{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d188191c",
   "metadata": {},
   "source": [
    "# Predicción de Cancelación de Reservas Hoteleras - Daniel García Nilo\n",
    "\n",
    "Máster en Data Science & IA · Evolve Academy\n",
    "\n",
    "Revenue Management, el equipo que gestiona precios e ingresos del hotel, necesita anticipar\n",
    "cancelaciones. El objetivo es estimar el riesgo al reservar para actuar a tiempo y reducir\n",
    "habitaciones vacías, sin molestar innecesariamente a los clientes.\n",
    "\n",
    "Es un problema de **clasificación binaria**: aprendemos de reservas anteriores para distinguir\n",
    "dos resultados. La variable que queremos predecir, o **target**, es `is_canceled`:\n",
    "**1 = cancelada; 0 = no cancelada**.\n",
    "\n",
    "**Reserva → estimación del riesgo → acción preventiva → posible reducción de pérdidas.**\n",
    "\n",
    "Seguimos el PDF del caso práctico y conservamos los 13 encabezados de\n",
    "`solucion_cancelacion_hotelera.ipynb`. Todos los resultados proceden del CSV local."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61cb96f1",
   "metadata": {},
   "source": [
    "## 0. Entorno y dependencias\n",
    "\n",
    "Preparamos las librerías que necesitamos para leer los datos, crear gráficos y entrenar modelos.\n",
    "Mostramos sus versiones para poder repetir el trabajo en el mismo entorno.\n",
    "\n",
    "Ejecutar de arriba abajo desde la carpeta del CSV. Usamos `random_state=42` para repetir las\n",
    "operaciones aleatorias con el mismo resultado. Los textos resumen la ejecución del CSV entregado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4115484",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:07.317704Z",
     "iopub.status.busy": "2026-09-24T13:11:07.317020Z",
     "iopub.status.idle": "2026-09-24T13:11:11.938950Z",
     "shell.execute_reply": "2026-09-24T13:11:11.936471Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas           3.0.6\n",
       "numpy            2.5.3\n",
       "scikit-learn     1.8.0\n",
       "matplotlib      3.10.9\n",
       "shap            0.52.0\n",
       "Name: Versión, dtype: str"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Python 3.14.5. Las versiones efectivas se muestran debajo. Si faltan dependencias:\n",
    "# %pip install pandas==3.0.6 numpy==2.5.3 scikit-learn==1.8.0 matplotlib==3.10.9 shap==0.52.0 ipywidgets==8.1.9\n",
    "import os\n",
    "os.environ.setdefault(\"OMP_NUM_THREADS\", \"4\")\n",
    "import calendar\n",
    "import hashlib\n",
    "from importlib.metadata import version\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "import shap\n",
    "from IPython.display import display, Markdown\n",
    "from scipy.special import expit\n",
    "from sklearn.base import clone\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, HistGradientBoostingClassifier\n",
    "from sklearn.model_selection import TimeSeriesSplit, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.metrics import (roc_auc_score, precision_score, recall_score, f1_score,\n",
    "                             confusion_matrix, ConfusionMatrixDisplay, RocCurveDisplay)\n",
    "\n",
    "# Fijamos la semilla y el umbral para comparar todos los modelos igual.\n",
    "SEED = 42\n",
    "THRESHOLD = 0.50  # Referencia inicial; después elegimos el umbral solo con validación.\n",
    "MIN_PRECISION = 0.70  # Supuesto de trabajo: al menos 7 aciertos por cada 10 alertas.\n",
    "MIN_RESERVAS = 100  # Solo afecta a gráficos de categorías, nunca al dataset.\n",
    "pd.set_option(\"display.max_columns\", 12)\n",
    "pd.set_option(\"display.float_format\", \"{:.3f}\".format)\n",
    "plt.rcParams.update({\"figure.figsize\": (8, 3.5), \"axes.spines.top\": False,\n",
    "                     \"axes.spines.right\": False, \"figure.dpi\": 110})\n",
    "display(pd.Series({p: version(p) for p in\n",
    "                   [\"pandas\", \"numpy\", \"scikit-learn\", \"matplotlib\", \"shap\"]}, name=\"Versión\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b1b7d9f",
   "metadata": {},
   "source": [
    "## 1. Carga y primera inspección\n",
    "\n",
    "Cargamos el CSV y revisamos su tamaño, los tipos de datos, los valores ausentes y el resultado\n",
    "de las reservas. Así sabemos con qué información contamos antes de analizarla.\n",
    "\n",
    "**Aquí solo detectamos posibles problemas; todavía no los solucionamos.** Más adelante\n",
    "decidiremos qué significan los nulos y las filas idénticas, y cómo tratarlos. Las relaciones\n",
    "con la cancelación se estudiarán solo en los datos de entrenamiento, llamados TRAIN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b2ad6b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:11.944391Z",
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     "iopub.status.idle": "2026-09-24T13:11:12.911983Z",
     "shell.execute_reply": "2026-09-24T13:11:12.910436Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensiones: (119390, 32)\n",
      "SHA-256 del CSV: d9514af026b8fc13851c028e9870cd9fb08ff1883c609259293d41a07625c7cb\n"
     ]
    },
    {
     "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",
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       "    }\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>hotel</th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <th>...</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>reservation_status</th>\n",
       "      <th>reservation_status_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0  Resort Hotel            0        342               2015               July   \n",
       "1  Resort Hotel            0        737               2015               July   \n",
       "2  Resort Hotel            0          7               2015               July   \n",
       "\n",
       "   arrival_date_week_number  ...  customer_type    adr  \\\n",
       "0                        27  ...      Transient  0.000   \n",
       "1                        27  ...      Transient  0.000   \n",
       "2                        27  ...      Transient 75.000   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  reservation_status  \\\n",
       "0                            0                          0           Check-Out   \n",
       "1                            0                          0           Check-Out   \n",
       "2                            0                          0           Check-Out   \n",
       "\n",
       "   reservation_status_date  \n",
       "0               2015-07-01  \n",
       "1               2015-07-01  \n",
       "2               2015-07-02  \n",
       "\n",
       "[3 rows x 32 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 119390 entries, 0 to 119389\n",
      "Data columns (total 32 columns):\n",
      " #   Column                          Non-Null Count   Dtype  \n",
      "---  ------                          --------------   -----  \n",
      " 0   hotel                           119390 non-null  str    \n",
      " 1   is_canceled                     119390 non-null  int64  \n",
      " 2   lead_time                       119390 non-null  int64  \n",
      " 3   arrival_date_year               119390 non-null  int64  \n",
      " 4   arrival_date_month              119390 non-null  str    \n",
      " 5   arrival_date_week_number        119390 non-null  int64  \n",
      " 6   arrival_date_day_of_month       119390 non-null  int64  \n",
      " 7   stays_in_weekend_nights         119390 non-null  int64  \n",
      " 8   stays_in_week_nights            119390 non-null  int64  \n",
      " 9   adults                          119390 non-null  int64  \n",
      " 10  children                        119386 non-null  float64\n",
      " 11  babies                          119390 non-null  int64  \n",
      " 12  meal                            119390 non-null  str    \n",
      " 13  country                         118902 non-null  str    \n",
      " 14  market_segment                  119390 non-null  str    \n",
      " 15  distribution_channel            119390 non-null  str    \n",
      " 16  is_repeated_guest               119390 non-null  int64  \n",
      " 17  previous_cancellations          119390 non-null  int64  \n",
      " 18  previous_bookings_not_canceled  119390 non-null  int64  \n",
      " 19  reserved_room_type              119390 non-null  str    \n",
      " 20  assigned_room_type              119390 non-null  str    \n",
      " 21  booking_changes                 119390 non-null  int64  \n",
      " 22  deposit_type                    119390 non-null  str    \n",
      " 23  agent                           103050 non-null  float64\n",
      " 24  company                         6797 non-null    float64\n",
      " 25  days_in_waiting_list            119390 non-null  int64  \n",
      " 26  customer_type                   119390 non-null  str    \n",
      " 27  adr                             119390 non-null  float64\n",
      " 28  required_car_parking_spaces     119390 non-null  int64  \n",
      " 29  total_of_special_requests       119390 non-null  int64  \n",
      " 30  reservation_status              119390 non-null  str    \n",
      " 31  reservation_status_date         119390 non-null  str    \n",
      "dtypes: float64(4), int64(16), str(12)\n",
      "memory usage: 29.1 MB\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "\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>tipo</th>\n",
       "      <th>nulos</th>\n",
       "      <th>% nulos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>float64</td>\n",
       "      <td>4</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>str</td>\n",
       "      <td>488</td>\n",
       "      <td>0.409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agent</th>\n",
       "      <td>float64</td>\n",
       "      <td>16340</td>\n",
       "      <td>13.686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company</th>\n",
       "      <td>float64</td>\n",
       "      <td>112593</td>\n",
       "      <td>94.307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tipo   nulos  % nulos\n",
       "children  float64       4    0.003\n",
       "country       str     488    0.409\n",
       "agent     float64   16340   13.686\n",
       "company   float64  112593   94.307"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filas idénticas adicionales: 31994\n"
     ]
    },
    {
     "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>Reservas</th>\n",
       "      <th>Proporción</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_canceled</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 · No cancelada</th>\n",
       "      <td>75166</td>\n",
       "      <td>0.630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 · Cancelada</th>\n",
       "      <td>44224</td>\n",
       "      <td>0.370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Reservas  Proporción\n",
       "is_canceled                           \n",
       "0 · No cancelada     75166       0.630\n",
       "1 · Cancelada        44224       0.370"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cargamos el CSV; cada fila representa una reserva.\n",
    "CSV = Path(\"reservas_hoteleras.csv\")\n",
    "df_raw = pd.read_csv(CSV)\n",
    "print(\"Dimensiones:\", df_raw.shape)\n",
    "print(\"SHA-256 del CSV:\", hashlib.sha256(CSV.read_bytes()).hexdigest())\n",
    "display(df_raw.head(3))\n",
    "df_raw.info()\n",
    "# Revisamos los nulos sin rellenarlos todavía.\n",
    "resumen = pd.DataFrame({\"tipo\": df_raw.dtypes.astype(str),\n",
    "                        \"nulos\": df_raw.isna().sum(),\n",
    "                        \"% nulos\": 100 * df_raw.isna().mean()})\n",
    "display(resumen.loc[resumen.nulos > 0])\n",
    "# Contamos filas idénticas; más adelante decidimos cómo tratarlas.\n",
    "print(\"Filas idénticas adicionales:\", df_raw.duplicated().sum())\n",
    "# Comparamos cuántas reservas cancelan y cuántas no.\n",
    "target = df_raw.is_canceled.value_counts().sort_index().rename(\"Reservas\").to_frame()\n",
    "target[\"Proporción\"] = target.Reservas / len(df_raw)\n",
    "display(target.rename(index={0: \"0 · No cancelada\", 1: \"1 · Cancelada\"}))\n",
    "assert set(df_raw.is_canceled.unique()) == {0, 1}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6103b7ae",
   "metadata": {},
   "source": [
    "**Resultado:** hay 119.390 reservas y 32 variables. Aproximadamente el **37 % cancela y el 63 % no**.\n",
    "Hay más ejemplos de una clase que de otra: esto se llama **desbalanceo**. No es extremo;\n",
    "comprobaremos durante el modelado si dar más importancia a las cancelaciones ayuda.\n",
    "\n",
    "Detectamos 4 nulos en `children`, 488 en `country`, 16.340 en `agent` y 112.593 en `company`.\n",
    "También hay 31.994 filas idénticas adicionales. Por ahora registramos estos hallazgos;\n",
    "no eliminamos ni rellenamos datos en esta primera revisión."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "740f257e",
   "metadata": {},
   "source": [
    "## 2. EDA Tecnico\n",
    "\n",
    "Antes de buscar patrones de cancelación, revisamos la calidad de los datos. Partimos de los\n",
    "nulos y posibles duplicados detectados, y comprobamos valores poco habituales e información\n",
    "que no podríamos usar al predecir. Esto evita tomar decisiones basadas en errores.\n",
    "\n",
    "**ADR** significa *Average Daily Rate*: precio medio por habitación y noche. Comprobamos\n",
    "si hay precios negativos o superiores a 1.000 €, reservas sin huéspedes y estancias de cero noches.\n",
    "Un valor extraño merece revisión, pero no es necesariamente un error.\n",
    "\n",
    "También creamos la fecha de llegada y reservamos desde ahora el periodo más reciente para\n",
    "TEST, la evaluación final. Así los gráficos de negocio no utilizan ese periodo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "499fabcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:12.916238Z",
     "iopub.status.busy": "2026-09-24T13:11:12.915974Z",
     "iopub.status.idle": "2026-09-24T13:11:13.203872Z",
     "shell.execute_reply": "2026-09-24T13:11:13.202448Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Filas con conteos negativos                        0\n",
       "ADR negativo                                       1\n",
       "ADR superior a 1.000 € (revisar, no eliminar)      1\n",
       "Reservas sin huéspedes registrados               180\n",
       "Reservas con cero noches                         715\n",
       "Name: Filas, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El EDA usa 97192 reservas anteriores a 2017-05-01\n"
     ]
    }
   ],
   "source": [
    "# Trabajamos con un DataFrame separado para conservar los datos originales.\n",
    "df = df_raw.copy()\n",
    "conteos = [\"lead_time\", \"adults\", \"children\", \"babies\", \"stays_in_week_nights\",\n",
    "           \"stays_in_weekend_nights\", \"previous_cancellations\", \"previous_bookings_not_canceled\"]\n",
    "auditoria = pd.Series({\n",
    "    \"Filas con conteos negativos\": int(df[conteos].lt(0).any(axis=1).sum()),\n",
    "    # Buscamos precios por noche negativos: no los interpretamos como tarifas válidas.\n",
    "    \"ADR negativo\": int(df.adr.lt(0).sum()),\n",
    "    # Un precio muy alto puede ser real; lo señalamos para revisión.\n",
    "    \"ADR superior a 1.000 € (revisar, no eliminar)\": int(df.adr.gt(1000).sum()),\n",
    "    # Revisamos registros sin huéspedes; si falta un componente, no suponemos cero.\n",
    "    \"Reservas sin huéspedes registrados\": int(df[[\"adults\", \"children\", \"babies\"]]\n",
    "                                                .sum(axis=1, min_count=3).eq(0).sum()),\n",
    "    # Contamos reservas con cero noches de estancia.\n",
    "    \"Reservas con cero noches\": int((df.stays_in_week_nights + df.stays_in_weekend_nights).eq(0).sum())\n",
    "}, name=\"Filas\")\n",
    "display(auditoria)\n",
    "assert not df[conteos].lt(0).any().any(), \"Revisar conteos negativos antes de modelar.\"\n",
    "# Marcamos el ADR negativo como ausente: se omite en EDA y se imputa dentro del pipeline.\n",
    "df.loc[df.adr < 0, \"adr\"] = np.nan\n",
    "\n",
    "# Creamos la fecha completa de llegada y dejamos TEST fuera del EDA.\n",
    "MESES = {calendar.month_name[i]: i for i in range(1, 13)}\n",
    "df[\"arrival_date\"] = pd.to_datetime(dict(year=df.arrival_date_year,\n",
    "                                         month=df.arrival_date_month.map(MESES),\n",
    "                                         day=df.arrival_date_day_of_month), errors=\"raise\")\n",
    "df = df.sort_values(\"arrival_date\", kind=\"stable\")\n",
    "CUTOFF = pd.Timestamp(\"2017-05-01\")\n",
    "eda = df.loc[df.arrival_date < CUTOFF].copy()\n",
    "print(\"El EDA usa\", len(eda), \"reservas anteriores a\", CUTOFF.date())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06bdd96f",
   "metadata": {},
   "source": [
    "**Qué encontramos y qué decidimos:**\n",
    "\n",
    "- **31.994 filas idénticas adicionales:** no hay un identificador único de reserva. Pueden ser\n",
    "  errores de duplicación o reservas distintas con las mismas características, por ejemplo de\n",
    "  un grupo. Las conservamos para no borrar reservas reales sin evidencia. Al compartir fecha\n",
    "  de llegada, las filas idénticas quedan en el mismo lado de cada corte temporal.\n",
    "- **1 ADR negativo:** no lo interpretamos como un precio por noche válido; podría ser un error\n",
    "  o un ajuste contable. Lo dejamos vacío, conservando la fila. En EDA no entra en la media; para el modelo se rellena con la mediana aprendida en TRAIN.\n",
    "- **1 ADR superior a 1.000 € (5.400 €):** es extremo, pero no podemos confirmar que sea un error.\n",
    "  Lo conservamos e indicamos que puede afectar a la media.\n",
    "- **715 reservas con cero noches:** podrían corresponder a usos sin pernoctación o incidencias\n",
    "  de registro. Sin información adicional no sabemos su origen, así que no las borramos.\n",
    "- **180 reservas sin huéspedes registrados:** requieren revisar el registro de ocupantes;\n",
    "  no basta para afirmar que toda la reserva es inválida. Las conservamos y anotamos esa limitación.\n",
    "\n",
    "No hay conteos negativos en las variables revisadas. Tampoco recortamos automáticamente grupos\n",
    "grandes: un tamaño poco habitual puede ser real. El ADR vuelve al modelo como aproximación al precio de la reserva; su versión inicial debe verificarse."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97eac7a6",
   "metadata": {},
   "source": [
    "### Revisión de Data Leakage\n",
    "\n",
    "**Data Leakage**, o fuga de información, significa usar al entrenar datos que no conoceríamos\n",
    "al predecir. Sería dar al modelo parte de la respuesta y sobrevalorar lo que puede hacer.\n",
    "**«¿Se conoce al reservar?» pregunta si el hotel dispone del dato al crear la reserva.**\n",
    "\n",
    "| Variable | Qué representa | ¿Se conoce al reservar? | Riesgo de Data Leakage | Decisión |\n",
    "|---|---|---|---|---|\n",
    "| `reservation_status` | Estado final de la reserva | No | Revela directamente el resultado | Excluir |\n",
    "| `reservation_status_date` | Fecha en que se registró el estado final | No | Hecho posterior a la reserva | Excluir; tampoco usar para dividir datos |\n",
    "| `assigned_room_type` | Habitación finalmente asignada | No siempre | Puede decidirse después | Excluir |\n",
    "| `booking_changes` | Cambios hasta la llegada o cancelación | No | Cuenta cambios futuros | Excluir |\n",
    "| `days_in_waiting_list` | Días que acabó esperando hasta confirmarse | No al crearla | Duración futura de la espera | Excluir |\n",
    "| `deposit_type` | Tipo de depósito realizado | No siempre | Puede reflejar pagos posteriores en este dataset | Excluir por precaución |\n",
    "| `adr` | Precio medio por habitación y noche | Aproximación al precio conocido al reservar | No es fuga por sí mismo; verificar precio inicial | Conservar |\n",
    "| `total_of_special_requests` | Número de peticiones especiales del cliente | Sí, si se registran al reservar | Verificar que no incluya peticiones añadidas después | Conservar bajo ese supuesto |\n",
    "| `required_car_parking_spaces` | Plazas de parking solicitadas | Sí, si se solicitan al reservar | Verificar que sea la solicitud inicial | Conservar bajo ese supuesto |\n",
    "| `previous_cancellations` | Cancelaciones del cliente anteriores a esta reserva | Sí, como historial anterior | No es fuga si solo recoge hechos anteriores | Conservar |\n",
    "| `previous_bookings_not_canceled` | Reservas anteriores del cliente no canceladas | Sí, como historial anterior | No es fuga si su resultado ya era conocido | Conservar |\n",
    "| `is_repeated_guest` | Indica si el cliente había reservado anteriormente | Sí, como historial anterior | No es fuga si se calcula al crear la reserva | Conservar |\n",
    "| Fechas previstas, antelación, huéspedes, noches, habitación reservada y comidas | Datos de la solicitud | Sí en la solicitud inicial | Pueden cambiar después; necesitamos su versión inicial | Conservar como aproximación |\n",
    "| Hotel, país, canal, segmento, agencia, empresa y tipo de cliente | Origen y características de la reserva | En principio sí | Verificar su versión inicial | Conservar |\n",
    "| `is_canceled` | Resultado que queremos predecir | No | Es la respuesta | Solo target, nunca predictor |\n",
    "\n",
    "**Decisión:** reincorporamos precio, solicitudes e historial; no son automáticamente fuga.\n",
    "El historial anterior puede ser útil para anticipar el comportamiento del cliente.\n",
    "\n",
    "**Limitación del dato:** el [artículo original](https://pmc.ncbi.nlm.nih.gov/articles/PMC6297060/)\n",
    "describe extracciones cercanas a la llegada. El CSV no permite comprobar todos los instantes\n",
    "de registro. Conservamos estas variables como aproximación académica; antes de usar el modelo\n",
    "en el hotel hay que obtener su valor al reservar, sin incorporar novedades posteriores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61078e87",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:13.207935Z",
     "iopub.status.busy": "2026-09-24T13:11:13.207411Z",
     "iopub.status.idle": "2026-09-24T13:11:13.214130Z",
     "shell.execute_reply": "2026-09-24T13:11:13.212345Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excluidas: reservation_status, reservation_status_date, assigned_room_type, booking_changes, days_in_waiting_list, deposit_type\n",
      "Reincorporadas: adr, total_of_special_requests, required_car_parking_spaces, previous_cancellations, previous_bookings_not_canceled, is_repeated_guest\n"
     ]
    }
   ],
   "source": [
    "# Variables que vamos a excluir por posible Data Leakage.\n",
    "LEAKAGE = [\"reservation_status\", \"reservation_status_date\", \"assigned_room_type\",\n",
    "           \"booking_changes\", \"days_in_waiting_list\", \"deposit_type\"]\n",
    "REINCORPORADAS = [\"adr\", \"total_of_special_requests\", \"required_car_parking_spaces\",\n",
    "                  \"previous_cancellations\", \"previous_bookings_not_canceled\", \"is_repeated_guest\"]\n",
    "print(\"Excluidas:\", \", \".join(LEAKAGE))\n",
    "print(\"Reincorporadas:\", \", \".join(REINCORPORADAS))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87ac001a",
   "metadata": {},
   "source": [
    "## 2. EDA — Análisis exploratorio con foco de negocio\n",
    "\n",
    "EDA significa **análisis exploratorio de datos**. Buscamos patrones que ayuden a entender\n",
    "las cancelaciones y a proponer acciones. Respondemos cinco preguntas usando solo TRAIN.\n",
    "\n",
    "La **tasa de cancelación** es cancelaciones divididas entre reservas del grupo. Permite comparar\n",
    "grupos de distinto tamaño; `n` indica cuántas reservas hay. Una relación entre dos variables\n",
    "no demuestra que una provoque la otra.\n",
    "\n",
    "**Gráficos de categorías:** mostramos solo grupos con al menos 100 reservas. Es un criterio\n",
    "sencillo para no destacar porcentajes basados en unos pocos casos, no una garantía estadística.\n",
    "Los registros se mantienen para entrenar y evaluar.\n",
    "\n",
    "### ¿Se cancela más en un tipo de hotel?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62f3191c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:13.218268Z",
     "iopub.status.busy": "2026-09-24T13:11:13.217607Z",
     "iopub.status.idle": "2026-09-24T13:11:13.615817Z",
     "shell.execute_reply": "2026-09-24T13:11:13.613570Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 880x385 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculamos cancelaciones / reservas para comparar grupos de distinto tamaño.\n",
    "def tabla_tasas(columna, datos=eda):\n",
    "    return datos.groupby(columna, observed=True).is_canceled.agg(\n",
    "        Reservas=\"size\", Cancelaciones=\"sum\", Tasa=\"mean\")\n",
    "\n",
    "# Reutilizamos el mismo formato de gráfico para facilitar la lectura.\n",
    "def barras_tasa(tabla, titulo, ax=None):\n",
    "    # Ocultamos grupos pequeños solo al dibujar; las tablas y el dataset no cambian.\n",
    "    tabla = tabla.loc[tabla.Reservas >= MIN_RESERVAS]\n",
    "    ax = ax if ax is not None else plt.subplots()[1]\n",
    "    posiciones = np.arange(len(tabla))\n",
    "    ax.bar(posiciones, tabla.Tasa, color=\"#277DA1\")\n",
    "    ax.set_xticks(posiciones, [f\"{x}\\n(n={n:,})\" for x, n in zip(tabla.index, tabla.Reservas)],\n",
    "                  rotation=40, ha=\"right\", fontsize=8)\n",
    "    ax.yaxis.set_major_formatter(mticker.PercentFormatter(1))\n",
    "    ax.set_ylabel(\"Tasa de cancelación\")\n",
    "    ax.set_title(titulo)\n",
    "    for p, v in zip(posiciones, tabla.Tasa):\n",
    "        ax.annotate(f\"{v:.1%}\", (p, v), xytext=(0, 4), textcoords=\"offset points\", ha=\"center\", fontsize=8)\n",
    "    ax.set_ylim(0, min(1.1, max(0.1, tabla.Tasa.max() * 1.22)))\n",
    "    return ax\n",
    "\n",
    "hotel_rates = tabla_tasas(\"hotel\")\n",
    "barras_tasa(hotel_rates, \"Cancelación por hotel · TRAIN\")\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8c9ec60",
   "metadata": {},
   "source": [
    "**Resultado:** City Hotel tiene un **41,5 %** de cancelaciones\n",
    "y Resort Hotel, un **26,0 %**. La diferencia no explica por sí sola la causa.\n",
    "\n",
    "**Acción:** comenzar una prueba de recordatorios en City Hotel y medir si reduce cancelaciones."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bef9e0ae",
   "metadata": {},
   "source": [
    "### ¿Hay meses con mayor tasa de cancelación?\n",
    "\n",
    "Comparamos el mes previsto de llegada para saber cuándo podría necesitarse más seguimiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5214354b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:13.618781Z",
     "iopub.status.busy": "2026-09-24T13:11:13.618361Z",
     "iopub.status.idle": "2026-09-24T13:11:13.795671Z",
     "shell.execute_reply": "2026-09-24T13:11:13.794459Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x385 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>arrival_date_month</th>\n",
       "      <th>January</th>\n",
       "      <th>February</th>\n",
       "      <th>March</th>\n",
       "      <th>April</th>\n",
       "      <th>May</th>\n",
       "      <th>June</th>\n",
       "      <th>July</th>\n",
       "      <th>August</th>\n",
       "      <th>September</th>\n",
       "      <th>October</th>\n",
       "      <th>November</th>\n",
       "      <th>December</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Reservas</th>\n",
       "      <td>5929.000</td>\n",
       "      <td>8068.000</td>\n",
       "      <td>9794.000</td>\n",
       "      <td>11089.000</td>\n",
       "      <td>5478.000</td>\n",
       "      <td>5292.000</td>\n",
       "      <td>7348.000</td>\n",
       "      <td>8952.000</td>\n",
       "      <td>10508.000</td>\n",
       "      <td>11160.000</td>\n",
       "      <td>6794.000</td>\n",
       "      <td>6780.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tasa</th>\n",
       "      <td>0.305</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.408</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "arrival_date_month  January  February    March     April      May     June  \\\n",
       "Reservas           5929.000  8068.000 9794.000 11089.000 5478.000 5292.000   \n",
       "Tasa                  0.305     0.334    0.322     0.408    0.350    0.396   \n",
       "\n",
       "arrival_date_month     July   August  September   October  November  December  \n",
       "Reservas           7348.000 8952.000  10508.000 11160.000  6794.000  6780.000  \n",
       "Tasa                  0.375    0.382      0.392     0.380     0.312     0.350  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ordenamos los meses de enero a diciembre, no alfabéticamente.\n",
    "month_rates = tabla_tasas(\"arrival_date_month\").reindex(MESES)\n",
    "ax = month_rates.Tasa.plot(marker=\"o\", color=\"#277DA1\")\n",
    "ax.set_xticks(range(12), [\"Ene\", \"Feb\", \"Mar\", \"Abr\", \"May\", \"Jun\", \"Jul\", \"Ago\", \"Sep\", \"Oct\", \"Nov\", \"Dic\"])\n",
    "ax.yaxis.set_major_formatter(mticker.PercentFormatter(1))\n",
    "ax.set(title=\"Cancelación por mes de llegada · TRAIN\", xlabel=\"Mes\", ylabel=\"Tasa de cancelación\")\n",
    "plt.tight_layout(); plt.show()\n",
    "display(month_rates[[\"Reservas\", \"Tasa\"]].T)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b2c22ac",
   "metadata": {},
   "source": [
    "**Resultado:** abril tiene la tasa más alta de TRAIN (**40,8 %**) y\n",
    "enero la menor (**30,5 %**). No todos los meses incluyen los mismos años;\n",
    "la diferencia no tiene por qué deberse solo a la época del año.\n",
    "\n",
    "**Acción:** planificar el seguimiento por mes y comprobar si el patrón se repite con más datos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57226694",
   "metadata": {},
   "source": [
    "### ¿Cambia la cancelación según el canal por el que llega la reserva?\n",
    "\n",
    "Comparamos el canal de distribución y el segmento comercial. TA/TO agrupa agencias y\n",
    "operadores turísticos; Direct corresponde al canal directo y Groups al segmento de grupos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90a137ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:13.799816Z",
     "iopub.status.busy": "2026-09-24T13:11:13.799481Z",
     "iopub.status.idle": "2026-09-24T13:11:14.119641Z",
     "shell.execute_reply": "2026-09-24T13:11:14.117681Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1540x495 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Canal y segmento son dos formas distintas de describir el origen de la reserva.\n",
    "channel_rates = tabla_tasas(\"distribution_channel\").sort_values(\"Tasa\", ascending=False)\n",
    "segment_rates = tabla_tasas(\"market_segment\").sort_values(\"Tasa\", ascending=False)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 4.5))\n",
    "barras_tasa(channel_rates, \"Canal de distribución · TRAIN\", axes[0])\n",
    "barras_tasa(segment_rates, \"Segmento de mercado · TRAIN\", axes[1])\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d8577b",
   "metadata": {},
   "source": [
    "**Resultado:** TA/TO registra **40,3 %**, frente a\n",
    "**16,8 %** de Direct; el segmento Groups alcanza\n",
    "**60,2 %**. Canal y segmento describen aspectos distintos de una reserva.\n",
    "\n",
    "**Acción:** coordinar reconfirmaciones con agencias y responsables de grupos.\n",
    "`Undefined` tiene solo 5 reservas en canal y\n",
    "2 en segmento: se oculta en los gráficos, pero no se eliminan esas filas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c90490",
   "metadata": {},
   "source": [
    "### ¿La antelación de la reserva está relacionada con la cancelación?\n",
    "\n",
    "`lead_time` indica cuántos días pasan entre la reserva y la llegada. Lo agrupamos en intervalos\n",
    "para comparar reservas hechas con poca y mucha antelación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67bfcba7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:14.123638Z",
     "iopub.status.busy": "2026-09-24T13:11:14.122923Z",
     "iopub.status.idle": "2026-09-24T13:11:14.318863Z",
     "shell.execute_reply": "2026-09-24T13:11:14.316616Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x385 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrupamos los días de antelación en intervalos fáciles de comparar.\n",
    "eda[\"lead_group\"] = pd.cut(eda.lead_time, [-1, 7, 30, 90, 180, np.inf],\n",
    "                            labels=[\"0–7\", \"8–30\", \"31–90\", \"91–180\", \">180\"])\n",
    "lead_rates = tabla_tasas(\"lead_group\")\n",
    "barras_tasa(lead_rates, \"Antelación y cancelación · TRAIN\")\n",
    "plt.xlabel(\"Días entre reserva y llegada\")\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f295d4",
   "metadata": {},
   "source": [
    "**Resultado:** la cancelación pasa del **9,4 %** con 0–7 días\n",
    "al **60,0 %** con más de 180 días. En estos datos, más antelación se relaciona con más cancelaciones.\n",
    "\n",
    "**Acción:** programar reconfirmaciones durante las esperas largas sin penalizar automáticamente a quien reserva con tiempo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70f1490",
   "metadata": {},
   "source": [
    "### ¿El precio medio por noche cambia entre reservas canceladas y no canceladas?\n",
    "\n",
    "**ADR (*Average Daily Rate*)** es el precio medio por habitación y noche. Comparamos la media\n",
    "del ADR de las reservas que **no cancelaron** con la de las que **sí cancelaron**.\n",
    "El gráfico muestra solo esos dos importes, sin mezclar el número de reservas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "62e7c87a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:14.322947Z",
     "iopub.status.busy": "2026-09-24T13:11:14.322224Z",
     "iopub.status.idle": "2026-09-24T13:11:14.489949Z",
     "shell.execute_reply": "2026-09-24T13:11:14.488525Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 660x385 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculamos el ADR medio de cada grupo, usando solo TRAIN.\n",
    "adr_stats = eda.groupby(\"is_canceled\").adr.agg(Reservas=\"count\", Media=\"mean\", Mediana=\"median\")\n",
    "\n",
    "# Mostramos solo dos barras de precio; el eje comienza en cero para no exagerar la diferencia.\n",
    "fig, ax = plt.subplots(figsize=(6, 3.5))\n",
    "barras = ax.bar([\"No cancelada\", \"Cancelada\"], adr_stats.loc[[0, 1], \"Media\"],\n",
    "                color=[\"#277DA1\", \"#F9844A\"], width=0.55)\n",
    "ax.set(title=\"ADR medio según cancelación · TRAIN\", ylabel=\"ADR medio (€ por noche)\")\n",
    "ax.set_ylim(0, adr_stats.Media.max() * 1.20)\n",
    "\n",
    "# Añadimos el importe sobre cada barra para que la pequeña diferencia se lea con claridad.\n",
    "ax.bar_label(barras, labels=[f\"{valor:.2f} €\".replace(\".\", \",\")\n",
    "                            for valor in adr_stats.loc[[0, 1], \"Media\"]], padding=5)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "807905a5",
   "metadata": {},
   "source": [
    "**Resultado:** ADR medio de **92,64 €** en no canceladas y\n",
    "**96,08 €** en canceladas: diferencia de **3,43 €**,\n",
    "calculada antes de redondear. Las medianas son **87,00 €** y\n",
    "**90,00 €**. La mediana es el valor central y depende menos de precios extremos;\n",
    "la media conserva el ADR máximo de 5.400 €.\n",
    "\n",
    "**Interpretación:** las medias son parecidas; no demuestran que subir el precio provoque cancelaciones.\n",
    "**Acción:** estudiar ingresos en riesgo usando tarifas iniciales verificadas.\n",
    "El ADR se utiliza también en el modelo como aproximación al precio conocido al reservar."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9bb3c6a",
   "metadata": {},
   "source": [
    "### Tratamiento de nulos\n",
    "\n",
    "Ahora decidimos cómo tratar los valores ausentes según el significado de cada variable.\n",
    "No todos los nulos representan lo mismo; rellenarlos sin criterio podría inventar información.\n",
    "\n",
    "- **`agent`** identifica la agencia. Usamos `Sin_agente` cuando no hay una agencia identificada.\n",
    "- **`company`** identifica la empresa. La mayoría está vacía; usamos `Sin_empresa` en lugar\n",
    "  de inventar un código. Estas etiquetas significan «no registrada», no ausencia confirmada.\n",
    "- **`country`** indica el país. Usamos `Desconocido` para no atribuir uno que no conocemos.\n",
    "- **`children`** cuenta los niños y tiene cuatro nulos. No suponemos que sean cero. Al sumar\n",
    "  huéspedes, esas cuatro sumas también quedarán vacías; después rellenaremos **`total_guests`**\n",
    "  con la mediana aprendida solo en TRAIN. No rellenamos `children` directamente porque sus\n",
    "  componentes se sustituyen por el total en el modelo.\n",
    "\n",
    "Los códigos de agencia y empresa se tratan como categorías, no como cantidades.\n",
    "Conservamos `Undefined` como categoría tal como aparece en el CSV.\n",
    "\n",
    "El ADR negativo queda nulo: el pipeline lo rellenará con la mediana calculada solo con\n",
    "los datos de entrenamiento de cada ajuste. No usamos TEST para calcularla."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "31d5a9e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:14.493047Z",
     "iopub.status.busy": "2026-09-24T13:11:14.492603Z",
     "iopub.status.idle": "2026-09-24T13:11:14.567454Z",
     "shell.execute_reply": "2026-09-24T13:11:14.566310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "children    4\n",
       "country     0\n",
       "agent       0\n",
       "company     0\n",
       "Name: Nulos pendientes, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Los códigos identifican agencias o empresas; no son cantidades.\n",
    "for col, ausencia in [(\"agent\", \"Sin_agente\"), (\"company\", \"Sin_empresa\")]:\n",
    "    df[col] = df[col].astype(\"Int64\").astype(\"string\").fillna(ausencia).astype(object)\n",
    "# Si falta el país, indicamos que es desconocido.\n",
    "df[\"country\"] = df.country.fillna(\"Desconocido\").astype(object)\n",
    "# Los cuatro nulos de children se tratarán a través de total_guests en el pipeline.\n",
    "display(df[[\"children\", \"country\", \"agent\", \"company\"]].isna().sum().rename(\"Nulos pendientes\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55c3215",
   "metadata": {},
   "source": [
    "**Comprobación:** país, agencia y empresa ya no tienen nulos. Permanecen los cuatro de `children`;\n",
    "es intencionado. Se trasladarán al total de huéspedes y se completarán durante el entrenamiento,\n",
    "para no calcular la mediana con información de validación o TEST.\n",
    "El nulo de ADR se trata de la misma forma dentro de su columna numérica."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c61589",
   "metadata": {},
   "source": [
    "## 3. Feature Engineering\n",
    "\n",
    "**Feature Engineering** consiste en crear nuevas variables a partir de las existentes.\n",
    "Creamos dos resúmenes fáciles de interpretar para representar mejor la reserva:\n",
    "\n",
    "- **`total_guests = adults + children + babies`:** sumamos adultos, niños y bebés para conocer\n",
    "  el tamaño del grupo. Organizar un viaje puede ser distinto según cuántas personas participan.\n",
    "- **`total_nights = stays_in_week_nights + stays_in_weekend_nights`:** sumamos las noches\n",
    "  entre semana y de fin de semana. La duración indica cuántas noches de habitación están comprometidas.\n",
    "\n",
    "Usamos los totales en lugar de sus componentes para no repetir esa información.\n",
    "La fecha `arrival_date` solo sirve para dividir los periodos, no entra al modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8aa944c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:14.569949Z",
     "iopub.status.busy": "2026-09-24T13:11:14.569550Z",
     "iopub.status.idle": "2026-09-24T13:11:14.603992Z",
     "shell.execute_reply": "2026-09-24T13:11:14.602024Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features del modelo: ['hotel', 'lead_time', 'arrival_date_year', 'arrival_date_month', 'arrival_date_week_number', 'arrival_date_day_of_month', 'meal', 'country', 'market_segment', 'distribution_channel', 'is_repeated_guest', 'previous_cancellations', 'previous_bookings_not_canceled', 'reserved_room_type', 'agent', 'company', 'customer_type', 'adr', 'required_car_parking_spaces', 'total_of_special_requests', 'total_guests', 'total_nights']\n"
     ]
    }
   ],
   "source": [
    "# Sumamos huéspedes; min_count=3 mantiene el nulo si falta algún componente.\n",
    "df[\"total_guests\"] = df[[\"adults\", \"children\", \"babies\"]].sum(axis=1, min_count=3)\n",
    "# Sumamos las noches entre semana y de fin de semana.\n",
    "df[\"total_nights\"] = df.stays_in_week_nights + df.stays_in_weekend_nights\n",
    "COMPONENTES = [\"adults\", \"children\", \"babies\", \"stays_in_week_nights\", \"stays_in_weekend_nights\"]\n",
    "# Retiramos fugas, componentes sustituidos, respuesta y fecha de partición.\n",
    "feature_cols = df.columns.drop(LEAKAGE + COMPONENTES + [\"is_canceled\", \"arrival_date\"]).tolist()\n",
    "assert not set(LEAKAGE + [\"is_canceled\", \"arrival_date\"]) & set(feature_cols)\n",
    "assert df.total_guests.isna().sum() == df.children.isna().sum()\n",
    "print(\"Features del modelo:\", feature_cols)\n",
    "assert set(REINCORPORADAS).issubset(feature_cols)\n",
    "assert len(df) == len(df_raw)  # El filtro visual de categorías no elimina reservas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d002eda9",
   "metadata": {},
   "source": [
    "## 4. Train y Test\n",
    "\n",
    "Comparamos modelos con reservas posteriores a las utilizadas para aprender. No mezclamos fechas al azar.\n",
    "\n",
    "- **TRAIN/FIT:** datos con los que aprenden los modelos durante la comparación.\n",
    "- **VALIDATION:** periodo posterior para elegir modelo, configuración y threshold (umbral de decisión).\n",
    "- **TEST:** periodo final, que no usamos para ninguna de esas decisiones.\n",
    "\n",
    "Revisamos cortes a principio de mes y elegimos el más cercano al 80 % de desarrollo.\n",
    "Se mantiene **01/05/2017**, una fecha clara: desarrollo termina en abril y TEST empieza en mayo.\n",
    "Dentro de desarrollo, reservamos aproximadamente el último 20 % para validación.\n",
    "Al final reunimos ajuste y validación para entrenar el modelo ya elegido.\n",
    "\n",
    "El corte usa fecha de **llegada**, no de creación. Una reserva de llegada posterior puede haberse\n",
    "creado antes del corte: necesitamos fechas originales para simular exactamente qué sabía el hotel\n",
    "en cada momento. Además, TEST ya se evaluó en versiones anteriores del trabajo; no interviene en\n",
    "la selección de esta revisión, pero un periodo nuevo sería necesario para una comprobación externa.\n",
    "\n",
    "**Porcentajes reales del total:** ajuste **65,08 %**, validación **16,33 %** y TEST **18,59 %**. Desarrollo completo: **81,41 %**.\n",
    "\n",
    "| Conjunto | Reservas | Llegadas desde | Hasta |\n",
    "|---|---:|---|---|\n",
    "| TRAIN ajuste | 77.698 | 2015-07-01 | 2016-12-25 |\n",
    "| TRAIN validación | 19.494 | 2016-12-26 | 2017-04-30 |\n",
    "| TEST reservado | 22.198 | 2017-05-01 | 2017-08-31 |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "31c0146e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:14.609200Z",
     "iopub.status.busy": "2026-09-24T13:11:14.608622Z",
     "iopub.status.idle": "2026-09-24T13:11:14.840938Z",
     "shell.execute_reply": "2026-09-24T13:11:14.839206Z"
    }
   },
   "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>Corte</th>\n",
       "      <th>% desarrollo</th>\n",
       "      <th>Distancia a 80 %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>81.407</td>\n",
       "      <td>1.407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>76.666</td>\n",
       "      <td>3.334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>86.695</td>\n",
       "      <td>6.695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Corte  % desarrollo  Distancia a 80 %\n",
       "0 2017-05-01        81.407             1.407\n",
       "1 2017-04-01        76.666             3.334\n",
       "2 2017-06-01        86.695             6.695"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Reservas</th>\n",
       "      <th>Inicio</th>\n",
       "      <th>Fin</th>\n",
       "      <th>% total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conjunto</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TRAIN ajuste</th>\n",
       "      <td>77698</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>2016-12-25</td>\n",
       "      <td>65.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAIN validación</th>\n",
       "      <td>19494</td>\n",
       "      <td>2016-12-26</td>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>16.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEST reservado</th>\n",
       "      <td>22198</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>18.593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Reservas      Inicio         Fin  % total\n",
       "Conjunto                                                   \n",
       "TRAIN ajuste         77698  2015-07-01  2016-12-25   65.079\n",
       "TRAIN validación     19494  2016-12-26  2017-04-30   16.328\n",
       "TEST reservado       22198  2017-05-01  2017-08-31   18.593"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN completo: 97,192 (81.4%); TEST: 22,198 (18.6%)\n",
      "Cancelaciones en ajuste: 36.1%; en validación: 36.7%\n"
     ]
    }
   ],
   "source": [
    "# Comparamos cortes mensuales usando solo fechas y cantidades, no resultados de cancelación.\n",
    "cortes = df.arrival_date.dt.to_period(\"M\").drop_duplicates().dt.to_timestamp()\n",
    "opciones_corte = pd.DataFrame({\"Corte\": cortes.to_numpy()})\n",
    "opciones_corte[\"% desarrollo\"] = [100 * (df.arrival_date < c).mean() for c in cortes]\n",
    "opciones_corte[\"Distancia a 80 %\"] = (opciones_corte[\"% desarrollo\"] - 80).abs()\n",
    "display(opciones_corte.sort_values(\"Distancia a 80 %\").head(3).reset_index(drop=True))\n",
    "assert opciones_corte.sort_values(\"Distancia a 80 %\").iloc[0][\"Corte\"] == CUTOFF\n",
    "\n",
    "# Reservamos las llegadas más recientes para evaluar al final.\n",
    "train = df.loc[df.arrival_date < CUTOFF].copy()\n",
    "test = df.loc[df.arrival_date >= CUTOFF].copy()\n",
    "# Dentro de TRAIN, usamos el último periodo para comparar modelos.\n",
    "val_cut = train.arrival_date.iloc[int(0.8 * len(train))]\n",
    "fit = train.loc[train.arrival_date < val_cut].copy()\n",
    "valid = train.loc[train.arrival_date >= val_cut].copy()\n",
    "\n",
    "# X contiene las variables de entrada; y contiene si la reserva canceló.\n",
    "X_fit, y_fit = fit[feature_cols], fit.is_canceled\n",
    "X_valid, y_valid = valid[feature_cols], valid.is_canceled\n",
    "X_train, y_train = train[feature_cols], train.is_canceled\n",
    "X_test = test[feature_cols]  # Las etiquetas de TEST se usan solo tras elegir el ganador.\n",
    "# Comprobamos que los periodos están ordenados y no comparten fechas.\n",
    "assert fit.arrival_date.max() < valid.arrival_date.min()\n",
    "assert train.arrival_date.max() < test.arrival_date.min()\n",
    "assert not set(train.index) & set(test.index)\n",
    "particiones = pd.DataFrame([\n",
    "    {\"Conjunto\": nombre, \"Reservas\": len(datos), \"Inicio\": datos.arrival_date.min().date(),\n",
    "     \"Fin\": datos.arrival_date.max().date(), \"% total\": 100 * len(datos) / len(df)}\n",
    "    for nombre, datos in [(\"TRAIN ajuste\", fit), (\"TRAIN validación\", valid), (\"TEST reservado\", test)]\n",
    "]).set_index(\"Conjunto\")\n",
    "display(particiones)\n",
    "print(f\"TRAIN completo: {len(train):,} ({len(train)/len(df):.1%}); TEST: {len(test):,} ({len(test)/len(df):.1%})\")\n",
    "print(f\"Cancelaciones en ajuste: {y_fit.mean():.1%}; en validación: {y_valid.mean():.1%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c64a3e6",
   "metadata": {},
   "source": [
    "## 5. Preprocesado\n",
    "\n",
    "Preparamos los datos para que los modelos puedan utilizarlos. Separar números y categorías\n",
    "permite aplicar a cada grupo el tratamiento adecuado.\n",
    "\n",
    "### Variables numéricas\n",
    "\n",
    "Incluyen antelación, fechas numéricas, huéspedes y noches; también ADR, peticiones, parking,\n",
    "cancelaciones anteriores, reservas anteriores no canceladas y el indicador de cliente repetidor.\n",
    "\n",
    "1. **`SimpleImputer`:** rellena posibles nulos con la mediana, el valor central de TRAIN.\n",
    "2. **`StandardScaler`:** pone variables con escalas diferentes en una escala comparable.\n",
    "   Esto ayuda a la regresión logística. Los árboles no lo necesitan, pero conservar el mismo\n",
    "   paso no cambia el orden de los valores y facilita usar una preparación común.\n",
    "\n",
    "### Variables categóricas\n",
    "\n",
    "Incluyen hotel, mes de llegada, país, comidas, canal, segmento, agencia, empresa,\n",
    "tipo de cliente y tipo de habitación reservada. Sus valores son grupos o etiquetas.\n",
    "\n",
    "1. Completamos posibles ausencias con una categoría de desconocido.\n",
    "2. **`OneHotEncoder`** convierte cada categoría en columnas de 0 y 1. Por ejemplo,\n",
    "   `hotel=City Hotel` activa la columna City Hotel y deja a 0 la de Resort Hotel.\n",
    "   Agrupamos categorías poco frecuentes para evitar demasiadas columnas; también admitimos\n",
    "   categorías nuevas al predecir.\n",
    "\n",
    "**`ColumnTransformer`** aplica estos tratamientos distintos a números y categorías.\n",
    "**`Pipeline`** une la preparación y el modelo para repetir siempre los mismos pasos.\n",
    "Las medianas, escalas y categorías se aprenden **solo con los datos usados para entrenar en\n",
    "cada ajuste**, nunca con validación ni TEST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d7c4f4e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:14.845602Z",
     "iopub.status.busy": "2026-09-24T13:11:14.844684Z",
     "iopub.status.idle": "2026-09-24T13:11:14.854667Z",
     "shell.execute_reply": "2026-09-24T13:11:14.852757Z"
    }
   },
   "outputs": [],
   "source": [
    "# Separamos variables categóricas y numéricas.\n",
    "cat_cols = X_fit.select_dtypes(include=[\"object\", \"string\", \"category\"]).columns.tolist()\n",
    "num_cols = [c for c in feature_cols if c not in cat_cols]\n",
    "preprocessor = ColumnTransformer([\n",
    "    # Completamos nulos numéricos con la mediana y ponemos las escalas al mismo nivel.\n",
    "    (\"num\", Pipeline([(\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "                       (\"scaler\", StandardScaler())]), num_cols),\n",
    "    # Completamos categorías ausentes y las convertimos en columnas de 0 y 1.\n",
    "    (\"cat\", Pipeline([(\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"Desconocido\")),\n",
    "                       (\"onehot\", OneHotEncoder(handle_unknown=\"infrequent_if_exist\",\n",
    "                                                min_frequency=30, max_categories=20,\n",
    "                                                sparse_output=False))]), cat_cols)\n",
    "], verbose_feature_names_out=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28fd4ebd",
   "metadata": {},
   "source": [
    "La preparación queda definida, pero todavía no aprende valores: lo hará al entrenar cada modelo.\n",
    "Las funciones siguientes evitan repetir código. `pipeline` une preparación y modelo;\n",
    "`metricas` calcula los mismos indicadores y `evaluar_validacion` entrena y guarda la comparación.\n",
    "Explicamos el significado de las métricas al presentar el baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "743d17a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:14.859336Z",
     "iopub.status.busy": "2026-09-24T13:11:14.858924Z",
     "iopub.status.idle": "2026-09-24T13:11:14.871393Z",
     "shell.execute_reply": "2026-09-24T13:11:14.869463Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numéricas: ['lead_time', 'arrival_date_year', 'arrival_date_week_number', 'arrival_date_day_of_month', 'is_repeated_guest', 'previous_cancellations', 'previous_bookings_not_canceled', 'adr', 'required_car_parking_spaces', 'total_of_special_requests', 'total_guests', 'total_nights']\n",
      "Categóricas: ['hotel', 'arrival_date_month', 'meal', 'country', 'market_segment', 'distribution_channel', 'reserved_room_type', 'agent', 'company', 'customer_type']\n"
     ]
    }
   ],
   "source": [
    "# Cada modelo aprende su propia preparación al entrenar.\n",
    "def pipeline(modelo):\n",
    "    return Pipeline([(\"prep\", clone(preprocessor)), (\"model\", modelo)])\n",
    "\n",
    "# El umbral cambia Precision, Recall y F1; ROC-AUC usa las probabilidades sin convertirlas en 0/1.\n",
    "def metricas(y, probas, umbral=0.50):\n",
    "    pred = (probas >= umbral).astype(int)\n",
    "    return {\"ROC-AUC\": roc_auc_score(y, probas),\n",
    "            \"Precision\": precision_score(y, pred, zero_division=0),\n",
    "            \"Recall\": recall_score(y, pred, zero_division=0),\n",
    "            \"F1\": f1_score(y, pred, zero_division=0)}\n",
    "\n",
    "modelos, resultados, probabilidades_valid = {}, {}, {}\n",
    "def evaluar_validacion(nombre, modelo):\n",
    "    # Entrenamos con ajuste y guardamos probabilidades únicamente de validación.\n",
    "    modelos[nombre] = modelo.fit(X_fit, y_fit)\n",
    "    probabilidades_valid[nombre] = modelo.predict_proba(X_valid)[:, 1]\n",
    "    resultados[nombre] = metricas(y_valid, probabilidades_valid[nombre])\n",
    "    return pd.DataFrame(resultados).T.rename_axis(\"Modelo · validación a 0,50\")\n",
    "\n",
    "print(\"Numéricas:\", num_cols)\n",
    "print(\"Categóricas:\", cat_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff6d0b98",
   "metadata": {},
   "source": [
    "## 6. Modelo Baseline — Regresión Logística\n",
    "\n",
    "El **baseline** es la referencia sencilla para comparar mejoras. Usamos regresión logística,\n",
    "fácil de interpretar y exigida en el caso. Primero evaluamos todos los modelos con umbral 0,50:\n",
    "una reserva se señala si su riesgo estimado es al menos 50 %. Después revisaremos ese umbral.\n",
    "\n",
    "| Métrica | Qué significa | Cómo la usamos |\n",
    "|---|---|---|\n",
    "| **ROC-AUC** | Capacidad global de distinguir cancelación/no cancelación; 0,5 es similar al azar y 1 es separación perfecta | Comparamos la capacidad general y optimizamos hiperparámetros |\n",
    "| **Recall** | Porcentaje de todas las cancelaciones reales que detectamos | Es especialmente importante para negocio: si es bajo, muchas cancelaciones pasan desapercibidas |\n",
    "| **Precision** | De las reservas señaladas, qué proporción realmente cancela | Controla intervenciones sobre clientes que no iban a cancelar |\n",
    "| **F1** | Resume el equilibrio entre Precision y Recall | Comprueba el compromiso entre ambas |\n",
    "\n",
    "**No elegimos solo por ROC-AUC ni solo por Recall.** Buscamos equilibrio entre detección,\n",
    "precisión, F1 y volumen de reservas señaladas. El mínimo provisional de Precision del 70 %\n",
    "es una referencia de trabajo; los costes y la capacidad del hotel deberán validar la decisión.\n",
    "\n",
    "**Recorrido:** Baseline → candidatos → comparación inicial → finalistas → optimización de\n",
    "RF y HGB → comparación posterior → thresholds → comparación modelo + threshold → selección\n",
    "final → reentrenamiento → TEST → interpretabilidad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e66e609",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-09-24T13:11:18.737918Z",
     "shell.execute_reply": "2026-09-24T13:11:18.735402Z"
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>ROC-AUC</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Modelo · validación a 0,50</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logística</th>\n",
       "      <td>0.871</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            ROC-AUC  Precision  Recall    F1\n",
       "Modelo · validación a 0,50                                  \n",
       "Logística                     0.871      0.723   0.732 0.727"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creamos la regresión logística y la evaluamos con la función anterior.\n",
    "baseline = pipeline(LogisticRegression(C=1.0, solver=\"lbfgs\", max_iter=2000, random_state=SEED))\n",
    "display(evaluar_validacion(\"Logística\", baseline))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97bdfd22",
   "metadata": {
    "result_key": "baseline"
   },
   "source": [
    "**Resultado en validación a 0,50:** ROC-AUC **0,8714**, Precision **72,3 %**, Recall **73,2 %**, F1 **72,7 %**.\n",
    "Detectamos aproximadamente 73 de cada cien cancelaciones;\n",
    "de cada cien alertas, unas 72 son correctas.\n",
    "\n",
    "**Decisión:** usamos estos resultados recalculados como referencia. Los árboles pueden mejorar\n",
    "la combinación de detección y falsas alarmas; no basta con superar una sola métrica."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fc9a971",
   "metadata": {},
   "source": [
    "## 7. Modelos candidatos\n",
    "\n",
    "Además del baseline, comparamos Logistic Regression Balanced, Random Forest e HistGradientBoosting.\n",
    "La logística balanceada da más peso a las cancelaciones al aprender; comprobaremos si esto\n",
    "compensa el aumento de falsas alarmas.\n",
    "\n",
    "**Random Forest:** combina muchos árboles de decisión entrenados de forma independiente\n",
    "y utiliza su resultado conjunto para realizar la predicción.\n",
    "\n",
    "**HistGradientBoosting:** construye árboles de forma secuencial, donde cada nuevo árbol\n",
    "intenta corregir los errores de los anteriores.\n",
    "\n",
    "Conservamos las configuraciones iniciales. En boosting, `early_stopping=False` evita\n",
    "una partición aleatoria interna: todas las comprobaciones se hacen por fechas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e90d5d36",
   "metadata": {
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     "shell.execute_reply": "2026-09-24T13:11:23.237376Z"
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    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>ROC-AUC</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Modelo · validación a 0,50</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logística</th>\n",
       "      <td>0.871</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logística balanced</th>\n",
       "      <td>0.872</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            ROC-AUC  Precision  Recall    F1\n",
       "Modelo · validación a 0,50                                  \n",
       "Logística                     0.871      0.723   0.732 0.727\n",
       "Logística balanced            0.872      0.654   0.833 0.732"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Damos más peso a las cancelaciones durante el entrenamiento de la logística.\n",
    "balanced = pipeline(LogisticRegression(C=1.0, class_weight=\"balanced\", solver=\"lbfgs\",\n",
    "                                       max_iter=2000, random_state=SEED))\n",
    "display(evaluar_validacion(\"Logística balanced\", balanced))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c7575092",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:11:23.245485Z",
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     "shell.execute_reply": "2026-09-24T13:11:32.947068Z"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>ROC-AUC</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Modelo · validación a 0,50</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logística</th>\n",
       "      <td>0.871</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logística balanced</th>\n",
       "      <td>0.872</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.895</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HistGradientBoosting</th>\n",
       "      <td>0.902</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            ROC-AUC  Precision  Recall    F1\n",
       "Modelo · validación a 0,50                                  \n",
       "Logística                     0.871      0.723   0.732 0.727\n",
       "Logística balanced            0.872      0.654   0.833 0.732\n",
       "Random Forest                 0.895      0.832   0.571 0.677\n",
       "HistGradientBoosting          0.902      0.810   0.621 0.703"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Probamos dos modelos de árboles sin cambiar datos ni criterio de evaluación.\n",
    "forest = pipeline(RandomForestClassifier(n_estimators=160, max_depth=14, min_samples_leaf=15,\n",
    "                                          max_features=\"sqrt\", n_jobs=4, random_state=SEED))\n",
    "boosting = pipeline(HistGradientBoostingClassifier(max_iter=150, learning_rate=0.08,\n",
    "                                                   max_leaf_nodes=15, l2_regularization=1.0,\n",
    "                                                   early_stopping=False, random_state=SEED))\n",
    "# Guardamos sus métricas en la misma tabla que el baseline.\n",
    "evaluar_validacion(\"Random Forest\", forest)\n",
    "display(evaluar_validacion(\"HistGradientBoosting\", boosting))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf502406",
   "metadata": {},
   "source": [
    "### Comparación inicial · VALIDATION, threshold 0,50\n",
    "\n",
    "Los cuatro indicadores se calculan sobre las mismas reservas. Una buena discriminación\n",
    "no garantiza detectar muchas cancelaciones a un umbral concreto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6d06c50b",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-09-24T13:11:32.961169Z",
     "shell.execute_reply": "2026-09-24T13:11:32.960124Z"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>ROC-AUC</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Modelo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logística</th>\n",
       "      <td>0.871</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logística balanced</th>\n",
       "      <td>0.872</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.895</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HistGradientBoosting</th>\n",
       "      <td>0.902</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ROC-AUC  Precision  Recall    F1\n",
       "Modelo                                                \n",
       "Logística               0.871      0.723   0.732 0.727\n",
       "Logística balanced      0.872      0.654   0.833 0.732\n",
       "Random Forest           0.895      0.832   0.571 0.677\n",
       "HistGradientBoosting    0.902      0.810   0.621 0.703"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Random Forest:** ROC-AUC 0.8950, Precision 83.2%, Recall 57.1% y F1 67.7%."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**HistGradientBoosting:** ROC-AUC 0.9018, Precision 81.0%, Recall 62.1% y F1 70.3%."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparacion_inicial = pd.DataFrame(resultados).T.rename_axis(\"Modelo\")\n",
    "display(comparacion_inicial)\n",
    "for nombre in [\"Random Forest\", \"HistGradientBoosting\"]:\n",
    "    r = comparacion_inicial.loc[nombre]\n",
    "    display(Markdown(f\"**{nombre}:** ROC-AUC {r['ROC-AUC']:.4f}, Precision {r.Precision:.1%}, \"\n",
    "                     f\"Recall {r.Recall:.1%} y F1 {r.F1:.1%}.\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef59039d",
   "metadata": {},
   "source": [
    "### Finalistas: Random Forest e HistGradientBoosting\n",
    "\n",
    "Optimizamos ambas familias antes de compararlas como alternativas operativas.\n",
    "La logística y su versión balanceada quedan como referencias iniciales.\n",
    "**Todavía no hay ganador: la unidad que seleccionamos es MODELO + THRESHOLD.**\n",
    "\n",
    "## 8. Optimización de ambos finalistas\n",
    "\n",
    "Los hiperparámetros son configuraciones elegidas antes de entrenar.\n",
    "RandomizedSearchCV prueba 12 combinaciones de Random Forest con rangos moderados;\n",
    "GridSearchCV mantiene las 4 combinaciones actuales de HistGradientBoosting.\n",
    "\n",
    "En Random Forest variamos número de árboles (120, 160, 220), profundidad (10, 14, 18),\n",
    "mínimo para dividir (2, 10, 20), mínimo por hoja (5, 15, 25), variables por división\n",
    "(`sqrt`, 0,5) y peso de clases (sin ponderar o `balanced`). En HGB probamos 15 y 31 hojas,\n",
    "y regularización L2 de 0,1 y 5,0; mantenemos 150 iteraciones y tasa de aprendizaje 0,08.\n",
    "\n",
    "Ambas búsquedas utilizan **ROC-AUC medio en tres ventanas temporales de TRAIN/FIT**.\n",
    "El preprocesado se aprende dentro de cada fold. TimeSeriesSplit se aplica a los días\n",
    "ordenados para mantener las reservas de un mismo día juntas. VALIDATION y TEST quedan fuera.\n",
    "[Documentación de TimeSeriesSplit](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html).\n",
    "\n",
    "ROC-AUC se utiliza durante la optimización porque mide la capacidad general del modelo\n",
    "para separar reservas canceladas y no canceladas sin depender de un threshold concreto.\n",
    "La selección final también considera Recall, Precision, F1 y porcentaje de reservas señaladas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5ecc66f8",
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     "iopub.execute_input": "2026-09-24T13:11:32.964688Z",
     "iopub.status.busy": "2026-09-24T13:11:32.963691Z",
     "iopub.status.idle": "2026-09-24T13:14:20.060258Z",
     "shell.execute_reply": "2026-09-24T13:14:20.059427Z"
    }
   },
   "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>Train hasta</th>\n",
       "      <th>Validación desde</th>\n",
       "      <th>Validación hasta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-13</td>\n",
       "      <td>2015-11-14</td>\n",
       "      <td>2016-03-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-28</td>\n",
       "      <td>2016-03-29</td>\n",
       "      <td>2016-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-11</td>\n",
       "      <td>2016-08-12</td>\n",
       "      <td>2016-12-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Train hasta Validación desde Validación hasta\n",
       "1  2015-11-13       2015-11-14       2016-03-28\n",
       "2  2016-03-28       2016-03-29       2016-08-11\n",
       "3  2016-08-11       2016-08-12       2016-12-25"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest optimizado · mejores hiperparámetros: {'model__n_estimators': 120, 'model__min_samples_split': 2, 'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 18, 'model__class_weight': None}\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>params</th>\n",
       "      <th>ROC-AUC CV</th>\n",
       "      <th>Desviación CV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'model__n_estimators': 120, 'model__min_sampl...</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.030</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'model__n_estimators': 120, 'model__min_sampl...</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.020</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'model__n_estimators': 220, 'model__min_sampl...</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.037</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'model__n_estimators': 120, 'model__min_sampl...</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'model__n_estimators': 120, 'model__min_sampl...</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'model__n_estimators': 120, 'model__min_sampl...</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>{'model__n_estimators': 120, 'model__min_sampl...</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>{'model__n_estimators': 160, 'model__min_sampl...</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>{'model__n_estimators': 220, 'model__min_sampl...</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>{'model__n_estimators': 120, 'model__min_sampl...</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>{'model__n_estimators': 220, 'model__min_sampl...</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>{'model__n_estimators': 220, 'model__min_sampl...</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                               params  ROC-AUC CV  \\\n",
       "0   {'model__n_estimators': 120, 'model__min_sampl...       0.875   \n",
       "1   {'model__n_estimators': 120, 'model__min_sampl...       0.884   \n",
       "2   {'model__n_estimators': 220, 'model__min_sampl...       0.864   \n",
       "3   {'model__n_estimators': 120, 'model__min_sampl...       0.873   \n",
       "4   {'model__n_estimators': 120, 'model__min_sampl...       0.874   \n",
       "5   {'model__n_estimators': 120, 'model__min_sampl...       0.875   \n",
       "6   {'model__n_estimators': 120, 'model__min_sampl...       0.879   \n",
       "7   {'model__n_estimators': 160, 'model__min_sampl...       0.865   \n",
       "8   {'model__n_estimators': 220, 'model__min_sampl...       0.862   \n",
       "9   {'model__n_estimators': 120, 'model__min_sampl...       0.865   \n",
       "10  {'model__n_estimators': 220, 'model__min_sampl...       0.872   \n",
       "11  {'model__n_estimators': 220, 'model__min_sampl...       0.876   \n",
       "\n",
       "    Desviación CV  \n",
       "0           0.030  \n",
       "1           0.020  \n",
       "2           0.037  \n",
       "3           0.022  \n",
       "4           0.027  \n",
       "5           0.030  \n",
       "6           0.031  \n",
       "7           0.041  \n",
       "8           0.038  \n",
       "9           0.036  \n",
       "10          0.029  \n",
       "11          0.028  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HistGradientBoosting optimizado · mejores hiperparámetros: {'model__l2_regularization': 0.1, 'model__max_leaf_nodes': 15}\n"
     ]
    },
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>params</th>\n",
       "      <th>ROC-AUC CV</th>\n",
       "      <th>Desviación CV</th>\n",
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       "      <th>0</th>\n",
       "      <td>{'model__l2_regularization': 0.1, 'model__max_...</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.025</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'model__l2_regularization': 0.1, 'model__max_...</td>\n",
       "      <td>0.885</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'model__l2_regularization': 5.0, 'model__max_...</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.034</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'model__l2_regularization': 5.0, 'model__max_...</td>\n",
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       "                                              params  ROC-AUC CV  \\\n",
       "0  {'model__l2_regularization': 0.1, 'model__max_...       0.885   \n",
       "1  {'model__l2_regularization': 0.1, 'model__max_...       0.885   \n",
       "2  {'model__l2_regularization': 5.0, 'model__max_...       0.881   \n",
       "3  {'model__l2_regularization': 5.0, 'model__max_...       0.883   \n",
       "\n",
       "   Desviación CV  \n",
       "0          0.025  \n",
       "1          0.027  \n",
       "2          0.034  \n",
       "3          0.032  "
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "# Creamos tres comprobaciones temporales: aprendemos con días anteriores y validamos después.\n",
    "dias = np.sort(fit.arrival_date.unique())\n",
    "folds, ventanas = [], []\n",
    "for tr_dias, va_dias in TimeSeriesSplit(n_splits=3).split(dias):\n",
    "    tr = np.flatnonzero(fit.arrival_date.isin(dias[tr_dias]))\n",
    "    va = np.flatnonzero(fit.arrival_date.isin(dias[va_dias]))\n",
    "    assert fit.iloc[tr].arrival_date.max() < fit.iloc[va].arrival_date.min()\n",
    "    assert fit.iloc[va].arrival_date.max() < valid.arrival_date.min()\n",
    "    assert y_fit.iloc[tr].nunique() == y_fit.iloc[va].nunique() == 2\n",
    "    folds.append((tr, va))\n",
    "    ventanas.append({\"Train hasta\": fit.iloc[tr].arrival_date.max().date(),\n",
    "                     \"Validación desde\": fit.iloc[va].arrival_date.min().date(),\n",
    "                     \"Validación hasta\": fit.iloc[va].arrival_date.max().date()})\n",
    "display(pd.DataFrame(ventanas, index=[1, 2, 3]))\n",
    "\n",
    "espacio_rf = {\"model__n_estimators\": [120, 160, 220],\n",
    "              \"model__max_depth\": [10, 14, 18],\n",
    "              \"model__min_samples_split\": [2, 10, 20],\n",
    "              \"model__min_samples_leaf\": [5, 15, 25],\n",
    "              \"model__max_features\": [\"sqrt\", 0.5],\n",
    "              \"model__class_weight\": [None, \"balanced\"]}\n",
    "search_rf = RandomizedSearchCV(clone(forest), espacio_rf, n_iter=12,\n",
    "    scoring=\"roc_auc\", cv=folds, random_state=SEED, n_jobs=1,\n",
    "    refit=True, error_score=\"raise\", verbose=1)\n",
    "search_hgb = GridSearchCV(clone(boosting),\n",
    "    {\"model__max_leaf_nodes\": [15, 31], \"model__l2_regularization\": [0.1, 5.0]},\n",
    "    scoring=\"roc_auc\", cv=folds, n_jobs=1, refit=True, error_score=\"raise\", verbose=1)\n",
    "busquedas = {\"Random Forest optimizado\": search_rf,\n",
    "             \"HistGradientBoosting optimizado\": search_hgb}\n",
    "for nombre, busqueda in busquedas.items():\n",
    "    busqueda.fit(X_fit, y_fit)\n",
    "    modelos[nombre] = busqueda.best_estimator_\n",
    "    probabilidades_valid[nombre] = busqueda.best_estimator_.predict_proba(X_valid)[:, 1]\n",
    "    resultados[nombre] = metricas(y_valid, probabilidades_valid[nombre])\n",
    "    print(nombre, \"· mejores hiperparámetros:\", busqueda.best_params_)\n",
    "    display(pd.DataFrame(busqueda.cv_results_)[[\"params\", \"mean_test_score\", \"std_test_score\"]]\n",
    "            .rename(columns={\"mean_test_score\": \"ROC-AUC CV\", \"std_test_score\": \"Desviación CV\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4454e565",
   "metadata": {},
   "source": [
    "### Comparación después de optimización · VALIDATION, threshold 0,50\n",
    "\n",
    "La optimización busca mejorar la métrica utilizada durante la validación cruzada.\n",
    "Esto no garantiza que todas las métricas mejoren en una ventana concreta de validación.\n",
    "\n",
    "Comparamos ambas versiones de cada familia. El RF optimizado pasa al análisis de umbrales.\n",
    "Para HGB conservaremos la versión inicial si el ajuste no aporta una mejora operativa útil;\n",
    "comprobaremos también sus umbrales antes de cerrar esa decisión."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d0964cfc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:14:20.062891Z",
     "iopub.status.busy": "2026-09-24T13:14:20.062439Z",
     "iopub.status.idle": "2026-09-24T13:14:20.076028Z",
     "shell.execute_reply": "2026-09-24T13:14:20.075151Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROC-AUC</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Modelo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.895</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest optimizado</th>\n",
       "      <td>0.893</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HistGradientBoosting</th>\n",
       "      <td>0.902</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HistGradientBoosting optimizado</th>\n",
       "      <td>0.900</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 ROC-AUC  Precision  Recall    F1\n",
       "Modelo                                                           \n",
       "Random Forest                      0.895      0.832   0.571 0.677\n",
       "Random Forest optimizado           0.893      0.834   0.571 0.678\n",
       "HistGradientBoosting               0.902      0.810   0.621 0.703\n",
       "HistGradientBoosting optimizado    0.900      0.811   0.618 0.701"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Random Forest optimizado:** mejor ROC-AUC medio en CV temporal = 0.8843."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**HistGradientBoosting optimizado:** mejor ROC-AUC medio en CV temporal = 0.8853."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "nombres_post = [\"Random Forest\", \"Random Forest optimizado\",\n",
    "                \"HistGradientBoosting\", \"HistGradientBoosting optimizado\"]\n",
    "comparacion_post = pd.DataFrame(resultados).T.loc[nombres_post].rename_axis(\"Modelo\")\n",
    "display(comparacion_post)\n",
    "for nombre, busqueda in busquedas.items():\n",
    "    display(Markdown(f\"**{nombre}:** mejor ROC-AUC medio en CV temporal = {busqueda.best_score_:.4f}.\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69644bb6",
   "metadata": {},
   "source": [
    "## 9. Análisis de thresholds y comparación final\n",
    "\n",
    "El modelo devuelve una probabilidad. El threshold es el punto a partir del cual convertimos\n",
    "esa probabilidad en una predicción de cancelación.\n",
    "\n",
    "- Threshold = 0,50: riesgo ≥ 50 % se marca como cancelación.\n",
    "- Threshold = 0,30: riesgo ≥ 30 % se marca como cancelación.\n",
    "\n",
    "Bajar el threshold suele aumentar Recall, pero también genera más falsas alarmas y reduce Precision.\n",
    "Un Recall muy alto permite detectar más cancelaciones, pero puede conseguirse marcando\n",
    "demasiadas reservas como riesgo. Esto aumenta los falsos positivos y el número de clientes\n",
    "sobre los que habría que actuar. Por este motivo buscamos un equilibrio entre Recall y\n",
    "Precision, teniendo también en cuenta cuántas reservas quedarían señaladas.\n",
    "\n",
    "Probamos **0,20; 0,25; 0,30; 0,35; 0,40; 0,45; 0,50; 0,55; 0,60** solo en VALIDATION.\n",
    "La tabla auxiliar permite revisar las dos versiones de HGB; después dejamos únicamente\n",
    "los dos finalistas en la tabla y en los gráficos.\n",
    "\n",
    "**Criterio de lectura, no máximo Recall automático:** buscamos buena discriminación,\n",
    "Precision al menos cercana al 70 %, Recall razonablemente alto y F1 competitivo.\n",
    "Entre alternativas con equilibrio parecido preferimos menos falsas alarmas e intervenciones.\n",
    "Los umbrales candidatos y la decisión se documentan tras leer esta comparación.\n",
    "Sin una estimación económica del coste del falso negativo y del falso positivo,\n",
    "no existe un único modelo objetivamente perfecto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "85106aba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:14:20.078536Z",
     "iopub.status.busy": "2026-09-24T13:14:20.078160Z",
     "iopub.status.idle": "2026-09-24T13:14:20.454479Z",
     "shell.execute_reply": "2026-09-24T13:14:20.453190Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Modelo</th>\n",
       "      <th>Threshold</th>\n",
       "      <th>ROC-AUC</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
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       "      <th>% reservas señaladas</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.730</td>\n",
       "      <td>55.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.751</td>\n",
       "      <td>48.543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.746</td>\n",
       "      <td>39.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.730</td>\n",
       "      <td>32.882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.714</td>\n",
       "      <td>29.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.700</td>\n",
       "      <td>27.573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.678</td>\n",
       "      <td>25.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.658</td>\n",
       "      <td>23.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.617</td>\n",
       "      <td>20.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.689</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.759</td>\n",
       "      <td>44.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.761</td>\n",
       "      <td>40.474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.757</td>\n",
       "      <td>36.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.747</td>\n",
       "      <td>33.739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.735</td>\n",
       "      <td>31.779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.725</td>\n",
       "      <td>30.404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.703</td>\n",
       "      <td>28.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.686</td>\n",
       "      <td>26.649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.669</td>\n",
       "      <td>25.203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HistGradientBoosting optimizado</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.757</td>\n",
       "      <td>45.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HistGradientBoosting optimizado</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.760</td>\n",
       "      <td>40.315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HistGradientBoosting optimizado</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.757</td>\n",
       "      <td>36.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HistGradientBoosting optimizado</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.738</td>\n",
       "      <td>32.964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HistGradientBoosting optimizado</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.726</td>\n",
       "      <td>31.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HistGradientBoosting optimizado</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.717</td>\n",
       "      <td>29.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>HistGradientBoosting optimizado</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.701</td>\n",
       "      <td>27.927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>HistGradientBoosting optimizado</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.685</td>\n",
       "      <td>26.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>HistGradientBoosting optimizado</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.670</td>\n",
       "      <td>25.218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Modelo  Threshold  ROC-AUC  Precision  Recall  \\\n",
       "0          Random Forest optimizado      0.200    0.893      0.608   0.913   \n",
       "1          Random Forest optimizado      0.250    0.893      0.659   0.872   \n",
       "2          Random Forest optimizado      0.300    0.893      0.718   0.778   \n",
       "3          Random Forest optimizado      0.350    0.893      0.772   0.692   \n",
       "4          Random Forest optimizado      0.400    0.893      0.793   0.648   \n",
       "5          Random Forest optimizado      0.450    0.893      0.816   0.614   \n",
       "6          Random Forest optimizado      0.500    0.893      0.834   0.571   \n",
       "7          Random Forest optimizado      0.550    0.893      0.848   0.538   \n",
       "8          Random Forest optimizado      0.600    0.893      0.872   0.478   \n",
       "9              HistGradientBoosting      0.200    0.902      0.689   0.845   \n",
       "10             HistGradientBoosting      0.250    0.902      0.725   0.800   \n",
       "11             HistGradientBoosting      0.300    0.902      0.762   0.753   \n",
       "12             HistGradientBoosting      0.350    0.902      0.780   0.717   \n",
       "13             HistGradientBoosting      0.400    0.902      0.792   0.687   \n",
       "14             HistGradientBoosting      0.450    0.902      0.800   0.663   \n",
       "15             HistGradientBoosting      0.500    0.902      0.810   0.621   \n",
       "16             HistGradientBoosting      0.550    0.902      0.814   0.592   \n",
       "17             HistGradientBoosting      0.600    0.902      0.821   0.565   \n",
       "18  HistGradientBoosting optimizado      0.200    0.900      0.686   0.845   \n",
       "19  HistGradientBoosting optimizado      0.250    0.900      0.726   0.798   \n",
       "20  HistGradientBoosting optimizado      0.300    0.900      0.763   0.750   \n",
       "21  HistGradientBoosting optimizado      0.350    0.900      0.779   0.700   \n",
       "22  HistGradientBoosting optimizado      0.400    0.900      0.791   0.670   \n",
       "23  HistGradientBoosting optimizado      0.450    0.900      0.800   0.650   \n",
       "24  HistGradientBoosting optimizado      0.500    0.900      0.811   0.618   \n",
       "25  HistGradientBoosting optimizado      0.550    0.900      0.815   0.591   \n",
       "26  HistGradientBoosting optimizado      0.600    0.900      0.822   0.566   \n",
       "\n",
       "      F1  % reservas señaladas  \n",
       "0  0.730                55.027  \n",
       "1  0.751                48.543  \n",
       "2  0.746                39.730  \n",
       "3  0.730                32.882  \n",
       "4  0.714                29.958  \n",
       "5  0.700                27.573  \n",
       "6  0.678                25.115  \n",
       "7  0.658                23.233  \n",
       "8  0.617                20.088  \n",
       "9  0.759                44.942  \n",
       "10 0.761                40.474  \n",
       "11 0.757                36.221  \n",
       "12 0.747                33.739  \n",
       "13 0.735                31.779  \n",
       "14 0.725                30.404  \n",
       "15 0.703                28.127  \n",
       "16 0.686                26.649  \n",
       "17 0.669                25.203  \n",
       "18 0.757                45.122  \n",
       "19 0.760                40.315  \n",
       "20 0.757                36.068  \n",
       "21 0.738                32.964  \n",
       "22 0.726                31.051  \n",
       "23 0.717                29.784  \n",
       "24 0.701                27.927  \n",
       "25 0.685                26.572  \n",
       "26 0.670                25.218  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC-AUC se calcula una vez por modelo y se repite como referencia en la tabla.\n",
    "UMBRALES = np.array([0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60])\n",
    "filas = []\n",
    "for nombre in [\"Random Forest optimizado\", \"HistGradientBoosting\", \"HistGradientBoosting optimizado\"]:\n",
    "    probas = probabilidades_valid[nombre]\n",
    "    auc_modelo = resultados[nombre][\"ROC-AUC\"]\n",
    "    for umbral in UMBRALES:\n",
    "        pred = probas >= umbral\n",
    "        filas.append({\"Modelo\": nombre, \"Threshold\": umbral, \"ROC-AUC\": auc_modelo,\n",
    "                      \"Precision\": precision_score(y_valid, pred, zero_division=0),\n",
    "                      \"Recall\": recall_score(y_valid, pred, zero_division=0),\n",
    "                      \"F1\": f1_score(y_valid, pred, zero_division=0),\n",
    "                      \"% reservas señaladas\": 100 * pred.mean()})\n",
    "tabla_umbrales_auxiliar = pd.DataFrame(filas)\n",
    "display(tabla_umbrales_auxiliar)\n",
    "assert tabla_umbrales_auxiliar.groupby(\"Modelo\")[\"ROC-AUC\"].nunique().eq(1).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "693de941",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:15:40.509569Z",
     "iopub.status.busy": "2026-09-24T13:15:40.509028Z",
     "iopub.status.idle": "2026-09-24T13:15:40.518962Z",
     "shell.execute_reply": "2026-09-24T13:15:40.517392Z"
    }
   },
   "outputs": [],
   "source": [
    "# Decision razonada con VALIDATION; no usa etiquetas de TEST.\n",
    "umbrales_candidatos = {'Random Forest optimizado': 0.3, 'HistGradientBoosting': 0.3}\n",
    "ganador = 'HistGradientBoosting'\n",
    "justificacion_validacion = \"\"\"**Decisión razonada con VALIDATION, antes de consultar TEST:**\n",
    "\n",
    "- **Random Forest optimizado a 0,30:** ROC-AUC 0,8933, Precision 71,8 %, Recall 77,8 %, F1 74,6 % y 39,7 % de reservas señaladas. A 0,25 consigue Recall 87,2 %, pero Precision cae al 65,9 % y señala el 48,5 %. A 0,35 mejora Precision al 77,2 %, a costa de Recall 69,2 % y F1 73,0 %. Elegimos 0,30 como su candidato operativo dentro del rango probado.\n",
    "- **HistGradientBoosting inicial a 0,30:** ROC-AUC 0,9018, Precision 76,2 %, Recall 75,3 %, F1 75,7 % y 36,2 % señaladas. Frente a RF optimizado pierde 2,5 puntos de Recall, pero gana 4,4 puntos de Precision y 1,1 de F1, y reduce en 3,5 puntos el porcentaje de reservas que habría que atender. Su discriminación global también es mejor.\n",
    "- **Por qué HGB a 0,30 y no a 0,25:** a 0,25 obtiene Recall 80,0 %, Precision 72,5 %, F1 76,1 % y 40,5 % señaladas. El F1 solo mejora 0,35 puntos, mientras las intervenciones aumentan 4,25 puntos. A 0,30 se conserva un Recall del 75,3 % y se reduce la carga. Es una preferencia operativa provisional, no un óptimo económico demostrado. A 0,35 el Recall ya baja al 71,7 % y el F1 al 74,7 %.\n",
    "- **Por qué conservar HGB inicial:** el ajuste obtiene ROC-AUC 0,8998 frente a 0,9018. A 0,30 su Precision es 76,3 %, Recall 75,0 %, F1 75,7 % y señala 36,1 %. Esa variación mínima en Precision y volumen no compensa la menor discriminación y detección. Su mejor resultado dentro de la búsqueda no obliga a sustituir la configuración inicial.\n",
    "- **La optimización tampoco garantiza mejorar RF en VALIDATION:** su ROC-AUC pasa de 0,8950 a 0,8933; a 0,50 Precision y F1 mejoran apenas. Se mantiene el RF optimizado como finalista solicitado, sin presentar el tuning como una mejora universal.\n",
    "\n",
    "**Elegimos HistGradientBoosting inicial + threshold 0,30.** Mantiene un Recall razonablemente alto, mejora la calidad de las alertas y F1 frente al RF optimizado y requiere menos intervenciones. Una operación que valore mucho más los falsos negativos podría preferir otro umbral. Sin costes de FP/FN y capacidad de contacto no existe una única alternativa objetivamente perfecta; no seleccionamos por máximo Recall ni solo por ROC-AUC.\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b67f06f",
   "metadata": {},
   "source": [
    "### Precision y Recall frente al threshold\n",
    "\n",
    "La línea vertical marca el umbral candidato de cada finalista; la horizontal recuerda\n",
    "el mínimo provisional de Precision. La tabla final resume solo las dos alternativas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "06d03cd9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:15:40.524222Z",
     "iopub.status.busy": "2026-09-24T13:15:40.522996Z",
     "iopub.status.idle": "2026-09-24T13:15:41.053826Z",
     "shell.execute_reply": "2026-09-24T13:15:41.053031Z"
    }
   },
   "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>Modelo</th>\n",
       "      <th>Threshold</th>\n",
       "      <th>ROC-AUC</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>% reservas señaladas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.730</td>\n",
       "      <td>55.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.751</td>\n",
       "      <td>48.543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.746</td>\n",
       "      <td>39.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.730</td>\n",
       "      <td>32.882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.714</td>\n",
       "      <td>29.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.700</td>\n",
       "      <td>27.573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.678</td>\n",
       "      <td>25.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.658</td>\n",
       "      <td>23.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.617</td>\n",
       "      <td>20.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.689</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.759</td>\n",
       "      <td>44.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.761</td>\n",
       "      <td>40.474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.757</td>\n",
       "      <td>36.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.747</td>\n",
       "      <td>33.739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.735</td>\n",
       "      <td>31.779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.725</td>\n",
       "      <td>30.404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.703</td>\n",
       "      <td>28.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.686</td>\n",
       "      <td>26.649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.669</td>\n",
       "      <td>25.203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Modelo  Threshold  ROC-AUC  Precision  Recall    F1  \\\n",
       "0   Random Forest optimizado      0.200    0.893      0.608   0.913 0.730   \n",
       "1   Random Forest optimizado      0.250    0.893      0.659   0.872 0.751   \n",
       "2   Random Forest optimizado      0.300    0.893      0.718   0.778 0.746   \n",
       "3   Random Forest optimizado      0.350    0.893      0.772   0.692 0.730   \n",
       "4   Random Forest optimizado      0.400    0.893      0.793   0.648 0.714   \n",
       "5   Random Forest optimizado      0.450    0.893      0.816   0.614 0.700   \n",
       "6   Random Forest optimizado      0.500    0.893      0.834   0.571 0.678   \n",
       "7   Random Forest optimizado      0.550    0.893      0.848   0.538 0.658   \n",
       "8   Random Forest optimizado      0.600    0.893      0.872   0.478 0.617   \n",
       "9       HistGradientBoosting      0.200    0.902      0.689   0.845 0.759   \n",
       "10      HistGradientBoosting      0.250    0.902      0.725   0.800 0.761   \n",
       "11      HistGradientBoosting      0.300    0.902      0.762   0.753 0.757   \n",
       "12      HistGradientBoosting      0.350    0.902      0.780   0.717 0.747   \n",
       "13      HistGradientBoosting      0.400    0.902      0.792   0.687 0.735   \n",
       "14      HistGradientBoosting      0.450    0.902      0.800   0.663 0.725   \n",
       "15      HistGradientBoosting      0.500    0.902      0.810   0.621 0.703   \n",
       "16      HistGradientBoosting      0.550    0.902      0.814   0.592 0.686   \n",
       "17      HistGradientBoosting      0.600    0.902      0.821   0.565 0.669   \n",
       "\n",
       "    % reservas señaladas  \n",
       "0                 55.027  \n",
       "1                 48.543  \n",
       "2                 39.730  \n",
       "3                 32.882  \n",
       "4                 29.958  \n",
       "5                 27.573  \n",
       "6                 25.115  \n",
       "7                 23.233  \n",
       "8                 20.088  \n",
       "9                 44.942  \n",
       "10                40.474  \n",
       "11                36.221  \n",
       "12                33.739  \n",
       "13                31.779  \n",
       "14                30.404  \n",
       "15                28.127  \n",
       "16                26.649  \n",
       "17                25.203  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1320x440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Modelo</th>\n",
       "      <th>Threshold</th>\n",
       "      <th>ROC-AUC</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>% reservas señaladas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest optimizado</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.746</td>\n",
       "      <td>39.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.757</td>\n",
       "      <td>36.221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Modelo  Threshold  ROC-AUC  Precision  Recall    F1  \\\n",
       "0  Random Forest optimizado      0.300    0.893      0.718   0.778 0.746   \n",
       "1      HistGradientBoosting      0.300    0.902      0.762   0.753 0.757   \n",
       "\n",
       "   % reservas señaladas  \n",
       "0                39.730  \n",
       "1                36.221  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Decisión razonada con VALIDATION, antes de consultar TEST:**\n",
       "\n",
       "- **Random Forest optimizado a 0,30:** ROC-AUC 0,8933, Precision 71,8 %, Recall 77,8 %, F1 74,6 % y 39,7 % de reservas señaladas. A 0,25 consigue Recall 87,2 %, pero Precision cae al 65,9 % y señala el 48,5 %. A 0,35 mejora Precision al 77,2 %, a costa de Recall 69,2 % y F1 73,0 %. Elegimos 0,30 como su candidato operativo dentro del rango probado.\n",
       "- **HistGradientBoosting inicial a 0,30:** ROC-AUC 0,9018, Precision 76,2 %, Recall 75,3 %, F1 75,7 % y 36,2 % señaladas. Frente a RF optimizado pierde 2,5 puntos de Recall, pero gana 4,4 puntos de Precision y 1,1 de F1, y reduce en 3,5 puntos el porcentaje de reservas que habría que atender. Su discriminación global también es mejor.\n",
       "- **Por qué HGB a 0,30 y no a 0,25:** a 0,25 obtiene Recall 80,0 %, Precision 72,5 %, F1 76,1 % y 40,5 % señaladas. El F1 solo mejora 0,35 puntos, mientras las intervenciones aumentan 4,25 puntos. A 0,30 se conserva un Recall del 75,3 % y se reduce la carga. Es una preferencia operativa provisional, no un óptimo económico demostrado. A 0,35 el Recall ya baja al 71,7 % y el F1 al 74,7 %.\n",
       "- **Por qué conservar HGB inicial:** el ajuste obtiene ROC-AUC 0,8998 frente a 0,9018. A 0,30 su Precision es 76,3 %, Recall 75,0 %, F1 75,7 % y señala 36,1 %. Esa variación mínima en Precision y volumen no compensa la menor discriminación y detección. Su mejor resultado dentro de la búsqueda no obliga a sustituir la configuración inicial.\n",
       "- **La optimización tampoco garantiza mejorar RF en VALIDATION:** su ROC-AUC pasa de 0,8950 a 0,8933; a 0,50 Precision y F1 mejoran apenas. Se mantiene el RF optimizado como finalista solicitado, sin presentar el tuning como una mejora universal.\n",
       "\n",
       "**Elegimos HistGradientBoosting inicial + threshold 0,30.** Mantiene un Recall razonablemente alto, mejora la calidad de las alertas y F1 frente al RF optimizado y requiere menos intervenciones. Una operación que valore mucho más los falsos negativos podría preferir otro umbral. Sin costes de FP/FN y capacidad de contacto no existe una única alternativa objetivamente perfecta; no seleccionamos por máximo Recall ni solo por ROC-AUC."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tabla_umbrales = tabla_umbrales_auxiliar.loc[\n",
    "    tabla_umbrales_auxiliar.Modelo.isin(umbrales_candidatos)].copy()\n",
    "display(tabla_umbrales)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4), sharey=True)\n",
    "for ax, (nombre, umbral) in zip(axes, umbrales_candidatos.items()):\n",
    "    datos = tabla_umbrales.loc[tabla_umbrales.Modelo == nombre]\n",
    "    ax.plot(datos.Threshold, datos.Precision, marker=\"o\", label=\"Precision\")\n",
    "    ax.plot(datos.Threshold, datos.Recall, marker=\"o\", label=\"Recall\")\n",
    "    ax.axhline(MIN_PRECISION, color=\"gray\", ls=\":\", label=\"Precision de referencia\")\n",
    "    ax.axvline(umbral, color=\"black\", ls=\"--\", label=f\"Candidato: {umbral:.2f}\")\n",
    "    ax.set(title=nombre, xlabel=\"Threshold\", ylim=(0, 1), xticks=UMBRALES)\n",
    "    ax.yaxis.set_major_formatter(mticker.PercentFormatter(1))\n",
    "    ax.legend(fontsize=8)\n",
    "axes[0].set_ylabel(\"Precision / Recall\")\n",
    "plt.tight_layout(); plt.show()\n",
    "comparacion_final = pd.concat([\n",
    "    tabla_umbrales.loc[(tabla_umbrales.Modelo == nombre) & np.isclose(tabla_umbrales.Threshold, umbral)]\n",
    "    for nombre, umbral in umbrales_candidatos.items()], ignore_index=True)\n",
    "assert len(comparacion_final) == 2\n",
    "display(comparacion_final)\n",
    "display(Markdown(justificacion_validacion))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df78bfb6",
   "metadata": {},
   "source": [
    "### Curva ROC de los modelos principales · VALIDATION\n",
    "\n",
    "ROC-AUC mide la capacidad global de discriminación y no depende del threshold seleccionado.\n",
    "El eje horizontal muestra falsas alarmas entre las reservas que no cancelan y el vertical\n",
    "las cancelaciones detectadas. La curva complementa la comparación operativa; no decide por sí sola el ganador."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "271ca846",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:15:41.057463Z",
     "iopub.status.busy": "2026-09-24T13:15:41.057038Z",
     "iopub.status.idle": "2026-09-24T13:15:41.282659Z",
     "shell.execute_reply": "2026-09-24T13:15:41.280595Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x495 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4.5))\n",
    "for nombre in [\"Logística\", \"Logística balanced\", *umbrales_candidatos]:\n",
    "    curva = RocCurveDisplay.from_predictions(y_valid, probabilidades_valid[nombre], ax=ax, name=nombre)\n",
    "    curva.line_.set_label(f\"{nombre} · AUC {resultados[nombre]['ROC-AUC']:.3f}\")\n",
    "ax.plot([0, 1], [0, 1], \"--\", color=\"gray\", label=\"Referencia al azar\")\n",
    "ax.set(title=\"Curvas ROC · validación\", xlabel=\"False Positive Rate · falsas alarmas\",\n",
    "       ylabel=\"True Positive Rate · Recall\")\n",
    "ax.legend(fontsize=8, loc=\"lower right\")\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdc4566d",
   "metadata": {},
   "source": [
    "### Selección final · MODELO + THRESHOLD\n",
    "\n",
    "Fijamos la combinación justificada en validación. No se ordenan los candidatos por máximo Recall.\n",
    "El código comprueba que el umbral elegido pertenece a la comparación anterior.\n",
    "Los hiperparámetros son los del pipeline de esa versión; no se modifican tras consultar TEST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a79c4744",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:15:41.285316Z",
     "iopub.status.busy": "2026-09-24T13:15:41.284983Z",
     "iopub.status.idle": "2026-09-24T13:15:41.295180Z",
     "shell.execute_reply": "2026-09-24T13:15:41.293917Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Decisión cerrada antes de TEST: HistGradientBoosting, threshold 0.30.**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "categorical_features    from_dtype\n",
       "class_weight                  None\n",
       "early_stopping               False\n",
       "interaction_cst               None\n",
       "l2_regularization            1.000\n",
       "learning_rate                0.080\n",
       "loss                      log_loss\n",
       "max_bins                       255\n",
       "max_depth                     None\n",
       "max_features                 1.000\n",
       "max_iter                       150\n",
       "max_leaf_nodes                  15\n",
       "min_samples_leaf                20\n",
       "monotonic_cst                 None\n",
       "n_iter_no_change                10\n",
       "random_state                    42\n",
       "scoring                       loss\n",
       "tol                          0.000\n",
       "validation_fraction          0.100\n",
       "verbose                          0\n",
       "warm_start                   False\n",
       "Name: Configuración final, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "THRESHOLD = float(umbrales_candidatos[ganador])\n",
    "seleccion = comparacion_final.loc[comparacion_final.Modelo == ganador].iloc[0]\n",
    "assert np.isclose(THRESHOLD, seleccion.Threshold)\n",
    "parametros_finales = modelos[ganador].named_steps[\"model\"].get_params()\n",
    "display(Markdown(f\"**Decisión cerrada antes de TEST: {ganador}, threshold {THRESHOLD:.2f}.**\"))\n",
    "display(pd.Series(parametros_finales, name=\"Configuración final\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c14fbfc9",
   "metadata": {},
   "source": [
    "## 10. Reentrenamiento y evaluación final en TEST\n",
    "\n",
    "Reentrenamos con todo TRAIN de desarrollo (ajuste + validación), incluyendo el preprocesado.\n",
    "Después obtenemos las probabilidades de TEST una sola vez y las reutilizamos para métricas,\n",
    "matriz e interpretabilidad. Ningún resultado de TEST cambia el modelo, sus parámetros o el umbral."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d12596da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:15:41.299494Z",
     "iopub.status.busy": "2026-09-24T13:15:41.298808Z",
     "iopub.status.idle": "2026-09-24T13:15:47.212655Z",
     "shell.execute_reply": "2026-09-24T13:15:47.210069Z"
    }
   },
   "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>ROC-AUC</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>% reservas señaladas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEST final · umbral 0.30</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HistGradientBoosting</th>\n",
       "      <td>0.891</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.773</td>\n",
       "      <td>52.910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          ROC-AUC  Precision  Recall    F1  \\\n",
       "TEST final · umbral 0.30                                     \n",
       "HistGradientBoosting        0.891      0.682   0.890 0.773   \n",
       "\n",
       "                          % reservas señaladas  \n",
       "TEST final · umbral 0.30                        \n",
       "HistGradientBoosting                    52.910  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x385 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Resultado final:** ROC-AUC **0.8914**,\n",
       "Precision **68.2%**, Recall **89.0%**,\n",
       "F1 **77.3%**, reservas señaladas **52.9 %**.\n",
       "\n",
       "- **True Positive (TP): 8,014** reservas que cancelan y fueron detectadas.\n",
       "- **False Negative (FN): 987** reservas que cancelan pero no fueron detectadas;\n",
       "  se pierde la oportunidad de actuar y revender la habitación.\n",
       "- **False Positive (FP): 3,731** reservas que no cancelan pero se marcaron como riesgo;\n",
       "  implican contactos innecesarios y carga para el equipo.\n",
       "- **True Negative (TN): 9,466** reservas que no cancelan y fueron correctamente clasificadas.\n",
       "\n",
       "El **31.8%** de las alertas son falsas.\n",
       "No se mantiene\n",
       "la referencia provisional de Precision del **70%** en este periodo.\n",
       "No reajustamos usando TEST. El volumen de intervención y los costes deben comprobarse en un piloto."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Volvemos a entrenar el ganador con todo TRAIN, incluida su validación.\n",
    "modelo_final = clone(modelos[ganador]).fit(X_train, y_train)\n",
    "assert modelo_final.named_steps[\"model\"].get_params() == parametros_finales\n",
    "# Evaluamos una única vez en TEST con modelo y umbral ya fijados en validación.\n",
    "y_test = test.is_canceled\n",
    "p_test = modelo_final.predict_proba(X_test)[:, 1]\n",
    "pred_test = p_test >= THRESHOLD\n",
    "metricas_test = {**metricas(y_test, p_test, umbral=THRESHOLD),\n",
    "                 \"% reservas señaladas\": 100 * pred_test.mean()}\n",
    "display(pd.DataFrame([metricas_test], index=[ganador]).rename_axis(f\"TEST final · umbral {THRESHOLD:.2f}\"))\n",
    "# Las filas son resultados reales y las columnas son predicciones.\n",
    "cm = confusion_matrix(y_test, pred_test, labels=[0, 1])\n",
    "ConfusionMatrixDisplay(cm, display_labels=[\"No cancela\", \"Cancela\"]).plot(cmap=\"Blues\", colorbar=False)\n",
    "plt.title(f\"TEST · {ganador} · threshold {THRESHOLD:.2f}\")\n",
    "plt.xlabel(\"Predicción\"); plt.ylabel(\"Resultado real\")\n",
    "plt.tight_layout(); plt.show()\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "assert cm.sum() == len(test)\n",
    "display(Markdown(f\"\"\"**Resultado final:** ROC-AUC **{metricas_test['ROC-AUC']:.4f}**,\n",
    "Precision **{metricas_test['Precision']:.1%}**, Recall **{metricas_test['Recall']:.1%}**,\n",
    "F1 **{metricas_test['F1']:.1%}**, reservas señaladas **{metricas_test['% reservas señaladas']:.1f} %**.\n",
    "\n",
    "- **True Positive (TP): {tp:,}** reservas que cancelan y fueron detectadas.\n",
    "- **False Negative (FN): {fn:,}** reservas que cancelan pero no fueron detectadas;\n",
    "  se pierde la oportunidad de actuar y revender la habitación.\n",
    "- **False Positive (FP): {fp:,}** reservas que no cancelan pero se marcaron como riesgo;\n",
    "  implican contactos innecesarios y carga para el equipo.\n",
    "- **True Negative (TN): {tn:,}** reservas que no cancelan y fueron correctamente clasificadas.\n",
    "\n",
    "El **{1-metricas_test['Precision']:.1%}** de las alertas son falsas.\n",
    "{'Se mantiene' if metricas_test['Precision'] >= MIN_PRECISION else 'No se mantiene'}\n",
    "la referencia provisional de Precision del **{MIN_PRECISION:.0%}** en este periodo.\n",
    "No reajustamos usando TEST. El volumen de intervención y los costes deben comprobarse en un piloto.\"\"\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfcb2f24",
   "metadata": {},
   "source": [
    "## 11. Interpretabilidad\n",
    "\n",
    "**SHAP es una técnica que permite entender por qué el modelo realiza sus predicciones.**\n",
    "Muestra qué variables aumentan o reducen el riesgo estimado de cancelación.\n",
    "\n",
    "- **Summary Plot:** qué variables influyen más globalmente. Usamos 250 reservas de TEST\n",
    "  elegidas al azar con semilla fija, sin mirar si cancelaron.\n",
    "- **Waterfall Plot:** qué variables suben o bajan el riesgo de una reserva concreta.\n",
    "  Elegimos la de mayor y menor riesgo estimado del modelo final.\n",
    "\n",
    "En el Summary, **derecha = sube el riesgo; izquierda = baja**. Rojo indica un valor alto y azul\n",
    "uno bajo; en columnas de categorías, 1 significa que está presente. En las variables numéricas, los valores del gráfico están estandarizados: un valor negativo\n",
    "no significa peticiones o plazas negativas. Las tablas locales muestran los valores originales. Se explica el modelo recién entrenado con las variables reincorporadas.\n",
    "\n",
    "SHAP explica la probabilidad o puntuación, no el umbral que usamos para actuar.\n",
    "La celda indica su escala: si aparece **log-odds**, es una escala interna del riesgo, no puntos\n",
    "porcentuales. SHAP muestra asociaciones del modelo, **no causas**.\n",
    "[Referencia técnica de SHAP](https://shap.readthedocs.io/en/latest/generated/shap.TreeExplainer.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2a894160",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:15:47.218957Z",
     "iopub.status.busy": "2026-09-24T13:15:47.217867Z",
     "iopub.status.idle": "2026-09-24T13:15:47.983817Z",
     "shell.execute_reply": "2026-09-24T13:15:47.981494Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SHAP expresado en log-odds de cancelación · aditividad verificada contra predict_proba.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 990x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>SHAP absoluto medio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>1.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agent</th>\n",
       "      <td>0.909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <td>0.622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <td>0.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lead_time</th>\n",
       "      <td>0.568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_type</th>\n",
       "      <td>0.447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>market_segment</th>\n",
       "      <td>0.405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_year</th>\n",
       "      <td>0.310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             SHAP absoluto medio\n",
       "Variable                                        \n",
       "country                                    1.238\n",
       "agent                                      0.909\n",
       "required_car_parking_spaces                0.622\n",
       "total_of_special_requests                  0.585\n",
       "lead_time                                  0.568\n",
       "customer_type                              0.447\n",
       "market_segment                             0.405\n",
       "arrival_date_year                          0.310"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Recuperamos el modelo entrenado y los nombres de las columnas que recibe.\n",
    "prep_final = modelo_final.named_steps[\"prep\"]\n",
    "estimador = modelo_final.named_steps[\"model\"]\n",
    "nombres = prep_final.get_feature_names_out().tolist()\n",
    "# Elegimos la muestra para SHAP sin mirar si las reservas cancelaron.\n",
    "X_explain = X_test.sample(n=min(250, len(X_test)), random_state=SEED)\n",
    "X_explain_t = prep_final.transform(X_explain)\n",
    "indices_casos = [int(np.argmax(p_test)), int(np.argmin(p_test))]\n",
    "casos = X_test.iloc[indices_casos]\n",
    "casos_t = prep_final.transform(casos)\n",
    "\n",
    "# Elegimos el explicador adecuado para el tipo de modelo ganador.\n",
    "if isinstance(estimador, LogisticRegression):\n",
    "    background = prep_final.transform(X_train.sample(n=min(100, len(X_train)), random_state=SEED))\n",
    "    explainer = shap.LinearExplainer(estimador, background, feature_names=nombres)\n",
    "    unidad_shap = \"log-odds de cancelación\"\n",
    "else:\n",
    "    explainer = shap.TreeExplainer(estimador, feature_perturbation=\"tree_path_dependent\", feature_names=nombres)\n",
    "    unidad_shap = \"probabilidad de cancelación\" if isinstance(estimador, RandomForestClassifier) else \"log-odds de cancelación\"\n",
    "\n",
    "def explicar(matriz):\n",
    "    resultado = explainer(matriz)\n",
    "    if resultado.values.ndim == 3:\n",
    "        resultado = resultado[:, :, 1]  # Clase positiva en Random Forest.\n",
    "    return resultado\n",
    "\n",
    "shap_global = explicar(X_explain_t)\n",
    "shap_casos = explicar(casos_t)\n",
    "assert shap_global.values.shape == X_explain_t.shape\n",
    "assert shap_casos.values.shape == casos_t.shape\n",
    "assert list(shap_global.feature_names) == nombres\n",
    "reconstruido_global = shap_global.base_values + shap_global.values.sum(axis=1)\n",
    "if unidad_shap.startswith(\"log-odds\"):\n",
    "    reconstruido_global = expit(reconstruido_global)\n",
    "np.testing.assert_allclose(reconstruido_global, p_test[X_test.index.get_indexer(X_explain.index)], atol=1e-5)\n",
    "# Comprobamos que la suma de los aportes reproduce la predicción.\n",
    "reconstruido = shap_casos.base_values + shap_casos.values.sum(axis=1)\n",
    "if unidad_shap.startswith(\"log-odds\"):\n",
    "    reconstruido = expit(reconstruido)\n",
    "np.testing.assert_allclose(reconstruido, p_test[indices_casos], atol=1e-5)\n",
    "print(\"SHAP expresado en\", unidad_shap, \"· aditividad verificada contra predict_proba.\")\n",
    "shap.summary_plot(shap_global.values, X_explain_t, feature_names=nombres,\n",
    "                  max_display=12, show=False, plot_size=(9, 5))\n",
    "plt.title(\"SHAP Summary · \" + ganador)\n",
    "plt.xlabel(\"Impacto SHAP: \" + unidad_shap)\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "# Reunimos las columnas One-Hot de cada variable para resumir su importancia.\n",
    "origen = []\n",
    "for nombre in nombres:\n",
    "    if nombre in num_cols:\n",
    "        origen.append(nombre)\n",
    "    else:\n",
    "        origen.append(next(c for c in sorted(cat_cols, key=len, reverse=True) if nombre.startswith(c + \"_\")))\n",
    "shap_importance = (pd.DataFrame({\"Variable\": origen, \"SHAP absoluto medio\": np.abs(shap_global.values).mean(axis=0)})\n",
    "                   .groupby(\"Variable\")[\"SHAP absoluto medio\"].sum().sort_values(ascending=False))\n",
    "display(shap_importance.head(8).to_frame())\n",
    "\n",
    "# Resumimos el sentido de los aportes para contrastar las explicaciones con el gráfico.\n",
    "shap_direccion = []\n",
    "for j, nombre in enumerate(nombres):\n",
    "    valores = X_explain_t[:, j]\n",
    "    mediana = np.median(valores)\n",
    "    altos = valores > mediana\n",
    "    if altos.any() and (~altos).any():\n",
    "        shap_direccion.append({\"variable\": nombre,\n",
    "                               \"importancia\": float(np.abs(shap_global.values[:, j]).mean()),\n",
    "                               \"aporte_valores_altos\": float(shap_global.values[altos, j].mean()),\n",
    "                               \"aporte_valores_bajos\": float(shap_global.values[~altos, j].mean())})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "afd0699f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:15:47.987946Z",
     "iopub.status.busy": "2026-09-24T13:15:47.987494Z",
     "iopub.status.idle": "2026-09-24T13:15:48.026763Z",
     "shell.execute_reply": "2026-09-24T13:15:48.024784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Ranking SHAP de HistGradientBoosting:** `country`, `agent`, `required_car_parking_spaces`, `total_of_special_requests`, `lead_time`, `customer_type`, `market_segment`, `arrival_date_year`. Se recalcula sobre 250 reservas del modelo final reentrenado, en log-odds de cancelación."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>variable</th>\n",
       "      <th>aporte_valores_altos</th>\n",
       "      <th>aporte_valores_bajos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>country_PRT</td>\n",
       "      <td>1.393</td>\n",
       "      <td>-0.802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>required_car_parking_spaces</td>\n",
       "      <td>-5.227</td>\n",
       "      <td>0.369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>total_of_special_requests</td>\n",
       "      <td>-0.830</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lead_time</td>\n",
       "      <td>0.629</td>\n",
       "      <td>-0.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>agent_9</td>\n",
       "      <td>0.789</td>\n",
       "      <td>-0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>previous_cancellations</td>\n",
       "      <td>2.411</td>\n",
       "      <td>-0.286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>agent_240</td>\n",
       "      <td>0.889</td>\n",
       "      <td>-0.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>market_segment_Groups</td>\n",
       "      <td>0.726</td>\n",
       "      <td>-0.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>market_segment_Offline TA/TO</td>\n",
       "      <td>-0.355</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>customer_type_Transient-Party</td>\n",
       "      <td>-0.249</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         variable  aporte_valores_altos  aporte_valores_bajos\n",
       "31                    country_PRT                 1.393                -0.802\n",
       "7     required_car_parking_spaces                -5.227                 0.369\n",
       "8       total_of_special_requests                -0.830                 0.057\n",
       "0                       lead_time                 0.629                -0.150\n",
       "63                        agent_9                 0.789                -0.341\n",
       "4          previous_cancellations                 2.411                -0.286\n",
       "51                      agent_240                 0.889                -0.122\n",
       "38          market_segment_Groups                 0.726                -0.156\n",
       "39   market_segment_Offline TA/TO                -0.355                 0.113\n",
       "71  customer_type_Transient-Party                -0.249                 0.081"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Cómo leerlo:** un aporte medio positivo aumenta el riesgo y uno negativo lo reduce. La tabla contrasta valores por encima de la mediana con el resto de la muestra. El ranking agrupado suma magnitudes de columnas de la misma variable; no mide causas."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- **total_of_special_requests (puesto 4):** valores por encima de la mediana tienen aporte medio -0.830; el resto, +0.057 (log-odds de cancelación)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- **lead_time (puesto 5):** valores por encima de la mediana tienen aporte medio +0.629; el resto, -0.150 (log-odds de cancelación)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- **previous_cancellations (puesto 9):** valores por encima de la mediana tienen aporte medio +2.411; el resto, -0.286 (log-odds de cancelación)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- **required_car_parking_spaces (puesto 3):** valores por encima de la mediana tienen aporte medio -5.227; el resto, +0.369 (log-odds de cancelación)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- **adr (puesto 14):** valores por encima de la mediana tienen aporte medio +0.040; el resto, +0.000 (log-odds de cancelación)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(f\"**Ranking SHAP de {ganador}:** \" +\n",
    "                 \", \".join(f\"`{x}`\" for x in shap_importance.head(8).index) +\n",
    "                 f\". Se recalcula sobre {len(X_explain)} reservas del modelo final reentrenado, en {unidad_shap}.\"))\n",
    "direccion = pd.DataFrame(shap_direccion).sort_values(\"importancia\", ascending=False)\n",
    "display(direccion.head(10).drop(columns=\"importancia\"))\n",
    "display(Markdown(\"**Cómo leerlo:** un aporte medio positivo aumenta el riesgo y uno negativo lo reduce. \"\n",
    "                 \"La tabla contrasta valores por encima de la mediana con el resto de la muestra. \"\n",
    "                 \"El ranking agrupado suma magnitudes de columnas de la misma variable; no mide causas.\"))\n",
    "for variable in [\"total_of_special_requests\", \"lead_time\", \"previous_cancellations\", \"required_car_parking_spaces\", \"adr\"]:\n",
    "    fila = direccion.loc[direccion.variable == variable]\n",
    "    puesto = shap_importance.index.get_loc(variable) + 1\n",
    "    if not fila.empty:\n",
    "        r = fila.iloc[0]\n",
    "        display(Markdown(f\"- **{variable} (puesto {puesto}):** valores por encima de la mediana \"\n",
    "                         f\"tienen aporte medio {r.aporte_valores_altos:+.3f}; el resto, \"\n",
    "                         f\"{r.aporte_valores_bajos:+.3f} ({unidad_shap}).\"))\n",
    "    else:\n",
    "        display(Markdown(f\"- **{variable} (puesto {puesto}):** no hay variación suficiente \"\n",
    "                         \"en esta muestra para contrastar los dos grupos.\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ef0eb01e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:15:48.032517Z",
     "iopub.status.busy": "2026-09-24T13:15:48.031254Z",
     "iopub.status.idle": "2026-09-24T13:15:49.136604Z",
     "shell.execute_reply": "2026-09-24T13:15:49.134970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Mayor riesgo estimado · fila CSV 11901 · riesgo estimado 99.4%**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Valor original</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hotel</th>\n",
       "      <td>Resort Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lead_time</th>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_year</th>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_month</th>\n",
       "      <td>June</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meal</th>\n",
       "      <td>BB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>PRT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>market_segment</th>\n",
       "      <td>Groups</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distribution_channel</th>\n",
       "      <td>TA/TO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_cancellations</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reserved_room_type</th>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agent</th>\n",
       "      <td>Sin_agente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company</th>\n",
       "      <td>Sin_empresa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_type</th>\n",
       "      <td>Transient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adr</th>\n",
       "      <td>72.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_guests</th>\n",
       "      <td>2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_nights</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Valor original\n",
       "hotel                            Resort Hotel\n",
       "lead_time                                 323\n",
       "arrival_date_year                        2017\n",
       "arrival_date_month                       June\n",
       "arrival_date_week_number                   22\n",
       "arrival_date_day_of_month                   3\n",
       "meal                                       BB\n",
       "country                                   PRT\n",
       "market_segment                         Groups\n",
       "distribution_channel                    TA/TO\n",
       "is_repeated_guest                           0\n",
       "previous_cancellations                      0\n",
       "previous_bookings_not_canceled              0\n",
       "reserved_room_type                          A\n",
       "agent                              Sin_agente\n",
       "company                           Sin_empresa\n",
       "customer_type                       Transient\n",
       "adr                                    72.000\n",
       "required_car_parking_spaces                 0\n",
       "total_of_special_requests                   0\n",
       "total_guests                            2.000\n",
       "total_nights                                7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eeeI42SISER2E8zVo0CBm/SOPPOK+M2HChNDz0CHx2h/lZV/umQcdDdb9888/Md/t2bNn0us6OxCm1DOdDwYaZs+e7doMYqJcuXLu3AyueFxwwQVu3aGHHuqE8cyZM13dUVd0lrJ7Lv1cdtll7tlZtGhRjiLSe2Y5H+3be94+++wzd04EP8/qXXfd5Z5VxOmVV17phFEicD2cO1F4HyF+eT/5hSBiP9jh5bi0oW+++SZ08TqKQRHJ+yps8Gffffd1dRQcPPLafXBBCDAwlJPI9s6HIA4+i7TBSpUqOcEePB+iLwjnPOGEExKqy+C77YEHHnDr3njjjZh9vc4zQgDomPP/pZdeGrMvoik3IvLiiy92+/O9IN5gzYcffuj+53eI/ylrkNdeey1GRObmeef5439+m3IzEJmXcyGAEeynnnpq6DuY/XMSHzx3PMc77bRT6CBaPDZv3ux+jzgPzwzluvrqq2NEJIPAQbyBUu/9m5syMODE+yHsej0hN23atKz9c2pDq1atcr+ZfJ+6550QHIhMpA0GRWRe+x5hg0U8H2zzD3omgtxZhRBFFlxrcNvAvQTXDeJCcC+MB+4iuEDhxhkWy+GPucB1Dtck3GFw+yNWgUyGuCIRV+llicQVDNcTz7XVD+40ZGbEnQp3MX/MVl7mcML1BBeZRCDGEbJzu8GdFnCDxLUH91cSFuEeigspLqu4jHrgJkYd4zoYhFhLXJv8+4Lnahx0a8Tlj7i87MC9j4U659qz2w/3OlxycM/CzRYXHlyXcBPCJYlYxfwSjPED7gfnDlvvuQp7EHNC/C0xKUwEHZzuwt/+qBvq2p/11IM2hZtTMus6O3DdI14K90ViLr22TfvAhRU3cH+WZNx8cWnEpYznEthOVlQv6UxOkBQLlzBcr4LuWPHgmcW1ywP3O1zRcFfHRYy6x+0S91jKQ4wnsU/BeKhkgLua57LG/aEN867CjZp31G+//RaVAZj3CTFXYcSLo+J4fI93jz+ekVgq4qdwLcX9LixOj7aF+ytupm3bto2b8THsnYK7NEt275ScnhveIf5nIzdk1969dV579/bF5TPsnRWE93kwNsxzC87Neb1P6jbs+Q2Sm+ed3yJcePktY6odzs37Gjfj7Fwf83Iu7z2FGy5LGGH5B4LvZ37/eC/ntK/Xrnmn4CIffFcE4yKza1/gtbHclIF2Tl8hu98c2nmrbH7DCV3AhRn3YvoHfnh3+usXvJCM3JDXvkdO9ZVdiEwQiUghRJGFRAm8QEl0QEwYweTZTc1AVkhiYYgFQDjxQ0pnlw4dndtgp54OKD9mxDIQb+UlrAhmhM0LOcUC5vc73rVQZjodYdAB8eAa6WwTX0KnFUFJhxvhQP2mAu8auFckW4iH92NPe6AzTSeIDjSxJ8Qg8mNOJz6sI5kb4mVtzS6bqz/mkCQ3xIMhvq6//vqsxCTEpxALk585FAsaxDlxbMT60SlBmJKQgTjQYIccQYIwI2Zs0qRJrkNDrA6dc549/8BEPIj1ogPevXv3KIFEp5I6ZR3xUv4so0GIwUNkeXMn0jnlmeY6gNgxkmSRjMnfsQsjkdjReNBpYyFukYENBDJxXnT88woxvkwlQp2GdSK9Zz9MRHLe7OotHl77JE6cbK5hhA1yxXs+8lOnBQWDEAwapns5GSRgEIT3NQMT7733nvutYoCCRGc5tefc3nOec+L3CxoGoIhfZkCF6+E3iveJF8cZ9o5M9P2bKJyDOOvsBpfiPXNAngCeMcQY7yB+dxjgQrwy0OklKksVyawviUghRJG3Rnpp/Pk7HgTVIyCxLgYzNJLgIQxGeLEmIKj4MSBZChng/CPRCBiS4tBZDnshs56OVaIWxGThjZJy3ngWjiD82LFQn2QLJbkAHWwyCpIhj44w1lhGK4PWSM9KERztDKsXjs2ofk5JTqhbLCWIlkSvAaGNSPCEApYwOh/33XdfVgbWZHWwcgvtBxERzIIYNtE7Io26JmFC0GIQrNNk1HWinQ9/2yeJDh0iEoewBMHa5xeMCHqeCdpVTtCR55mNN9rP+Rg0iJekB6FG4g/K501/Q3ILfwIQLMgkymJEP8wqk2zoRPL+oGwMHOQHrKkISKy7YYKQQSESZoQ9q3nFuxfUZ6LPY0HAs+G192Bn3suY7e3jPRthmYiD7yzA2haWKTl4Xtpeduf1Phl0CU63Evac5uZ592BwkEERFkQWA1GUn8EzLG7xyM25eG/QbqmTvN5zBmv5HSLhCwItO0sg70h+a8je698v7F4VVBlo57wrqMPsptSIB88dFkI8pIKDOGTiDnumSKyWW9Kh76EpPoQQRRpG93HtoTMV5ibk4f1oBEfaeNljcYsHVkc6fPxQ09nE2hk8Lm5EdFLoJPvhBx3XITLNFbZwYeSYzh4j1sGpJoBOgedmg4tQcIQXC47niuW5BHGdwBQhfrDkBlPc8wOHYKIzgNXFDxlDKRPz02UHdYvoJ3ueNy1Hdu5z3J8gpL33XwN47jphrlEFCSIs2P6Y8J3R6SBeVs9gyn4yv5J9ML91nZspPuKBJY96JSNuTrAf+1NWniU/ZIakLNSFB5kuEdvBhe/jXsjfYfUGuKhi8cRyghXbA6sGnUi/yxmWYDqYyYRBgbARfVzkvKktwtwcE4VjIxIRwUzTgGU7uOByj8j3ZyDOL2QgxbX4wQcfDJ3rEiFTGM8U7yHeDbgne1PLePVL2XjOvLAGnn/qiXrwT3lAHZLJN8x1G7HkX8KeSf/7ElfJ//u//3MDXrjPey7FWJ+wZjEY4kF5KXd+nneO539WALHjTSmTk5twbs7FAASDPli98e5I1IXZD/eKaZYYzGJQMoi/Lj0rmX8d9wpPoPyQmzLQp2DgEot7Xq639H/XEHwH8F4Ivp+5ZwyEkPmVLLfZlSvsmlLd95AlUghRpMHXH6GUE4zYkdKdlyvuMYwk06mgM5ZdzBViDMscnVri6kg7HoQfY1woib3jhxZrjZdmmw5MvB+jggRXQ+LJiHXENYfpMRiVp5PHjw6jpYyUMtrKtSGk+UFilBprHsIQdzh+5IgnA9ynqC86asSU8l1GbHE7IjaOdOL+HyzcYOnEMxqLe6I37QRxlxwXC2dOUHdYbhhlp7yIJTpnCA86PIhdr3ODSyX3FVc7rDNcqyc+uX4PrEF07ojrwkpAmne+x9QmBQlTTmAdo5NPJ5Py0R69+Fo/lJe6xr2LOvbS8NNZZcoW0sL7yW1d52aKD8BCjXUXywQDELjMYfGgDul0BWPusPp6sanMgca1MF3LJ598EmMZ4/sci/2wQoBnSQ6CSzAdZuowuzbDuYKDHZyHuSYZmcdKyn4MUiQSp5UbvBhc4pEZ2OJ9wz3kXuD1QFsOi6tLFN411BXvpXgdRNoXZeAZxj0wGfBeQIwh0HjWeB/QLhiMosPKOwWLf27mXcwLtEEsOsQE8iwTpuBN8cGAE/fVa0d06Hk2GEThHcWgIPefd4k3uJZoJ5tniylUqAOmasDF25viA6se71FvgIq6p/0xbQQustQVAxaUMcydMDfPO+87Bkg4P+92xCsWT+L3ee/nZDHM7buF4+KeSbwlQozr4Znh/cG0RNSr5+URD547pjOiLVJ+3NSpD6xlWEU98co7Eld/Bix4xhkY4F3DwFB+SbQMDMzwN/0KwiB4luhnUFf8FiFEvfjYMPB84TeKtsJgDoNU1Cnvep572qiHN28jbYsBDH6veV8zxyVeFrzHg++xtOp75CoNjxBCpFF21uwIy85KNkwyR5IWv3z58i6rGdnzhg4dGjNthx+yOLKd7HzxmDdvnkunTaY6skGSbZHssf608v5riHeu7K4nu+yQ8TK8MTVG7969I3Xr1nUpz/kkpT91SH14GeJI0850BGRNJJvnLrvs4qaeCGaDJVveRRdd5FLaU4e77babS4V+1VVXhWb8Y1qDPn36uCyrnJ9rYF9/2vucIPsqUxaQ0p907pSvRYsWLmPgV199lbXfvffe6+qJsnEusomSIfTrr7+OOh5Z7cjyRzsg86D/fmSXndW/zoPr4ZxBwr7DdTBFC98hqykZAMmQy/QRwWyNsHLlSpc1j3tGXZNhlayPYanec1vXuZniA2hbZPtjSg+yrHLN/nT7wSlfyNro3QeeBdrXn3/+mW3m4eD1hJFddlaYOHGiOyfT1oRlxL3uuutceagjnlcyJyYbpjvp169fpF27di47Iu8DMjIeeOCBrl0Es0PmdE3e/fays5544onufzLAZoeX1dXLJuplS+VdlR9orxyLKXaoa+qSjJa05blz5yaUBTnec5ObdxsZgckQzDuBZa+99grN2Opl6d1jjz3cc1S7dm13f7wsp7zPEoV7R+bXDh06uGPxviQrtP89FMzEyr487/w28FsyadKkfD3vZDu+8MIL3ZRWPItcO+/D/v37J3xvc/tu4Xfgf//7n/tdYH+ygvI3v3HB6ZeyOyeZQVu1auXqgylRuH9k+/XX7/333+9+izgPzyr3h/MH24GXnTVYj9m9sxMpA5CpmHW0Ke4x7z3e10x3FJzOyULaJxmtySrO/aGuePZZF++Z4N3INtqI9ztN9l36Jh7x7k0y+h7xjp0TGf9VgBBCiDiQdIfRQEb4OnbsqHoKAYse1iQsQMm27AghRLLBcwKvBaynWL+EELlDv/RCCJENuNPg+kOCEAnIzPoIQiwHCWxwo5KAFEKkE8QPBqeKINbMiwnEdVIIkXsUEymEECGQbY9pQ4hjInFCTjEfJQXi7Ug6QIwMcT/UEzFBxPURoySEEOkEsXvEMDLHKVl9STxDTCRJToiT9WK+hRC5QyJSCCFCINvZ7bffbvXr13dZ/IJp2ksqJBkgAQPuvbiuknCABCJkyM1u7iwhhEgFJHIi6RTvdDJrEsXF1ApkSU1W0iEhSiKKiRRCCCGEEEIIkTCKiRRCCCGEEEIIkTASkUIIIYQQQgghEkYiUgghhBBCCCFEwkhECiGEEEIIIYRIGIlIIYQQQgghhBAJIxEphBBCCCGEECJhJCKFEEIIIYQoYYwePdoOOeQQq1OnjmVkZNixxx5rK1assFq1atl9992Xp2OOGjXKHeunn35KenlFeqF5IoUQQgghhChBLFy40Fq3bm0NGjSw/v37W/Xq1a1NmzY2ePBge/vtt23GjBlWqVKlPB370EMPdWL0559/doKyMHn++efjbuvdu7ftuOOOCR1n7NixtnTpUresWrXKXcd5552X7303bNjg6oV9165da1u2bHH1XLt2bevcubMT8EWFMqkugBBCCCGEEKLweOWVV2z16tX28ssv29577+3WLVq0yJ555hkbMGBAngUkXH311XbwwQfbZ599ZkceeaQVNvXq1XOCOEiVKlVyZaUtV66cE3WbN2924i8Z+27cuNFWrlxpDRs2tKpVq1qZMmXcfZg+fbp98MEHToA3btzYigISkUIIIYQQQpQgRo4c6UTMXnvtlbVu4MCBzjJ25pln5uvYBx10kDVq1MiefvrplIjIatWqWcuWLfN1jJNPPtkdBz755BMnsJOxb/Xq1e2YY46JWd+2bVt74403bNy4cUVGRComUgghhBBCiBLAPffc49wtv/jiC2cBK1WqlPv/rrvusiFDhli7du2sadOmUd/BuoYoq1GjhnNT9fP444+771955ZVZ6zgmFrWvvvrK/v33X0sF27Zts02bNuX5+54oTPa+8ahYsaKzSmKpLCpIRAohhBBCCFEC6Nmzpz311FPu78MPP9xeffVVt5BgZ9KkSVmurX7Kli1r9957rxOE999/f9Z6XGERj+ecc4498sgjUd/Zd999nZDD4pkTuH8mumApzYmZM2faiy++6MrH8u233zrBnE5s27bNXc+6dets8eLFroyI9aCAT2fkziqEEEIIIUQJYM8997Q1a9a4v0844QQ77bTT3N/Dhw93ny1atIiblAZh+MQTT9jll1/usq/269fPHYNkNsEEOp476YQJE+zoo4/Otkwk80mU3XbbzXbfffe420lQ07x5c+c2unXrVudaOnXqVJs3b55zI91hhx0sHVi0aJF9+umnWf8TU9mpUyd3fUUFiUghhBBCCCFKCL/99pv77NKlS9a6JUuWuM+aNWvG/d7DDz9s++yzj51yyilORPbq1ctef/11K126dMy+3nGwsuUEFtFkuY4ed9xxUf8jips0aWJffvml/fjjj7k6V0FSs2ZNVxYsklh4//zzT2eJRPjiDlwUkIgUQgghhBCiBInI8uXLu/jHIJFIJO73cHUl1hFBhlXy/fffdxa0MLzjJDLFB0l4ChJEJHNhLliwwLnDEnuYasqXL5913ZSP6Vbeffdd53Z72GGHWVEg9bUohBBCCCGEKDQR2bFjxygxhRsoLFu2LO73vv76axe7B4hHksHEwzsO4i0niAtMFOIzWXILmWixipK4Jh1EZJioJB5y8uTJTkhS3nQn/WpRCCGEEEIIkXSYo3DGjBl2/vnnR633rJK4VYaBKyiuorizdu7c2WVl/fjjj+PGO3rH6dChQ45leu2115IWE5nddeMmWqFCBUtXtm7d6j4RuhKRQgghhBBCiLTg999/d66m/nhIzxKJkEQsBhk7dqwdccQR1qZNGzcPIi6hJMO5/vrrXVxfmGWPmElEW7du3QotJpJsp2Ei8a+//rKlS5c6t9Fg/CbxiJQzGdN0JAJW10qVKsWsx/o4e/ZsZ+FNl+Q/OSFLpBBCCCGEECUoqU5YFtCTTjrJbrnlFie6vCyt06dPd9N/1KtXz8VCehay//3vf05Ekpn14osvjjoOyWKYh5LvkSW1sGIiEcj//POPNWjQwKpUqeLKQRbUWbNmOeFGHGeQt99+2+3bt2/fqPVct5fF1vvk+B67+eovN/siyInNbNy4cVZdImQ5BuK8R48eaeluG0ZGJLsIWiGEEEIIIUSxgMyq7733nhM7waQ4CC6sdQjEO+64w+bOnWv777+/S47z/fffO+HjsX79emvVqpVzvUR0+i1533zzjR188MHOannkkUcW2rVhyZsyZYotX77cWSUBgcg14YIbFsOJCA4TkZR94cKFcc91vs8dODf7zp8/35URyyh1iNBF4CLS27dvn1AMabogESmEEEIIIYRwc0C+9dZbNnPmTKtcuXKeaoQMrgi5X375JaHsrKJoUjQmIhFCCCGEEEIUKLfddptL8PLEE0/k6fujRo2yr776yiXekYAs3sgSKYQQQgghhBAiYWSJFEIIIYQQQgiRMBKRQgghhBBCCCESRiJSCCGEEEIIIUTCSEQKIYQQQgghhEgYiUghhBBCCCGEEAkjESmEEEIIIYSIYf78+dajRw/3KYQfiUghhBBCCCFEDAsXLrQRI0a4TyH8SEQKIYQQQgghhEgYiUghhBBCCCGEEAkjESmEEEIIIYQQImEkIoUQQgghhBAx1K9f3/bee2/3KYSfjEgkEolaI4QQQgghhBBmNmbMGNtjjz1UFyIKWSKFEEIIIYQQodSuXVs1I2KQJVIIIYQQQggRyrp166xSpUqqHRGFLJFCCCGEEEKIUCZNmqSaETFIRAohhBBCCCGESBiJSCGEEEIIIUQoNWvWVM2IGBQTKYQQQgghhAhlw4YNVqFCBdWOiEKWSCGEEEIIIUQoEyZMUM2IGCQihRBCCCGEEEIkjESkEEIIIYQQIpQddthBNSNiUEykEEIIIYQQIpTNmzdb2bJlVTsiClkihRBCCCGEEKGMHTtWNSNikIgUQgghhBBCCJEwEpFCCCGEEEKIUKpVq6aaETEoJlIIIYQQQggRyrZt26xUKdmdRDRqEUIIIYQQQohQfvvtN9WMiEEiUgghhBBCCCFEwkhECiGEEEIIIUKpXLmyakbEoJhIIYQQQgghhBAJI0ukEEIIIYQQIpRff/1VNSNikIgUQgghhBBChBKJRFQzIgaJSCGEEEIIIUQoFStWVM2IGBQTKYQQQgghhBAiYWSJFEIIIYQQQoTyxx9/qGZEDGViVwkhhBBCCFHITFtgtnq9qj2V1Klu1qR21KotW7akrDgifZGIFEIIIYQQqeeSgWbDxqe6FCWbw3Yz+3xA1Kpy5cqlrDgifZE7qxBCCCGESAuUBzTFjJpstnVr1KpOnTqlrDgifZGIFEIIIYQQ6UFGqgtQwlmzwWzC3KhV48aNS1lxRPoiESmEEEIIIYTIZOTkqJrYtGmTakbEIBEphBBCCCGEMCuVYTZyUlRNlCmjFCoiFolIIYQQQgghhNm2iNnwiWaR7dGpu+66q2pGxCARKYQQQgghhMhk2Rqz6X9n1cbEiRNVMyIGiUghhBBCCCFEaFzk+vWau1PEIhEphBBCCCGE8MVFbheRGRlKmStikYgUQgghhBBCbI+L/G5CVm3svvvuqhkRg0SkEEIIIYQQYjsLlpvNWez+nDw5esoPISQihRBCCCGEELGMmuI+1q5dq9oRMcgSKYQQQgghhNhORnRcpBBBJCKFEEIIIYQQ24n8N1+kmXXp0kU1I2KQiBRCCCGEEEJE8+dCs8X/2p9//qmaETFIRAohhBBCCJETe7Qwe+wcsxF3mq18zSzyvtlNvXNfb5XKm919qtn0p8w2DDFbPMjsravNWtTP+bv1a5iteDXv584to6bYqlWrCv48osghESlEmsBI36hRo+yDDz6wF1980Z5//nn79ddfU12sYsdzzz3n5ry6+uqrLR3L5V/KlCljNWvWtAMOOMCGDRuW0P41atSwbt262Zdffun2W716tdWqVStm3+yWZ599NkW1IIQQaczhXcwuPcysdnWz32fm7RgVypkNv9PsxhPMJs4zu3KQ2VNfmPVoZ/bzfWatGmT//WcuMCtdSN13podUXKSIQ5l4G4QQhcuYMWNszZo1Vr58eatcubJG/koozZo1s169erm/Gf39/fffbfjw4TZixAh79dVX7dRTT427P4Jx3LhxbjDi8MMPt0GDBtnJJ59sZ5xxhmtbft5//31btmyZHXHEEdagQXSnZbfddivw6xRCiLQBq95tQ8xuH5L9fs98afbQR2ZrN5h1b2c2vH3uz3V+r0yL5nNfm13oG7B7dYTZxMfMnuxndsgd4d89aT+zI7qYXTfY7JGzrcCJmNmISdb5oTMK/lyiyCERKUSagPWoevXqVrVqVZs2bZoTDaLk0aFDB2eF9ti2bZudcsop9vbbb9u1114bIyLD9j/rrLOc4Pzf//5nZ555pj3yyCMx56F9ISKvueYa69GjRwFflRBCFAMWr8z/MQ7skPk56Nvo9bP+MRs12axXJ7NGNc3mL4veXrOq2RP9zB7/LO9W0LwwfrbNnTDFdt6tY+GdUxQJ5M4qRJrQqFEjJyCLArNmzbLTTjvNGjdu7CynlSpVsubNm1u/fv1i5pPCKnbQQQc5N0vcLXfccUdnJZs0aVKokMadcvbs2Qm5of79999uHZnjEN6HHXaY7bDDDla2bFlr0qSJPfbYYzHHv/DCC93fCCu/C6d3Tq8MU6dOtauuusodp1y5cu4cTzzxhNvWp0+fuC7JFStWtLp16zoxlwxKlSqVdc0LFy60JUuW5Lh///793d+LFi2ypUuXJqUcQgghkkD5spmf6zbGblu3iZe42V6tYrc9fm6mBfSWNwv3NkTMto6M/b0WQpZIIUSu+P777+3YY491VqyWLVta7969bfPmzTZjxgx75ZVXbMCAAc4dF4YMGeKsYhs3brT99tvPdtppJyfOiNfDRXPo0KG277775vsO/Pvvv9a9e3erUKGCc+1ct26dO/6VV17pRO5FF13k9jvhhBOcwPz222+tU6dOtueee7r1pUuXduLTDyJ5ypQprnxY6rgmruWuu+6yTz/91LmOBkU/sYQbNmxwIg4xlywipFr/j0SO69+f6xVCCJEmTJpnduiumRbJCXO2r69Yzmyvlpl/N6kV/Z3DdjM7tZvZoXeGi88CpurY+YV+TpH+SEQKUQyZMGGCE26JQuIVYutyAoGEkEJAIhbvvPPOqO1Y8+rVq5cVz3fFFVe475Ao6Jxzzsna7/bbb7fbbrvN+vbt66yaWPfyw8yZM52Yfeutt5wghI8//tiOO+44u//++7NE5OWXX+6EJiIS6+jDDz8c95iU648//rBWraJHhI866ih76aWX7IUXXnAi1V83xBki8rjuZIFF0ysnsYsk2slpfyymgGjHRVoIIYoO/B5sHwhLOriFhlGpXOw2BNv6Tck9/9NfmF3Qy+yOkzMti0PHm9WqZnb7SdvPT/ZWj6oVzZ670OyNkWZf/WGFTkaG1Z2SvQeMKJnInVWIYioiSciS6BLmPhrGhx9+6CyOrVu3tjvuiA38R4gi0uDdd9917pRY/PwCEojVw4o5Z84c++GHH/J9vVgbEaqegIQjjzzSdt55Z3eOYFKZRMCFNCgg4dJLL3VuubjX+kG0Uo9YRIOJanJ7784//3y3kBRnl112sXfeeceJ0zDR69+f2Ml27drZ66+/7txqEboFDe6yQRdm6twPAworVqyIWjd37twoiymDHrQXP//8848T59kdm+MG089zbD9YpoNuwAsWLLAtW7Zk/c/frNO16b6pTabuedu6dWvBCkh3oldiF7juuNj1rEs2M/8xO+wus4UrzAZebDbrWbMxD2S6ud73fuY+q9Zt3/+BMzJF5RWDLBVEMsw2ZkSS/p4URR9ZIoUohmDhKwg8wXfggQfmaD387bff3GdY0hZEHzGGxBD++OOPtv/+++erXMRmVqtWLWodoqt27druHPx4ValSJVfHjJdspnPnzrbXXnu5uvjll1/c3+AJNqyd+QEhOnDgQPc3ohhLIvV98803h5bJv78HrrdMCeKVrSDBih2kadOmUf8H7w0QaxpsE54V24PY0pyOTaxtTscmZpfFT8OGDaP+Z2AguE7XpvumNlnIz1vp0gUtIc163ha7buhtZoOHZy5BwVcQfD/FbJf+Zq0bmtWpnplEh8Q695+euX3Kf0Jt150ys7ne9a5ZtYqZCzT8zyNlxypmO9fLTPizen2BFDVjW8SWd6hnDZP8nhRFH4lIIUTCrFy5MrTTEIY3Wh3PKlenTh33uXz58nzfgXg/UJ5lMnN0O3fUrx8+6TPimQRCiEgsg2RNxTpLAiFED+6u+YHvY9XM7f5Y9XDBffTRR+3pp592ltjx48fHvQ4hhCiRDBsfvh7BGG9bQTFtQebij338d63ZD1O2x0YSB39Ln8wlyFVHZy5MFcKUIQXE6s45/+aLkodEpBDFkIKKifTi64LuS9kJOzKohrF48eKYkW0vaYzf7cmjsF1hsrO0nnTSSc4y6CXYeeaZZ5zb5WWXXZbUhDq5LS8xkE8++aRzLWKOyNNPP90lLxJCiKJDAcdEFiaNa2W6os5YZLYlh8HM/oebdWiamX3Vi8Mc/adZ7wdj923X2Oz2k83eHGX23s8FO+VH6VLWpM9BBXd8UWSRiBSimIrI3MQBEvuXiIgkw+pTTz3lEtNg+cpOaHkT1pMlNQgCl1hM8Gdn9YQn8RQtWrSI+o63f37BHSev1kkP4g1J5MMUImRkJaEOVs/8urImiwceeMA+//xz59JK/WseSCFE0SGNBWST2mand8/8u3mmN411b7d9+8jJmXM9egy+zKxHe7NmF5jN8cWJjrjT7I9ZZlMXuMQ1dkhns2P2NPvwF7N73tu+H3GT7/0UW46lq7Zneg3bniwoW5edbPHaVdas9o4Fdx5RJJGIFKIYUlAxkUztgbhjTsZbb701JrkO4g+XTpLrnHjiiS6D67hx42zw4MF2xhlnRImc6dOnO7dYfzwkwvOTTz5xLplkT/XAXTM3Lp7Z4cVvBAP/c8sll1ziBOS9997rrKTELeYnoU4y4RqZD5MsuCQI8uJThRBC5AOE412B39denTIXuG1ItIiMx0/TzY7ew6xfT+ZkyhSD5z9j9sLQzP/TBcrSvb1LlJTIQLMoWUhECpEmMH+il53Siz30ZxZlHkOSuqQSxCFuksccc4yb3oMpNfbYYw9n1WOajbFjxzpxyI8NVsVHHnnEZWZlWhCypzZv3txd5+jRo50177XXXouyZl5wwQVOmH322WfWsWNH69Chg82bN89++uknZ7EcOXJkvq8Bayp1yTkQxcQMYp1k/sfcTIeBmCYT61dffeX+T+a0HsngmmuucXWOBRdhnt9YTSGEKLZkHJ/YfiMmJb4vHHBL+Pr/vZq55JXcliM/dGub3tZhkTI0xYcQaQICEgHGwtQGwHyM3jrEVDqA5ZCspEw/QTwgiWUQKaT5Pvvss7MS5gBTTnz55ZdObGGRRDRixTz44IPdMbp27Rp1bAQd4g6hR7IapragXsh8mizrKla6559/3k0xggBEtOKim5eYS8QxkMHxiCOOsHSCbLT9+/d3f1933XWpLo4QQiRI/uYNFkm+Ffvt4qaaEiJIRsQ/UZcQQoiEwVUUa+v1119v9913n2pOCCHyQ8/bLPLtePtvWkKRato3MZvwmBvEZiotIfzIEimEEHkAy+Wbb75pZcuWtSuvvFJ1KIQQovhAqEmPzKRBXqiNEH4UEymEELmAOFBiM1kWLlxo5513nksmJIQQQhQbcFTs5ss8K0QAiUghhMgFH330kROSxBz26dPHzcsYBhlpv//++xyPRzxlMMutEEKUXIrRPJFFna5tsqYBEyKIYiKFEKIAIKkQYjMnsGLKVUgIIRQTmVbsVNdsxjPuzwULFljDhg1TXSKRZigmUgghCgDiJclbltMiASmEECKtKJVhdkD7rH///vvvlBZHpCcSkUIIIYQQQohMtkX+mx9SiPhIRAohhBBCiDRB80SmBV23i8idd945pUUR6YlEpBBCCCGESBOUVCfl1K9h1qxO1r+bNm1KaXFEeiIRKYQQQgghhNgeD8k8kf8xb9481YyIQSJSCCGEEEIIkRkP6XNlFSIeEpFCCCGEECItyFBMZOoJJNVp3rx5yooi0heJSCGEEEIIkRYw9ZFIITWqmLVpFLVq27ZtKSuOSF8kIoUQQgghRFqg3KwppnvbqHhImDNnTsqKI9KXMqkugBBCCCGEEDbkaps8dry1bdtGlZEqKldQ3YuEyIjIb0AIIYQQQggRwj///GN169ZV3Ygo5M4qhBBCCCHSgrFjx6a6CCJAmTJyXBSxSEQKIYQQQoi0YPPmzakugggwc+ZM1YmIQSJSCCGEEEKkBWXLlk11EYQQCaCYSCGEEEIIIUQoCxcutPr166t2RBSyRAohhBBCiLRgwoQJqS6CCFCxYkXViYhBIlIIIYQQQqQFGzZsSHURRIA///xTdSJikIgUQgghhBBpQalS6poKURRQTKQQQgghRCo5+h6ztRtL3j04fm+zSw5LdSlEDsyfP98aNWqkehJRaOIXIYQQQohUMnyiRVZvsIyMEnQbIma2bmOMiJw0aZK1a9cuZcUSsVSrVk3VImKQz4AQQgghRIrJ8IRVSVlgzF+ZQtLHunXrUlH9IhumTZum+hExSEQKIYQQQojCZ+s2s5+nq+aFKIJIRAohhBBCiNQwanLUv126dNGdSDPq1q2b6iKINEQiUgghhBBCFD6lMsxGTIpaJdfJ9KNmzZqpLoJIQyQihRBCCCFE4bMtYvbjNLNNm7NWrVmzRncizZg8OdpaLARIRAohhBBCiNSwcbPZ7zNV+0IUMSQihRBCCCFE6hi53dK166676k6kGbVq1Up1EUQaIhEphBBCCCFSFxfpE5EzZ8oqmW7Uq1cv1UUQaYhEpBBCCCGESF1cJCJy61b378qVK3Un0oyJEyemuggiDZGIFEIIIYQQqWP1erOJc3UHhChCSEQKIYQQQojU8p9La6dOnXQn0owdd9wx1UUQaYhEpBBCCCGESIu4yHnz5ulOpBmNGzdOdRFEGiIRKYQQQgghUhsXOXySWSRiy5cv151IM8aNG5fqIog0pEyqCyCEEEIIIQqIPVqYndrNbNfmZp2bm1WrZDbgDbO73038GA12NDutu9khnc1aNzDbsYrZvGVmP0zNPM6MRdH7D7rU7KwD4x9v8xazcn2i1y1dZfbnwlxenBAiVcgSKYRISzp06GAZGRlWUsp0/vnnu2O/+24uOnZCCJETh3cxu/Qws9rVzX7P4/QZR+1udsfJmQlwHv3UrP+LZl/+YXbyfmYTHzPr2jZ6/+e+Njvtsdjl0U8yt388Jvw8IydZ+/btdU/TjOrVq6e6CCINkSVSiHywYcMGmz17ts2dO9e54Kxdu9ZKlSrlgtBbt27tltyIjj///NMWLVpkS5cudcfbunWr7bbbbrb77rtn+70tW7bYhAkT3PxapEfnnJUrV3ZzO+2///6uTH7+/vtv556yePFi27x5s1WpUsWaN2/uJnkuV65cnutDxAdxeOKJJ9p5551nzz//vKpKCJE/Iu+b3TbE7PYh2e/3zJdmD31ktnaDWfd2ZsPzINJGTTFrdqHZohXR6z/91ezrW80eOtNsr+u3r/95euYSBEsmPP9N3LjIRQe1cL9HIn3YaaedUl0EkYbIEilEPkC0jRw50v755x+rU6eOs1Tx47dixQq3fujQoRaJRBI+3pgxY2zKlCm2atUqJwITAeH6wQcfuO8iANu2bWtt2rSxGjVq2KxZs5wQ9TN58mT79NNPbcGCBdawYUM36su5EJUff/yxbdq0Kdf1IPLPLbfcYr/88osdfPDBqk4hRPJYvDJTQOaHyfNiBSR8M85s+Wqzjk1zPkb1Sma99zGbvTjze3HiIhlEFenFH3/8keoiiDRElkgh8uniQae/SZMmUda+devWOWGHiGNJdBSvW7du7phVq1a1adOm2YgRI7LdH4GKUEV0HnbYYTEZ1LZt2xZlCaVcP/30k1t39NFHO+Hr/5FAiLLst99+uagFkQwaNWrkFiGEKDIQX1mlgtnCf3Pel5jKiuXNXhzmEuiEMm+plVu0MunFFEIkH1kihcgHWPKaNWsW4y5aqVIlZxGEhQsTTxSAiEBAJgoCFSsoFtCwFNyUyy8icbvFMkmZ/QLSm5urfPnyTrziHpvODBo0yLp06eLccMuWLWsNGjRwMYWrV6+O2feee+5xopjrxVLLvaG+snMpfeyxx2znnXd2+1erVs169eplf/31V57Le+ihhzpXVhg4cKC7J94yceLEuDGRbGMd3//111/ddWA15hr22WefrO8yAMAABG2He7jHHnvEzaaHBfq0005zdUbdcbw999zTPv/88zxfnxCihHJLH7NyZc0GfZvzvuf1Mtuy1eylYdnu1nrJtuSVTyQFfmuFCCJLpBAFhCcsCzI5zIwZM9wnggcr45w5c1ycJsIAUVmxYsWo/devX+8+EUZh5eWHYtmyZS5WEpGRjvTr189efPFFdw1du3Z1brsIJsTZDz/8YL///rsTUh633XabNW3a1IlOhCTX9v3339sFF1zg5iO78847o45/7bXX2kMPPeSEGuINyzD7I9oSdTEOcvzxx7vPr776yrkPcyyP2rVr5/j9+fPnW48ePdx9PuaYY1zs7M8//2w9e/a0Dz/80H1i7T7qqKPcNgTn4Ycf7gYNSpcunXWc8ePH24EHHujucceOHa179+7277//2qhRo5xl+rnnnrNzzz03T9cohChAasYZXKxULnbbuo1m6wshLKFvN7Orjzb7ZbrZve/lnCG2U7PMhDp/ZzOFR6kM2/zteKtwjtz60wnyOwgRRJZIIQoA3EinT59e4JP0LlmyxH1ijXzzzTedGMAddfjw4fbGG2+4+Eo/FSpUcJ9hFjtcY9esWeP+RlgkAkmFECyJLiT/yQ9cIwKyXbt2TkB/8cUX7jonTZpkF110kYv3HDBgQNR3qA+EFfu+8sor7hMLLvflkUceiaoLLHuPPvqoE4vEJxIj+uqrrzorJD+iiPS8gJUR8QsISKyg3lK3bt0cv8/1IXoRy1wv13TkkUe6+37QQQe5bQhE/zaSJyGs/ZxyyikuYdMzzzzjjkV9Uh/8jVi++uqrs9pAohC/RFyun2A94W5NnLAfBK4/Xnjjxo0uqZQfro9BkeyOzXE5fvDYfhhg8Z4Vv0XWb3Hnb9bp2nTfUtUms2XpK7ELXHdc7HrWFTTH72328qVmE+aYHXmP2aYcvFf69cz8HBiSUMcH74QI8Zf/oXdJerwnf/vtt6S/J0XRJyOSm6wfQoiEwEpEpx6hQqxiXvBiIrPLzoqgwj0VayfCCjdNXBT5ccAqR+bVI444wrndAoLprbfecvtj0fJbwcaOHWujR492f+MOSabWnECsemI5EbB09u3bN6F9uRZEnf8Vte+++7qYTgQeLph+uNaaNWs6a2MirqfXXHONPfzww/b+++/bccdldrouueQSe/rpp53gCwowhBZ14jo5eXht5pSdFaHJOd955x3r3bu3W8f1Uw/cJ9yi/VZFLJCUm+tFMPq3EY+L9dN/ru+++85ZIbFo8neQG264we677z576aWX7Oyzz8719Qkh8kG1vmars0l+c1DH2HVDbzMbPDxz8TPzH7NZ/4Qfx2VnvTP380T6OXFfs9evMJs836znbZnzO2ZHZWImXzRbuc6s6QWMssbfN8Ns0Sl7Wr3X/5e3sokCgcFJ+gVC+JE7qxBJho4/AhLLzgEHHFCg9euJGWIpEVgerVq1cqIKIYk49EQkMXO4dWIV/Oijj1wmWaxujCQiRJiaBEtVoi64CBKWwgKLXJkyZeyFF15wSxC24foZvB833XSTE8i4cVIvQWuqhxdHSAxkEGJGEampyBzYsmXLKJEIuOhCixYtYrYR8wrcUw9PODIFDII1LNMweHGWQog0Ytj48PUIxnjbCoJTupoNvsxs3GyzXrebrUjAc+Hk/c2qVsycIzI7AQkRs2pH7JW04orkQHiHEEEkIoVIInTAf/zxR9thhx2cS6HnPlpQEPtHnKMnKPywDhEZdE/Bskn5KCsWS1xvEUfE/3nzXQZjKdMFXC0pb9BKGA8ENPNk4qaDRY8YSmIpif/k+rFq+t2APFfOeFlSEdmpEJFhyZawOOe0ze+uiYD2kvBkl649t+6sQohiRuNaZpXKm81YlJkIx+PUbmav9Df7dYbZIXdkWhYTAVdWppoiK2sC/Nu+vkmypBd4OgkRRCJSiCRBvB+ihEQvCMjCEGJYOxGR/kQyHt664DyRQBKWsGlHEF2JJnvxrHi5EVWUCTGXn9FQ5rFEFAatb2GQNIdYjfvvv9+uu+66qG39+/d39yssA13QmumBwC6q0Fa8637iiSdSXRwhRGHRpLbZ6d0z/25eZ7tbq8fIyWajJm//H0tjj/ZmzS4wm/PfIOSRu2cKyHWbzF4Znvl/kNdHxq5r38Rs71ZmX/xuNjeBGNA2DW3BxtWWnmndSi7EROLFJIQfiUghkoAXT4hFjxjEgrZAeuCmSpB9WMC6ty7R1NwEvhOgj7WNJVERmduYyPyIyDZt2rjYDGJFie9LpHwQlnGUuNUwl1Wst99884316dMnahuurp41Ly/gahtP1BcGZGK99957Xf0JIUoQCMe7ArHovTplLnDbkGgRGUaXnc0YuMMt9elYd/i4IpJpPRJIqOMgjKJ7+5z3E4UOHkBCBJGIFCKfMKUEMYa1atVy0yrkJCB5GSPYcKkMm2ojN5AxFAFLrCB/e66NCBXKBEwL4QdLHvMf+sGl89tvv3UxlsGENekUE3n55Ze7OQ6J6UPoEdPpBxFMXXgCE7dU7g9ZVv3JYh544IGs+vFDhlemuSBrKediOg6vPkm6k588ZN68nMHMdoXFIYccYrvssosTz3fffbeLEw3CXJG4/+a3XQohCoGMzKmDcmTEpMT3hQNuiV13+5DMJbdc/mLmkgi8X7u1dTHgIr0orIFxUbSQiBQiH2CFQ4yQiKZevXqhSUkQdv45lnCvfPvtt0MzlU6dOjUrfTcJULxU2V6cGrGMnTt3ztqfYzAB/ciRI+29995zCVUQiLhjMk0HwsW/PyCqmB+RqSX4YfDmlyR1+N57721NmjRJ2zZx6qmnumlMEHpYJSkvGXAR5VwzCY0Q8p6IvOqqq5wwYgoMhCFuutwjXI8RS8z/6AfReMUVV7isrXvttZebfxFBxX7cA+JM8zrNB5ntcHUeNmyYm5MRKzLt5vbbb0/YfTi/kCGWZE9MgzJo0CB3vbQhEvAwHQxtj6lTJCKFECmhaxsXosFvnUgf8uNBJIovEpFC5ANv3iUsVPGyWtavXz/hiXrpxAfdQ3Gh9NwoOVZQFGJdQgjgbon7JslUEAHEL+Ce6blRejRo0MDFMbIvVkniFFnH5POJzFmYap599lk3N+Ljjz/urLAIPDLMIsQQmRdffHGUCydTZiCaPMHIKPeQIUPcNCBBEQkPPfSQE3hPPvmkE6DEtmKd5bzeVCB5gWQ3r7/+uovNxIrqJfShvIUlIkmOgKWW+vj666/dHJFAh416ufLKK+MmFRJCiAKlaW2zRrVs/pgx7rdOpA/81gb7HkJonkghhBBCiHSeJ7K4UyrD7PQeZi/315yEaYjmiRRhlApdK4QQQgghRGGwLTMeEsIyh4vU4k0bJYQfiUghhBBCCJFa/hOR/vltRXogV1YRhmIihRAiD3z33XcuWU8iPP/886pjIYSIR+1qZjvXc3/OnTu3SMTnlyRIWkfeBCH8SEQKIUQeYKqMgQMHJrSvRKQQQmQTD3lA+8x5IkVaQvZ2IYLInVUIIfLADTfc4LLyJrIIIYTILh6yXda/TKUk0ovSpUunuggiDZGIFEIIIYQQKY+HhFKl1DVNN3bbbbdUF0GkIXpShRBCCCFEaqheyaxd46x/Z82apTuRZjC/sBBBJCKFEEIIIURq4iGxQsr6mNasW7cu1UUQaYhEpBBCCCGESOn8kB6NG2+3Sgoh0heJSCGEEEIIkRq6RovIcuXK6U6kGbvvvnuqiyDSEIlIIYQQQghR+FQoZ7bbTlGrZsyYoTuRZkydOjXVRRBpiOaJFEIIIYRIKRnGZEAZJWmuRKY/2q+1WVl1RdOdNWvWpLoIIg3RkyuEEEIIkUouOcyWzltgtWvVKln34YAOMasaNGiQkqIIIXJHRkQzYQshhBBCCCFC2Lp1q5UuXVp1I6JQTKQQQgghRIoZO3ZsqosgRCiKUxVhSEQKIYQQQqSYzZs3p7oIQoSycuVK1YyIQSJSCCGEECLFlC1bNtVFEEKIhFFMpBBCCCGEECKUTZs2af5OEYMskUIIIYQQKWbChAmpLoIQocydO1c1I2KQiBRCCCGESDEbNmxIdRGECGXFihWqGRGDRKQQQgghRIopVUpdMiFE0UExkUIIIYRIfzZtNhs+yYoFnZuZ1dkh1aUQImEreYUKFVRbIgqJSCGEEEKkP0tWmtU524oFD5xhdu2xUasmTZpk7dq1S1mRhIjHrFmzrHnz5qogEYV8J4QQQgghCotSGWYjYy2q69at0z0QacnSpUtTXQSRhkhECiGEEEIUFtsiZiMmm23dqjoXQhRZJCKFEEIIIQqT1evNJkZPm9ClSxfdA5GWtG3bNtVFEGmIRKQQQgghRGEzakrUv9OmTdM9EGmJ3FlFGBKRQgghhBCFHhc5OWrVmjVrdA9EWrJ48eJUF0GkIRKRQgghhBCFHRf53USzSET1LoQokkhECiGEEEIUNktXmf21MOvfXXfdVfdApCWtW7dOdRFEGiIRKYQQQgiRCnwurTNnztQ9EGnJqlWrUl0EkYZIRAohhBBCpDgucuXKlboHIi1ZuHC7xVwID4lIIYQQQohUxUUKIUQRRCJSCCGEECIVzFuauZhZp06ddA9EWtKyZctUF0GkIRKRQgghhBCpYlSmS+u8efN0D0Rasm7dulQXQaQhEpFCCCGEEKkgY3tc5PLly3UPRFqyYMGCVBdBpCESkUIIIYQQqYB5IkdMUt0LIYocEpGi2PLcc89ZRkaGXX311akuihBCiHRhjxZmj51jNuJOs5WvmUXeN7upd96O1bWt2bDbzVa9nrnw9/5tcv5epfJmM5/JPPfVx5gtWWnt27fPWxmEKGB22mkn1bGIQSIyTfnzzz9t1KhR9sEHH9iLL75ozz//vP3666+pLpb4j//9739OoD777LOqk2JyDzZu3GgPP/yw7bffflarVi0rX768lStXzmrXru3W3XPPPbZ69eqknEsIkUIO72J26WFmtaub/Z6PuRkP7mw27Dazneqa3f622Y2vm9WuZvbt7WYHdcz+u/eeZlar2vb/v59iixYtyntZhChAtmzZovoVMZSJXSXSgTFjxtiaNWtcR7Zy5cqa6FWIAuSPP/6wPn362F9//WUVKlSwXXfd1Zo1a2ZlypSxv//+28aNG2c//vij3X///bZixQorVUrjb0KkHVj1bhtidvuQ7Pd75kuzhz4yW7vBrHs7s+F5sADyDnj2ArONm826DcjKsGqDh5tNetzsmfPNWvfPdFcNsk9rs0sONbvqZbPHzzXLMBcXubRRaWvevHnuyyJEATN37lyrW7eu6llEIRGZpnTr1s2qV69uVatWtWnTptmIESNSXSQhiu2P41FHHeUSBxx66KH26quvOkukn61bt9orr7zirJHbtm2TiBSiKLN4Zf6P0bWNWfO6ZoO+3S4gYdU6sxeGmt12ktm+rc1+mBr9vXJlzF642OzD0WYfjc4UkUBcZN8O+S+XEEIUEhpOT1MaNWrkBGQ6g2sfne169eq5jvgxxxxjNWrUcNbTXXbZxT799FO3H5acE044wWrWrGlly5a1nXfe2T788MOY402YMMHOOecca9WqlRPQWIH4zpFHHuksREFOOeUU5844bNgwu/vuu61FixbOikR5suOff/6x3Xff3X33vPPOs8h/I8VcD/GTlI/jcB2tW7e2p556Kur7rMMiBRdddJE7jrfklvnz59vZZ59tjRs3duesWLGiK/9BBx1kv/zyS8z+b731lu29995Z9VOnTh07/fTTQ7P6IXzuvPNON7KNWybfOfzww10aea4hWF5/fd5xxx3OEkcdcI8vv/xyJ55Y7rvvPhcfwTbuN3XAucJItLyck3Mfd9xx7roZRKH9016Yn4rjFNQ9uP76652A3G233eyzzz6LEZBQunRp1zYnT57sriMs7va9996zLl26OM8B1s2ePdvts2HDBud6S7uizlho49Qj9RmvHsLI6b7deuut7r559/vYY491z18QrKoIZkaWqeMqVapY06ZNrXfv3s7SKoTIgb1a/fcwBUSif92eIXPr3dLHrMGOZv1f2L6On6Bxs61tI1khRXrC74oQQWSJFPlm8+bNdsABB7jPXr16uU7o8OHD7fjjj7dvvvnGdb7ZhjDytp144onOwuoP1qYT/vrrr7vOPJMu0xGeOnWqff755y4+FJfCsBfZjTfe6NwR9913X7fQaY/H9OnTnZCaOXOmE0o333yzW7906VLr0aOHTZo0yZo0aeL2gZ9++sn69+/vxMPTTz/t1p188sn2xRdfOJfjrl27OsEMlDc3rF271tUbArlNmza2//77OwsXgob41x9++MH22muvrP0RKo8++qgTm+xLrB7leu2112zkyJE2fvx4JxwAYXz++efbSy+95ATCEUccYZUqVXLH3GOPPZxwiMctt9xiv//+uxNye+65p7OCP/HEE1lxg1jk2IYQZxsxiTvssIPde++9UcfJTXk9Zs2a5dpQgwYNXJm5L5yjb9++TrAecsghSb0Hq1atsq+++sr9jeDOyU013vFp09QRiTEQbohk9qW+uJ7vv//eXT/ikNgS9r/hhhvcem+wJb/Qlmk3PXv2dPdn9OjR9tFHH7lPBmgYkAHqjDJRNtoX4pY5wBgI+uSTT9wgC3UthMiGRpnPk81fFrvNW+ft49Gxmdl1x2YKyIUrzJrW3r5tW8TWfvObVT4j8x0nhBBpT0SkPVOnTo0899xzkTFjxuTp++PHj3ffTXSZNWtWQsddtWpVpGbNmoyhRg466KDIpk2bsrYNGDDAra9UqZLbtnHjxphtZ511VtTxZsyYEVm5cmXMeZ566qlIRkZG5KSTTopaf/LJJ7vjlC9fPvLTTz/FfO/ZZ59126+66ir3//fffx+pW7dupGzZspGXXnopat8+ffq4fc8999zI1q1bs9YvX7480qlTJ7ftl19+yVp//fXXu3XPPPNMJK+899577hgHHnhgzLb169dHFi9enPX/Z599FildunSkcePGkQULFmSt37ZtW+Smm26Kqc+hQ4dGSpUqFalWrVrU/eS4++67r9s/+Ph79VmlSpXIpEmTstZPmzbNrStXrlxkhx12iEycODFmG+fZvHlzvsrrlcm7Xx6PP/64W7/ffvtFrU/GPfjuu+/cMSjrhg0bcv19r42xPPTQQzHbb775Zrdtl112iaxZsyZr/fz58yNNmzZ1215++eWYejj22GNDz9eqVau4941r+Pnnn7PWcz+OOuoot83/7Fx88cVu3d133x1z/CVLlrg2IkRasvjfSMSOy1xqnhG7wP3vx66veNL27wWX7gMyv3fT6/H3CVte+CbzewfcHLut+YWZ2575cvu60idEIr/+FYmMmrx9XdPzM/cb+I37f8FZD6aiVoXIkdGjR6uWRAxyZy0BYIXAspTo4rnh5YaBAwdGWbcuuOAC5wKIhYNtfguOtw1LlB+sktWq+bLV/ce5557rLFNYr8I49dRTnctkduA+i2WLZEUff/yxcyH1WLx4sXNjxFKDe6LfGoVFxrNWkiW3IMCFNQjWOyxXHlhBcRn9v//7P1cXHrgxUj7cYcnk6/Hyyy87V0mskX7rLcfF9Tc7ixvfadu2bdb/uF5iddy0aZOzKrdr1y5qGy6cWPRwzc1reT1wr3zggQei1p111lm24447unacbLwy0+5wMw1CDCT14V+w7AXB5TY4lQzXj3UdyPqKm6tHw4YN7dprr3V/Jyu7LBZQv+Uat1vqkvZF+8cbwA+W6SC48tJGcgPWYqzqfubMmRP1P+0j6CaL5dNzJQcso8HslFhFg54FwWNzXI4fPLYf3kNLliyJWofF359xkL+DE2rr2tLvvm2/Oa/ELnDdcbHrWZds1m3K/Cwf4tVR4b916zZuX3fNMWbtm5id/0zcQ26rWl5tUu+SEvOeFEUfubOWAHAFLEhwlwxmlCOejYUXTrxtvPj8IHoQHQggXDwRfP6YMX8smh/cGbPj22+/daKGcn733Xcu86YfYvB4AeJaSXxdEO/liGttMjnwwAOd6ywCFmF22GGHOVdEXFz9goY6xM0W3nnnHedyGATBsnLlSlu2bJkTw1OmTHHrPfdPP4g+xDH7hrHPPvvErPMEbdg24hyBHxYEa17K64F4ZYDBD/eN8uLqWtgMGTIkZrADd2vcfIPrgnB9/JAi2HEfDUIyn8suuyzrXuWXgw8+OGYdIh/Bjhs37uO42xJDyYDINddc44Q832Px4oRzS1gMKfGVfsIGh2j7fmjzwXjmsGyAwWOHud4Gj41gDopmhLwf3i/Bdbq29LtvWfS8LXbd0Nsys6Oy+JkZ/VuTFOK5rPrXefvUq2F2ax+z10aYbdpitvN/9dVwx8zPahXduhondIv6nVOb1Lsk1c+b907kdyTZbVIUfSQiRb7xW1g86DgjBsKsO9624LxDCDjmw0RgErfHi8qzirz//vtxRU/wRRg25yajdIgnvxXNwxt5IwEJVtN4BEfa8gtxhMTG0ZlH3GKtYsFyRDwpVlHqlpFHRgiBzKHZgfBGlHllrV+/fsw+HJ8Xerz6jJdYJqdtnqUrL+X1CMZI+ttLMAlNMvB+DBklpdzB9kocrgfiK5jgx8NvbfXAcrt+/fqsBEFBuAcIZOogWcm4wurO6zwgaoH4VAYubrvtNhcfiYV/wIABWQmU+FuItGdY9OBOlGCMty2ZjP5z+3QdZGP1wzr/PnWrm1Usb3Zuz8wlSJ/93LL13yRkjRWiAMhtvgFRMpCILAHgBkgHOVHoTBZ2Jq6FCxc6wYHbIlYs/0gbggQX1HjklAwFd1isiF9//bVL7jN06NAoseB1sknKQ+KZwgQrLS6PCCQ69CRZIbkQCwKEbby8PTFNPeWUfdYv7Nk/KJw5btCtJZnkpbypAgsi9x83GyzWWIPzQpgFj3pAsJP1F4EdFJLcAwQkQjIoyMOy3frFeRh+d2IP2pXnQuQX6DwHLLQFBjJIwINAxtUYUR9mkReixNK4llml8mYzFplt+e/ZHDnZbPZisz77mt361narY9WKZv16Zu7747TMdbMWm/V+MPa4tauZPXOB2dDxZt9Ptrm921v7HWIH0oRINTNmzHD9MyH8KCayBFAYMZH5hXPSoaVTHxQdZF6NGxeTALhf4FLJVCFkw+zevbs7lwdxZIgeModml9nVj9fZD1pT8wpCmHKQIZSMtohcRC8gPrzso3T4E8Hb38s86ue3334r0NiEvJQ3LyTjHiCsPJdfstIm09rJsbGSc0wGLoJgDWQbmXmDbsNhgpCMwkEXcD9ee/GDGytTunjT1QRB5CKcict88MHMTu67776bi6sUogjSpLbZTb0zl9O7Z67r3m77uq7bY8Idgy8zm/rkdvdT4F1x8fNmFcqZjbrb7MqjzC45zOyHe8zq7WB20XOkwNo+d+R7P8UuX/yeuR0xmpER9bskhBDpjiyRJYCCjolMBlg+ER8IXlwxPUsanWam2Mhv5x6rEC6xJ510kosDI46SqSM4jzedBFY/5jBkCoqgWyPJVBiFYy5K8Ebk8hOnx3QMWKE8weVB0DzCyG+huvTSS50Qwd2wc+fOMd8hsJ0BAC8ujmQ0b775pnMP5rtefALWrJtuuqlAXEP95La8eSEZ9wCYmgSXTu7H0UcfbYMHD44ZcaW+sCjmVuQyP+pdd93lku4weOHFm2Ch9UQbyXr8lmkGUXCjJRazY8eObj3PBM9BMDmOH5LnEN/rJdehDV133XWuY0q79yyhDCwQ0xmMkaFM8RI9CVGsaF7H7K7A72KvTpkL3DbEbNTknI+DCOx1u9ktJ5rdcXLmujF/mfUcmGmpzA1d21rLlplxZ0KkG8E4SCFAIjJNwf3Sy8DlxTKRpMOLnyKejs55cYHAcCyFCDzmrWPORjrOdIoRgAR1Y1HJD3Si3377bTvttNNcwhTcV7FMErNG/CHJfLDCYD0jyQhWIUQsFiBcOQYNGpQlIkmUwvGw4GA1wgWQcj722GMJlweRxRyXZPYkMy11wPkoE+6MV155ZVQSFmInH3roIevQoYMTAQgO6ojzI76J+fREGa6KCGLmdCSZCuVFMHNsRAXxc8EMa8kkt+XNC8m4B94ABu7SCC2sg/xYYhGnvLipYgVH0CGysC76M9fmBIJ92LBhbr5RjkcyJW+eSIT0oYce6jLeeiAyuW8ITNon9xFLOoMYlIX7FmalBOqY+SH5Di7pfIdkOsTFPvnkk1n78TdWS9oFllJiM2nf7E99Mn+lEEWSjOMT22/EpMT3hQNuib9t+MTMJS/MWZJZjtKlzFa9butXLne/7UIUhdwXQkhEpikISFzR/JAIxUuGQsewOIlI3DmxACE86NBjFUTckan0mWeeyTEDa6LQISfeEEsj52NqEOIgESA///yzm9KBc2OZIjEKHWw67tdff31UvBwWokceecQJFixA7Au5ETAICOI/6bz/+OOPLt6N8xHDiOXqxBNPjNr//vvvd/VA8h1EDQIbqxGCgUQ8F154Yda+CI4XXnjBidOXXnrJufOy73777edEhN8qVlDkprx5IRn3wANBO3HiRJcdmPvPIA4xqqRWRzgi9Mmkeskll7h2mSi4SSMicZXluFjDAfF2xRVXhAo2LKO0U9rn559/7n68EYaIZZLixANXaNot2Y0RvNQ1llWeH/90MUxvQ/vA2onIxLqJ5ZX2eMcdd8RkLxZCFCDEU+/RwsVczp80PzQZmhCphv4oCQ+F8JPBZJFRa4QQxRqmlOjUqZOLkSuIuRdF4eJljSXDLxZ8IYotS1aa1dk+x2+x4frjzO473Q1cqaMu0hG1TRGGEusIUUxhIuFgDB1xfVjUWH/ccQUwAbcQQojc0TUzuRaeI0KkI+mcZV2kDrmzClFMwY2UmEhGtnGR8pLZEHeJe6Zi34QQIsUwO9B+bZKa7VuIZKNYXRGGRKQQSeTxxx93cY45wbQO/sQ5BQFxdMRbMqWHl5CJ0cQLLrjAHnjggWKbhTOd7oEQQmRLh6ZmO1TO8h4hwZoQ6QZ5AuRqLYIoJlKIJEJ2zFGjRuW4H9k/EXci+egeCFFMKW4xkSTVufQwsyf6uX8VdybSFbVNEYYskUIkEbJjitSieyCEKBKQ17Db9imDvPl8hUg36tSpk+oiiDREiXWEEEIIIVJB17ZRU10JkY74p4kSwkNvLCGEEEKIwqZlfbO6O2T9O2vWLN0DkZYkkmdAlDwkIoUQQgghCrX3lWHWvZ3qXAhRZJGIFEIIIYQoTLZFx0NC48aNdQ9EWlKzZs1UF0GkIRKRQgghhBCFTUBElitXTvdApCUNGjRIdRFEGiIRKYQQQghRmDTY0axpdMbLGTNm6B6ItGTChAmpLoJIQyQihRBCCCEKreeVYXZAe9W3EKJIIxEphBBCCJHCeEiQy6BIV2rUqJHqIog0JCMSYbZbIYQQQog0hu7Kpi3Ota5Dhw5WpClbmokhU10KIRJi8+bNVrZsWdWWiEIiUgghhBBCCBHKmDFjbI899lDtiCg0DCaEEEKIIsPYsWNTXQQhhCjxSEQKIYQQoki51gkhCo9q1aqpukUMEpFCCCGEKDIoNkuIwqVFixaqchGDRKQQQgghigydO3dOdRGEKFH8/vvvqS6CSEMkIoUQQghRZNDE50IIkXokIoUQQghRZNiwYUOqiyBEiaJKlSqpLoJIQyQihRBCCFFkKKX5FYUoVNq0aaMaFzFonkghhBBCZM8Nr5l9/lvh1lJGhtngy8w6Nivc8wohovj1119t9913V62IKMpE/yuEEEIIEWDOEouMn2MZhV0x306IEZGTJk2ydu3aFXZJhCixRCKRVBdBpCFyZxVCCCFEAj2GjMI/38jJMavXrVtXuOUQooRTsWLFVBdBpCESkUIIIYRIP7ZFzIZPxAyS6pIIUaJp3759qosg0hCJSCGEEEIkFqNY2KxYazZlftSqLl26FH45hCjBaJ5IEYZEpBBCCCHSl1HRLq3Tpk1LWVGEKIls3bo11UUQaYhEpBBCCCFyJhVupSFxkWvWrCn8cghRgilXrlyqiyDSEIlIIYQQQqRvXOR3iosUIpV06tRJN0DEIBEphBBCiPSMiYSFK9wUIx677rprasohRAll3LhxqS6CSEMkIoUQQgiR3oyclPXnzJkzU1oUIUoamzZtSnURRBoiESmEEEKInEnVVBuBuMiVK1emphxClFDKlCmT6iKINEQiUgghhBBpPl/kdkukEKJwkQu5CEMiUgghhBDpGxMJMxaZLVrh/lSSDyEKlwkTJqjKRQwSkUIIIYRIf0ZNcR/z5s1LdUmEKFFs2LAh1UUQaYhEpBBCCCHSNyYSMrYn11m+fHnqyiFECSQjlV4IIm1RpKwQQggh8seOVczu6mt2zJ5mNauazV5s9uIws0c+Ntu6LfHjlCtjdtkRZqd2M2tZ32zLVrNZi81e+c5s2HjdJSFSwO677656FzHIEimKHdOmTbNatWpZw4YNC+2c69atswEDBlizZs2sQoUKbtSuf//+VtxZvXq17bvvvu56Z8+enefj/Prrr+4YHEsIkabEs0ZUqWA28i6z83qZvfuT2SUDzX750+yBM8wGXpz48atWNBt+p9ntJ5v9PN3sipfMbnzdbNgEs+Z1zSbONVuxxtq3b5+0SxJC5MzkyduzIwtRYiyRkUjEiQoegH///ddKlSplderUcZmm6tevn+vjbdy40U26SoeZDnTp0qWtatWq1qhRI9trr71i9mc+q0mTJtnSpUtt27ZtVq1aNWvRooV17NjRfdfPli1bXDnZd9myZa68lP/II4+0Bg0aWLrw999/2wEHHGDTp0935Uu346WCZ555xu655x7Xpvr06eOE5KGHHprqYokSCO+lQw45xH766SebNWuWG9gQokC59lizdk3Mrhpk9ugnmeteHGq2cp1Z/8PNBn1rNiqBTuijZ5t1aGK27w1m4+IMSv0w1Ra1q2nNmzdP7jUIIeKydu1a1Y4oeSKSjtTEiROtSpUq1qZNG9u8ebPNmDHDPv30UzvooINsp512SvhYK1assM8++8zWr1/vRGPTpk1t69attmrVKnfMoIgcPXq0jR071sqWLet+8MqXL2+LFi2yMWPGOOF02GGHOVHrwXF//vln93flypWdEGGdSG9oA99++60TwO+8806JsqbRTl966SXXTgvT8iuESAHxBvnO6GG2doPZM19Fr3/4o0wRyfacRGTjWmZnHmD21BeZAhKrZ+XyZmsCCT1GTbalddtIRAohRIop1iISwYaArF69uh133HFWrlw5t75t27b20Ucf2ahRo5wY9NZnB+Lzq6++coLh2GOPtdq1a0dtx8roB2siApJjH3/88c4CCQiN77//3qZMmeLKhkXSA9F4+OGHO1dM/h4+fLizzon0BgGF1RjSyWJcGDAIsssuu6S6GEKIVFGnulmzOs5CaBs2RW+bs8Ts7+Vme7bI+TiH7mpWprTZ+NlmL1xsdkpXs0rlzZauMnt1RKZb68bNZiMmmfVuU2CXI4SIpUuXLqoWUbJiIj0fblxX/UIRkbbzzjs711TcTRMB0YfFcc8994wRkOC3KIIXH0YH2xOQQNwXxwDcXP1gsUTUIiDTlbfeestZnDxxy/V4y6mnnpq136ZNm+z//u//rHPnzs5axbXxvUsvvdRWrlyZq+PhRvHggw9at27drF69es6iW7FiRWdZ5hwFAVbnK6+80lmqOR8L9/LRRx+NGjC44IILnDvzjz/+6P7H4uyVPzcxggwufPPNN9arVy/nbk19YT3HFZF6oDweRx11lDv+F198Yddff72ziNO+d9hhB+dK+88//4SeAzfs3r17O5db7/j777+/G9QIg/L369fP1QFtkqVx48Z25plnZp0jXkwkovrmm2921nmeF8pHO+BZfPvtty3ZeHXy5ZdfuvPyfFNev3WUMhOn6l0P95Q2NHDgwNBjTp061Y455hjbcccdXX1x7Xfffbe98cYbMe09pzhcr3wMDAUHpwYNGuTeCbwnypQpY3Xr1nX17r/n3jP1/PPPux9zr0zcc67hhhtuyIot5Th4YATbo2chZyAMi/l+++3nysxxPDf78847z20XIpSwkMhGNTM/5y8L/w7rvX2yo02jzM/7Tjfr2tbsshfNTno4M7byyqPMPrw+0xL66wxr2zRxDyIhRP6RQUOUOEskLqOAMAvCOh4K9knEkvLXX3+5TzqgiEnmqaIDiICgcxm0ZpJoBfwC0sMTJXTAOVbYPukKVlxEBO7AxG3S6fRAAAHinM76Cy+84DrrPXv2dIIFV11EHx19Ort0gBM53sKFC+2RRx5xdYYoRWRh6eV4iFLuDeIuWSxevNiJB45Phx4rNteE5fqqq65youu9995z+3JtxLJ+/fXXNn/+fDv55JNdm6hUqZK7vkT5/PPPnQDkPPvss4/r/COeEWbvv/++E0Y1atSI+g7rmACYMnTt2tW5TyMOEBBYwWvW3N5xw/J+zjnnuNT4CDliULlO9uXvd999112zB9d+4oknumtq0qSJ24YQYdAFEXj22We7uokH5b7//vvddSAkET08axz3pJNOcl4Cl112mSWbG2+80Vn4EUh+t2LayNFHH+0GgxDdRxxxhBNLlOf88893gvHhhx/O2p/98ArgOhCkxCVTX8S9JiupB/f68ssvd8KQ54R7SD0x+PXiiy/ad999Z7///rvzpGCQ4a677nILbYsBFcQ59/PPP/90LsX33nuvs4Rzn4PtEXbbbTf3yXPJc8PAF+dE+CL6ubcvv/yyPfnkkzHx2kJY1QpmNatlZlpdt9Fs/X9WR6yFrkFvDq8krJPePjkl1YHyZcz2uzHTAglv/2A24k6zQ3Y169XJ7JtxtmboH1a5b0/dFCEKCfqrQsQQKaZs2rQp8txzz0Veeuml0O2LFy92299///0cj7V169bI888/Hxk8eHDkjz/+cH/zXW95+eWXI7Nnz476zi+//OK2/fTTTzHH27BhQ9Z3586dG/e83333ndtnwYIFCV1z8BxjxozJ1bJkyZKEjk15WrVqRXBM6PYhQ4ZEypQpE6lcuXJk2rRpWetXrFgROeCAA9z3zj333ISPt27dusjUqVNj1s+cOTPSpk2bSOnSpaPqkX1r1qwZadCgQSS3bNu2LXLZZZe5snTo0CGyZs2arG3jx4+PNGnSxG178803s9avWrUqss8++7j1s2bNiuSFU0891X3/4YcfjtnGfVm/fn3W/0ceeaTblzr+7bffstavXLkycvDBB7ttZ599dtb6efPmRVq0aBEpVapU5O2334469pdffhmpUaOGqy/vHMuXL4/sueee7jj9+vVzdeLn77//dvcyu2un3qZPnx5zLb/++qu7L1WrVnX31YP2xzE4Vl7w6qRChQruHH64rqOOOsptv+SSS9zz7G9D7du3d9u8uuTdccIJJ7h1RxxxRNT+77zzTqRSpUpuW9++fRNuc175eKY9Bg0a5Npus2bNIosWLcpav3Hjxqw2eNFFF7l1y5Yti+y6665u3dixY6OOTfnmz5+fUHvk2D179nTbgu8+7jPPYvB+5wTt0/+cQPB9SNukXfmZM2dO1Ll4Zy1cuDBqH+rF3/bDjs1xOX7w2H7Wrl3r3vl+qLPNmzdn/c/f/nosideWMLe+FYnYcZnLbldnrnvr++3r/Msv0yORZavCt/mXJz7LPM5rI2K3nfVk5rZ733P/z7rhuWyvjesqSfdN16b7VtBtcvTo0Ulvk6LoU2zdWXH9gnjxjt56b7/s2LBhg7ME8ElSHObLOe2009yy9957O4vk0KFDo9zPsN4AFg7/CA7H4Rh+a0RBwHVhxcjNgiUwv2DdwUUV6xzWjlatWmVtwzJ30003OavL66+/7uotEXBdbd26dcx6rFyeRQmrTTLA5RG3UsD6iQumR4cOHZyboZeNtSDAyhTEi5ENgpXJsy4BFm3cGqmvN998M6t+P/74Y2eJw5qIddEP1l4sbtz7YcOGuXU//PCDa6NYfB9//PGYSYZxh83Jykq9tWzZMmY95cX6xTPhfw6SBfcnGLuBqymJj7zr8bue04YuueQS9/drr73mPrE+YnVmv+D+WDM9C3l+4F3C+Wi7tCW/VZd3E67UeDgMGTIk5rvcXz+ULy9JjYJtjfuMJTO3k0rTPv3PCWDt9UPbDFrSeUf6z4WnAe7qfqiXYNsPHpvjBr05vPev/1qDYQjUGe7DHvwdrMeSdm3Zcs97Fjn4drOet5kN9rlle26s8VxWWR/P1dXPvKWZnwuj3bij1jEXJZnM998l22vjukrSfdO16b4VdJvECyzZbVIUfdLanZXYozVr1iS8P25mBZkZEwGI+yXugB4kxsF1dfz48c61EDcz4IcH4UMHFldBf3ZWXNDohONCltsOW6LgwoabXmFDvKMXGxfW2SZ+i84xLni4E7dr1y6h4/7222/OXQ/3wyVLlsSI/wULFiSl/LjJzp0713XMu3fvHrOda7rtttvcvU4muMzSThDeCAemaGCKkE6dOsVtI2H1i2infqlb2h7PBG6ugDtpWJvw4oIZ8ECU4+JKW8clNEzUJgLfR9gTy/rHH3+4Nh8cNEjWPfMT9vzjOo1rMD9gF110Ucx2b/DHi1GeM2eOc11FLOPK6geBR/wi7sH5geOT0dl7z+Gy7IdBGBbqDYFP2ckmTV0ixGkb3H8+gz/W2UH5cc1FVCOIcYU++OCD3XHCBmqEyII5Gr8db7Y1kKF18crMBDqdm5lVKBedXKdJbbMGO5p9/nvOFcm8kF6W1iCN/xOo/6x0bq8NuinJhxCFCf26sIFhUbJJaxHJyEVuYnP8I/Q5WRpzslT68e8T1mEj+QkiEnHjB0GJ9YPOuddRZxSTThydQURk0KpQ1KFevWlJgiO4gCghxgu8jKY5QQwiGXHpTCPiiRn02gZ1i9UoWRZdr/yUkYQjQTgv8Z3EsiZbRCIeiSNEKJOABasio4LXXXedXXPNNTHfCYtJpH690UYvgZFXz7/88otb4uEN2HjfC45S5gZiM4nNY4CFEUwEOQMbiHNEHe2/IKzwYW0OIQbECMZLogOex4AnKsMSaMU7R27h2r246VdffTXbfbkvxLcSD8k9JxHPhx9+mBWXS0z3Y4895gYeEgHLK/fiqaeeclZ34nG9+805Tj/99HxfnyiuMKAVMs3Hq8PNBpxodtEh2+eJhKuP3r7dD0KROMkZi8y2/JfI6fspZn8tNDtq92jrZelSZucfnPn357+ZdWtr/65aaZWq5MKKKoTIF4n210TJIq1FJGIrryAA6FDTUWMJWlS8jrInaLID8z2meqwZWBPjiUwsB36wIGF5Ywnr2LIdN4CCgE5qbq1liOH8loe68IQxVtegpZF7kZu6JxMqiT4QkCSSueOOO6K2k5wFEZksvPJTRixnQSGJeKRTn+xkSIgr3E1ZEDNYirB2YaG69tprnYggmY2fsCys1K8ncL369RKrkBTGy+KZHd73sMjmBUQ4Lpo8LyR88ZcbCyUZYhGRBUGY1da7HhIIUa854bncBAeFPGjXYedlCcts6heM/nbmuR+ROCoRYcq7h8EEFqzKJKiifTD1EFZFBrISsSbyPkPgs/BcISQ/+OAD++STT1ySKyzZPXr0yPE4ogQSb57IBz40672P2QNnZE73wTyP3dtlzg+J6+vIwByRgy8z69HerNkFmVZM79gXPGv2+QCzn+8ze/pLs9Xrzfp2M9ujhdnzX2daK+8/3XkxlLTplIQQIt0otjGR4P3IYIEI4q1L9IfI8wH3rBp+PMuF11nPCTqACJGwrK5FISbS6zBDsNNMh92z1hInGgQXSzLb0oH2OrzZHY/YMa/THoznQ6TQcU4miGjuC+J15MiRMdu5Jrb55/dMNrQjxCQC7H//+59bh6trWFmC4MYarF9ieCHsesLA0sv9IDYyKH4SARGLuyYgGP0gzAo7VTgxktQHU5zQnnKC9osHASLdczn1P1eee3BQ4DFQhTt00G2Xa8ZF1g9WTlzcwZseJjd4WVixSDLYRrkQlcC98+I4c5qug8EJYmuJYz7rrLOcyA+61gqRI4i9rgPMXhxmduK+Zk+fb7Z3K7P/vWp2zlOJV+C3E8y6DTAbP8fs2mPNHjzTrFI5s0sHZgpM6NZWN0SIQqYg+zyi6FKsRaRnAcTq4XdrpaNH55COH1N2+KHTjNk+6AaLGyUEO6Ls51lVgvFTYa60WJnozNPJ22OPPayg8GIic7MkGhNFvXlB1AgWP7iY0inlE3c5fycc6x7uctTfKaeckmXly+54WAU966g/eQ5W3yeeeCKpVkjAXZA4MWA6D7+IImaOqRe8+SGTBR19rEn++TMBsepZw8ISYJA8B/HvF2/EjWIJ5B549XvCCSe4tonICEsIhKWMbd61EgtJ20QIMgUFwsIPoj5Y1mDb8+6Zf15E7jv3nyk4ChOs4cQTMgDEFDJBN1rqGTdfL5YXizzJf1h/xRVXRM0LSpIiL/FSmCjkXjKFhv+a77vvvhgxiuA844wz3HuAONjgdm+ga8SIEVn3lnlBg94OXIvnZuR5W3iCNux5om1wrz2Xcw+ErzdAltc4WFECyC6Enyk5LnzWrMG5ZhVOMmt9qdn9H2ROCRLkgFvMMo7fboX0M/pPs8PvMqtxeuZxOl1l9n9fZG4rX9Zst50UmyVEIRNmjBEird1Z8wuJMUgsQqfVS25DZ4kOGx1DOopBSyBWBiwlxHD5RRWWCRLqIBg5FtYKRvyxMGARo+Ppz0QKdACxONKh9uaFZH/OjWudfx4/D+LhPJHqWeCY88+z3nAellRCUiAylSJgiMPy6hHxdfzxxzsrGm5xWNIYvSIBCCKIayM2lPvgn5Mvp+NhIaHjS8ZK3O64r8RCUi9kx/UmVk8G3FPcZhEVZA9lkIF7RWcd8Y+1luvr27dv0s6JMHjggQecIKYeaFvUF8mHSChEXYTFRJJ0B6sh9YQLJm2X7zAHKgltPPifmDncSi+++GI3pybPBdY53MKoSwQjbpUICI5FVlIsvwgi5hz0rJMILQZSiKOL5/LIMagfLGwIWAQcFmrcqzkXArUgMrPGg+vkenj2yIiKYKcMzMnoWRtnzZrlEtzwbCG+77zzTlf3zF/Kc03CHuqIe+RPVuS/Zto81ltiDnlH8Nzz7qHNc1+D7uWnnnqqO8ezzz7r4hqZT5Pz857gB5tBC86LCy4Cj4EexCTH8ua+5V5Qp3hKMM8oUH6OhdglgzTPH4MxxKbyPGHd5n3Cs4nVnXcT5+JYuGlfeOGFhXZvRDGJiSwMSmWY7dvarFxZW79sfa7m4RVC5A9+g4KGEiGKtYgEOr90FukkMYk3I/8IQjIcIkZyg9fxpGPIlAmIQX7I6Ixh7QjGYyEGmLScTiTilY4cAorOXLxUx+wbzEjrHwEiqUuqRSR1SHwdnV2EMok+qAusU3RS6bQ//fTTTuR4IoTrx1pDdky+6+8A5HQ8MoYyCTpWNpKycCxeZggCRE0yRSTQPhCtZGElTowEJtxb6p0OOBbKZEKnH6FMvXlCi2tkkAFXRQRN2AT3rMfShysiApD2hfsoFuBgbCvHQQBRz9TxZ5995tbTDrGy33777VHfQZyzHzGoTP2BeOc+cQ9POumkLMt8PHC1BMSrZ03jO9QrAqswRSTQXhgEIHER1kTqDU8BxC1CioEDzwLteTEglLnffA+rL3GLxJQysIAADIJlkWMi0kkGhTUeV1qms+H4QRGJeEPckoCL54XtDLRwH6lnRLw3/Qj3BnFH/TFQwHNAm8RyTlwwx/fHGF999dXObZ77TOIeLKS8CxG6WGO5n7ybcAfnWeP4JNThfucm26soYcSLiSwMtkUy4yz/+03M7e+3EEKI5JLBZJFJPqYQogA56qijnIUM914lQCl8EO24Y2NtRSAKUSLo+6hFhnxvGYi5VDHsdrMDO7hBqIIMBxFCRBOWoFKIYh0TKYQQQogkUTDTGicGU32QrMcsJpeBEKJg8ZLlCeFHIlIIIYQQ6asiCRVhmg/mlgyZTksIUbDEm/JKlGyKfUykEEx8H5ZRMyyrqD/hT34gAzDxh8H41jBwi5RbajTPPfecSzqTE02aNLEBAwbk404JIRImVdEvke3xkN4ctsQDCyGESB0SkaLYw3yKAwcOTGjfZIlIsrgOHjw4obk3mfMvNyKSZD/FHWI+WRKpu8IWkUyfwiKEKES6Zk7ZJYRI3ZR5QvhRYh0hhBBCpG9iHbxol79qtkPlrPgssmgLIQoHrP94/gjhRzGRQgghhMiZwDRWhUaHplkCEphuSAhReDCnshBB9CYWQgghRHrGRCJce0TPkztr1qzCL4cQQogoJCKFEEIIkb7CVfGQQqSUVq0yp9cRwo9EpBBCCCHSl65to/5t3LhxyooiREkkkUzzouQhESmEEEKIHMlIRUxky/pmdXeIWlWuXLnCL4cQJZi///471UUQaYhEpBBCCCFyJFLYMZGlMqLmh/SYMWNG4ZZDCCFEDBKRQgghhMiRQp/eg/N1i3ZlFUIUPi1atFC1ixjKxK4SQgghhPDRex9btENpq1evXuFWS69OMasaNGhQuGUQooSzcePGVBdBpCEZkUL3TxFCCCGEEEIUBcaMGWN77LFHqosh0gy5swohhBAiR8aOHataEkII4ZCIFEIIIUSObN68WbUkRAmkefPmqS6CSEMkIoUQQgiRI2XLllUtCVEC2bZtW6qLINIQiUghhBBC5Ejnzp1VS0KUQObMmZPqIog0RCJSCCGEEDkyYcIE1ZIQQgiHRKQQQgghcmTDhg2qJSFKIE2bNk11EUQaIhEphBBCiJw7DKXUZRCiJFK6dOlUF0GkIWVSXQAhhBBC5IPV680++y25VbhTXbM9W0at6tKlS3LPIYQoEsycOdNq1qyZ6mKINEMiUgghhCjKLFphdsojyT3mvq3Nfrg3atWkSZOsXbt2yT2PEEKIIol8U4QQQggRzZi/zNZvjFq1bt061ZIQJZBGjRqluggiDZGIFEIIIUQ0m7eajf5LtSKEsAoVKqgWRAwSkUIIIYSIZeSkqH8VEylEyeSvvzSgJGKRiBRCCCFEoHeQYTYiWkROmzZNtSSEEMIhESmEEEKIaLZFzH6cZrZ5S9aqNWvWqJaEKIHUr18/1UUQaYhEpBBCCCFiWb/J7I9ZqhkhSjhVq1ZNdRFEGiIRKYQQQogc4yJ33XVX1ZIQJZDp06enuggiDZGIFEIIIURIDyHDbNSUqAnHhRBCCIlIIYQQQsSPiyS5zrZt7t+VK1eqpoQogdStWzfVRRBpiCyRQgghhAhn5TqzSfNUO0KUYHbcccdUF0GkIRKRQgghhIjPyMnuo1OnTqolIUogU6Zsd2sXwkMiUgghhBDZxEVmish582SRFEIIkYlEpBBCCCHix0V+N9EsErHly5erloQogdSuXTvVRRBpiESkEEIIIeKzeKXZjEWqISFKKEqsI8KQiBRJ5fzzz7eMjAx79913U1azHTp0cGUQBT8ymejo5L333uvuCZ9CiCLIqMnWvn37gjv+stVmFz1n1uBcs/J9zFpfavbAB2Zbtub9mCc+aJZxvFmLi2O39bg5c1u8peUl+bocIYoTEydOTHURRBpSJlUnjkQiNm3aNJs8ebL9+++/VqpUKatTp46bzLh+/foJH2fRokU2e/ZsW7hwoa1evdo2bdpklStXtgYNGljnzp2tevXqcb/LeceOHWsLFiyw9evXW7ly5WyHHXawXXbZxVq1apWkKxVCCCGKeFzkyMm2qMdO1rx58+Qff/V6s24DzKYtMLv4ULOOTTOT+Vz/qtmU+WaD+uf+mB/8bPbBL2YVy4Vvv+kEs349Y9d/+YfZ6yPNjt499+cUQogSRMpE5E8//eRGNqpUqWJt2rSxzZs324wZM+zTTz+1gw46yHbaaaeEjvPNN9/Yhg0bnABt0aKFE6P//POPE6gc74gjjgg1w7Ptu+++s9KlS1vTpk1dOTZu3GgrVqywuXPnSkTmkVtuucX69evnhLgQHrQJnusC6YAKIQolLnLpxXvn7hnGondrH7PbTs5+vwc/NJs8z+zhs8yuOjpzXb9eZtUrmT31hdnZB5p1a5f4ef9da3bJQLP+h2cKyTB6dQ5f/9zXmZ/n9Ur8fEIUczTFh0gbEYn1EAGJlfC4445zFkBo27atffTRRzZq1Chr1KhR1vqcXBdbtmzprI9+/vjjDxszZoyNHDnSTjzxxKhty5YtcwKSh+Kwww6zihUrRm3f9t/EyiL3cN9YhMir66sQIg2Zs8TKLl5dMMcePNysUnmziw6JXn/1MZkiku25EZFXDTIrW9rsrr7xRWQYU+ebfT/FrGtbs130OyaEh/p1Im1iInFhBVxX/UKxVq1atvPOOzuL4MyZMxM6Fi6rQQHpzWeFlRHLIpZKP4hLhOKBBx4YIyABa2Y6dbyXLl1qJ598svubayLu0D93zzHHHOO2lS1b1qpVq2bdu3e3n3/+OfSYQ4cOtS5duliFChXctVN/n332WWgsI3+zzn++sPL5CTsOAwasO/TQQ+3XX3+1Hj16uHJSz/79PvnkE9tvv/3c4EKZMmXcsU866SQ36BDGY4895toLbYjj9erVy/766y9LBq+//rrttdde7rjUa82aNW2fffaxN998M2sfXKBvuOEGV5+0XfbDor3HHnvYBx98EHNMf31++eWXtueee7r9WZfbeAOv7pcsWWInnHCCGxDh/I0bN7YBAwaEDoTcc889rn6x2lNnlSpVcoMwzz//fMy+id6zMIh7pJ02a9Ys637Ei4lkHWWYP3++HXXUUc6d3LsOyhvGunXrXB1yHezL5znnnOPWe8fLCzxnF1xwgTVp0sQ9Hyx4MRxwwAH27bffht5Hnh2eIZ4llt13392GDRsWc2zqgf1bt26d1b5pUwxiZXfvn3jiCfcuo51wrdxz3lt4YAR54IEHrF27du6+cn/xsLjuuuts69bomDLaBnVLWfztm3b81FNP5anuRMlglyUFMMD6z79OoFrnZmYVy0dva1bHrH4Ns9G5eK9/M9Zs0LdmT59vVrlC7srywtDMz/NC3FyFKMGMHz8+1UUQaUhKLJF///133JEN1k2fPt3tkx+XSDp5dHjpQPmTrBAzyVxXdJrosCJQFi9e7GI0WUcsZbqISNiyZYvtvffetmrVKvdZtWpVJ5yAjiSWXDrPCBfceXHlxZJLpx9RhrDy4P/evXu7Y+6///7OLWnSpEl27LHHFsok0tQ7Ardhw4au84y4r1Gjhtt2xx132G233Wbly5d3QqdevXo2depUe/vtt+377793sat+wXrttdfaQw895DrMCB065uyH0AsbVMgN5513nr3wwgtORHTt2tW1CdrJuHHj7JVXXrFTTjnF7Ucc7v333+/csTkvQo74WspBPSPOzj333Jjj//bbbzZo0CBneT/66KNd2nzOlVu4j/vuu6+tWbPGDjnkEOcSjoC5++673TNE3fmhfhEWiAWEF+2esiKcuDd33nlnru5ZGJdccok9/fTT1rFjR1cWxHVOUH4ENcKqZ8+ebhAJ0XbTTTc5MXr99ddHCSBE3ejRo13sNF4GXPeHH37oXNjzCsflmeAYiCueCcrDe+j333+3ESNGOPHmh/XcRwbDGOyYNWuWq8/DDz/cCU1EsQdu+rQdBCcil/vNPfrqq6/shx9+sF9++cW1I395OA7beea5B9Ql7YvzvvPOO1HPNvf/66+/zhKZPAMMlj344INuEMAvgs866yx79dVX3b7UN8fnvcG74L333rNLL700z/UoijEZGbZ52DircFaSBdb8ZZmfjWqGb2f9Xwlmhl27wez8Z81O2s/siFzGNG7abPbKcLMaVcxO3Dd33xVCiBJIoYtIOnyIHka/EQBBvEQ4K1euzNd5sGRyLjrLCBO/tQHByMg+nS6S8vihg0znDIGZCHyfYyYKZcmNpYTkP3T86Qj6y0RH+9RTT3XXiDgk9tMDKyQiEssHHVuvU3rhhRc6Ef3cc89FWRfppGPFKAwL9Jlnnmkvv/xy1HoEASISUUAn3B9zg1BEMCJOPFGE5ebRRx91HWWu1csYyIABnW065fmxQCIgsUBRFoS5H79QQejyP+7Ufv78808nim6++eZQEYkIuPXWW52oyw+0DeqM+F7vWUL0IGoQGbQLv5BBVAQHC2i7u+22mz3yyCPOaoWgSOSeBaEdIuhwR0fkYaELs/LHe4YQTAhB3gtezDSi7sknn4wSkbQH2gsCnGfCOwciiOvIKz/++KO7l4jyYPvh2nCBDxsMwBLtt5gycIAo51njmfQGpBjsoR6DAhzLNs8x1/jxxx9nrUfQIyBJ8EV5/GKc8hC37XHXXXe5dxnPPPXutQWeB8QwAvbZZ591ZYL333/flQPrKJZIP1iEhQglErHSP2YzULN0Vfj6dZtit+G6yuK2b8z8LJ/57MdQodz2fXLixtfNVq4zezz2vZsjH47OLCdxlJxTCJFFon1iUbIodJMbIgbixTt667398gJWOzpeWCCx3vnBBRHohGFdYiSekXncRemw4f6Kq2HQBSy7DjCiINFlwoQJub6exx9/POYBxqqBKyMWEL+ABK6ZziNlo3MMdCQRGLi7Bd1T6YQWxhxADBBgpQqCMKC+77vvvpikDddcc41bxz3xeOaZZ9z+WAT9KeexWv3f//1fvqb3wH0QELVBAQlYqTzorAcFJLCOdoWlEnfjILh55ldAeiBg/IMxWE2vuOIK9zeDBX7CrM2Ikz59+riBHVydE71nfsiKjHhHQPIccZxEBaT3zNOePQEJWHaxzGF545n0eOONN7LarP8ctN+rrrrK8ot/wMmDcjFgEIR1QestzxZtkmeNZ86DgaAwCy5tGNfdoPv5iy++6D4Rf0FrLuXxvBG8fWn7DBz42wLrGBDhefDqzQMrK0sy4l4YiFi7dm3Uujlz5sS8k/330XsHM6DnHxgLuq4zOBAMRwgem+Ny/OCx/dC+eV/6oW1hzffgb9YVxWvDm6HAKZVhm3esHP/aap8Vu3hJc4LrH/hg+7V5YnLj5tBr27xm3fZ9srtvP07NjJ984HRbWnpL7u/bf66sEV/GVrVJPW8l7V0S79q8vlkyr02UUEskHRJc0BKFThUj/IUBIvGLL75wDxTnDHb+vIeRT9wmvSywdGTpCNPIeYiw4IWJiCCM/rMUFHQYcakMgsuq96CGxSx6MaW4gVIPWHYgKKq9c+Bmh+WjIKHjG2Z9pozA+b3r8oPlBaHCiwu3StxKwe/O5xdKuCXnxjrsB9FHp9tzWc0JrJW33367uwYsg/4fEu8++N0UIa8xe0GwcgUHEADr44033ujcgf1gwcVFFEseljXq1U/QKp/dPfNgsAerK1Y8BD/uk7kF4Rvm9sqzi4slP5CeAMN6xv0Ju27cmilDXiD+FXFPwi3uF+6hZJNlMCCeIOaZQagFQQBT1whD3JU9Bg4c6CyVuLHy/vTHrfoFHe0IN2IEPFbd7OCHmXcAnhXc8zB4vv33Frdk3G3pFFBnXCOfeU18FHbvEM1+ghZPIPY0KOCD7+uwwa3gscPEefDYtOFgO+Zd4od7EFxXZK5txx2twNkWsfIH7xb/2r65NfY7vW43O7272RmB38id6m6/tkb/PQfzl4Vemy1aGeXqGve+HX6lWdtGZt3bWa1/N5ux2KrMeSYjEWu6uZzZ/KVmjWrF3rdZ/5gNHW+2dyvL6Ngs9tp8qE3qeSuJ14YnE2FTybw2UUJFJI0hrPMUD38nLCdLY06WypwEJKP/uMLSkQubGNl/3GAjp3PKOkQk8WKJiMiCxktmEsQb0SH7LEs8EF9+9+Awiwrg9lvQxIuP88qIK2l20Lnm5eUNYMSzmhCbmFcRyWgaL9GgW2cYuA7ipsiABK6UdMrpzHO/vClsPMu3n2RZfSmj33rngRjyrsUDkYt7KCONiFgGJry2RTkpb3AkE3KKaeQ7njst1u+8QJ2F4QkrvzCnPtk/7P0QfJ5zA/VIHVx++eXOkor1n4V315FHHunEX9AbIN4z481z63fJ92J4vcRLtGPvvUgSJn979UaCGQzJCQQ27Y9nCJEaD/+9ZRCQtop7+ODBg91CO0BIY+UvjPhoUTRZ3amhxY3e7hmn3exUN/42qLuDWZNaZmNnm63fGJ1cZ85is4UrzA5PwFWd5Dy4sraKE9Pb8hKzvVqa/Xx/7LYXhzmhqWk9hBCigEUknaq84sVC0pllCY56eB0vLzYyUTgWAhKhgYURt80wvI4gIjjMnctzZ8uNO2tBxkTGc830RnWIDfTcF7PDq894mU4RzUG8gYKgdc0DkZKbJDbxrsU7BmIkkflBPdERL34rP65dlAWRSqc8JyGJyyvWPERAUEBhgYqXdTM/7rZ+KCPnDwpJz+rkvze4XXK/SARE7KOf/v37Z1mqc1tW2iEuwLiEY+HyYiILCoQX94fBpqCQDLrP5BYGWIYMGeL+xvWc9wkCCzdR6hoPh5yeGcCN2f/MUVbcrHn3IOaDYtfv9gqeRTAsDjOeyMeaG3QxigfthRhoFuqMwRCEJSEA3EO8MMLcekUJp0xpm1u/ghXIRD2n9zC7+12zZ77aPk8kPPzx9u1+5i7JjJPcuZ5Z2f9+xwdfZrYp5Lfq4ufNSpcye7KfWc2Qdzq/9S9/a1atUmZCHiFEDIkMrIuSR0rSkNLhiScCvHXePolAp5IkIghILCzxBKTX6eVhQCQG/cP9Fr5EH5jCiIkMw3MP9mIecwLLLIRN/YEQ8VxKwzqzYZ1T5uEMs7LlBTJ5AslBEsGzlIRNc4CrayKd73jgyohlxz+VRzxoqwi1oICkbYXVZ7LBHRIBEIRnAfzZjT1hGZboJ950MImC6y8iBLGEK23YfUkWuNdyf8Ku2x83m1+wLN9yyy3umUVQhVn7ucdhg01B13HaCc8K76WggCQJE9ZEP4hN4iQZUMO9NjtwUcIiz+BQXkQ05bn44oudWzbPIQI4v+1BFEMYTNpjZ9tWIU7ym/xy3bFmrRuaXTfY7LIXzF4canbG42ZPfp7pDts98Jt+xhNmbS4zW+AbMDx6T7Pe+8YuxFMy1Qd/HxAygPv575nH6ds191OCCFFCIGeIEGkhIr0YMYSI360Vix7WKDptQYsUlkZEYtANFgsBnWYEITGNwfizMMjsCMSG+eOSEJDEdmF9CSZ4iYeXBTXRpW/fvpYMEAOIPFLyv/baazHb6dz65/PDeowwJ8YsOC8g8woGO7KA2x2WH+IU/R1U6jze3JF5AVc/3OnIZurFO/rh3n7++edZ/1900UXOSorQ81v7uGayuPqD0HPLZZdd5j4RELTFIHT6/ZYr2mUwmydTJBDTVhgQB0cZPEjoghsmkCXUw3P99WcABaxRZDnNL8xVicWO54l5S8NEXjLwnh/arH8QAxdQMszmZw6ssLbHc4ElPswyh3Cjzfrh2aJN8qx5HhsINaymvFt4h/nb9WmnnRY6p2e/fv3cJxlVg54ODPr42yb7cgwSJIV5RRCX6wlb2kqwDXjJGrxBNY04ixh4p3ZvH5pILClgBRx1l9m5B5m982Om9fDn6Wb3nmb2UgFPOTPwv0EvzQ0pRFzIRi5EWswTScwQ8Yp0trwED17HiM4Q1sSgqxqCj4QUCEV/hkxcwRA1uHXxGdYhZn9/xwh3Ujr5dK4Y7ac8dKJw40KIYEHIrTttYYO4Q0QRk3f66ae7zKZYnnBVw3JI3VIfzFcIiDTinZiGARFG/KE3TyQWFeYODL4k6DjTaSf7I9NG4KZIhxpLBfWd3XyBuQErKa6hCDcma0e8UjZEAtdC5x4rDtNAAG0HF96HH37YxXGRGAQLM9YUrNJ02vPq2sh0C8yp99JLL7nz0BaJX8N1kXqi7XgWL9xAzz77bJfgh7n5cLNlYIR2RLIZ2mxBgsWK5wbrHIMZ3Bvi+RAq3Gf/9B5kLkWIIyxpNwxA0EawjBMrSd3lF86Hay+CksVrn8mEAQfEKm01eN0MICGi8zLPK20awUYcNANYDBAgTBlA4Z3gF+QePDMkEuLcDEx580Ty7uJZ88rBgAcCj8Ee3kWUmRhFhB3biGENJjVCJHMsrLqUp1u3bu6Zw1LItR9//PFZg0FMF0Nb4/6yL20PEYtFnux7TNPC4ArPGe8ERD7Xx/uCwQXefQyEUHeEAuRnqhRRjOnaxr2Tc5XqP/J+4vvWrm723EWZS04Mj53TNi6zo7NUx/BxeEIqIYQQaWiJBDo0dIwQKnRyEJAkqmD0PpG4uGBSFkbg47mQevt40Lkj9gexQgeRjJx04jg/WQo998p0h+yRiBbcCbGiYmGgE08GSzqCzLEXjNPDQkRWSSY3f+utt7Imao/XcaSjilhCnBLvRmcT0eR1gJMFWUPpMDNIgMWG2DTcW7EEUW5Esh+SlGB5IkkNnWfKhqigXPm1pCCamUQeYYC7MJ1/RAad/XPOOSdrP+IAmUYDkYmQYECDDhZZZgsjOQkxvZSPZwlhy33EvZa5C7m3fqhXxBcuKYgT2glQz544TwZkNcUzACHFdB/e3J7Jgmd3+PDhzhKPeOSacPlEGHlzWeYmTtdfP2eccYY7PveaQRY+EX3ERd57770x3+GZoc4RYdQ3zxTPFs+YPyur16awcnPPmKcRccpgFRnvwhILUQ7aP+2cDHoMbFAmnnfOwdQ+fjgnwhVLEeXAvZi2wfONAMZlFWifJA+izTKAQLlpO5QBMTps2LBc150oARAavd8umkdUiBJKdlnaRcklI5If3z9RbMA9leyOdMo966VIX7x41eCcTiUZhBOWZMQg804WFHhPYOk977zzYlzDhUgJf/4dPytpMujUzGzsI1lp/oUQQoiUWSKFECIvhMWq4vbsxScmOsenECJBK2SPzMQ2ufESEkIUHxQTKdImJlIIIfIKiWRIcITLMHMpEsuH6ylTmOBGiku6ECJJ4KvUtW220z0JIYo3YQnghJCIFMUa4uUSma4DUumayNyN/syd8Tj44INLvLsxyXpeeOEFF1dItlHidUmmhCsrsbX++5lI5lmSOBFHKoSIQ9fMrOckaiIOXQhRsqhQQdPfiFgUEymKNSREYRqMREhleDAxjmHTMwRRHF7iYJEkyVFOkIU3WfO3ClHsYiJb1jeb/n/uT8VECiGE8JCIFEIIIYoyBSUiMzLM+vU0ez5z2g2mOiKLuRCiZEFmcKZ6E8KPEusIIYQQIha8M7q13d5hyMMcrEKIoo/ioUUY+kUQQgghRDg+ETlr1izVkhAlEHIPCBFEIlIIIYQQsTSqadYkc05aIUTJpXPnzqkugkhDJCKFEEIIEegdZJj1aB+1qnHjxqolIUog48ePT3URRBoiESmEEEKIaLZFx0NCuXLlVEtClEA2btyY6iKINEQiUgghhBCxBETkjBkzVEtClEBKly6d6iKINEQiUgghhBDR1Kxq1qqBakUIYbvttptqQcRQJnaVEEIIIYoMzOdYqZxt27LNShHLmAx6dcw8ro8GDSQqhSiJTJw40dq3j46RFkIiUgghhCjKtKhvtvYtmzh+vHXs2LHATtOwYcMCO7YQIn1Zv359qosg0hC5swohhBDFgIIUkEIIIYSfjEgkEolaI4QQQogix9ixYzWfmxBCiEJBlkghhBCiGLB58+ZUF0EIUQyZMmVKqosg0hCJSCGEEKIYULZs2VQXQQhRDFmzZk2qiyDSEIlIIYQQohjQuXPnVBdBCCFECUEiUgghhCgGTJgwIdVFEEIUQzRPpAhDIlIIIYQoBmzYsCHVRRBCFEP++uuvVBdBpCESkUIIIUQxoFQp/aQLIZLPqlWrVK0ihjKxq4QQQgiRVDpdabY8SckpSmWYfX2rWeuGUau7dOmSnOMLIYQQOSARKYQQQhQ0c5aYrVyXvOONmBQjIidNmmTt2rVL3jmEEIIxsE6dVA8iBvm+CCGEEEUJLJGjYudtW7cuiSJVCCH+Y+7cuaoLEYNEpBBCCFGU2BYx+06ZWIUQhcOKFStU1SIGiUghhBCiqLFgudmcxVGrFBMphBCisJCIFEIIIYoiAZfWadOmpawoQojiS4cOHVJdBJGGSEQKIYQQRY2MDLORk6NWrVmTpOyvQgjh4++//1Z9iBgkIoUQQoiiRoS4yImpLoUQogSwbNmyVBdBpCESkUIIIURR5K+FZv/8m/XvrrvumtLiCCGEKDlIRAohhBBFle+3x0XOnDkzpUURQhRP2rZtm+oiiDREIlIIIYQoimRYVFzkypUrU1ocIUTxZOnSpakugkhDJCKFEEKIokjEzIYrLlIIUbAsXhw9nZAQIBEphBBCFFUmzDFbudb92alTp1SXRgghRAlBIlIIIYQoytbIH6a6P+fNm5fq0gghiiG77LJLqosg0hCJSCGEEKIo819c5PLly1NdEiFEMUTx1iIMiUghhBCiqJKRYTZiUsGeY9lqs4ueM2twrln5PmatLzV74AOzLVvzfswTHzTLON6sxcXZT2Fy9pNmjfplnrfu2WYH327224y8n1cIkWsWLlyoWhMxlIldJYQQorBYvXq1NW/e3MqUKWOLFi1SxYvcEYmY/TrDbN1Ga9++fQE00PVm3QaYTVtgdvGhZh2bZlo+r3/VbMp8s0H9c3/MD342++AXs4rl4u/z3QSzo+41a7ij2QUHmzWqabZ8jdnvM80WrsjXJQkhhMg/EpFpyJ9//uk6k6RUxj1p69attttuu9nuu++e6qKlJa1bt7bp06dbhM6UcPz999/WsGFD125+++23YlErp5xyir311lsJ71+crr2g6Natm40aNcpmzZplzZo1S3VxRF7BIvjLdFvUrIobkEgIrIC39jG77eTs93vwQ7PJ88wePsvsqqMz1/XrZVa9ktlTX5idfaBZt3aJl/XftWaXDDTrf3imkIxn+TzpYbO9Wpp9dpNZhWzEphCiwGnZsqVqWcQgEZmGjBkzxtasWWPly5e3ypUr26pVq1JdJCFSzmGHHWZVq1aNWjd27Fj3vOy000520EEHRW1r06aNFQV4xr/55hsrW7ZsqosiijIjJ9vSKjslLiITZfBws0rlzS46JHr91cdkiki250ZEXjXIrGxps7v6xheRz35ltmRVpnBFQG7YlOm2W17PiBCpYN26dbbDDjuo8kUUEpFpah2oXr266zBPmzbNRowYkeoiCZFyzjjjDLf4+d///udEZMeOHe3555+3okipUqVs1113TXUxRFGmVEami+nhOyX3uP/8azZnidm+rc0qlo/e1qyOWf0aZqP/Svx434w1G/St2ac3mlWuEH+/z34zq1rRbP0msz2vMxvz3zk6NM20np6wTx4vSAiRFxYsWGANGjRQ5YkolFgnDWnUqFGMxSVdwQ3utNNOs8aNGzvLaaVKldxIeL9+/Wzt2rVZMV+1atWyevXqxXVTzMjIsOHDh2etw4X38ccftw4dOjhBjZWmRo0aTizcd999bp9hw4a57+HKCvztLccdd1zWsXBzfemll1xHHasPx+JleN5559m///4bVRZ/WefOnWvHHHOMOy/XRorrTz/9NMtd9IQTTrCaNWu64+2888724Ycfhl4fx7z66qvdPhUqVHDHwgX3qaeeilsXXNvdd99tLVq0cN+JV3dhIKxwZYXff/89ql6effZZe+KJJ9zfffr0ietOXbFiRatbt65t27Yta2CD70ydOtUuvfRSV39cN3V11llnxbWW//rrr3bEEUdY7dq13f7cSyyG48aNs4KEOqC8zzzzjLvmdu3aubbJOn/76tGjh6tb7gnXzH15+OGHY47H/ea7Xbp0cQM7WEUZleWamjRpYo899ljMdzZu3Gi33nqrtWrVyj3P7Et7wS39xRdfzNovu+dj8+bNduedd1rbtm1d+cuVK+fcTjmud2+C18z1Dho0yF0zbYfr2meffaLq3LseXFmBZ9ZrI1yjKGJsi5j9ONXatmiV3OPOX5b5STxiGKz39smJtRvMzn/W7KT9zI7IITSDWMut28x63W7Wsr7ZO9eYPXWe2dJVZr0fNHtdA6tCCJFqZIkUeeb777+3Y4891pYtW+b85Xv37u06vTNmzLBXXnnFBgwY4ERbXrjqqquc2KGjTkcfIbd48WLXgX/99dddh5nONELw/fffd2Xgb4/9998/6lgIBoRCz5493TF/+ukne+GFF5wbIS6RQTcNruOAAw5wn7169bIVK1Y4kXv88ce775xzzjluG4LI23biiSe68uFa6UFcK+WfNGmSExuHH364W8/5+/fvb5MnT7ann3465vpvvPFG++OPP2zfffd1y4YNGxKuOwQfMbXcgzp16jgh7I8TRAzfddddThAjYIIDFogQzkf5sJL5OfPMM921UI/cW9oA58EaiGCljj3eeecdJzDXr19ve+65pxPRZHhDuOy999727bffOnFTkLz88stOyHJ+RL+XuAaBd++997ryMkE7Ao42xH255pprnJCmHoIw6NC9e3cnzmgXuPhw76+88kp3rIsuuihr4KJv376ubSLGDz74YFdf//zzj6u/9957z84999xsy04ZDznkEOeJwH2kzjnv6NGj7Y477nB1/vnnn8d874033rAff/wx65oR/j///LMr919//eUEK/ec5+XLL790cwuefPLJWe2gqLgBiwAbNtvqEeOs8rHdYqsG8RXGuk2x23BdZXHbN2Z+xnMjxdXU2ycnbnzdbOU6s8ezb/dZyXwQkad0NXv9yu3re3Y0a39FZlKfvt0yXVyFEAUOv99CxBARac3UqVMjzz33XGTMmDF5+v748ePddxNdZs2aldBx169fH9l5553JZBMZMGBAzHaOwz6watWqSM2aNSN169YNPdbJJ5/sjvPdd9+5/zdu3Bhp0qRJpEyZMpGFCxdG7btt27bIvHnzota1atXKfT+M4cOHu+NUqlQpMnHixKz1mzZtihxyyCHue3379s1a75WV9QcddJDbz4PrZD3HYhvlDG4766yzos7fp08ft/7cc8+NbN26NWv98uXLI506dXLbfvnll5i6KF++fOSnn36K5JUFCxa44+y2226h28855xy3/ZFHHolazz1r1qxZpFSpUu4YHl27dnX7V61aNTJjxoys9WvXro3ssccebtsNN9yQtX7x4sWRWrVqRcqVKxf59ttvo87x+eefu+tr0aJFJL9cf/317tzHHnts6PqMjIzIe++9F/O9LVu2RCZMmBCzfsWKFZF27dq56587d25MfbL07t3bfd/jo48+cvs3bdo0a92yZcsilStXdnVAHfmhHcyfPz/r/3jPx9VXX+3Od+ihh0Y2bNgQdY969uzptr355psx10xZPvnkk6jznXDCCW7bzTffHHUO774m+tyHsWTJksiaNWui1s2ePTvq/5UrV7o272fOnDnuefbgGoPP+6JFi7LeI/GOzXE5fvDYfrgHtEk/3IPNmzdn/c/f/vuS7GvbWq1vJGLHFejy592vhF5bro5z6/Y2tXr4uMx1fR4Mv7Y9ro1sq3FazvfthymRbaWOj0QGfp21ijrb2qRfJLLzRbH3rcop7rzrP/455r5t2OvazDJNmVco9604t0ldm+5bom2Sdp/sNimKPhKRxVxEvv766+77iS6ekMsJOq90Plu3bh31wxRGXkUkQoPOeE5kJyJPP/10t61///4x2xCVFSpUcOfxxKJfRM6cOTNqf8Rr6dKls93mF23//POPExIczy86PN599113rPPPPz+mLhB5+SEnEfn77787cc398zNkyBD3vQMOOCBUbFx77bUxx/rmm2+cWGvYsGHWunvvvdftf9FF2zuIfo488ki33S/sC0JEHnjggbk+5t133+2+y/MQrE/aSrBziEhr2bKl27569eooEVmvXr0oAZjo80EntVGjRu4eBc8H48aNc3V+8MEHx1zzUUcdFbP/0KFD3bZevXolXUSKBKl+aoGLyLGfDA0/9zdjYxe+c/pjsetn+ETTohWZ++37v/Dj1j8nEulwRc7X3vGKSKT95ZHI9AWRyJ9/b18anhuJNDkv8+95S7bv36Z/5nknRHdEHSc9lLnthyk5n1cIkRRGjx6tmhQxyJ21mINLXUHwww8/uM8DDzwwK84sWRD3hRvfwIEDnQsF8We48vFZv379XB0Ld1E46qijYrbhtkcsJ66LxFUSQ+ZRpUpsqnzc/Vi2bNkSdxvuih6//PKLiwslDtBzc/TjxYzibhika9euVpB07tzZ9tprL3cfKSd/Ay6+cPnll4d+z3PH9cN3d9xxRxd477nHckzAvff888+P+Q77wvjx46PqPdngNhsP7vnNN9/sXD9xld60aVPU9jlz5sR8h/ZSrVq1qHW4/BLzSTvCtZm2g3s0bsVffPGFNW3a1NUbbfjQQw91dZUTnJs6oi5xrw1CGyTGEvfUILixxnNFCsYAi2JEk1rWvNse4dt6dgpfv1Pd+Nug7g7uuDZ2ttn6jdHJdeYszpyv8fDdci4byXlwZW11afj2lpdkTufx8/2Z/+/dKjMuct4ys/ZNo/edt3R72YQQQqQMiUiRJ1auXOk+ifMrCIgTJDnMa6+95uYGfPPNN51YRfyQkIY4wUTwhFpYVjE6/16HPti5DovlZP/SpUtHxf0Ft9G591iyZElWEhMEcU5l9FNQ9epBXZL8CBFJIpm3337bxbISr0gMX5johjART+IWhBUxhSTYQfh47YO4R5Z4ePsVFF6CoSCIW+JmiVllMAEhjNjnHlIPlDksDjUoID34npewx2sP7777rt1www0u4RKJbljYj7ZLeyCJTzwQtXiKUJ/ZtR1iMoMQ8xg2MOMvnyiG2Vl7tHftJV4bzTOn9zC7+12zZ77aPk8kPPzx9u1+5i7JjJPcuZ5Z2f+6GIMvM9u0/d2YxcXPm5UuZfZkP7OavtjsMw/IzOL69Jdmh+66Pfbx9xlmP00326Vh5vGFEIWC5hEWYUhEFnMmTJjgEnQkCh3QRF4WdLiBDKY54WV9jNeBxXoVpEyZMi4DJcv8+fNdAhE65UOHDnUWyZkzZ7pMlznhiUGEXNDiRXbL5cuXR11PMiEZECAaPMttogQT2hQEJ510krPEeQl2yGSKcLrsssvinp/EOEHxQ+IcLzur14H1krSQ2IZkPKki3nU8+OCDTkBee+219sADD0Rtu+WWW7IVvolCNlUSOrFgMfzss8/cYAhCnaQ8iNV4c0N6bZvBhDCLqBAx2Vm7tXXPJ9m9k8p1x5q9+5PZdYPNZi8269TMbMQks1dHmJ3e3ax7wJPgjCcyt896NnMaEDg61jruuOYVszKlzXoHBgU55rkHmb04zOyQO8yO2TNzupEnP8+cY/LpWO8GIYQQhYum+CgBIpKsmYkus2fPTui4++23n/uks43FJDuwgtChxtWPjKZ+cCHEKpQddIpwifzqq6+ciyCC5bvvvosRCmEi1cs0SQc+CG6kZKb0ptxINli3yKaJS21usqsmg5yEu2dBJKMuIpBMpGQSxVIWz5UVwrKB4rqKGMfq54lHprEA/7Qt6YQnzE4//fSo9bRlzxU3mTBVC/VKNluy49LuPFfrMBjIweqLS6vfRTrZBC2oogjTrW3BHLdaJbNRd2WKund+zLQe/jzd7N7TzF6K456aDJ670OyxczJdZq9+2eyJz8z228Xs+7vNDuhQcOcVQsSQaN9QlCxkiSzmFFRMJFN70DFGAGItZMqBYCcdt0hEFCISkYbVEldUpkPwLIFYfYJxXYiar7/+2o4++uioeEsEp+d26nc39YQLHfOgFZVpFLD+4BJIXKInFnE7JdYMcYdFLp5FKD/gQssciUzngFjBNTfoCst0DbjUUpfJhPN4U0pkxyWXXOIEJNNdIPKJcc1uQmH2vfDCC7OmMcGd8qabbnLiyy/IEP1Y4F599VXnGsvUKH64l1hAg+sLC8/NlZhF5iL1eO6555y1O7/gyswULUzt4WfNmjVuAQZW4sE22iXzTyL0qaugtXzKlCnuWWHalrziHZMOglK4F2FqVzNrUd9arozfpmKIvJ+L41c3e+6izCUnht+Z+HFnPxd/GwMclx+ZuQghhEg7JCLTECxk3nx2XswYoszrfJK0g9jAVII4JMaLOQiZDJ24xT322MNZNHA1Ze5FEpd4og4XSayHV199tX3yySdOYDJfHvswuflvv/0WFSN4yimnOEsZLqgkM0H0MS/erFmz3MTruAN6MNcg21iHpRIBhaX01FNPdXM0Xnzxxfbkk0/arrvu6pKb0HFm3jzEK8fGjbOgQJRwHlxxscphoSMJC+KOesKlkXpMtoikjVB3WNVI0sPfWGwR1f7J5Dkv8wdi5YUrrrgi2+Ni2e3YsaOra4QOljUGB1h/2223Ze3H/eXamSeSuQoRagh4LF9Y17j3tOdgMpvCgjbBPJbELGJdxdrNgAjtkFhJXE7zA26FJIfCmojlkeMjuEniwzbaIXOrZsd9993nPAmGDRvm3FpJmMPxcMPlfcB7gvLnR0Ry7z/66CM744wzsu4p95I5QkXRiockbpBBheCct0IIkV8KOk+DKJpIRKYhCEjElR+SlrAAHclUi0igs41IIa4OgURyFqyOCIizzz7bTZDuceSRR7r4uLvvvtvFByJouAY664888kiUiMSyiOsfopOOMufAUshxr7vuOhswYICLmfRAxFJndLY5BxZOOuqISMAi1r59e5esBysTLrXEfp5zzjn20EMPZcUuFgTEtiFY77nnHmeRHDlypBNOxA4iLK6//noX45lsqF8yrWJ9RdBT51gLEYB+EQkIPURkvXr1nOU0O1555RVnTeZaEDNcBxZI1gWtrIhHhCNWas6PWEFEUt8MODBQkCoQZFwDVlTaHveIjLvUGe0jvyKSwYlLL73UicZx48a54zEogpUXgRaWcTUI9fnll1+6AZDBgwe7MiISeD54B2BFpg3nB8rIu4bkP1jKGQRClEpEFr14SCB+PLcZrIUQIicKwltLFH0ymOcj1YUQQqQOrMMIeQQt1q8wsPAihLAEK0ubEHlgh9Myp7koCMY9YtaxmfPIYIBGCCGSid4tIgwl1hGiBEMcJDGjjDJ6sapCiCJEtYpm7TNdzbxYZSGEEKKgkTurECUQYlhxrWXB9fe8885z7sJCiCIWD4kr638Zqv3z1AohREHPuSxKNhKRQhQBSMBz//33J7TvjTfemKPLKfGJCMkqVapYnz59XNxdqiC7L0I2kRhcEsAIIfzxkNvnaSTJlQaDhBDJJrts4qLkophIIYoAJA0io2cikJCIrLRFBZLvBBNJxZtW5oMPPiiUMglRZGIif77PbK9W7k/FLQkhCgK9W0QYskQKUQQ46KCDXHbV4ghTawgh8kCFsma7bY+DbNq0qapRCCFEoaDEOkIIIURRjIfcdxezsmWipvYRQohko6mDRBj6xRFCCCGKYjxk9+3xkMAUPEIIkWyqV6+uShUxSEQKIYQQRZGubVJdAiFECWDq1KmpLoJIQyQihRBCiKJGmdJZCXU8GjdunLLiCCGEKFlIRAohhBBFiYwMsz12NqtUPmp1uXLlUlYkIUTxpU6dOqkugkhDJCKFEEKIogSZmru3D51PVgghkk2tWrVUqSIGiUghhBCiMKyHyUTxkEKIQmLy5MmqaxGD5okUQgghCpoHzrAFM2Zbw4YNknO8rm1jVjVokKRjCyGEEDmQESmuM5gLIYQQQggh8sXMmTNtp512Ui2KKOTOKoQQQhQCY8eOVT0LIYoc8nIQYUhECiGEEIXA5s2bVc9CiCLHhAkTUl0EkYZIRAohhBCFQNmyZVXPQgghigUSkUIIIUQh0LlzZ9WzEKLIUaNGjVQXQaQhEpFCCCFEISCXMCFEUaRJkyapLoJIQyQihRBCiEJgw4YNqmchRJFj3LhxqS6CSEMkIoUQQojC+MEtpZ9cIYQQxYMyqS6AEEIIkVY8+5VZfqdQ7tXJrEX9qFVdunTJ3zGFECIFVKtWTfUuYsiIRPL7SymEEEIUI8r0Ntu6LX/HuKWP2e0nR62aNGmStWvXLn/HFUKIQmbr1q1WunRp1buIQr41QgghRDIplWE2YlLM6nXr1qmehRBFjt9//z3VRRBpiESkEEIIkUy2Rcx+nma2abPqVQghRLFEIlIIIYRINhu3mP06I2qVYiKFEEWRKlWqpLoIIg2RiBRCCCEKgpGTo/6dNm2a6lkIUeRo06ZNqosg0hCJSCGEECLpv64ZZqOiReSaNWtUz0KIIseYMWNSXQSRhkhECiGEEAURF4klcutW1a0QQohih0SkEEIIURCs2WA2fk7Wv7vuuqvqWQhR5KhYsWKqiyDSEIlIIYQQohDiImfOnKl6FkIUOdq3b5/qIog0RCJSCCGEKIS4yJUrV6qehRBFDs0TKcKQiBRCCCEKKi5y+ESzSET1K4QosmxVbLcIQSJSCCGEKCiWrTGbtsD92alTJ9WzEKLIUb58+VQXQaQhEpFCCCFEQTJqivuYN2+e6lkIUeTo2LFjqosg0hCJSCGEEKLAfmUzspLrLF++XPUshChyjB07NtVFEGmIRKQQQghRkHGR301Q/QohiiybN29OdRFEGiIRKUQx5vzzz7eMjAx79913C+2cEydOdOc89NBDk35sroNjc11CFBkWLDebs7jg0uQvW2120XNmDc41K9/HrPWlZg98YLZla96PeeKDZhnHm7W4OJklFUIUQcqUKZPqIog0RK1ChPLnn3/aokWLbOnSpc4Fi8xcu+22m+2+++55rrG//vrLpk6dasuWLbMtW7ZYpUqVrFatWrbHHnvYDjvsELXvtm3bbMKECTZ9+nRbtWqVe4HVr1/fnX/HHXcMPT7lHTdunDv++vXrrUKFCm5fOm6NGzfWnRZZdOjQwYndiLJmisJi1BRbtF8Ta968eXKPu3q9WbcBmcl7Lj7UrGPTTPfZ6181mzLfbFD/3B/zg5/NPvjFrGK55JZVCFEk2XXXXVNdBJGGSESKUMaMGWNr1qxxGbkqV67shFxeQRAOGzbMZs2a5cRiixYtnChct26dE37MneYXkXTshw4darNnz7YaNWo4Ebh27Vo3UTeJKY444girV69e1DkmT55s33//vTtus2bNrEqVKq78HIPvID4RwSWNW265xfr162e77LJLqosihJX0uMilrSslLiKxAt7ax+y2k7Pf78EPzSbPM3v4LLOrjs5c16+XWfVKZk99YXb2gWbd2iVe1n/Xml0y0Kz/4ZlCUghR4mFQn8FXIfxIRIpQunXrZtWrV7eqVavatGnTbMSIEfkSpAhIRFyXLl2cO2JQZAYtlog/LI+HH364lS5d2q1HfH755Zc2cuRI6927t5UqVSrr+6NHj3b7HX/88VGC9N9//7X33nvP/vjjD5de3ztWSaFRo0ZuEUKkwXyR5+XdkyMug4ebVSpvdtEh0euvPiZTRLI9NyLyqkFmZUub3dVXIlII4diwYYNqQsSgmEgRCsIDAZlfsDYyglW3bl1nDQwKSNcI/xODfqsisL9f9DVp0sQJS4ThwoULo15umzZtcqI36BbL/yy446ZDYDgWVgRw06ZNnYW3bNmy1rBhQzv77LNDrb2M/FFnuOdeeOGF1qBBA/cdL96wdu3absHt+OSTT3Z/U2dezGAwJhI3Zeq7ZcuWccvYunVrt8+UKZnTEnDuG264wQ0A4H7M+bH04ob8wQcfFEg9MeiAxZl7Wq5cOWe9uf/+++Pu/84777j9qUvcmLGg850rr7zStY1gvCafwN/eEozh/PHHH61nz57OJRoLN5+cg0GVvODVKy7dYVxwwQWuHHfeeWeey5FoPXjk1H5EEvlzobWt1TC5VfrPv2Zzlph1bmZWMTCPW7M6ZvVrmI3+K/HjfTPWbNC3Zk+fb1a5QnLLKoQosgT7aUKALJGiQMEFFUvhzjvv7OIg58yZ48QSnVsEUVD0sc/ixYtdZxnhGYTYRgTk33//7TrKULFiRddhxi2WBeHhwf+Izpo1a7p9Us2TTz7prLqdO3e2/fbbzwlbrKQvv/yy/fzzzy6mE9EUpEePHk407LPPPk7I1alTJ6rO9t57b1evfCL+qe8wEI+cm3Nivd1zzz2jtv/6668uDpU5odq0aePWUd8IOP7n/IiYBQsWOPdhBPHzzz9v5557btLqaP78+e46aAdt27Z1gwn//POP3XzzzU7IhnHfffc5t2XKzUADrsxc32OPPeaudfjw4W4/RNJ5553nxC/Cib89/PG+gwYNcqIO12qumQEM2i6W8B9++MEt7drlwrpj5uro+uuvt4cfftgGDhwYtY1BDizmtOX+/fvnuRyJ1oOf3LQfkT/WffWbVb6wQfKqcf6yzM9GNcO3s/6vRYkda+0Gs/OfNTtpP7MjCsBiKoQossT77RUlG4lIUaAsWbLEfWIFGTJkiItt9EOs3v777581ykVHlg4zHdmwka9q1apliUMPrDcc49tvv7X333/fxURi5eNcXlzlQQcdlHCZ+Q4CI1EQxInGCtxzzz3WqlUrZ83zg5h54YUX7P/+7/+c1SgIsaNYz8JcUxHJWDYRgEFRHsapp57qBMXTTz8dIyJZ5+3jQfwpAjZovcSqyfcRd8kUkRdddJETkH369HFtxuObb76Jm/EVsUXsrL/NMHiBVQ7B9dlnn7m/GZhA9P7000/uHvN3EATyJZdc4gYdEF3+WFqEHuUizpRj5IaLL77Y7rjjDtdGqWd/Gxg8eLBLCHXMMcdk3cO8lCPReshP+xF5Z+uoSWYXHhW7YWmcmPN1m2K34brK4rZvzPwsH/0+yaJCue375MSNr5utXGf2ePKeZSFE8WDSpEm5HjgVxR/Zp0Wh+NH/9ttvroOK5QrXzSOPPNJZDHHt+/3337P291zuwqxx/vVB17yddtrJdY7ZjrhhYlw+sWjiRuiJz0RFJGVKdMFdN1F4CQcFJNx+++3u8+uvvw793k033ZRtbOPjjz+esABAsCJMPv3006h4VP7++OOPnSj2uzOSRTfM/ZV1uFliqfRcX/PLxo0bXR1QvqeeeipqW69evdwSBpa34KAD/19zzTXu708++SThMlCXuPAi5oPJmE444QRnEfzll19yNdAAuAAfffTRLtsxlmc/npi9/PLL81WOvNZDbtpPEM4fHBzCWuqHwaEVK1ZErZs7d25UdlzuPYMlfrBAB2NxgsfmuEFXcI4ddKv3BrQ8EOlYYT34m3UFyaY6VcOvrfZZsYuXNCewfuVNvrbzn5jcsGpN+LVt2OT2Cbu2qPv241QXP7ns+iPM6m5vB9si22xrIGa9KNy34twmdW26b6lok2xLdpsURR9ZIkWB4v0gIwoQAJ4IxJWV/7GoIMJIH52fpDe4YI4aNcrFf9Hh9rKzIvJw+UPoIHgSAddRloKATsltt93mrFG4HfLi93dasMCFkZ0lFVHatWvXhMuAoD7wwAPt888/d7GSWLSAe4E17JBDDokRFLiuInQR51iu/B0dz23Zc3/NDyRhYoAAN1ZcT4N0797dvvrqq5j1/DjdeOONbhuuztSzH9YlCu6fQNsJiw2kjrhn1EWibcrj2muvtbfeestZnT1XWhJJcU5E+QEHHJCvcuSlHnLbfoLgXh0Ey6afsEEcXHP9MHgRzLoc5tIePDaeBjkdm4EQFj+eO7wHA07Bdclmx2O7hl/bN7fG7tzrdrPTu5udEf0uqr6T73v/ubFWWBItIrOuA3fXRjVDry3qvjHHZNtGVvP4rmZ/bY83L7WVd9O2zHUVypo1qlUk7ltxbpO6Nt234tImRdFHIlIUKH7RGLQuEluH2yojVF7cYjxLY3aWSr5LxlaORyfcS96DEOJ/tiNy6EBTjlRCeXA/5EeAvxFK3rUQJxfvurObFoCXc26D3nGDRES+9NJLWSLyxRdfdJ9B11RcII877jgnWBDolAWRzjm5FtxssZglA4QR0BbCCLPGIpRI8jNjxgznyowI5vv82HHvSTYTr17D8FylsdQmsl9uYLCEBbGMFR53bmIksQKfccYZ+SpHXushL+1H5IHK5e3fptWtSti2np3Cv4NgjLcNsBo2qWU2drbZ+o3RyXXmLDZbuMLs8ASmNiI5D66srS4N397yErO9Wpr9HD+5lRCi+KKYSBGGRKQoUDyLVjz3VEZ6wbNs0aFFBK5evdp1rIOdW88dw588h0Qs7EsikWD2V/5nPe4WLImIyIKKiSSmD9GFECOJjt+tlY5/MNmKn+w6+WEZb3OC2DsELLF21Cn1x9+MKOIq6Yc4PhIAkYzm2GOPjdqGe6aX6TQZeOLRE5NBuNdBsOpRf0wHg+D1w/+Ip9yAQPa+yzGTDYlyWBCPzz77rLMGk1Dn0ksvzVc58loPeWk/Ig/zRO7XxhYuWWyNmkWP0Oeb03uY3f2u2TNfbZ8nEh7+ePt2P3OXZMZJ7lzPrOx/XYDBl5ltivYucFz8vFnpUmZP9jOrmf9s3UKIogneXppvWgSRiBQFCqINd7wwX3gyUnpWFG86EawmZB7Fx58FAegHF1DvuB5eXF+8eYw8K1mi1hZEJC/MRKGzn4iIJDAdDj744Ji4yGCnv6ChLhCECFfi8ahDLFkk1AnWE8KNREVBAcn9w5UymWBJY8AByzGxGUGX1rD5Sr2pLpgjNEy4h+FdI+I4eC/I0or77nfffVcgIpKYYKZMwaWZuEYGLKjboAtxbsuRl3oQhThPZPe2BXPs6441e/cns+sGm81ebNapmdmISWavjsh0h+0eSIZxxhOZ22c9mzkNCBwdnWAri2teMStT2qz3vgVTdiFEkYCBfSGCyIdJJAUsibjMEYfoBxGImymB4MFAazKE4l7HPn4/fOLhgGyRCBV/EDixjVgh/eLSiw9AeAQtWHTQmXMQEnVlJR6SGLREl759+yZ0XLKyAjGaQXF51113WWFD5k+sUG+88Ya9+eabWeuCUL8E1QfLjeXME/XJAqsuIpsBgaBlDiEUJoZIqgRk5/WDi3M8665nyQ6ba/Hqq692lkEy5Q4bNixmO2L7ww8/tLyCaD3xxBNdgh0v4c0VV1yR73LkpR5EIdKtXbbzs+aZapXMRt1ldu5BZu/8mGk9/Hm62b2nmb0Uxz1VCCGEyCcZEX9WDyH+g3gtLwMY1kKsgrgaeu6GWE2Yb9CDeENitxB3Rx11VIyQIyskQpNga1xWsTIhCEm4g0uk3wpDkyRDJ6KTYHCCvxExuOohesjCGgwsxxUT6yEWJuLBsGwycoZVESsb0x7su29qR9OxemGxRLiQhAa3VuqYyeSxwNHZp5z+bK/sj7tovMfUs9QFM6p5IHIRELgykhk3LFusl1mVLLZhWVbJJIr1DEFDQh4srwwAIM6J7yMBjP/4lJdyE5PHtBK5Bcsn8RfBeSIRR6zHFZikNF5GU6zcuNmwP9upWwYcqFfuOfUaLMstt9xid955pxNeJOuhHfJdLx701VdfdedgkINrpPNPOyIr3fjx410bzk8mT9oygwock88wMZvbcuSlHnJqPyWWMr3NtkZnJc0X5cqYrXrdFi5fGuNdIYQQ6Q79NzzFhPAjS6QIBXGDKGOhAw9Y+bx1ubFAEWeHix0TmHMsRAZxeHR4WR9040Mokrl1r732cuKJ/ekMIyZJ8BIUkIAQYCFLGSKEDjada/ZF+KRaQHoWqKFDhzqRTT0wByKi7ZxzzkmZu+Epp5zi6pjlpJNOCt3nrLPOsueee85lZKP8DBZwz8gA2qlTNkk/8gjJc4gdZU5I2hlWUkQW2WGxzgVhoAGBiQWZTKdvv/22ay9kKcWKF8aAAQPstNNOcwMkr7zyij3zzDNRMYOnn366KwP3ivZE5lqum+PSzh599NF8XSPPAuIYggl1/OSmHHmpB1EIEHNKUpryZUNjeoUQIt3xPLqE8CNLpBBCpMAqjUUKCzsDNkp/XswtkQN6m93Z12XlxetACCGKEnp3iTBkiRRCiELmySefdJZ9LIwSkCWAbm2j4laFEEKIoo4cnIUQopC47LLLnHhkuhTiMO+++27VfXGHKTL2aR01lZEQQhQlOv5/e/cBHkW1/nH83dA7KCK9gyK9iCBSFOwNsfcKf/XqtXDtDRVRbNcuiooVexcLKCKgIF0QuXSQ3nsnmf/zO3HC7maT7MImu0m+n+dZEmZnZ86cmezuO+8pLVokughIQgSRAHKVBsDRSLs5qVevnpv6Ij+5/fbb3ajEOdGIsxp4SBnIIkWKWK1atWzgwIHWsGHDPCknEkRTcLaqZ1a2lPuv+qiq3zYA5Cfqz62+/EAwgkgAuUrzIWoQnpxoZNr8FkQOGTLEjT4cDQWRDIZdyGhQ5W5h8zQCQD6jKakIIhGOIBJArtqfaT7yC6bGQLT9IUVTHAEAUBAwsA4AALmlU5N9H7gpfOQCyH/UUggIxycaAAC54YiaZgeXy/gvc60ByI/8+cKBYASRAADEWyBg1o279wDyP7puIBKCSAAA4s3zzDrv6w8pGpUXAICCgCASAIDc0Hlff0gpXrw49Qwg32nSJPS9DBCCSAAA4q1uFbMaB4csmj9/PvUMIF9O8QGEI4gEACDe/SGPpT8kgIKBgXUQCfNEAgAQrPpBtnv7DiterNj+B5E9WmRaXL16deoZAFAgBDxPvf8BAIBv+vTp1qJF5kAQAAqbTZs2WYUKFRJdDCQZgkgAAAAAES1dutRq1qxJ7SAEfSIBAAgzbdo06gQAzGzFihXUAzIhiAQAIMyePXuoEwAAskAQCQBAmGL7O6gOABQwDRs2THQRkIToEwkAAAAgy+as1apVo3YQgkwkAABhZsyYQZ0AwD8D6wDhCCIBAAizc+dO6gQAgCwQRAIAEP7hmMLHIwBI/fr1qQhkQp9IAABycNyHe23CyqyfD5jZI51T7N9tCD4BFCyrV6+2KlWqJLoYSDJ82gEAEGbmzJkZv2/f49nopWbb9mT92LrHbN3GLdQjgAJn8eLFiS4CkhBBJAAAYbZv357x+/gVnqV6OVfRxo0bqUcAQKFAEAkAQDbGMDAhgEKsTp06iS4CkhBBJAAAYdq2bZvx+y9L0qL6sKxUqRL1CKDAYaAxREIQCQBAmNmzZ7ufu1M9G7fcLI0aAlBILVy4MNFFQBIiiAQAIMzWrVvdzymrzHamRlc9GzZsoB4BAIUCQSQAAFkYvTSKEXUAoACrVatWoouAJEQQCQBAmNatW7ufo5emWYomgYxCxYoVqUcABU6JEiUSXQQkIYJIAADCLFiwwFLT0ueHTIsyGcngEwAKonnz5iW6CEhCBJEAAITZtGmT/bnWbMvu6Ktm/fr11CMAoFAgiAQAIAL6QwKAWfXq1akGZEIQCQBAmJYtW7ogMtr+kFKhQnnqEUCBU7Zs2UQXAUmIIBIAgDB//73ERi3xou4PKcWKFaceARQ4c+bMSXQRkIQIIgEACDNt2TZbuyO2alm7di31CAAoFIomugAAgMJh8SbP7hqTZiMWe7Z1j9lhlcxuaJ1i17SI7n7m3A2evfdX+uvnb0wf9KZOebMedQJ211EpVq1saNvTUX+n2bEfpUXc1sGlzNb+K+uPwKmbaL4FAHLooYdSEciETCQQB2+++aYFAgG74oorqM98ROdL5+21117LWLZo0SK37Oijj7bly5fn2r47d+7s9jNq1CgrDJZu8azD0FT7Yp5nvVsE7LnjUqxmuYD1Hp5mD/4WOdAL9/qMNHtykme1y6cHjf89NsU6VA/YS9M8a/pmqv1vXeS2p31aBOydU1JCHoN6ZP/xtzhQM6b+kFKufLnYXgAA+cBBBx2U6CIgCZGJjAPP82z27Nn2119/2caNG91cYVWqVHGTVVerVi3q7WzevNnmzp3rmkTpsW3bNre8T58+Wb5m8eLFtmzZMrf+unXrbM+ePda4cWPr1q1blsPWL1y40JYuXep+37Fjh5tEVuVt3rw5I3ABcdKvXz978MEH7Z577rH+/fsX+nq9e0yardxm9ukZKdarcXoA17uF2Rmfp1r/8Wl26REBq18x+6jtnMYpdmd7s4ol963Xp6VZh2pp9n8j0uz+X9PsozOKZHpdx+oBu+SI2O6Zjvw7zdK82F5TskTJmNYHgPxg1qxZduSRRya6GEgyBJFxMG7cOPvzzz/d6FVNmjRxgdz8+fPtm2++se7du1v9+vWj2s6KFSts8uTJLjtRoUIFK1KkiKWmpmb7munTp7vXFStWzMqUKeOC2OxMnDjRTaJdsWJFq1WrlpUsWdK9RsGoHsq+NGvWLKbjh9lZZ53lgvDKlStTHYjKO++8Y6tXry4Uf2/b93j2yRzP6lWwjADSd2vbgH0937Ohszy7t2P2QWS7qpGfv+DwgP3fCLPpa71sy6BXlyqWc3rx782erdgR+8fjmjVrzKxOzK8DACC/IYg8QCtXrnQBpII+BRLFi6ePznfEEUfYl19+aWPGjLGaNWtmLM+OspZnnnmmHXzwwVa0aFEbOnSobd26NdvXtGvXzkqVKuX2r2BSgWt2FDi2atUqU7CjZnvffvutjR8/3gW9pUuXjur4kU7137ZtW6oDUatbt657FAYz1prt2JueEQynZVo6YWUMw6CGWfbP2+ShWbxt3TQyza78Pv33muXMLmkSsPs6pljpLALKMUv3vywAUNAccsghiS4CkhB9Ig+QmrCKmq4GB4oK0ho0aGC7du1ymb9olC9f3nVeVgAZLQWeyioqexmNww47LGK2TBPJaltpaWkuME40Bbsqp/qLXXXVVS6wVra1Ro0adv311/9zx38frac6OP300+3nn3+2Ll26uMxweH83BfYdO3Z0z2l7VatWtQsvvDBk+GpllvU6Beh///13prKpjlRXCt5/+OGHHPtETpo0yY4//ngXaGqfOq6TTz7ZfvnlF9cUOtp+clntY+TIkda1a9eMOtJ+GjZsaJdddplr6rw/lAF/9tlnrUWLFu6Ggq5tXZs9evSwL774IuTmQ9++fa1p06buOtS1q74Txx13nLspoboKb+KpY7j33nvtgw8+cDdb1JxaWXRlwYcPH56pTmT9+vV28803W7169dz6KlPt2rXt3HPPtRkzZoSsq2bd//rXv9w1pHJr2/r7fPXVV93f4/7SNfJ///d/rrl4uXLlXF3rg/WUU05x10ww7VtNWeWRRx5xx6yHzr2uh+zOtersueeec60a1FJAx6sbO//+979t1apVIesG99/8/fff7bTTTnPvIyqb6krNaP1m8T7VwWOPPWaNGjVydaPtq1xqqvTMM88cUB1l1x9SakYYq6ZE0YBVLrVvnf1x39j06+zKZqEfacWKBOzU+gF7tHOKfXVWir1+Yoq1OiRgj03wrOsHqS47GcmYZcpaxl4e5lIDUBCpyxMQjkzkAfIH3lC2MZyW6Yun1jn88MMt2akvZ/DPRNu7d6/70q7mtgqSFKD89NNP9vLLL7t6fffdd10QGEwB+wknnOC+IOvLvb5A+/1S7777bnv00UddANC+fXv32pkzZ7pgRl/sP/nkEzfBuIJM3QBQU+EpU6a4YCXYjz/+6ALtTp06uSArO8oM9+rVywVG2qeCgf/973/2/fffu6zviy++aBdddNF+19F3331np556qgtoFfTWqVPHZa/nzZvn9q1gSoF3LHbv3u3qUEGuAkL1r1WAqut4woQJLhDv2bOnW3f06NHuGBQMHnvssS4oUVNuBUZqOv3KK69EPL4RI0bY448/bm3atHH1o/4WCsQuv/xy++yzz9w58KkP7zHHHOP2r8ydbhQoSNJyBbQnnniia0rsr6vzoqy8/uY6dOjg+v3qWHTzQWV7+OGHo2oZEE7leu+991zArP0p+FK5dQ7UDP399993wbOo3j/++GN3/ag8qh8/yNANm+xccMEF7rWqcx2bzq3q8/nnn3eB4kcffeTOc3jgfNJJJ7nzpRsWOmZdp/fff787nw888IBrHi+XXHKJu9ZVDq2roFPNaqdOneqCeN2k0LFF0u/X7JvXB+tWK2Ddaqe/l2zfk76sRObuik7Jombb99p+GTA+zT6d61nPhgG7vGnozbRONQL2Ta/QnV7V3OzO0ak2cIJnz03x7M6jMt+AG/W3tx8hpLnrHwAKGn1Xok8kMvGw33bv3u298sor3htvvBHx+dWrV7vnP/vss/3a/nvvvedeH61ly5a59X/++eeY97V582bvtdde815//XVv586dUb9u4sSJMT1UxmjUrFlT3+G8Bg0aeJMnT85Yvm7dOq927druuQcffNBLS0tzy3XMWqbHlVde6W3atClkeyNGjHDPNWrUyPvtt99CnuvXr5977rzzzvO2bdvmlvXv398tu/TSS71du3aFrH/hhRe65+67776M/Q8ZMsQtu/zyyzPW27Jli1exYkUvJSXFGzBggLtefI888ohbv2nTpt68efMylh9zzDFueaRzGGkfPXv2dMueeuopLzU1NdP1t2PHDi9Wd9xxh9tmq1atvL/++ivkOV0bCxYsyPj/qlWr3Dn168E3fPhwd9ydO3f2Vq5cmbH8gQcecNsuWbKk9+KLL3p79uzJeE71r+euu+66kGM58sgjM87P+vXrQ/azZs0a9/B16NDBCwQC3q233ppxLmX58uVe1apVvfLly3sjR47MWK661LYHDx6csWzhwoVuWceOHUOu16VLl7o6Dae/G61/1llnuXMefqz33HNPxHqOdK6HDh3qltWpUyfkOt26davXokWLjPrxryW/rHqceeaZ7u/D9/3337vlzZo1y7jGNm7c6OpHf0NTp04NKY/O4YoVK0LOSTh7Yk/UjwfG7s143dtTt7plt4/at2zx4sUZ180hL+zxWr252+0/mK6d8Gt40aJFGb8/MynVbbfzuzu9bbvTQrYdTNdC8LnbuivNS3lyt3f0e/uOVcetc6wyVXwu+uMMfvz700WZjs3/u4n12ETXe/h7WU7HJjqO4PPoH1sw/d3ouspu/9p3+N8cx8Z545osfH9vEyZMiPuxIf8jE3kAdIdfsspq+Mv99ZKVmi6qSaR+KluWVRYiEmVaYpVTJiaYsiZqiuhTpkXNBK+88kqXzdLItcHZSDUjvOWWW1x2JZiaZoqaBAZnuURNK9V8UFkeZbKUabr00kvtvvvus99++81lN/1M8pYtW1z2S83/zjjjjGybEStjpyyq6lTbU/bMd/vtt9ugQYPc3b2xY8e6zOeBULPH8LLsTx8GXQMvvfSSK6vKqCaVwXRtqJlkTk1clOFSnSlTp5GAw+eYUtby4osvDmm6fe2117osm7Koqjeda2X4lNFUNlijnFaqVClkO8FNs6dNm+ayu8rIqulrcL9eZaPvuusuu+mmm+zrr792+49VVhldNbfW/tS0XdlSNXfdX4MHD3Y/lb096qijQjJcTz/9tGtOrKykBsFSk2WfspZ33nlnyDDsymKqzLqm1fxb15iuEcWCOr/h71t6LjyzH877z/59ZDR2nRVTbWlQF28/w79rr2drd5h1qJ6Saf+R5ibzs7BPT0qzvqPSrHvtgH11VvGQ/o3hrQd0LQRfD2WKB6xK6YCt2bFvHV2L/jnuXMNs2ALP0lxvzdiF719/N7Ecmy/8eo/m2CJdq8HH5ovUrSF8/+Hvo5H2z7Fx3rgmC/7fm//ZEs9jQ/5X6IPIaAavCaaRFNX/qKBQ/yv1IVRfKwUHas4Zi+ymHzlQalbr9xsL5k9fov6Kmtok+A1VzR0jBTYKLvTmp4GO1Ew10r7URNUf3VZvrupXqWaQarLpB5FqBqimghp1N6dgwQ+w9drwwFll0XFohM4//vjD9peCUwW1t912m/up4EgBnAZPiqVvrU9NbRUox3ItqJmnAhwNMKXXBveDVBnC++SJAiD13QymGwCyffv2jH55Om+iYDb8Ay+c3y9R+9ONhvBm2X7/1iVLltjOnTtd4B0LBV/qX6vgX82ptZ/g/pu6diIdayzUpFTXu66N8PKrSbeapKr8uu6Dg0g1nY8UAKpOFdj69akPcvXHVRNcBZm6XtQEV8GpvhhE27c6Vs0rpzdZHbc8cyPR8SvSU6ntsxh5NZKBv6fZnWPS7KS6Afu8Z4qVLBpbuTfv8mzVNrNGmb9XOV1qpdjXC6KbuzIYzVkBFESRumwBhT6I1Jcqv69QNNQ/KdpMY06ZymQJIJVpU189fZnMrS+R+0P9x9R/MZz/ZVmBQHjd63wGn6PgL/jqY6lMV3Y0PUtwhklBpDJX6qembb/xxhuujpSFzOnOmubh9LNEkfqZ+oGl5gfdX+pPqIyn+vkpm6zsrPp+Kpi+4YYbXD/ASPWRFT+I1iA50XwhVvCogXW0rgJXBXr6XXWkelNgHj64jkSqOz/o1fp+cBZcnpxGDNbgO6Lspx5Z0XWj8xxrEHnjjTe6/p+6o6tsp4Iu1a2OVTejdH3lNCVPThSEq/6yqh/dDVb/x/Dr3h/8KNJrVJfBwe6nn37qBjhS/07dxNBDmUllPjUIkG6exJuyhGc3Cth7szz7bE5ayDQfT01Ks6IpZhc2CX3vmb/Rsz2pZocfHMjUB/KesWl2Wv2AfXJGihuYJyvrdnh2cKnQ51UXt49Oc4Frz4aR+393qbl/74OR3q8AIL/TzXf6RCJcoQ8iNZrh/tIXL315U+ZEj/AvuX4QEZ5xSQb6oq6gQwGkMhqRMh/R8EeajJYCp2ibsypDrC/V4fzRYxUERGqSFykQ1pc7bU9BljI60Tj77LPtuuuuc9mwuXPnuiYbv/76q2sWqAxpTvzzri/9qu/w+vUHZQr+4umvExzM+rKaA1QBrR7KgummgLKlCmrUfFPXpI4hWgrWos2qKWhSMKLj1Mix/mA7vuBM2f4KLo/+xrK7IePXt6ba0aBL8ZymRgPPaEAn3Y3VQEwaLMenc6tALB50Leh9I9KNBdW3AmUFrgdyY0qvHzhwoHuoWeywYcPc+VOz6t69e7vg/0Ca5GZlQOcU+3Fxql36bZpNXqU5IwP25TzPvlng2X0dAtagYujfbfePUm3x5tAmtC9OTQ8g1Tq2V6OAfTzH7xKarmwxs56N9v2dnfRJqh1aJmDtDtXUHunNV7+cl2a/r1CgaPavVpGDxdZVzEqkpNmutNjeE9Pfm5gnEgBQ8BX6IPJAKSBSHy71+wr/4qVl/jrJRNkSjdyoL5AaxVQB5P5mIHOzT6S+nKv5aXiG1J8SQVmvSG3xI1G/RE3HodFFlWmJ5niVEVIgqQBBX6wV4CmLoZFClbnNid+Xc/bs2W600OB+BAoIlOUUZfB8fr8DTd0QTmXPqby6KaKHmqMqwFNdXX311VEHHWp6qyyYrl01s/VHFY1ETSoV5Os4g/utiqYW0fV1oDS6qiizqOaoflAZid+HUDdG/ObZ8aJt6npU/YT3E9WNFGU3wzO3fguHWLKTuhZ0znRtqIlp8I0HNavWtnTdK7sdD+q3omy1+qMq6FczXfXTzY0gsnb5gI27qIjdPTbNXpnu2dbdnjWuZPbK8SnWp2V0wdrEf+aSXLXd7KofMme465QPDSLPaZzeLPXlP8w27PTc6LBNDjb777EpLoDUFCCRaHmL8ltt8qbylsaUkQAKuew+e1F4EUQeIH2hVBCpvkxqQuh/WdcXbE0n4M/xFkwZFTVHy6oJWm7Sl1AN469+VZozMtqAKhF9IkUZJWXZNBWEKBOj4EjU9y9Sh/JIbr31VhdEqjmipuXQFBbBx61MlwZmUSAS3PxTA/goiNTUIgpilH1WE9Jozpsyc3rj1cAwOg41+/SbbKoZqLanIC04q6WgSX0bP//8czvnnHMyOsMrg/rhhx9m2oeWq+9ieD9QP8up6y+W86vAR0GF5hHUFBwKaoKDJl23ChAVoGmfygbrOBRk+R3pdX2rKbAC5QPVtm1bdwNAAbSaWmrKkOAPM2V5Fdj78xz662oqlyeffDJTs1Bl2xTMxxpg6m9b9DetANsP9rX/rP4G/HUU0KououmjqkyggkhlkjWViR9Eq051/Ygy6X55YqUBdjQAkAaXCr6GlaXXQ+c/N9+T6lUM2PunRdd9YFGfzPX15slF7M2To9/fHUeluMf+OL5BCZs4ObbXxNJ0HADyi/39zEHBRhB5gDTqowbb0aAiakaoL6dqiqgvm8pcqNlj+JcyfcnVHX99GVQg51M2wx9IxP+/BE9Gri/1wV+ilbHys1b6ouk3qfJfoy/5/hdRUWZPAaSWK3Oi0S8PpMlpblLzRH2p1UAgCrQUwCmDqkBSA9voy3u0AZKCRo22qr6DZ555pgtK9aaoOlZ9KOumL9bqMxn8RVBZWjVh1KAt/ui14Vm37Pp0qpmggkGNAKt5G3V9aPAaBZY6Po2oGdzsU0GrAjgNfKJ9K8hU0KL9qx9eeDZSI9Eq46dAT4OoqOwKEnRTQ4FV+Kiw0dCgNGq2q2tF168Ca2W+dF1pBFsF0UOGDHFZMs2H+NRTT9l5553nMrQ6HyqrAjs1+9XfwYHym4/q3Oj4VQ/6m9J1r3rUaLLXXHONW1frqBwa5VQ3S/S3qb8XlV3ZTAXXuikQaxCpfrhqJqvgXvNy+vNPavReBYuRMoMatEb1oYGHdJNCNzwU1GokV3/u0nAalVXvI9qPMso6Fn+eSB2DAuU77rgj5nPq0w0AXVcKpNUKQT/1N6AspwJM3Zih30u6NpU0dGv0I1VLxUrcrQdQ8OgmO58NCEcQGQcKPvRFUs3A9AVeX66VpVGgktWXxUgUfCq4DBe8TM3MgoNIZTzDX6P+VH6fKgUywUGk38dQXxyza4qaDEGkMjfKPL311lv21VdfuWPStBVqeqdAJ6vpJbLy0EMPucyrslQ6dgUkygYr2FKAoIAreIoEURBw+eWXuyyY+F/Ao6WAVdkvDXajgEcBloJHBbUKIP2RZn06Jq2vfoxaX01hFfAoc6lgIjyI1DbefvttdxNDHd9VXgU0CuqUudqfN30FaOovq2lRXn/9dZeFVVCorKgCyuC+jwp4FRhp1FL1rdONCe1Tk9urbPEIInX8M2bMcNvUIEJ66NrQtaDzFjxli7KhqosBAwa4AWR000FlV51rO2raqxsQ+0PBp86jAlVli/V3qBtBujGhGx0KKoPpBoCCWZVFNxD0961zc+6552b7vqAg8plnnnHXvm4m+H+PGihJ05zkNA1HdnTjRFO3KIOtGye61nTOdE3r70oBbvjfQGFVZeciK5rSyvbGMEjriuUr6BMJACgUAposMtGFAMIpq6Yv5d9//73LPAFAXtJNnJvntrZxy4OH7snev+svs2d7MbAOgIJFLaj8qc4A3/51FgEAoABTX/autQJRB5AS67QxAJAf5MZga8j/CCIBAAijwZA614ht0LF4jZoLAMkk0vgZAH0igQJO/fe+/fbbHNdTP70bb7wx6mlTgIJMow4f3aKKpQQs6mk+NHAR80QCAAoDgkgkJY2YivgFkRqcKJrmKhodlCASSFehRMBaHGI2bTU1AqDwCp8HGRAG1gEAIMzq1avdaMm3/Jxqz072ouob+Z/D19gTp0U/IjcAAPkVfSIBAAj/cExJ/3hUv8hoB9eJddohAMgPJk2alOgiIAkRRAIAEGbhwoXuZ+ea0Q+us3TpUuoRQIHDbICIhCASAIAsHFI6YIdVonoAFF5MX4RICCIBAAhTq1atjN81X2Q0+chixYpRjwAKnObNmye6CEhCBJEAAIQpXrx4xu9dakbXL7Jq1arUI4ACZ+rUqYkuApIQQSQAAGHmz58fEkRGY+ky+kQCKHj27t2b6CIgCRFEAgCQjVrlA1a7XM5V5KVFO44rAOTPlhmAr2jGbwAAwKlevXpITbx5coot35p95ZTeuIPaA1DgtGzZMtFFQBIKeIzbCwAAACCCP/74g0ASmdCcFQCAOJg2bRr1CKDA2b17d6KLgCREEAkAQBzs2bOHegRQ4BQpUiTRRUASIogEACAOmCcSQEHUpk2bRBcBSYg+kQAAAAAi+vPPP61Zs2bUDkKQiQQAIA5mzJhBPQIocHbsYORpZEYQCQBAHOzcuZN6BFDgBAKBRBcBSYggEgCAeHygpvCRCqDgadeuXaKLgCREn0gAAHLBa9PTbNX2yM9d3TxgVctwdx9A8ps1a5Y1adIk0cVAkima6AIAAFAQzJw505o2bep+37XXs3/9mGa70yKve2r9Ila1TN6WDwD2x9atW6k4ZELbGwAA4mD79n1px4krLcsAEgCA/I4gEgCAOBu91KNOARQIzBOJSAgiAQCIg7Zt22b8PnppmqXQ5RFAATBv3rxEFwFJiCASAIA4mD17tvu5N82zscvM0khGAigANm/enOgiIAkRRAIAEMfBJ/5YbbZtD1UKACi4CCIBAIijMctIQQIoOFq1apXoIiAJEUQCABAHrVu3dj9HL6E/JICCY/HixYkuApIQQSQAAHGwYMEC8zzPRi2hPySAgmPDhg2JLgKSEEEkAABxsGnTJpu1zmzDLqoTAFCwEUQCABAn9IcEUNA0b9480UVAEiKIBAAgDlq2bGmjl3rMDwmgQFm+fHmii4AkRBAJAEAc/P33Evv5b4/5IQEUKOvWrUt0EZCECCIBAIiDmSu22optVCUAoOAjiAQAIA6mbixLPQIocJo2bZroIiAJFU10AQAAyMriTZ7dNSbNRiz2bOses8Mqmd3QOsWuaRHdPdC5Gzx776/018/faLZlt1md8mY96gTsrqNSrFrZQMj6K7Z69uK0NJuyymzqas9WbjPrXjtgP55XJMd9LUqp5fpDpnmcTwAFx5o1a6xOnTqJLgaSDJlIAPvdR2L48OH21ltv2euvv24ff/yx/fHHH5aWlkaNxtErr7xigUDA+vbte8DbWrp0qV1yySVWvXp1K1asmJUpU8aOPPJI++qrrywZLd3iWYehqfbFPM96twjYc8elWM1yAes9PM0e/C266+z1GWn25CTPapdPDxr/e2yKdagesJemedb0zVT737rQiG/2es8eGe/Z9DWeta8aGmDmZNTfqQSQAAqc1atXJ7oISEJkIgHEbOXKlTZs2DA3sXr9+vVdMPL333/b77//bqtWrbLjjz/eBT5IHnPnzrVu3bq5UfZq165tvXr1ss2bN9vYsWPtzDPPtP/+97928803WzK5e0yaywR+ekaK9Wqcfs+zdwuzMz5Ptf7j0+zSIwJWv2L219k5jVPszvZmFUvuW69PS7MO1dLs/0ak2f2/ptlHZ+zLMratGrBV1xWxKmXS1w88uTeqsq7c5tnibcX280gBAMhfyEQCiIkyjb/88oulpqbaCSecYMcdd5wdddRRLiipWrWqLVq0yObNm0etJpmbbrrJBZDdu3e3+fPn24cffmjfffedjRs3zipVqmR33HGHLVmyxJLF9j2efTLHs3oVLCOA9N3aNmB708yGzsq53Wi7qoGQANJ3weHpy6avDd1GueKBjAAyFmOW0oYVQMF0+OGHJ7oISEIEkQBiokBk06ZNrkmkMlq+IkWKWLt27dzvs2bNSopaVTPNnj17unIqW6omnDVr1rRrr73Wtm/fHvE1CpA7duzo1i9RooQdccQRLuC68847XXZ10KBBmV7z448/2rHHHuuCsaJFi9pBBx3ksnsKqMPrTtto27atzZ49204++WSrWLGiK5fK+Mwzz4Ss36VLF1dWefrpp91r/Uf4trOjjOP48ePd79qHyuhr1qyZnXfeebZ792577rnnLFnMWGu2Y69Zx+qZAzot09IJK/c/cFu2Nf3noaUtLjQ/ZMAIJAEUPBs3bkx0EZCEaM4KYL8mHVYwFk6ZSAUoatKqTKUCy0R6/vnnbdKkSdaqVSvr3Lmz7dy506ZOner6Gf722282ZcqUkIDqhx9+cBlVBZgKJBs3bmwLFiywiy++OCNADqeg7Pbbb3dNezt16uTqRa/55ptvbMyYMTZ58mSrV69epg/krl27WsmSJV3TX+1v1KhRdsstt7jA9brrrnPrnX322S7AHDlypJvIvn379m656lXBZ7S0fQWS0qBBg0zPq0my/PTTT5ZM/SGlZoQBT0sUDVjlUvvW2R/3jU3vU3lls/jcS/1liUcICaDAdmGpVatWoouBJEMQCSAmykJK+fLlMz2XkpJi5cqVsw0bNrigRZm5nMyYMcN27doV9f4rV65sdevWjWpd9fNr0qRJSDCr5rgaXOb999+3d955x6688kq3XJk4BXEKuJ544gn7z3/+E7KdW2+9NWLZ7777bitdurT9+uuvIcOgDx482GURr7rqKvv5559DXqcg85xzzrEPPvggo2zKmp511lk2cODAjCBSTVAVaCqIVDPUp556yvaHtlG2bFl37rTv8OHatUxiyW5Gq9+vqVGv261WwLrVTg/qtu9JX1Yii/sQJYuabY+uu2ImA8an2adzPevZMGCXNw3Epentn2v1G/2AAQCFA81ZAcREwZYUL1484vP+cn+9nCgQU0Yw2kcsgY6aaoZnQxXo3nbbbe53ZQt9au6pZrg1atTIFDDecMMNrllruBdffNF27Nhh999/f6bA7Oqrr7bWrVvb6NGjM7KAPmUbNaJtcNlOO+00lyVcvHixbd36T1vLOFHWUk1oRYGyssS+P//8042sK1u2bIl522vXrrVt27aFLNMx+B4c50X9+GbWvuMu/c8YNdt2p0bc9s69ZqWLWsYNi2Aa5CmYbgxoiHp5dnKa3TM2zTpW2W1vnehlDAC1d+9eW7ZsWaZjCxd8bKJ979yy0Q4pHfvoxsqMZ7ftWI/Np+PQ8fiyOrbszpto3ypD+P6VdffpBpCyFMHUEoFj47xxTRacvze1yon3ewnyPzKRABLqoosuyrVt64OrX79+LljU9Bb6oA3+QPab5opGlhUFWwo0g6lJqZqT/vXXXyHL1TRW1Gz1f//7X8T9K/OpkVH9IE7ULCg8k6t9HnLIIW5dfdgqcxhPyq6q6eyIESNcsKrBkBQ0anTWgw8+2NavX5/puKPNDIcLnk/M+08sHzP76kRTecjKHUUybXvXXs/W7jA3VUekbHdwX11RpliPpyelWd9RaW7ex6/OKmWli+3LHKpZs24gZD620HRn+Fxp/nk8tlaqfTwnzdK86LKRqvOSYQP+hG87lmMLFn4cWR+bRXVs2e1fN0TUjD3YoYcemuO2OTbOG9dk/vl7U5BYoUKFuL6XIP8jiAQQk5wyjTllKvOK7thq5Fj1SdQgQPpdQZoCQpVR81sGH4M/cED4B7RPrw3nZ+6++OKLbMsSnlnM6gPVz0wGZwrjpU2bNi7Y1QBBGpH1008/deU49dRT7ZprrrEePXrE1M8ytzWvnN5kddzyzP0ex68w1/8wlnkcB/6eZneOSbOT6gbs854pVrJofJuedqkZsA9n05wVQMGj7KM+R4FgBJEAYqK7kRLeNEaUdVNgpSaC0d55zK0+kQqSFEB26NDBZduCm45qUB0FkZGOK7ypkC+8mY9oBFd/exqIJ9mpSa76XoZ7+eWX3U9lW5OFsoRnNwrYe7M8+2xOWsg0H09NSrOiKWYXNgkN2uZv9GxPqtnhBwcy9YFUE9bT6gfskzNS3MA88aYgEgCAwoIgEkBMdDdy2rRprnmoRj0NpgBMfSaUzYt2ZFYFkbH0AVTfjGiCyDlz5rifmmojvCyakiOcP/KpAk8Fw8FNO/fs2WN//PFHpte0aNHCjf6qUU1zK4j0R4/Njeyk6Fg1wI/4gwwliwGdU+zHxal26bdpNnmV5owM2JfzPPtmgWf3dQhYg4qhgVv3j1Jt8ebQJrQvTk0PIDWVR69GAft4jnKY+7KbZYuZ9WwU2oy3/7j0kVt9izZ7GcvqlDe7tGnmZr9HVDYrUyTVtqUmdkRiAIi3SKN6AwSRAGIOIpW1U39Cdcz3+1UoyFFAJRoRNdF9Iv1pKzQyqppwBvd91NQf4Y4++mg3obL6NmpOxuDRWTWATnh/SH/01KFDh9qjjz7q5onUFB/BFBxr/6effvp+H4ff3yR8oIJYKdurR3CGWAGkBgVSM9fmzZvb+eefb8mkdvmAjbuoiN09Ns1eme7Z1t2eNa5k9srxKdanZXT9Nyf+M5fkqu1mV/0QGhz6QWF4EHnfr6Hrzd+4b1nXmpGDyJRAwLrU8OyHJWZpTBcJoACJdqA8FC4BL3iUCQCIgjKOw4YNc4PU6A6lOuMroNSAMMoSagAXf9TLRFFzWw1mM2/ePDdyqrKmCnw1CuuRRx7pRk1VP0FlHn3fffedm3pDI67680TOnz/fNVfVPJEKQDXHZJ8+fTJeo4BUo73qQ1b7a9iwoctcal/Ksh500EEZAaCWaVCC8P36unTp4gK6hQsXZmRbNfJdo0aN3MAGp5xyilWrVs1lJ/v375/RBDcaCoI1WqyOQ4Me6NypDBrIRzcCNEVJpLk/Eb0HRm60h6ZENyDS1MuKWKsqNIEFkPwmTpzoPjeBYEzxASBmaq7as2dPF3woeNQ0EQpKNOKnBmhJdAApyrh9++23dvLJJ9uKFSvsww8/dE1cr7/+ejc/ZCRa9+uvv3bHoZFXlWVUX8j33nvPTRcSaZS7G2+80TVn1WtVF5988okLRpcsWeLmdoyU9Yw1E/nqq6+6QPKHH36wQYMG2QsvvBDzcOkaGEjZUs0Jqf6in332mWt6rCk/FGASQB64+l7o0PcAABRUZCIBIAdq9tm5c2eXkVRz18MOO4w6QybjJky07r+1th2hs4JERCYSQH6hm6mRRihH4UYmEgD+oWas4RMpK8OqJqxqBqsmpgSQyErjBvWtUw31j6SOAAAFGwPrAMA/NOKsBgVS/0l/NLrZs2fb9OnTXT9ENSUFsqLmwV1rakTZzAP4AEB+tWjRIjKRyIQgEgCC+h9qJFUNIqB+ghosp1y5cq4v4cMPP+xGcE0Wb7/9tpv/Mpr+qw899FCelKmwU5/YzjWrJLoYAADkOoJIAPiHBs35/PPP80V9aPAef37H7Bx66KEEkXmofVWzYilme0hGAigg/Km8gGD0iQSAfOj99993/TVzemg6FuQNTZ1SqljABZJ0iwRQUKg7BxCOIBIAgDhISUn/SO1aK2BMwAygoNDUUEA4gkgAAOJg4cKF7mfnmuQhAQAFG0EkAABxdHT1ANN8ACgwatSokegiIAkRRAIAEAe1atVyP8uXCFjLQ+gXCaBgKF26dKKLgCREEAkAQBwUL14843f1iwSAgmDu3LmJLgKSEEEkAABxMH/+/Izfu9RkcB0AQMFFEAkAQJwdU4NMJICCoVq1aokuApIQE78AABAH1atXz/j9kNIBO6We2aLNmdfbtWOHlSxSljoHkC+UL18+0UVAEgp4mo0aAADkienTp1uLFi2obQD5wsSJE+3II49MdDGQZGjOCgBAHiKABADkdwSRAADkoWnTplHfAPKNKlWqJLoISEIEkQAA5KE9e/ZQ3wDyjcqVKye6CEhCBJEAAOShYsWKUd8A8o2//vor0UVAEiKIBAAgD7Vq1Yr6BgDkawSRAADkoRkzZlDfAPINmrMiEoJIAADy0M6dO6lvAPlGtWrVEl0EJCGCSAAAgqWmxvcR/sGbwkcvgPyD1hOIpGjEpQAAFFYnPmz20/T4bKtEUbNN75mV2DeYTtu2beOzbQAAEoTboQAA5JZde80mzgtZNHPmTOobQL5RqVKlRBcBSYggEgCAcIFA/OpkdGjQuH37duobQL5Ru3btRBcBSYggEgCAXPuUDZiNZo41APnXH3/8kegiIAkRRAIAkFvSPLOxs8z27htghz6RAID8jiASAIDctG2X2R+LMv47e/Zs6htAvlGhQoVEFwFJiCASAIDcFtSkdevWrdQ3gHyjQYMGiS4CkhBBJAAAufpJGzAbQ79IAPnTlClTEl0EJCGCSAAAcrtf5Kg/zTzP/bd169bUNwAgXyOIBAAgt23YZjZrqft1wYIF1DeAfKNs2bKJLgKSEEEkAAB54Z8mrZs2baK+AeQbhx9+eKKLgCREEAkAQK5/2jJfJID8adKkSYkuApIQQSQAAGE8S++/GNd+kSNnuH6RLVu2pL4BAPkaQSQAAHlh5UazRattyZIl1DeAfKN06dKJLgKSEEEkAABhAhbInToZ85etX7+e+gaQbzRt2jTRRUASIogEACC3m7PmVb/IdVvMrnvFrPrVZiXOMzvsBrPHPzfbm7r/2zz3CbNAL7OG12d+TlOX6LlIj8qXH9ChAEgOzBOJSIpGXAoA2TjkkEPczzVr1iRlPa1du9auv/56GzVqlK1bt87S0tJs5MiRduyxxya6aCjM1C/y5z+t2fNX5c72t+ww63Kv2exlZtefZNaiTnrQesc76dOLDLkx9m1+Pt7s89/NShXPfr0+x5t1PiJ0Wclise8PQNJJTT2Am1AosMhEAvvhySeftJ49e1rjxo2tePHiFggErE+fPtm+RoFM//79rWHDhlaiRAkrU6aMtW3b1j7//PMsXzN27Fjr0aOHVa1a1e3noIMOshYtWthzzz2Xq2/qOhYd0yeffGL50dVXX20ff/yx1atXz6644grr3bu31alTJ0/2rXpr3rx5nuwL+dCCVbZmxpzYXqOsXr8Pcl7viS/M/lpi9vhlZs9dY3bN8WZv32R2w8lmb/5sNnpmbPvduM3sX4PNbjzFrEqF7NfteJjZJV1DH+ccHdv+ACQlfWcBwpGJBPbDwIEDXbZLgaACu1WrVuX4mgsuuMAFNpUrV7ZevXrZ1q1b7ccff7Szzz7bXnjhBZc5C/b666+7YC4lJcU6duxodevWdVm1X375xW666SYbPny4ffPNN5y/CMaMGWPVqlWz33//nfrBAfSJzIUmrWa2+6c/zNrnwgitb48yK13C7LoTQ5f3PdPshe/Sn+8SQ9+mW4eYFSti1v+i9GxkTrbvUsWZleILJ1CQ6OY1EI5MJLAflAmcMWOGCwQV0OXkyy+/dAGkApu//vrL3n//ffv666/t22+/tSJFitgdd9zhgtJg/fr1c9nLDz74wEaPHm1vv/22DRs2zGbOnGkVKlRwv8+aNYvzF8HGjRvt4IMPpm6QXH0iJRCwclNzYXTWVRvNFq8xa1U3cxBXt4pZtUpmE+ZFv70R08yGjDR7qY9ZmZI5r3/TG2ZlLjQrfaFZrd5md72bHlQCyPemTZuW6CIgCRFEAvvhwgsvtGbNmkW9/vPPP+9+3nzzzRn9CUV99NRcVcHooEGDQl6j7GapUqVcpjKYmmX6+162bFlMHeNPOOEEq1SpkhUtWtT91P/DO8yrfIMHD3a/n3vuua55ph7B5Q4O1i6++GKXXS1WrJhrdqugWsFvJAqcO3Xq5IJglUHbPP/8823lypUh6/35559unyeddJKb5Lhbt25Wvnx5l5XNromtmpHqdZ7nZWwjUvPSIUOGuKbEZcuWdeWuXr26y/pu2bIl0zYHDBjgylylShXXpFhDnWt7r776ash6jz76qNtXcPn9h54LXsf/f7hIZfWPaceOHXbttde6sqrMqhufroNLLrkk4zllyNu3b+9uUoRbsGCBy4prXTVR0vHUqFHDTj75ZFdu5DLPs8ozc265ELOl69J/1szi5omW++vkZNtOsz6DzM7vZHZqu+zXVaby1LZmj15s9tVdZq//y6xVPbPHPjPrei+BJFAA7NmzJ9FFQBKiOSuQB/xA7Zxzzsn03Iknnmjff/+9a6Z67733ZiyvXbu2zZ8/32UxzzzzzIzlS5cuddnIihUr2lFHHRXV/n/44Qc766yzbOfOna5prPplzps3zzWnPeaYY9w+jj/+eLfulVdead99950LKFQ2lUO0v2Dqk3n00Ue7DKp+KijUQDbK0mo/r7zySsj6Dz30kMuuKnBRUKaA83//+5999NFHru+n7nSGB6qaT69r164ZQY62q+A3KxdddJEtXLjQBcEKbHXMor6RvmuuucY1FVZQ2rlzZ7e9P/74w73m119/decquP+HyqzAXUGnAsnVq1e78v7f//2fK9/DDz/s1uvQoYPrexm+b/+5A6VAevbs2e78afsqi0yfPt2OO+4419RZTY5UXwru1aT3jDPOcOdBfURl8+bN7vU6htatW7ttKuDWNaXrb+rUqTHdHMH+SdEgNxu2mlUqm/nJtZsjv2j77szPqemqHu75f7J+JbIYzKZk8egDurvfM9u03ezZ9OsmW52amH1zT+iyq7qb3fmO2cDPzZ4bZnZnr+j2CyAp6eYkkIkH4IAMGDBA7d683r17R3x+w4YN7vkSJUpEfH7EiBHu+UaNGoUs//zzz72SJUt6RYsW9bp27epddtll3qmnnuqVK1fOq1q1qvf9999HVb7U1FSvevXqbh/PPvtsyHP6v5bXqFHDrefTsWj5xx9/HHGblStXds+3a9fO27x5c8byRYsWeaVKlXKPnTt3Ziz//fffvSJFirhyLFiwIGRbTzzxhNvWueeem7FsxowZbpkel19+uRcrva5Zs2aZlg8dOtQ917RpU2/NmjUhz1133XXuuf/85z8hy6dNm5ZpO3ptrVq1vNKlS4ccf3b7Dr5W9DPacuv/Wl67dm1vyZIlmV5zxBFHeIFAwHv55ZdDls+bN8876KCDvAoVKnhbtmxxy1577TW3rfPOOy/TdrZt2+atXbvWi5XqYuvWrSHLdB0E27Rpk7d+/fqQZYsXL/bS0tIy/q/rZcWKFSHrrFy50tuxY0e229Z2tf3wbYcf2+rVq0OWLV261NuzZ0/G//W7ljndH/C8QC/Ps7Ny7bF64szIxxbLdh54f9+xTZqXvuy8JyIe266WN3lepUtzPm+/zvK8lLM9b/Dw0PNWp4/nNbgu+vO2dkP6djrdlXfnLadjy8/XJMfGeStg1yTyP4JIIJeDyLlz57rnK1WqFPH5yZMnu+erVauW6blff/3VBXh+QOUHo9dff31GYJCTL7/80r2uSZMmEZ9XEKLnv/rqq5iDyClTpmR67oQTTnDPjR07NmOZAkQte/vttyNur169ei44Dg8iFQDpgy1WWQVyHTt2dM8pqA23e/duV4YGDRpEtY++ffu6bX322WdR7ftAg8hXXnkl0/ojR450z3Xr1i3i9u688073/BtvvBESRO5PYF6odH/ASwvkXgC565DLst73iGmZH3rdpc9kXj4/6Mvkyg3p6x19Z+TtVrvK85rfnPOxt7jZ85rd5Hlzlnne3OX7HjWu9rzavdN/XxJ6AyZLVa/0vEbXR7cugKQ1ffr0RBcBSYjmrECSGjp0qOund9hhh9lbb71lRx55pGuqqf50L730kpv3UM0w1U8vO+PGjXM/1YwxEjVF1WA/Gsn09NNPj7p86q+pJpHh1PQ0fA5Jv1O+mtWqmWWk/hbqj6i+ff7rpUGDBq7PXryoGbCa3b722mvuEU7PqWlnMDXrveeee2zChAmuyWh435BFixZZXujevXumZT///LP7uWnTpohTzKj/o/h9HdUs+rbbbnODNGlgKPWJ1XbVBJbmSnkkJWB2bDZNhntkMWpr/UOzfk4OrWhWu7LZtEVmO3aFDq6zeLXZig1mp7TJuXwanEdNWRvfEPn5Rv8yO6qR2fiB2W9n83azVZvMGlXPeZ8Akpq6kgDhCCKBXKb+a7J9+/aIz69fv9791CAvvuXLl7ugQAGiAgX135OWLVu60VrVF++3336zp556yu66665s968AQzQybCQaYEU2bNgQ03Fp8JZIFIjJ3r17M5b5A9a899572W5TffmCg0i/7uJFAxhp0B9/4KCcKPhVn1GdOw1woz6U/gA/CswUoOfVh2twv06fglpRX0Y9sjtuvz5V5ltvvdUF8+r/+dhjj1m5cuXcYFEaACqnmxI4QGmebWtT23Klli/tZvbIJ2Yv/2B26xn7lj/11b7ng/29Jr2fZIOqZsX++Trw9r/Ndu/7281w/atmRVLMnr/G7OBy+5av2xL6f1FS/fa303/2bB+/4wOQEPrMA8IRRAK5TAPSaPAWBWnKDNWvXz/keT9LpMFbfBpoZ9u2bdaqVauMADJYly5dXBA5efLkHPevkVBlxYoVEZ9XwBq8Xm7wA04NFBR+/NnxRzuNF2U1d+/e7YJCTa2SEw2ao/OgeUFvv/32kOduvPHGjCxvtPx9BgfYvpzmGo30Ie6fM5VFAxpFQ5ltTQ+jYFrl11yjb775ZsZos+EDIhVWuTlP5NL6FSzr4aEOwO09zT4Zlx7ALVpt1rKu2S8zzd75xezSrmZdw+aIvOy59OcXDkqfBkTOyCLo+89bZkWLmJ1zdOjykx5Kz4K2a5A+AuyazWZfTjD7fa5ZlyPM/nVybhwpgDykgeWAcNxaAPJAmzbpzcgiTU+hJp6iJoU+P7uVVXZQo2tKNE0Q/ZFBx48fH/F5BaPB6wUHOxqBNZ4TFQ8fPtwSqUmTJi6I1Eik0fCbqvqjmwbLqj4V+GY1xYk/d+Xff/+d6Tk1T46Vf81MnDgx5tcqKNUouWoe7e870eenUMwTeVBZ21nnoNzZdvnSZmP6m13d3ezj39Kzh+PnmD16idkbWTRPPVDndDTbuC09+3n9YLMBn7psq/33SrMf+2U9WiyAfENdQYBwBJFAHrjhhvQvcM8880xIX0E1VdU0G2rKqikjfOqrpi/5s2bNytQEVNNifPzxx+53TcGRE/VzVJNV9Xt88cUXQ57T/7Vcz5922mmZgh2/T92BUj88Hc99993n+nGG09QTkeY0jDfNYSlqKqz+pZGygcHBXM2aNd3Pr776pzngPx5//HE3f2UkOpfB5ziYptRQkKnsn445OBusfpex0vk//PDDXUD7yCOPRFxH9ervSzcMIh334sWL3c+SJaOYVB4H1h+yW1Nr1LhxbK/zPjPrd0F06x5SweyV68xWvGG26yOzOS+mT7GhLGK4UQ+nb9vPQmZn0Stm817KvPyOXmZjB5itGmK2+yOzLUPNJjxudvPp+5rIAsjXsuqOg8KNd3hgP6iZo+YUDM4qjRgxImMC+MaNG4c0L+zZs6ede+65Lvg74ogjrEePHq6fmgJIZfvUXDJ4jkTN46g5B9W08NJLL7UXXnjBLVMGUlm0Xbt2uSzSFVdckWNZFbxpEJlevXq5Zo/vv/9+xjyRCioUOOj54OaSp556qg0YMMAFS2puq2aTaparZftDg/ponsj777/f2rVr5wYJUh+/HTt2uMF0FFg2bdrUTjnlFMtNF198sesLqHpVVlLZ11q1arkgSwPqaM5FlUHzLor6DioIU4CvetM5Un1oUBr1ldR8kZGyzjpHGrBI/SiV1b3gggtcE2QNFKRzr2tFzUq1TNeBriU1XY4U4OVE2e1jjz3WzTE6ZMgQN8+jAlkFproJsXLlSteMWM2iv/jiC9ePVoFn3bp13fGo/nUcCm5vueWWuNQzsqAMXZem7roPn3cVAIB8JdHDwwL50Yknnhgy7Ub4I9IUD5qH8aGHHnLTWRQvXtzNpdi6detMU0QE07QObdu2dVNPpKSkuHkjDzvsMO++++5zU1LEYsKECV737t3dtBnaln7q/1oeyWOPPebmJtQ8lTomTevh0+/B/w+W3fQgP/30k9un5i/UvJFlypTx6tSp46YAGT58eKYpPlTP+yO7aTbko48+8jp16uTqQOUoX768m9pDU1+ET/+h+To1r6Q//2WLFi3c67OarmP+/PluXk+dM83fGL6Opiy56KKL3JQv2vehhx7q3XDDDd6uXbuyneIjO5rbq0+fPl7dunXdtaVHlSpV3DEOHDjQbVvGjRvn5ojUsap8Orc6j507d/aGDRsWUx0X/Ck+cmmeyCnzs/ybA4BkFDyPNOAL6J9EB7IAACSNHv3MGznd3C2AeCpb0mzjOzZxyhSXjQeA/ECtWtR6BwhGn0gAAPKiP+QxTTRqVUwjFANAovnTRAHBCCIBAMiL/pD/TLERaYoXAADyE4JIAAAyie8cpU7nJllO8QIAyap169aJLgKSEEEkAACZxLlDZImiZu0aUs8A8p14TfeFgoUgEgCAXP2kDZh1OMysRDH33zp16lDfAPKNTZs2JboISEIEkQAA5FF/SPfBGzQnKwAA+RGfZAAA5HafyH/6Q8rChQupbwD5RsuWLRNdBCQhgkgAAHKzT2SRFLOOh1HHAPKlJUuWJLoISEIEkQAA5JZAwKxNfbMyJTMW1apVi/oGkG+sX78+0UVAEiKIBAAgt3ih/SGlePHi1DcAIF8jiAQAIDd1OSLkv/Pnz6e+AeQbzZo1S3QRkIQIIgEACBPw4tic9Zh9g+oAQH6zcuXKRBcBSahoogsAAEBSGXCxzRl3uDVu3PjAt1W8qFmlsiGLqlevfuDbBYA8snbtWqtXrx71jRAEkQAABGvfyDYFNpod2SZX6qVGjRrUNwAgXwt4nnr9AwAA37Zt26xMmTJUCIBCj/dDREKfSAAAwqxbt446AQDeD5EFgkgAAMKsWrWKOgEA3g+RBYJIAAAAAEDU6BMJAECYzZs3W/ny5akXAIUe74eIhEwkAAARvjQBAHg/RGQEkQAAhFmxYgV1AgC8HyILBJEAAAAAgKjRJxIAgDAbN260ihUrUi8ACj3eDxEJmUgAAMLs2LGDOgEA3g+RBYJIAADCLF26lDoBAN4PkQWCSAAAAABA1AgiAQAIU79+feoEAHg/RBYIIgEACLN3717qBAB4P0QWCCIBAAjz999/UycAwPshskAQCQAAAACIGkEkAABh6tSpQ50AAO+HyAJBJAAA4R+OKXw8AgDvh8gKn5IAAIRZuHAhdQIAvB8iCwSRAAAAAICoEUQCABCmVq1a1AkA8H6ILBBEAgAQpnjx4tQJAPB+iCwQRAIAEGb+/PnUCQDwfogsFM3qCQAAsrN161YbNWpUgaykOXPm2KpVqxJdDAAosO+H3bp1s7Jly8Z9u8gbBJEAgP2iAPL000+n9gAAMfv666/ttNNOo+byqYDneV6iCwEAKHiZyLVr19qVV15pQ4YMscqVK1t+uuvet29fe+qpp6xx48ZWUOXX81NYcH6SG+fnwJGJzN8IIgEAuWLp0qVulNMlS5ZYzZo1800tT5w40dq3b28TJkywI4880gqq/Hp+CgvOT3Lj/KCwY2AdAAAAAEDUCCIBAAAAAFEjiAQA5Iry5cvbAw884H7mJ9WqVbOuXbu6nwVZfj0/hQXnJ7lxflDY0ScSAAAAABA1MpEAAAAAgKgRRAIAAAAAokYQCQAAAACIGkEkAAAAACBqBJEAAAAAgKgRRAIAct20adOsX79+1qlTJzd1RvHixa1GjRp24YUX2pQpUzgDeWj69OnWq1cvq1y5spUqVcqaNm1qTzzxhO3du5fzkEDr1q2z1157zc466yxr2LChOzcVKlSwY445xl5//XVLS0vj/CShd9991wKBgHvo/AGFBVN8AAByXYcOHez333+3Nm3auN/Lli3rAsvhw4db0aJF7cMPP3SBDXLXb7/9Zj169LDU1FQ777zzrHr16jZs2DCbOXOm9ezZ0z777DP3ZRh5b9CgQXbddddZ1apV7bjjjrPatWvbqlWr3DnZtGmTnX322fbxxx9zfpLIkiVLrHnz5u7vaevWrTZ48GC75pprEl0sIE8QRAIAct3zzz9vJ510kjVq1Chk+XvvvWeXXHKJHXzwwbZ8+XKXoUTu0BddZR1nz55t3377rZ188slu+e7du11gOWbMGJdVufjiizkFCTBy5EgXiJx66qlWpEiRjOUrV6609lTb1q8AAAq/SURBVO3bu4Dlk08+ccEkEs/zPDv++ONt4cKF7gbYk08+SRCJQoXmrACAXHfjjTdmCiBFAYuWqynfjBkzOBO56Oeff3YB5LHHHpsRQIoC94cffjgjG4bEUPbxjDPOCAkgRZnJa6+91v0+atSoBJUO4Z577jkX+A8ZMsTKlClDBaHQIYgEACRUsWLF3E81a0XuBpFywgknZHpO/e70RXjcuHG2a9cuTkOS4W8kucyaNcvuvPNOu+mmm6xLly6JLg6QEASRAICEGT9+vP31119ukJ1mzZpxJnLRnDlz3M9IGWFlv+rVq+eavC5YsIDzkEQ04NHbb7/tfleTcCT+fFx66aWuz+qAAQM4HSi0uO0LAEiI9evX22WXXeZ+f/rppzM140N8aXAW0YifkZQvX9793LhxI1WfRJTx+vPPP10T5BNPPDHRxSn0HnroIZs6daqNHTvWjaALFFZkIgEAUalbt27GUPbRPG6++eYst7Vt2zY788wzbe7cuda3b183UiiAzP3unnrqKTvssMMyspFIHI0wreyj3rM6duzIqUChRiYSABCVBg0aWMmSJaOurUMPPTTLAFIjUOpO/q233upGNUTu8zOQfkYy3ObNm93PihUrcjqSwAsvvOD63DVp0sQN4KJ5PZHYZqxqOdG4ceOMgaiAwowgEgAQlZ9++umAa2rLli0ugNR0ErfffrsNHDiQ2s8j+vIryv6GU19ITVWgJsX169fnnCTYM888Y7fccovrJ6y/uypVqiS6SIWepl/x+xVndTOtd+/e7vHAAw9Yv379Cn2doWAjiAQA5AllwDQwiAbTueeee6x///7UfB7S1B5qijd8+HDXzy6YssLKEGuU1hIlSnBeEkg3VnR+WrVqZSNGjCADmST0d3H11VdHfG7KlCmun6T+ftT0uE2bNnlePiCvBTzNlgoAQC7asGGDm1pi0qRJ9uCDD9r9999PfecxZRuPOOIIl0359ttvM+aK3L17t/Xo0cNlh9999103dycSQ80k9bfRtm1bF+wfdNBBnIp8QFlHva8NHjzYrrnmmkQXB8gTZCIBALmuV69eLoBUv8q0tLSITb169uzpsi/IHWqqqonRu3fv7ur6/PPPt2rVqtmwYcNs5syZbtlFF11E9SfIW2+95QJInafOnTu7QXUiDW51xRVXJKR8ABCMIBIAkOvU307mz5/v7thHoi/IBJG56+ijj3YjTKrPloJHNWFVH8jHH3/c9cHTqLpI7N+IMsbqExlJ165dCSIBJAWaswIAAAAAosY8kQAAAACAqBFEAgAAAACiRhAJAAAAAIgaQSQAAAAAIGoEkQAAAACAqBFEAgAAAACiRhAJAAAAAIgaQSQAoMB48803LRAIZDwGDRoUcb0JEybYiSeeaFWqVHHr9ezZ0y3fsGGDVa5c2R577LEDKseYMWPcdseNG5fpuR9//DGkjP369TugfQEAkNeK5vkeAQCI0Z49e6xcuXK2a9euLNd59NFHrWrVqu73u+++25o0aWJHHXVUpvVWrFhhPXr0sOrVq9sDDzxgFSpUcOuKArpixYrZv//970xBYZcuXax379726quvhjzneZ5ddtll9u6779ojjzzi9t25c2cXpN588802fvx4Fyz6mjVrZu+8846tXbvWbrnllnx9LUybNs0dhx6bN292x6k6yu3txLJ+vMoIANiHIBIAkPT+/PNPF0CeeuqpdsEFF0Rcp2vXrvbTTz+5348//njr1q1bxPXeeust27Jli8tadujQIWP5ypUr7eWXX7Z7773XSpcuHfIaBYXavl573333Wa1atTKeu+uuu1wAef3117sA0te3b1874YQTbNiwYXbaaadlLFege8kll9iiRYvyfRCpjG7x4sVd9laB/s6dO/NkO7GsH68yAgD2IYgEACS9SZMmuZ8KIBWAHYjRo0e7rGZ4lnLw4MG2d+9eu/zyyyO+7v7777fu3bu7pq4vvviiW/bSSy/ZwIEDXXPY559/PmR9rVuzZk23TnAQWZDofJQvX979/vXXX7tAPC+2E8v68SojAGAf+kQCAJLe5MmT3c9IzVOjNWDAANeU8bvvvnOZyJSUFPf//v37u+c//PBDa9q0qdWpUyfi64877jjr1KmTvf7667Z8+XL76quvXLPXo48+2oYOHeq2F0z/P+mkk+yHH36wjRs3WkHkB2d5vZ1Y1o9XGQEA+xBEAgDyRRCpYKBSpUoZ/duCH8og5kT9IF944QX3+ymnnOL6Jepx8cUX25o1a2zmzJkhzVsjUVNWNau95ppr7MILL7SGDRu6YLJUqVIR11eAmZaW5rKf8aQmmdE+oqkbAABiQXNWAEBS2717t82YMcMFb4ccckjEdZYuXWo1atTIdjvt27e3rVu3ut/PPvvskGaxo0aNcj8VFGZHg+UoG6pspvo2fv/993bwwQdnuX6jRo3cT5X/jDPOsHh5++23o163TZs21q5du7jtGwAAgkgAQL4YVOfKK6+0iy66KNPzRYoUyTGADG8W27Zt25DlykRKdgGhqD/d6tWrMwLRunXrZru+vz3/NfGiTGq8mnP6/U2joRFt9QAAFG4EkQCApOYHOQqc1CT1QCiILFGihOv7GImm68iK+lGefPLJrj9kvXr13OiumiIkq+xo8PaCp/iIBw3YEy9TpkyJaX2CSAAAQSQAIKn52cPmzZvHZVstWrSwokVDP/78QHDdunVZNqk966yzbPr06fbBBx+4uST1/6efftrNT5kVf3tVqlSxeNq+fXvU66qsemSlT58+cSoVAKCwIIgEACQ1BX4lS5bMsb9iTjZt2mTz58+PGDT5mcm5c+dGzCZq2g/NQfnss8/aueee65a1bNnSTfVx++23uwF/IvG3F48AOJjmpYwWfSIBAPFGEAkASPpBdRTkqe/jgTbbVPAX3h/Sz0RqH7/99lum5/r27euyj7fddpub0sNvnnrvvfe6gPK///2vPfTQQxH3OW7cODfVR5cuXSxZ+0TmJU11ovpIpjIBAGJHEAkASFoKIBVIFi9ePMvsm6bliCZL6TeLVWYukvPPP9/uv/9+mzdvXsb2nnzySRckahqQgQMHhqyvgXUUeD733HMu0KxQoULI85raQ6O4akTX8OeSqU/kgZgzZ07GiLf+z+A+luF1/dFHH1nZsmUzDZAU63ZiWT/WbQMAckYQCQBIWn7g9/vvv7tHJJqeI9ogUn0Ds2pa2rt3b3v44Yfd9BnKLA4dOtQ1Ve3evbsNGTIk0+A4+r/mjbzgggtcIKnfg6n567Jly2zQoEFWUM2ePdtWrFiR5Wiv0QZosW4nlvXjVUYAwD4BL7uh6AAAyEc0YqqmAvniiy+sU6dOLuul/pTRuummm1zT1QULFliZMmUOqCwnnXSSrV+/3gW/wQHonj17XP/MJUuWuABGI7z269fvgPYFAEBeSsnTvQEAkAd69uzp+jkqqIyFgrnU1FSXWTwQY8aMsR9++MENxBOewfzll19c2ciAAQDyKzKRAIACQ80WZ86cmfH/Jk2aWI0aNSyZKDsZ3Cevfv367gEAQH5BEAkAAAAAiBrNWQEAAAAAUSOIBAAAAABEjSASAAAAABA1gkgAAAAAQNQIIgEAAAAAUSOIBAAAAABEjSASAAAAABA1gkgAAAAAQNQIIgEAAAAAUSOIBAAAAABYtP4fYeNVCQQjHtIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 880x715 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aumentan el riesgo: country_PRT (+1.941); lead_time (+1.271); market_segment_Groups (+1.040)\n",
      "Reducen el riesgo: previous_cancellations (-0.248); agent_9 (-0.206); arrival_date_week_number (-0.129)\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Menor riesgo estimado · fila CSV 15149 · riesgo estimado 0.0%**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Valor original</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hotel</th>\n",
       "      <td>Resort Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lead_time</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_year</th>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_month</th>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meal</th>\n",
       "      <td>BB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>IRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>market_segment</th>\n",
       "      <td>Online TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distribution_channel</th>\n",
       "      <td>TA/TO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_cancellations</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reserved_room_type</th>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agent</th>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company</th>\n",
       "      <td>Sin_empresa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_type</th>\n",
       "      <td>Transient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adr</th>\n",
       "      <td>94.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_guests</th>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_nights</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Valor original\n",
       "hotel                            Resort Hotel\n",
       "lead_time                                  11\n",
       "arrival_date_year                        2017\n",
       "arrival_date_month                        May\n",
       "arrival_date_week_number                   21\n",
       "arrival_date_day_of_month                  24\n",
       "meal                                       BB\n",
       "country                                   IRL\n",
       "market_segment                      Online TA\n",
       "distribution_channel                    TA/TO\n",
       "is_repeated_guest                           1\n",
       "previous_cancellations                      0\n",
       "previous_bookings_not_canceled              1\n",
       "reserved_room_type                          A\n",
       "agent                                     242\n",
       "company                           Sin_empresa\n",
       "customer_type                       Transient\n",
       "adr                                    94.160\n",
       "required_car_parking_spaces                 1\n",
       "total_of_special_requests                   2\n",
       "total_guests                            3.000\n",
       "total_nights                                1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x715 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aumentan el riesgo: arrival_date_year (+0.137); customer_type_Transient (+0.105); market_segment_Offline TA/TO (+0.060)\n",
      "Reducen el riesgo: required_car_parking_spaces (-4.413); previous_bookings_not_canceled (-0.649); country_PRT (-0.577)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "        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>TEST completo</th>\n",
       "      <th>Decil superior de riesgo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lead_time</th>\n",
       "      <td>124.000</td>\n",
       "      <td>181.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adr</th>\n",
       "      <td>127.600</td>\n",
       "      <td>110.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_cancellations</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             TEST completo  Decil superior de riesgo\n",
       "lead_time                          124.000                   181.000\n",
       "adr                                127.600                   110.000\n",
       "total_of_special_requests            1.000                     0.000\n",
       "previous_cancellations               0.000                     0.000\n",
       "required_car_parking_spaces          0.000                     0.000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>TEST completo</th>\n",
       "      <th>Decil superior de riesgo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hotel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>City Hotel</th>\n",
       "      <td>0.685</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Resort Hotel</th>\n",
       "      <td>0.315</td>\n",
       "      <td>0.111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              TEST completo  Decil superior de riesgo\n",
       "hotel                                                \n",
       "City Hotel            0.685                     0.889\n",
       "Resort Hotel          0.315                     0.111"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "# Mostramos los dos casos y los factores que más suben o bajan su riesgo.\n",
    "explicaciones_locales = []\n",
    "for i, etiqueta in enumerate([\"Mayor riesgo estimado\", \"Menor riesgo estimado\"]):\n",
    "    idx = casos.index[i]\n",
    "    prob = p_test[indices_casos[i]]\n",
    "    display(Markdown(f\"**{etiqueta} · fila CSV {idx + 2} · riesgo estimado {prob:.1%}**\"))\n",
    "    display(casos.iloc[[i]].T.rename(columns={idx: \"Valor original\"}))\n",
    "    shap.plots.waterfall(shap_casos[i], max_display=10, show=False)\n",
    "    plt.title(f\"{etiqueta}: {prob:.1%} · SHAP en {unidad_shap}\")\n",
    "    plt.tight_layout(); plt.show()\n",
    "    valores = pd.Series(shap_casos.values[i], index=nombres)\n",
    "    aumentan = valores[valores > 0].nlargest(3)\n",
    "    reducen = valores[valores < 0].nsmallest(3)\n",
    "    explicaciones_locales.append({\"caso\": etiqueta, \"fila_csv\": int(idx + 2), \"probabilidad\": float(prob),\n",
    "                                  \"aumentan\": aumentan.to_dict(), \"reducen\": reducen.to_dict(),\n",
    "                                  \"perfil\": casos.iloc[i].to_dict()})\n",
    "    print(\"Aumentan el riesgo:\", \"; \".join(f\"{n} ({v:+.3f})\" for n, v in aumentan.items()) or \"Ninguna\")\n",
    "    print(\"Reducen el riesgo:\", \"; \".join(f\"{n} ({v:+.3f})\" for n, v in reducen.items()) or \"Ninguna\")\n",
    "\n",
    "# Describimos el 10 % con mayor riesgo estimado; no usamos esto para cambiar el modelo.\n",
    "# Tomamos exactamente ceil(10 % * n); los empates se resuelven por orden de fila.\n",
    "n_top = int(np.ceil(0.10 * len(X_test)))\n",
    "indices_top = np.argsort(-p_test, kind=\"stable\")[:n_top]\n",
    "alto = X_test.iloc[indices_top]\n",
    "perfil_riesgo = pd.DataFrame({\"TEST completo\": X_test[num_cols].median(),\n",
    "                              \"Decil superior de riesgo\": alto[num_cols].median()})\n",
    "display(perfil_riesgo.loc[[\"lead_time\", \"adr\", \"total_of_special_requests\",\n",
    "                           \"previous_cancellations\", \"required_car_parking_spaces\"]])\n",
    "display(pd.DataFrame({\"TEST completo\": X_test.hotel.value_counts(normalize=True),\n",
    "                      \"Decil superior de riesgo\": alto.hotel.value_counts(normalize=True)}).fillna(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6c6a9a4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:15:49.140536Z",
     "iopub.status.busy": "2026-09-24T13:15:49.139926Z",
     "iopub.status.idle": "2026-09-24T13:15:49.159618Z",
     "shell.execute_reply": "2026-09-24T13:15:49.158136Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Mayor riesgo estimado · fila CSV 11,901:** riesgo 99.40%. Aumentan: `country_PRT`, `lead_time`, `market_segment_Groups`. Reducen: `previous_cancellations`, `agent_9`, `arrival_date_week_number`."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Menor riesgo estimado · fila CSV 15,149:** riesgo 0.02%. Aumentan: `arrival_date_year`, `customer_type_Transient`, `market_segment_Offline TA/TO`. Reducen: `required_car_parking_spaces`, `previous_bookings_not_canceled`, `country_PRT`."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Los casos son extremos escogidos por el riesgo estimado, sin mirar su etiqueta;\n",
       "no son reservas representativas. La suma de SHAP y el valor base reproduce la predicción\n",
       "tras transformar log-odds a probabilidad.\n",
       "\n",
       "**Top 10 % de riesgo (2,220 reservas):** antelación mediana **181 días**,\n",
       "frente a **124** en TEST; peticiones medianas\n",
       "**0**, frente a **1**.\n",
       "City Hotel representa **88.9%** de este grupo,\n",
       "frente a **68.5%** en TEST.\n",
       "Este perfil es descriptivo, no un nuevo umbral ni una regla causal."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for caso in explicaciones_locales:\n",
    "    aumentan = \", \".join(f\"`{v}`\" for v in caso[\"aumentan\"]) or \"ningún factor positivo\"\n",
    "    reducen = \", \".join(f\"`{v}`\" for v in caso[\"reducen\"]) or \"ningún factor negativo\"\n",
    "    display(Markdown(f\"**{caso['caso']} · fila CSV {caso['fila_csv']:,}:** riesgo \"\n",
    "                     f\"{caso['probabilidad']:.2%}. Aumentan: {aumentan}. Reducen: {reducen}.\"))\n",
    "display(Markdown(f\"\"\"Los casos son extremos escogidos por el riesgo estimado, sin mirar su etiqueta;\n",
    "no son reservas representativas. La suma de SHAP y el valor base reproduce la predicción\n",
    "{'tras transformar log-odds a probabilidad' if unidad_shap.startswith('log-odds') else 'en escala de probabilidad'}.\n",
    "\n",
    "**Top 10 % de riesgo ({len(alto):,} reservas):** antelación mediana **{alto.lead_time.median():.0f} días**,\n",
    "frente a **{X_test.lead_time.median():.0f}** en TEST; peticiones medianas\n",
    "**{alto.total_of_special_requests.median():.0f}**, frente a **{X_test.total_of_special_requests.median():.0f}**.\n",
    "City Hotel representa **{alto.hotel.eq('City Hotel').mean():.1%}** de este grupo,\n",
    "frente a **{X_test.hotel.eq('City Hotel').mean():.1%}** en TEST.\n",
    "Este perfil es descriptivo, no un nuevo umbral ni una regla causal.\"\"\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b1d37d3",
   "metadata": {},
   "source": [
    "## 12. Conclusiones y aplicación al negocio\n",
    "\n",
    "El EDA, la limpieza, las variables reincorporadas, el split temporal y el preprocessing\n",
    "se mantienen. Los patrones de hotel, antelación y segmento describen asociaciones en TRAIN.\n",
    "\n",
    "La revisión corrige la comparación: ambos finalistas pasan por optimización temporal,\n",
    "y se selecciona una combinación de modelo y threshold después de valorar las alertas.\n",
    "Se abandona la regla anterior de máximo Recall entre alternativas con Precision mínima.\n",
    "No se fuerza ni el modelo optimizado ni una familia concreta.\n",
    "\n",
    "Sin costes económicos de FP y FN no hay un ganador universal. La elección busca un\n",
    "equilibrio operativo en la validación disponible, pendiente de comprobar capacidad y costes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "39fdd7b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-24T13:15:49.163589Z",
     "iopub.status.busy": "2026-09-24T13:15:49.162722Z",
     "iopub.status.idle": "2026-09-24T13:15:49.174997Z",
     "shell.execute_reply": "2026-09-24T13:15:49.173864Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Combinación elegida: HistGradientBoosting, threshold 0.30.**\n",
       "En validación: ROC-AUC **0.9018**, Precision **76.2%**,\n",
       "Recall **75.3%**, F1 **75.7%** y **36.2 %** señaladas.\n",
       "En TEST: ROC-AUC **0.8914**, Precision **68.2%**,\n",
       "Recall **89.0%**, F1 **77.3%** y\n",
       "**52.9 %** señaladas.\n",
       "\n",
       "**Cambio respecto a la versión anterior:** aquella elegía Random Forest inicial a 0,30\n",
       "priorizando Recall y sin optimizar sus hiperparámetros. Ahora se comparan ambas búsquedas\n",
       "y el equilibrio operativo; la decisión actual es **HistGradientBoosting a 0.30**.\n",
       "Matriz, SHAP, probabilidades, ejemplos y perfil de riesgo se recalculan con el modelo final real.\n",
       "\n",
       "**Principales variables SHAP:** `country`, `agent`, `required_car_parking_spaces`, `total_of_special_requests`, `lead_time`, `customer_type`.\n",
       "Son asociaciones aprendidas, no instrucciones para modificar artificialmente una reserva."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(f\"\"\"**Combinación elegida: {ganador}, threshold {THRESHOLD:.2f}.**\n",
    "En validación: ROC-AUC **{seleccion['ROC-AUC']:.4f}**, Precision **{seleccion.Precision:.1%}**,\n",
    "Recall **{seleccion.Recall:.1%}**, F1 **{seleccion.F1:.1%}** y **{seleccion['% reservas señaladas']:.1f} %** señaladas.\n",
    "En TEST: ROC-AUC **{metricas_test['ROC-AUC']:.4f}**, Precision **{metricas_test['Precision']:.1%}**,\n",
    "Recall **{metricas_test['Recall']:.1%}**, F1 **{metricas_test['F1']:.1%}** y\n",
    "**{metricas_test['% reservas señaladas']:.1f} %** señaladas.\n",
    "\n",
    "**Cambio respecto a la versión anterior:** aquella elegía Random Forest inicial a 0,30\n",
    "priorizando Recall y sin optimizar sus hiperparámetros. Ahora se comparan ambas búsquedas\n",
    "y el equilibrio operativo; la decisión actual es **{ganador} a {THRESHOLD:.2f}**.\n",
    "Matriz, SHAP, probabilidades, ejemplos y perfil de riesgo se recalculan con el modelo final real.\n",
    "\n",
    "**Principales variables SHAP:** {', '.join('`' + v + '`' for v in shap_importance.head(6).index)}.\n",
    "Son asociaciones aprendidas, no instrucciones para modificar artificialmente una reserva.\"\"\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b47c7512",
   "metadata": {},
   "source": [
    "### Aplicación y limitaciones\n",
    "\n",
    "Reserva → probabilidad → umbral → recordatorio o reconfirmación. Un piloto de contactos\n",
    "de bajo coste permite medir utilidad, falsas alarmas y carga de trabajo. Las predicciones\n",
    "no justifican automáticamente depósitos o restricciones.\n",
    "\n",
    "- Verificar que precio, solicitudes e historial estaban disponibles al crear la reserva.\n",
    "- Estimar costes FP/FN y capacidad antes de convertir el criterio académico en política comercial.\n",
    "- La validación se usa para varias decisiones: su rendimiento puede ser optimista.\n",
    "- El split es por llegada, no por creación; TEST ya apareció en versiones anteriores.\n",
    "  Esta ejecución lo consulta solo tras cerrar la decisión, pero hace falta otro periodo\n",
    "  para una comprobación externa sin exposición previa.\n",
    "- Revisar calibración, posibles duplicados y cambios de comportamiento con datos nuevos,\n",
    "  sin reajustar a partir de este TEST.\n",
    "- SHAP explica el modelo reentrenado: no prueba causalidad y su ranking depende de la muestra."
   ]
  }
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