notebook_unificada.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Desafío 3 - DH - ML Clasificación"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"tabla_contenidos\"></a> \n",
    "### Tabla de Contenidos\n",
    "\n",
    "#### <a href='#section_proposito'>0. Propósito de esta Notebook</a>\n",
    "- ##### <a href='#section_motivacion'>0.1 Motivación</a>\n",
    "- ##### <a href='#section_dataset_info'>0.2 Detalles del Dataset</a>\n",
    "- ##### <a href='#section_antecedentes'>0.3 Estado del Arte</a>\n",
    "\n",
    "#### <a href='#section_preparacion'>1. Preparación del Dataset</a>\n",
    "- ##### <a href='#section_importacion'>1.1 Importación</a>\n",
    "- ##### <a href='#section_analisis'>1.2 Análisis Exploratorio</a>\n",
    "- ##### <a href='#section_eliminacion'>1.3 Acotación de interés</a>\n",
    "- ##### <a href='#section_split'>1.4 Selección de Features y target</a>\n",
    "\n",
    "#### <a href='#section_algoritmos'>2. Algoritmos y predicción</a>\n",
    "- ##### <a href='#knn'>2.1 K Nearest Neighbors (KNN)</a>\n",
    "- ##### <a href='#reg_log'>2.2 Regresión Logística</a>\n",
    "- ##### <a href='#naive'>2.3 Naive Bayes</a>\n",
    "- ##### <a href='#naive_gaussian'>2.4 Naive Bayes Gaussian</a>\n",
    "\n",
    "#### <a href='#section_consolidacion'>3. Consolidación</a>\n",
    "- ##### <a href='#pipe'>3.1 Pipelines</a>\n",
    "- ##### <a href='#conclusion'>3.2 Conclusión</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='section_proposito'>0. Propósito de esta Notebook</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id=\"section_motivacion\">0.1 Motivación</a> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section_preparacion'></a> \n",
    "### 1. Preparación del Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"section_importacion\">1.1 Importación de datos</a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importación de datos de población y resultados de encuesta\n",
    "Los datos de población nos permitirán realizar una exploración geográfica de la ubicación de los encuestados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# librerías\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import unicodedata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultados_del_test = pd.read_csv(\"data/resultados-del-test.csv\", delimiter = \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "poblacion_por_barrio = pd.read_csv(\"data/caba_pob_barrios_2010.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unifico en un dataset\n",
    "Elimino tildes en los nombres de los barrios de cada dataset y agrego población por barrio al resultado de las encuestas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def elimina_tildes(cadena):\n",
    "    s = ''.join((c for c in unicodedata.normalize('NFD',cadena) if unicodedata.category(c) != 'Mn'))\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "poblacion_por_barrio.BARRIO = poblacion_por_barrio.BARRIO.apply(elimina_tildes).apply(lambda barrio: barrio.capitalize())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultados_del_test.barrio = resultados_del_test.barrio.apply(elimina_tildes).apply(lambda barrio: barrio.capitalize())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "poblacion_por_barrio.columns = ['barrio', 'poblacion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "poblacion_por_barrio = poblacion_por_barrio.set_index('barrio')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtener_poblacion_por_barrio(barrio):\n",
    "    return poblacion_por_barrio.loc[barrio].poblacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imputar_barrio(barrio):\n",
    "    barrios = poblacion_por_barrio.index.tolist()\n",
    "    if barrio == \"La boca\": return \"Boca\"\n",
    "    if barrio == \"Montserrat\": return \"Monserrat\"\n",
    "    if barrio == \"Villa general mitre\": return \"Villa gral. mitre\"\n",
    "    if barrio == \"La paternal\": return \"Paternal\"\n",
    "    if barrio == \"Otro\": return np.random.choice(barrios)\n",
    "    return barrio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultados_del_test[\"barrio_completo\"] = resultados_del_test.barrio.apply(imputar_barrio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultados_del_test[\"poblacion_por_barrio\"] = resultados_del_test.barrio_completo.apply(obtener_poblacion_por_barrio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exporto y resguardo el dataset unificado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultados_del_test.to_csv(\"data/dataset_joined.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"section_analisis\">1.2 Análisis Exploratorio</a> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importo el dataset unificado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>edad</th>\n",
       "      <th>genero</th>\n",
       "      <th>barrio</th>\n",
       "      <th>pregunta_1</th>\n",
       "      <th>pregunta_2</th>\n",
       "      <th>pregunta_3</th>\n",
       "      <th>pregunta_4</th>\n",
       "      <th>pregunta_5</th>\n",
       "      <th>pregunta_6</th>\n",
       "      <th>pregunta_7</th>\n",
       "      <th>pregunta_8</th>\n",
       "      <th>pregunta_9</th>\n",
       "      <th>pregunta_10</th>\n",
       "      <th>pregunta_11</th>\n",
       "      <th>pregunta_12</th>\n",
       "      <th>pregunta_13</th>\n",
       "      <th>barrio_completo</th>\n",
       "      <th>poblacion_por_barrio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-14</td>\n",
       "      <td>09:09:00</td>\n",
       "      <td>27</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>Constitucion</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>Constitucion</td>\n",
       "      <td>44107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>23:20:00</td>\n",
       "      <td>17</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>Otro</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>Mataderos</td>\n",
       "      <td>64436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>16:33:00</td>\n",
       "      <td>15</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>Recoleta</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>Recoleta</td>\n",
       "      <td>157932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fecha      hora  edad genero        barrio pregunta_1 pregunta_2  \\\n",
       "0  2019-06-14  09:09:00    27  Mujer  Constitucion          A          A   \n",
       "1  2019-06-13  23:20:00    17  Mujer          Otro          A          B   \n",
       "2  2019-06-13  16:33:00    15  Mujer      Recoleta          B          B   \n",
       "\n",
       "  pregunta_3 pregunta_4 pregunta_5 pregunta_6 pregunta_7 pregunta_8  \\\n",
       "0          A          B          A          B          C          C   \n",
       "1          B          B          B          C          C          B   \n",
       "2          C          B          C          B          B          A   \n",
       "\n",
       "  pregunta_9 pregunta_10 pregunta_11 pregunta_12 pregunta_13 barrio_completo  \\\n",
       "0          B           C           B           C           C    Constitucion   \n",
       "1          B           C           B           C           C       Mataderos   \n",
       "2          A           A           A           A           A        Recoleta   \n",
       "\n",
       "   poblacion_por_barrio  \n",
       "0                 44107  \n",
       "1                 64436  \n",
       "2                157932  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/dataset_joined.csv\", delimiter = \",\",  index_col = \"Unnamed: 0\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5676 entries, 0 to 5675\n",
      "Data columns (total 20 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   fecha                 5676 non-null   object\n",
      " 1   hora                  5676 non-null   object\n",
      " 2   edad                  5676 non-null   int64 \n",
      " 3   genero                5676 non-null   object\n",
      " 4   barrio                5676 non-null   object\n",
      " 5   pregunta_1            5676 non-null   object\n",
      " 6   pregunta_2            5676 non-null   object\n",
      " 7   pregunta_3            5676 non-null   object\n",
      " 8   pregunta_4            5676 non-null   object\n",
      " 9   pregunta_5            5676 non-null   object\n",
      " 10  pregunta_6            5676 non-null   object\n",
      " 11  pregunta_7            5676 non-null   object\n",
      " 12  pregunta_8            5676 non-null   object\n",
      " 13  pregunta_9            5676 non-null   object\n",
      " 14  pregunta_10           5676 non-null   object\n",
      " 15  pregunta_11           5676 non-null   object\n",
      " 16  pregunta_12           5676 non-null   object\n",
      " 17  pregunta_13           5676 non-null   object\n",
      " 18  barrio_completo       5676 non-null   object\n",
      " 19  poblacion_por_barrio  5676 non-null   int64 \n",
      "dtypes: int64(2), object(18)\n",
      "memory usage: 931.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizamos los nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "### librerías de visualización\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_columnas = df[[\"fecha\",'edad', 'genero', 'barrio','barrio_completo','poblacion_por_barrio']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "* ```fecha``` tiene 0 nulos, lo cual representa el 0.00%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* ```edad``` tiene 0 nulos, lo cual representa el 0.00%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* ```genero``` tiene 0 nulos, lo cual representa el 0.00%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* ```barrio``` tiene 0 nulos, lo cual representa el 0.00%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* ```barrio_completo``` tiene 0 nulos, lo cual representa el 0.00%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* ```poblacion_por_barrio``` tiene 0 nulos, lo cual representa el 0.00%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "\n",
    "nulls_by_column = df_columnas.isnull().sum()\n",
    "dataset_size = df.shape[0]\n",
    "\n",
    "for column in nulls_by_column.index:\n",
    "    column_nulls = nulls_by_column[column]\n",
    "    null_percentage = 100 * column_nulls / dataset_size\n",
    "    display(Markdown(f\"* ```{column}``` tiene {column_nulls} nulos, lo cual representa el {null_percentage:.2f}%\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Barrio de los encuestados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "barrio = df.barrio.value_counts().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e5403414c8>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=barrio.index, y=barrio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos los primeros quince barrios con más encuestados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e5402c1e88>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEKCAYAAADq59mMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEqpJREFUeJzt3X+w5XV93/Hni0URi0wgXCjZxSyT2bRZ0KCslGgmpprGbfNjqZW4pIY1MbMpQ9RkajqQaWJiu61JtSmYwHQn1d01VtzGRFZHEpmtmBqpeBeRdUHKNghsobCaH4JNaBff/eN8rn5Z7i7no/fsuXfv8zFz5ny/n+/3873vs2fved3vr89JVSFJUo8Tpl2AJGnpMTwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHU7cdoFTMoZZ5xRq1evnnYZkrSk7Nmz50tVNfNM6x234bF69WpmZ2enXYYkLSlJ7h9nPQ9bSZK6GR6SpG6GhySpm+EhSepmeEiSuhkekqRuhockqZvhIUnqZnhIkrodt3eY97jwl3ZMu4Tj3p5/d/m0S5C0gNzzkCR1MzwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3QwPSVI3w0OS1M3wkCR1MzwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3QwPSVI3w0OS1M3wkCR1MzwkSd0MD0lSt4mHR5IVST6b5CNt/vQkNye5tz2fNlj36iT7k9yT5FWD9guT7G3Lrk2SSdctSTqyY7Hn8Wbg7sH8VcDuqloD7G7zJFkLbATOA9YD1yVZ0fpcD2wG1rTH+mNQtyTpCCYaHklWAT8C/O6geQOwvU1vBy4ZtN9QVU9U1X3AfuCiJGcDp1bVrVVVwI5BH0nSFEx6z+M/AP8C+Nqg7ayqehigPZ/Z2lcCDw7WO9DaVrbpw9slSVMysfBI8qPAo1W1Z9wu87TVUdrn+5mbk8wmmT148OCYP1aS1GuSex4vA348yReBG4BXJPk94JF2KIr2/Ghb/wBwzqD/KuCh1r5qnvanqaqtVbWuqtbNzMws5GuRJA1MLDyq6uqqWlVVqxmdCP+vVfU6YBewqa22CbixTe8CNiY5Kcm5jE6M39YObT2W5OJ2ldXlgz6SpCk4cQo/8+3AziRvAB4ALgWoqn1JdgJ3AYeAK6vqydbnCmAbcDJwU3tIkqbkmIRHVd0C3NKmvwy88gjrbQG2zNM+C5w/uQolST28w1yS1M3wkCR1MzwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3QwPSVI3w0OS1M3wkCR1MzwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3QwPSVI3w0OS1M3wkCR1MzwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3SYWHkmek+S2JJ9Lsi/Jr7f205PcnOTe9nzaoM/VSfYnuSfJqwbtFybZ25ZdmySTqluS9MwmuefxBPCKqvpe4AJgfZKLgauA3VW1Btjd5kmyFtgInAesB65LsqJt63pgM7CmPdZPsG5J0jOYWHjUyONt9lntUcAGYHtr3w5c0qY3ADdU1RNVdR+wH7goydnAqVV1a1UVsGPQR5I0BRM955FkRZI7gEeBm6vq08BZVfUwQHs+s62+Enhw0P1Aa1vZpg9vlyRNyUTDo6qerKoLgFWM9iLOP8rq853HqKO0P30DyeYks0lmDx482F+wJGksx+Rqq6r6S+AWRucqHmmHomjPj7bVDgDnDLqtAh5q7avmaZ/v52ytqnVVtW5mZmZBX4Mk6RsmebXVTJJva9MnAz8EfAHYBWxqq20CbmzTu4CNSU5Kci6jE+O3tUNbjyW5uF1ldfmgjyRpCk6c4LbPBra3K6ZOAHZW1UeS3ArsTPIG4AHgUoCq2pdkJ3AXcAi4sqqebNu6AtgGnAzc1B6SpCmZWHhU1Z3Ai+Zp/zLwyiP02QJsmad9Fjja+RJJ0jHkHeaSpG6GhySpm+EhSepmeEiSuhkekqRuY4VHkt3jtEmSloejXqqb5DnAc4Ez2tDpc0OFnAp8x4RrkyQtUs90n8fPAb/AKCj28I3w+ArwOxOsS5K0iB01PKrqGuCaJG+sqncdo5okSYvcWHeYV9W7krwUWD3sU1U7JlSXJGkRGys8krwX+C7gDmBuvKm5L2aSJC0z445ttQ5Y277JT5K0zI17n8fngb89yUIkSUvHuHseZwB3JbkNeGKusap+fCJVSZIWtXHD49cmWYQkaWkZ92qrT0y6EEnS0jHu1VaPMbq6CuDZwLOAr1bVqZMqTJK0eI275/G84XySS4CLJlKRJGnR+6ZG1a2qDwGvWOBaJElLxLiHrV49mD2B0X0f3vMhScvUuFdb/dhg+hDwRWDDglcjSVoSxj3n8dOTLkSStHSM+2VQq5L8YZJHkzyS5INJVk26OEnS4jTuCfP3ALsYfa/HSuDDrU2StAyNGx4zVfWeqjrUHtuAmQnWJUlaxMYNjy8leV2SFe3xOuDLkyxMkrR4jRsePwP8BPC/gYeB1wCeRJekZWrcS3X/FbCpqv4CIMnpwDsYhYokaZkZd8/jhXPBAVBVfw68aDIlSZIWu3HD44Qkp83NtD2PcfdaJEnHmXED4J3Ap5L8PqNhSX4C2DKxqiRJi9q4d5jvSDLLaDDEAK+uqrsmWpkkadEa+9BTCwsDQ5L0zQ3JLkla3gwPSVK3iYVHknOSfDzJ3Un2JXlzaz89yc1J7m3Pw6u4rk6yP8k9SV41aL8wyd627NokmVTdkqRnNsk9j0PAP6+q7wEuBq5Msha4CthdVWuA3W2etmwjcB6wHrguyYq2reuBzcCa9lg/wbolSc9gYuFRVQ9X1e1t+jHgbkYj8m4AtrfVtgOXtOkNwA1V9URV3QfsBy5KcjZwalXdWlUF7Bj0kSRNwTE555FkNaM70j8NnFVVD8MoYIAz22orgQcH3Q60tpVt+vD2+X7O5iSzSWYPHjy4kC9BkjQw8fBIcgrwQeAXquorR1t1nrY6SvvTG6u2VtW6qlo3M+OI8ZI0KRMNjyTPYhQc76uqP2jNj7RDUbTnR1v7AeCcQfdVwEOtfdU87ZKkKZnk1VYB/hNwd1X9+8GiXcCmNr0JuHHQvjHJSUnOZXRi/LZ2aOuxJBe3bV4+6CNJmoJJDm74MuCngL1J7mhtvwy8HdiZ5A3AA8ClAFW1L8lORnexHwKurKonW78rgG3AycBN7SFJmpKJhUdVfZL5z1cAvPIIfbYwz4CLVTULnL9w1UmSvhXeYS5J6mZ4SJK6GR6SpG6GhySpm+EhSepmeEiSuhkekqRuhockqZvhIUnqZnhIkroZHpKkboaHJKmb4SFJ6mZ4SJK6GR6SpG6GhySpm+EhSepmeEiSuhkekqRuhockqZvhIUnqZnhIkroZHpKkboaHJKmb4SFJ6mZ4SJK6GR6SpG6GhySpm+EhSepmeEiSuhkekqRuhockqZvhIUnqZnhIkrpNLDySvDvJo0k+P2g7PcnNSe5tz6cNll2dZH+Se5K8atB+YZK9bdm1STKpmiVJ45nknsc2YP1hbVcBu6tqDbC7zZNkLbAROK/1uS7JitbnemAzsKY9Dt+mJOkYm1h4VNWfAH9+WPMGYHub3g5cMmi/oaqeqKr7gP3ARUnOBk6tqlurqoAdgz6SpCk51uc8zqqqhwHa85mtfSXw4GC9A61tZZs+vF2SNEWL5YT5fOcx6ijt828k2ZxkNsnswYMHF6w4SdJTHevweKQdiqI9P9raDwDnDNZbBTzU2lfN0z6vqtpaVeuqat3MzMyCFi5J+oZjHR67gE1tehNw46B9Y5KTkpzL6MT4be3Q1mNJLm5XWV0+6CNJmpITJ7XhJO8HfhA4I8kB4K3A24GdSd4APABcClBV+5LsBO4CDgFXVtWTbVNXMLpy62TgpvaQJE3RxMKjqi47wqJXHmH9LcCWedpngfMXsDQdRx542wumXcKy8Pxf3TvtErTILJYT5pKkJcTwkCR1MzwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3QwPSVI3w0OS1M3wkCR1MzwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3QwPSVI3w0OS1M3wkCR1MzwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3ZZMeCRZn+SeJPuTXDXteiRpOVsS4ZFkBfA7wD8E1gKXJVk73aokaflaEuEBXATsr6o/q6r/C9wAbJhyTZK0bJ047QLGtBJ4cDB/APh7U6pF0gJ52bteNu0Sjnt/+sY/nch2l0p4ZJ62etpKyWZgc5t9PMk9E61qus4AvjTtIsaVd2yadgmLyZJ67wB463y/gsvWknr/8qbu9+47x1lpqYTHAeCcwfwq4KHDV6qqrcDWY1XUNCWZrap1065D/Xzvljbfv5Glcs7jM8CaJOcmeTawEdg15ZokadlaEnseVXUoyc8DfwysAN5dVfumXJYkLVtLIjwAquqjwEenXccisiwOzx2nfO+WNt8/IFVPO+8sSdJRLZVzHpKkRcTwmLIkleS9g/kTkxxM8pEx+n5qstVpTpLHD5t/fZLfnsS2NR1JViW5Mcm9Sf5nkmuSPDvJBUn+0bTrW2wMj+n7KnB+kpPb/D8A/tc4HavqpeP+kIz4fi9RSZbM+cmlKEmAPwA+VFVrgO8GTgG2ABcA84bHcn5f/DBZHG4CfqRNXwa8f25Bkl9L8pbB/OeTrG7Tjw/afynJZ5LcmeTXW9vqJHcnuQ64nafeK6MFkuQ7k+xu//a7kzy/tW9Lcn2Sjyf5syQvT/Lu9p5sO2wb70xye+s/09puSfJvknwCeHOSmSQfbO/zZ5J4e/bCeQXwN1X1HoCqehL4ReBngd8EXpvkjiSvbb+TW5N8DNiR5DlJ3pNkb5LPJvn703sZx47hsTjcAGxM8hzghcCnezon+WFgDaMxwC4ALkzyA23x3wF2VNWLqur+Bax5uTm5fXjckeQO4G2DZb/N6N/4hcD7gGsHy05j9MH0i8CHgd8CzgNekOSCts7fAm6vqhcDnwDeOuj/bVX18qp6J3AN8FtV9RLgnwC/u+Cvcvk6D9gzbKiqrwBfBP418IGquqCqPtAWXwhsqKqfBK5s67+A0R9/29vv8nFt2e5yLSZVdWfbm7iMb+5y5B9uj8+2+VMYhckDwP1V9d8XoMzl7q+rau7DniSvB+buMv4+4NVt+r2M/lKd8+GqqiR7gUeqam/rvw9YDdwBfA2Y+1D6PUaHT+Z8YDD9Q8Da0REWAE5N8ryqeuxbe2liNATSfJeeHql9V1X9dZv+fuBdAFX1hST3MzrsdeckCl0sDI/FYxfwDuAHgW8ftB/iqXuI8/1FE+DfVtV/fErjKJC+upBFaizDD5sn2vPXBtNz80f6/Rv2H75/JwDfN/jQ0sLZx2hv7uuSnMroUO+T86w/fF+W5cBfHrZaPN4NvG3uL9OBLwIvBkjyYuDcefr+MfAzSU5p661McuYEa9VTfYrRkDkA/xT4ZGf/E4DXtOmfPEr/jwE/PzczOOylb91u4LlJLoevf4fQO4FtwCPA847S908Yve8k+W7g+cDxPCgrYHgsGlV1oKqumWfRB4HT23H2K4D/MezW+n4M+M/Are3wyO9z9P/sWlhvAn46yZ3ATwFv7uz/VeC8JHsYnR952xHWexOwrp2Yvwv4Z99swXqqGt0t/Y+BS5Pcy+j37G+AXwY+zuhw4R1JXjtP9+uAFe137wPA66vqiXnWO654h/kSleTbGZ1kHWv4ZElaSO55LEFJvgO4ldE5Ekk65tzzkCR1c89DktTN8JAkdTM8JEndDA9JUjfDQ1qkHAlZi5n/MaVOSX4lyReS3Jzk/UnekuS7kvxRkj1J/luSv9vW3Zbk2iSfaiPrvmawnbFGQk5yWRux9fNJfmM6r1p6KsND6pBkHaMxkF7EaDDEucERtwJvrKoLgbcwuut4ztmMBs/7UeDtbTtjjYQM/D/gNxjdeX4B8JIkl0zsBUpjcmBEqc/3AzfODU6Y5MOMBqt8KfBfBiPenjTo86Gq+hpwV5KzWtu4IyG/BLilqg62n/c+4AeAD03gtUljMzykPvONoHoC8JfDIdsPMxznKIPncUZCXpYjtmrx87CV1OeTwI+1b487hdE3QP4f4L4kl8LXT3R/7zNsZ9yRkD8NvDzJGW2k18sYfWGUNFXueUgdquozSXYBnwPuB2aBv2I0JPf1Sf4l8CxG3w75uaNs52NJvofRSMgAjwOv47Dvjqiqh5NczWhk1wAfraobF/yFSZ0c20rqlOSUqno8yXMZfZfD5qq6fdp1SceSex5Sv61J1jI6Ub7d4NBy5J6HJKmbJ8wlSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUrf/D6kFmAqJZdd6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='genero', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ploteamos cantidad de participantes de encuesta por barrio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Almagro      376\n",
       "Belgrano     354\n",
       "Palermo      332\n",
       "Caballito    309\n",
       "Agronomia    268\n",
       "Name: barrio_completo, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "porbarrio = df.barrio_completo.value_counts()\n",
    "#porbarrio = barioss.rename_axis('BARRIO').reset_index(name='counts')\n",
    "porbarrio.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "porbarrio = porbarrio.to_frame().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e5404c5408>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = df.barrio.value_counts(normalize=True).index[:14]\n",
    "sizes = list(df.barrio.value_counts(normalize=True)[:14])\n",
    "\n",
    "figura, axis = plt.subplots(figsize=(8,6))\n",
    "axis.pie(sizes)\n",
    "axis.axis('equal') \n",
    "axis.set_title('Género de encuestados')\n",
    "axis.legend(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizamos las edades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.edad.hist(bins=150);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Podemos escribir en el plot\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.hist(df.edad, bins=150);\n",
    "plt.annotate('¿Qué hacemos con esto?', xy=(15, 50), xytext=(2.5, 300),arrowprops=dict(facecolor='black', shrink=15), fontsize=16)\n",
    "plt.annotate('¿y esto?', xy=(100, 50), xytext=(78, 250), arrowprops=dict(facecolor='black', shrink=15), fontsize=16);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e540644c08>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.edad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Quitamos los outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos como quedaria la distribucion si usamos los rangos intercuartiles para quitar outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_edad = df[\"edad\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "q1 = df_edad.quantile(0.25)\n",
    "q2 = df_edad.quantile(0.5)\n",
    "q3 = df_edad.quantile(0.75)\n",
    "\n",
    "iqr = q3 - q1\n",
    "\n",
    "up_threshold = q3 + 1.5 * iqr\n",
    "low_threshold = q1 - 1.5* iqr\n",
    "\n",
    "no_outliers_mask = (df_edad > low_threshold) & (df_edad < up_threshold)\n",
    "\n",
    "ax = sns.boxplot(df_edad[no_outliers_mask], orient = 'v').set(\n",
    "    xlabel='', \n",
    "    ylabel='Edad'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El skew es 0.5893768101027447 cuando quitamos los outliers\n"
     ]
    }
   ],
   "source": [
    "print(f\"El skew es {df_edad[no_outliers_mask].skew()} cuando quitamos los outliers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5561.000000\n",
       "mean       27.400108\n",
       "std         7.758342\n",
       "min         4.000000\n",
       "25%        21.000000\n",
       "50%        26.000000\n",
       "75%        32.000000\n",
       "max        50.000000\n",
       "Name: edad, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_edad[no_outliers_mask].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sin outliers hemos tomado un 97.97% de los datos que teníamos, considerando solamente 115 registros como outliers\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n_without_outliers = df_edad[no_outliers_mask].shape[0]\n",
    "n_including_outliers = df_edad.shape[0]\n",
    "\n",
    "taken_percentage = 100 * n_without_outliers / n_including_outliers\n",
    "print(f\"\"\"\n",
    "Sin outliers hemos tomado un {taken_percentage:.2f}% de los datos que teníamos, considerando solamente {n_including_outliers - n_without_outliers} registros como outliers\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Género de encuestados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mujer     4736\n",
       "Hombre     912\n",
       "Otro        28\n",
       "Name: genero, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"genero\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e540705f08>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = df.genero.value_counts(normalize=True).index\n",
    "sizes = list(df.genero.value_counts(normalize=True))\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(8,6))\n",
    "ax1.pie(sizes, labels=labels)\n",
    "ax1.axis('equal') \n",
    "ax1.set_title('Género de encuestados')\n",
    "ax1.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Respuestas según género"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "preguntas = ['1. ¿Cuando no están juntos/as, ¿tu pareja te controla preguntándote con quién estás,\\n dónde y qué estás haciendo mensajeándote por celular?', \n",
    " '2. ¿Revisa los mensajes de tu celular o te pidió la contraseña de tu correo electrónico,\\n Facebook o Instagram como “prueba de confianza”?',\n",
    " '3. ¿Te acusa de haber sido infiel o coquetear con otras/os?',\n",
    " '4. ¿Sentís que están permanentemente en tensión y que, hagas lo que hagas, se irrita\\n o te culpabiliza de sus cambios de humor?',\n",
    " '5. ¿Le molesta que hagas actividades de manera independiente, como estudiar, trabajar,\\n visitar amigos/as, ir al gimnasio o elegir quedarte solo/a en tu casa?',\n",
    " '6. ¿Sentís que te alejaste o perdiste contacto con amigos/as o familiares desde que estás en pareja?',\n",
    " '7. ¿Te dice cómo tenés que vestirte o cómo debe ser tu apariencia?',\n",
    " '8. ¿Menosprecia en público o en privado tus opiniones?',\n",
    " '9. ¿Te dice que todo lo que hacés está mal o que no servís para nada?',\n",
    " '10. ¿Te amenaza con lastimarse/te si no lo/la obedecés o si querés terminar la relación?',\n",
    " '11. Después de un episodio violento, ¿se muestra cariñoso/a o atento/a\\n y promete que nunca más lo hará y todo cambiará?',\n",
    " '12. ¿Indaga o cuestiona tus noviazgos anteriores?',\n",
    " '13. ¿Te sentís presionado/a a realizar determinadas prácticas sexuales por temor a que te deje?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "respuestas = ['A = Siempre', 'B = \"A veces\"', 'C = Nunca']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df[['genero'] + list(df.columns[6:18])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\carlm\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1048: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item_labels[indexer[info_axis]]] = value\n"
     ]
    }
   ],
   "source": [
    "for columna in data.drop('genero',axis=1).columns:\n",
    "    data.loc[:,columna] = df.loc[:,columna].replace('A','Siempre').replace('B','A veces').replace('C','Nunca')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ploteamos las respuestas relativo a generos de los encuestados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for num,columna in enumerate(data.drop('genero',axis=1).columns):\n",
    "    # respuesta\n",
    "    respuesta = set(np.array(list(data.groupby(columna).genero.value_counts().index))[:,0])\n",
    "    # respuesta por hombres\n",
    "    n_varones = (data.genero == 'Hombre').sum()\n",
    "    n_mujeres = (data.genero == 'Mujer').sum()\n",
    "    n_otros = (data.genero == 'Otro').sum()\n",
    "    varones = list(data.groupby(columna).genero.value_counts())[1::3] / n_varones\n",
    "    # respusta por mujeres\n",
    "    mujeres = list(data.groupby(columna).genero.value_counts())[::3] / n_mujeres \n",
    "    # respusta por otros\n",
    "    otros = list(data.groupby(columna).genero.value_counts())[2::3] / n_otros\n",
    "\n",
    "    x = np.arange(len(respuesta))\n",
    "    width = 0.2\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(8,6))\n",
    "    barra1 = ax.bar(x, varones, width, label='Varones', color='blue')\n",
    "    barra2 = ax.bar(x+0.2, mujeres, width, label='Mujeres', color='green')\n",
    "    barra3 = ax.bar(x+0.4, otros, width, label='Otros', color='red')\n",
    "\n",
    "    ax.set_ylabel('Total respuestas')\n",
    "    ax.set_title(preguntas[num])\n",
    "    ax.set_xticks(x+0.2)\n",
    "    ax.set_xticklabels(respuestas)\n",
    "    ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline de preprocesamiento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separo en X e y y armo el pipeline a partir de ahi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('pregunta_13', axis = 1)\n",
    "y = (df.pregunta_13 != 'C').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline, make_union\n",
    "from sklearn.preprocessing import StandardScaler, KBinsDiscretizer, OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Genero un transformador que toma las columnas que quiero y asi reutilizarlo en varios steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ColumnSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, selected_features):\n",
    "        self.selected_features=selected_features\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        return X[self.selected_features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para la edad el pipeline va a crear 4 bins:\n",
    "\n",
    "* Menor de 25\n",
    "* De 25 a 49\n",
    "* De 50 a 74\n",
    "* De 75 en adelante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "edad_pipeline = make_pipeline(ColumnSelector(['edad']), KBinsDiscretizer(4, strategy = 'uniform', encode = 'onehot-dense'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para las preguntas, utilizamos OneHotEncoder y utilizamos todas las disponibles en el X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "preguntas_col = [col for col in X.columns if \"pregunta_\" in col]\n",
    "preguntas_pipeline = make_pipeline(ColumnSelector(preguntas_col), OneHotEncoder(sparse=False, handle_unknown='ignore'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para el barrio y el genero, lo transformamos tambien usando OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "barrio_pipeline = make_pipeline(ColumnSelector(['barrio']), OneHotEncoder(sparse=False, handle_unknown='ignore'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "genero_pipeline = make_pipeline(ColumnSelector(['genero']), OneHotEncoder(sparse=False, handle_unknown='ignore'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por ultimo, creamos un pipeline con la union de estos pasos anteriores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocess_pipeline = make_union(edad_pipeline, preguntas_pipeline, barrio_pipeline, genero_pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Divido en train y test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline de entrenamiento y prediccion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizamos la funcionalidad de GridSearch y Pipelines para probar varios modelos en gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(steps = [\n",
    "    ('preprocess', preprocess_pipeline),\n",
    "    ('estimator', GaussianNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizamos el placeholder None para utilizar la grilla de hiperparametros mas adelante para insertarle distintos modelos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GridSearch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_grid =[\n",
    "    {'estimator':[GaussianNB()]},\n",
    "    {\n",
    "        'estimator': [MultinomialNB()],\n",
    "        'estimator__alpha': np.linspace(0, 2, 20)\n",
    "    },\n",
    "    {\n",
    "        'estimator': [KNeighborsClassifier()],\n",
    "        'estimator__n_neighbors': range(1, 15),\n",
    "        'estimator__p': [1, 2]\n",
    "    },\n",
    "    {\n",
    "        'estimator': [LogisticRegression()],\n",
    "        'estimator__penalty': ['l2', 'l1', 'elasticnet', 'none'],\n",
    "        'estimator__C': np.linspace(0, 5, 100)\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GridSearchCV(pipeline, params_grid, cv = folds, n_jobs = -1, scoring='roc_auc', verbose = 1, return_train_score = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 449 candidates, totalling 2245 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    5.0s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:  3.2min\n",
      "[Parallel(n_jobs=-1)]: Done 784 tasks      | elapsed:  3.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1234 tasks      | elapsed:  3.9min\n",
      "[Parallel(n_jobs=-1)]: Done 1784 tasks      | elapsed:  4.4min\n",
      "[Parallel(n_jobs=-1)]: Done 2245 out of 2245 | elapsed:  4.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
       "             error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('preprocess',\n",
       "                                        FeatureUnion(n_jobs=None,\n",
       "                                                     transformer_list=[('pipeline-1',\n",
       "                                                                        Pipeline(memory=None,\n",
       "                                                                                 steps=[('columnselector',\n",
       "                                                                                         ColumnSelector(selected_features=['edad'])),\n",
       "                                                                                        ('kbinsdiscretizer',\n",
       "                                                                                         KBinsDiscretizer(encode='onehot-dense',\n",
       "                                                                                                          n_b...\n",
       "       4.04040404, 4.09090909, 4.14141414, 4.19191919, 4.24242424,\n",
       "       4.29292929, 4.34343434, 4.39393939, 4.44444444, 4.49494949,\n",
       "       4.54545455, 4.5959596 , 4.64646465, 4.6969697 , 4.74747475,\n",
       "       4.7979798 , 4.84848485, 4.8989899 , 4.94949495, 5.        ]),\n",
       "                          'estimator__penalty': ['l2', 'l1', 'elasticnet',\n",
       "                                                 'none']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='roc_auc', verbose=1)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7864256115526429"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_score_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'estimator': LogisticRegression(C=0.050505050505050504, class_weight=None, dual=False,\n",
       "                    fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
       "                    max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                    random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                    warm_start=False),\n",
       " 'estimator__C': 0.050505050505050504,\n",
       " 'estimator__penalty': 'l2'}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_params_ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verificacion de performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred_proba = model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e5430cbf48>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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90zkkJISHH344w8/94osv6N69O+XLl2fGjBmMGjUq2ywff/wxLVu2pHv3v+8ave666y51E5k1axY//fQTAPfeey/t2rVj7Nix57VLSkri1KlThIaGEh8fT6VKzoVNuyEtA0e3wYloZxiIpaMhtIgz2mdiPDx8GAqVcjuh8YiLO83IkT8wcaJzlFuvXlmmTeuRp7uaesuKQg5LTk7mxx9/pF+/foBz6qhp03MH3atRowYnTpwgLi6OqKgoHn/8ca8++5NPPmH06NGUL1+eXr16eVUUoqKizlt/Ro4fP07bthl/w/n444/Pm+XtwIEDqSO4VqxYkYMHD573vsqVKzNs2DCqVq1KoUKF6NSpU2pxNBmY1wcSTzhDO5yJg2bOuFUUv9wKQh6zdOluJk5cSUhIEE880YYnnmhLgQL5Y3eaP7Yivdd8cB7v8ayHAzk79tGOHTto2rRp6vDXqprpecULOd944MABtm7dSps2bRARQkJCiIqKokGDBhl+zoWeyyxatGjq0N855ciRI8yaNYvt27dTokQJbrvtNj788EPuueeeHF2P34rbBTvmO0cGi4YAAnf+6oOZvUxOSEhISh25tEuXmjz77HX06HEFV15Z3uVkOSt/FoVsduC+cPaawrFjx+jWrRvjx49nyJAh1K9fnyVLlpzTdtu2bYSHh1O0aFHq16/PqlWraNSoUZaf/+mnn3LkyBEiIiIAZ36EGTNm8Nxzz1G6dOlzLvTGxsZSpkwZAOrXr+/VtKAXeqRQvnx59u3bR8WKFdm3bx/lypU7730//PADERERlC1bFoBbbrmFpUuX5v+ioCnOjv7oVme+3Yxmnl3/HhzZAkUqwuU3Qv2+0HwkFHNhAH2TJVXls8/W89hj3zFr1h00b14ZgKeeyqfXw1TVr36aNm2q6W3YsOG813JbkSJFUh//8ccfetlll+mZM2c0Pj5eIyIidMGCBaqqGh8frzfeeKOOGzdOVVXXrFmjNWrU0E2bNqmqanJysr722mvnfX6LFi106dKlqc+3bdumNWrUUFXVOXPmaIcOHfT06dOqqvraa69p3759U9dXo0YN/eabb1LfO2/ePF27du0lbe+wYcP0xRdfVFXVF198UYcPH35em+XLl2u9evX05MmTmpKSon369End7rOuvfZaXbFiRabryQu/W6+kpKiueUd10T9VX+Xvn3frqi4Zef7P4hGqh9arpiS7ndxkYc+eOO3R4xOFZxSe0QcfnON2pIsGrFQv9rGu7+Qv9McfioKqardu3fSDDz5QVdW1a9fqtddeq7Vr19YaNWroM888oykpKalt58yZo1dddZXWqVNH69atq8OGDTvns7Zv366VKlU65z2qqk2aNNHly5erquozzzyjDRo00EaNGuktt9yiBw8eTG23ceNG7dy5s9asWVPr1q2rt99+u+7fv/+StvfQoUPavn17rVmzprZv314PHz6sqqp79uzRG264IbXd008/rVdccYXWr19f77nnHk1ISFBV1S+//FIrV66sYWFhWq5cOe3UqVOG68kLv1uvbJrpFIHvHlD933jb2fu5lJQUnTJllRYv/qLCM1q06Av6zjsrNTk5Jfs351HeFgUbOtvkaXn6d7vpM1g31RlX6Hg0lL0SbvjA7VTmEu3adYy+fWexcKHT7fvGG2sxaVI3qlTJZFhwP2FDZxvjK3uXw/ZvYflzzn0DVw50Xi9z4ZMhmbwnNDSIVav2UqZMYcaN68Idd2TcmSO/sqJgTGZO7IMvOkF8jNNN9KyjW6FyG2g+Clr/x8Ybygc2bTpEjRqlCAkJomLFonz11e00aFCOsmXz/gB2OS3f/DVrFl0/jX/K9VObx7bDn586jw+sdHoOhRWDXgugYMm/2wUXcGYQM37vzJlkXnzxZ55//mdeeKEDw4a1AuC66yJcTuaefFEUChYsyOHDhyldurQVhnxCVTl8+DAFC+bgrFSxm2Ht5IyXHVwFu3+CEjWgVi/n326fOvMIZzbbmPFrK1bs4f77ZxMV5dx4uWdPnMuJ8oZ8URSqVKlCdHT0JY/nY/KWggULUqVKDg0bkJIMn7d35g+o3fv85RE3QqtnoUqbnFmfybPi4xN5+ulFvPHGclJSlBo1SjJlSveAPjpIK18UhdDQ0NSbuozhVOy5k8yvmQC7FzmT0vRaAKXzaG8m43PR0XG0azedv/46QlCQMGxYS8aMuY7ChUPdjpZn5IuiYMw5Vr4Cf34C4Z6jjIRYaDsWKrWCkjXdzWZcValSUSpUCKdQoVCmTeuReney+ZsVBZN/qMJn7WDPL3Dta9D0MbcTmTzg228307BheapWLU5QkDBzZm9KlSpEWJhdK8qIFQXjf/6aA1/3gILpRg5NioekBLj9Z6jY3J1sJs+IiTnJY499x8cfr+OGG2ry7bd3ISJUqBCe/ZsDmBUF418ST8H3D8Bl7aD7zPOXhxSC0MK5HsvkHarKjBlRDBkyn0OH4ilUKISOHS9HFaxzYvasKJi879h2+Osbp9votm/h1CG4fQkUKu12MpPHREfH8dBD3/LNN5sBaN8+gilTunP55SWzeac5y4qCyVtWvQG7Fv39POEw7F0Khcs7Q0y3HQtV2kLJWu5lNHnS8eOnadx4EocPn6JYsQK89lon+vVrYvcuXSArCib3HVwDcTvOfe3IZtjwgTPxTPORUDrNOELhlaBCtuN4mQBXtGgBBg6MZN26g0yY0JXKlf17ADu3+LQoiEgX4C0gGJiqqi+lW14c+BCo6snyqqq+58tMxiWJJ+HPGbBwiHNBuGzjc4eKSEmEytdAx384BcDGEzLZSE5O4c03l1OjRiluusmZ43zMmHYEBYkdHVwCn/2fJyLBwHigIxANrBCR2aqa5q4iBgEbVLW7iJQFNonIR6p6xle5jAvidsEUz4xiDR+A5iOceYftf1xzkdatO0C/frNZsWIv5csXoWPHyylSJIzg4CC3o/k9X34daw5sVdVtACIyA+gJpC0KChQVp6yHA7FAkg8zmdykCnE7YXpdqNQabvsRQgq4ncr4sdOnk3jhhZ954YVfSEpKoUqVYrzzTjeKFAlzO1q+4cuiUBnYneZ5NHB1ujZvA7OBvUBR4HZVTUn/QSIyABgAULWqjU7pN7bPg1k3QcWr4Y6f3U5j/Nxvv0XTr99s1q93xjh76KFIXnrpeooVsy8aOcmXx1oZnRtIPxZyZ2A1UAloDLwtIuddHVLVyaoaqaqRZyeBN35g+1yo3tkKgrlkSUkp3HPPV6xfH0OtWqVYvPg+Jky40QqCD/iyKEQDl6V5XgXniCCtvsCXnilEtwLbgTo+zGRyS9JpWD0eKjRzO4nxYykpzvfIkJAgJk26kX/9qxVr1gzkmmuquZws//Ll6aMVQC0RiQD2AHcAd6VrswvoAPwsIuWBK4BtPsxkcssnLZ1/mw51N4fxS0ePJjB8+PcUKhTKuHE3ANChw+V06HC5y8nyP58VBVVNEpHBwHc4XVLfVdX1IjLQs3wS8CwwXUTW4ZxuGqGqh3yVyeSC5c/Br/92Ht+7DsJsnBlzYWbN+pOHHvqWfftOULBgCCNHtqFSpaLZv9HkCJ92BlfVucDcdK9NSvN4L9DJlxlMLppaA45tg5aj4arHoGAJtxMZP3Lw4EmGDJnHp5+uB6BlyypMm9bDCkIuszuEzKU7cxyW/AuO74ZBsefOZ2yMFz78cC2PPjqf2NhTFC4cyosvdmDQoGZ234ELrCiYSzevj9P9tMv7UMCODsyF+/bbLcTGnuL66y9n8uRuRETYFwu3WFEwl2bpGNj6NfT4Cmrd5HYa4ydSUpSYmJOUL+9ccxo3rgtdutSgT59GNkSFy6womIuz7l1YMtyZ6vKal60gGK9t3nyYBx6Yw9GjCaxc+QChocGULVuEe+9t7HY0g2/vUzD51ZGt8H0/uLwbPLALmg13O5HxA0lJKbz88q80ajSJJUt2sn//CbZsiXU7lknHjhTMhdnyNcy+Gco0gOsnQWghtxMZP7BmzX7uv382f/yxD4B7723E6693plQp+/vJa6woGO+oOj2M9i6D+n2h8zQb5dR4ZezYX3jqqUUkJaVQtWpxJk/uRufONd2OZTJhRcFkTxVmdoRdP8J1b8Jl7a0gGK+VKlWI5OQUBg9uxgsvdKBoURuvKC+zomCyd2y7UxBuWwhVr3M7jcnjTpw4w8qVe2nXrjoA/ftfRbNmlWncuIK7wYxX7EKzydrxPfB+A2d6TCsIJhsLFvxFw4YT6dr1I7ZtOwKAiFhB8CNWFEzm9v0Ok6tAaBG47Qe305g87MiRU/TrN4tOnT5kx46jXHFFGRISbL4sf2Snj8z5oqY7XU5VoXZv6Pp/EGwzW5mMffnlRgYNmsv+/ScoUCCY0aOvZdiwVoSGBrsdzVwEKwrmb6rO9YO1k6DJI3DtayBBdlHZZOqZZ35izJjFALRufRlTp/agTp0yLqcyl8JOH5m/bfoM3rsCDm+Aap0gKNgKgslS7971KVWqEP/97w0sWdLXCkI+kO2Rgog0UNWo3AhjXKLq9C5aMxEiboSbvnY7kcmjdu48ygcfrOGpp65BRKhXryy7dj1GkSJ2ejG/8Ob00SQRCQOmAx+r6lHfRjK5JnYTLBwCe36BpHiI6ApXj3I7lcmDUlKUiRNXMHLkj5w4cYaaNUtx550NAawg5DPZFgVVbSMitYD7gZUi8jvwnqou8Hk64ztrp8CCARBeBbp+BBWvhvCKbqcyedCmTYfo1282v/66G4BeverRvn2Ey6mMr3h1oVlVt4jIU8BKYBzQRJzxbZ9Q1S99GdD4yJEt0GwEtH3RrhuYDCUmJvPqq0sZM2Yxp08nU6FCOOPHd+WWW+q6Hc34ULYXmkXkShF5A9gItAe6q2pdz+M3fJzP5LSTB+BQFKx8xZkhzQqCycT48St44omFnD6dTN++jdmw4WErCAHAmyOFt4EpOEcFp86+qKp7PUcPxp+8WwtCCkOJGtD0n26nMXnYgw82Zf78rTz+eEs6dqzhdhyTS7zpkvqlqv5f2oIgIo8CqOr/+SyZyXmxm535lAfsgn5b7YY0c45fftlFhw4fcPRoAgCFCoUyf/49VhACjDdFoU8Gr92XwzlMbtj1g3OEYMXApHH8+GkGD55L27bvsXDhdl59danbkYyLMj19JCJ3AncBESIyO82iosBhXwczOeiHh5x5lE/uh/r3up3G5CHz52/lwQe/YdeuY4SEBDFyZGueeuoat2MZF2V1TWEpsA8oA7yW5vXjwFpfhjI5aMf3sGYS3DIPyjaCIuXdTmTygMOH4xk69Hs++GANAE2bVmTatB40amSjmQa6TIuCqu4EdgItcy+OyXF/zYGaN0NEF7eTmDzkjz/28cEHayhYMIQxY9oxdGhLQkJs1BuT9emjXzw3rh0HNO0iQFW1mM/TmUuTcAQOr4fKbdxOYvKAkyfPpN593LFjDV55pSM9elxB7dqlXU5m8pJMvxqoahvPv0VVtVian6JWEPzER81h9yKo0d3tJMZFqsp77/2PqlXfZOnS3amvDxvWygqCOY83N6+9JSJ2CslfnDoME8rB+1fC0a1w/xao0MztVMYl27cfoVOnD7n//tnExp5ixgwb29JkzZub1/4A/i0itYGvgE9VdaVvY5mLtnMBnIpxZkoLLggla7qdyLggOTmF8eNXMGrUj8THJ1K6dCHeeqsLd93V0O1oJo/zZkC894H3RaQUcCswVkSqqmotn6cz3tv4ERze6MyFULs3lL3S7UTGJdu2HeGee75k2bJoAO64owFvvdWFcuWKuJzM+IML6W5QE6gDVAf+9OYNItJFRDaJyFYRGZlJm3YislpE1ovI4gvIY86K2wlz73HuQyjXBBo/5HYi46IiRULZtOkwlSoVZdasO/jkk1utIBiveTPJzljgFuAv4DPgWW/mVBCRYGA80BGIBlaIyGxV3ZCmTQlgAtBFVXeJSLmL24wAlnQaplSHYtWg81S30xiXrF17gLp1yxAaGkz58uHMmXMn9eqVpUSJgm5HM37GmyOF7UBLVe2iqu9ewCQ7zYGtqrpNVc8AM4Ce6drchTO20i4AVT3obfCApgq7F8MXXWCCp/fIfRuyfo/Jl06dSmTEiAVcddX7K6DEAAAfpklEQVQ7vPbastTXW7W6zAqCuShZ3adQR1X/BH4HqopI1bTLVfWPbD67MrA7zfNo4Op0bWoDoSLyE87wGW+p6gcZZBkADACoWrVq+sWBJ/4gfNEJyl3lTJBT7ioILex2KpPLlizZSf/+s9myJZagICEu7rTbkUw+kNXpo6E4O+LXMlimOPMpZCWjgfo13fMQoCnQASgELBOR5aq6+Zw3qU4GJgNERkam/4zAoylQsBTctSz7tibfiYs7zciRPzBxotMJsF69srz7bg+uvrqKy8lMfpDVMBcDPA9vUNWEtMtExJvj0mjgsjTPqwB7M2hzSFVPAidFZAnQCNiMyZgqzL3LuahsAs7OnUdp0+Y9oqPjCAkJ4skn2zJqVBsKFPBqEkVjsuXNNYWMxtH1ZmzdFUAtEYkQkTDgDmB2ujazgLYiEiIihXFOL2304rMD17IxsPsnuOMXt5MYF1x2WXFq1ChJZGQl/vhjAM88084KgslRWV1TqIBzXaCQiDTh79NBxYBsT2CrapKIDAa+A4KBd1V1vYgM9CyfpKobRWQ+zqirKcBUVbVbLrOy51do9i+o3NrtJCYXqCqff76BZs0qERFRkqAgYebM3pQoUdAGsDM+kdVXjM44k+lUAV5P8/px4AlvPlxV5wJz0702Kd3zV4BXvPk8gzNRTvMRbqcwuWDv3uM8/PC3zJq1ieuvv5zvv78HEaFMGetUYHwnq2sKZ+9kvlVVv8jFTCYrYcVsLKN8TlV5993/8fjj33Ps2GmKFSvAbbfVczuWCRBZnT66R1U/BKqLyND0y1X19QzeZnzhxF5Y8KAzrpEmQ5CdQ86vtm07wgMPzGHhwu0AdOtWm4kTb6RKFRuY2OSOrPYuZ++LD8+NICYD+1fAX7Nh+XNQuDx0/RCqtINQG7IgPzp2LIGmTSdz9GgCZcoUZty4LtxxRwNEMurdbYxvZHX66B3Pv2NyL45JlZIEa6dA3A647k1o8giIXVjMz4oXL8ijj17Nli2xvPlmZ8qWteJvcp838ym8LCLFRCRURH4UkUMick9uhAto//svbJoBVw6Aqx61gpAPnTmTzH/+s5iZM/8eomT06Gv56KNbrCAY13izp+mkqnFAN5ybzWoDw32ayjgD3TV6GGr3cjuJ8YEVK/bQtOlkRo/+iUGD5hIfnwhgp4qM67wpCqGef7sCn6hqrA/zmNjNsO5d+GUUBIdm3974lfj4RIYN+54WLaYRFXWQGjVK8umnvShc2H7XJm/wphvLHBH5EzgFPCwiZYGEbN5jLtbvL8GhtVD/PucmNZNv/PTTDvr3n81ffx0hKEgYNqwlY8ZcZwXB5CnezLw20jOnQpyqJovISc4fAtvkhF9Hw/r3oPtMqH2r22lMDkpKSmHAgDn89dcRGjYsx7RpPWjWrLLbsYw5j7cd3uvi3K+Qtv15Q1ybS7B9Hiz/D7T/rxWEfCQ5OYXg4CBCQoKYMqU7ixfvZOTINoSFBbsdzZgMeTPz2v8BNYDVQLLnZcWKQs6KWQfVO0OTwW4nMTkgJuYkjz46n2LFCjBpUjcArr22OtdeW93dYMZkw5sjhUignqraPAa+su93WDcFatkRgr9TVWbMiGLIkPkcOhRPkSKhPPNMOypUsHtAjX/wpvdRFFDB10EC1u6fYNZNUKQiRJ43mojxI9HRcfToMYO77vqSQ4fi6dAhgrVrH7KCYPyKN0cKZYANIvI7kDrfn6r28FmqQPHjYFg9Hqp1gjbPQeFybicyF2ny5FUMH76AuLjTFC9egNdf70zfvo3tvgPjd7wpCs/4OkTA2vQZdBgPjR92O4m5RL/8sou4uNP07HkFEybcSKVKRd2OZMxF8aZL6mIRqQbUUtUfPDOkWdeJS3U8Gk7FQA3r3euPkpJS2L//ROropW+80ZkePa7g1lvr2tGB8WvejH30ADATeMfzUmXga1+GCghbZ0HB0lDU+qr7m3XrDtCq1TQ6d/6Q06eTAChdujC9etWzgmD8njcXmgcBrYE4AFXdAtjJ70vxx39h4WCo3sntJOYCnD6dxOjRi7jqqsmsWLGX48dPs337UbdjGZOjvLmmcFpVz5z9BuS5gc26p16M5DMwvy9s+hTavACRw9xOZLz022/R9Os3m/XrYwB4+OFIXnzxeooVK+ByMmNyljdFYbGIPAEUEpGOwMPAHN/Gyqc2fw5/fgyd34V6/7AZ1PzEmDE/MWbMYlShVq1STJ3ag2uuqeZ2LGN8wpvTRyOBGGAd8CAwF3jKl6HypeREWDsZIrpCg75WEPxItWolCAoSRoxozZo1A60gmHzNm95HKcAUEXkfqA/ssbubL9CpWJhQ2nnca4G7WUy2jh5NYPnyaLp0qQnAvfc2okWLKtSpU8blZMb4XqZHCiIySUTqex4Xxxn76APgfyJyZy7l839bvnYKQkgheFyh2vVuJzJZmDXrT+rVG8/NN3/K5s2HAWfiGysIJlBkdfqoraqu9zzuC2xW1YZAU8AG+vfG1tlOL6OGD8DA/W6nMVk4ePAkd9wxk5tu+pR9+07QpEkFrHepCURZnT46k+ZxR+BzAFXdb32xvbTx/5wjg3avQZjd4ZoXqSoffbSORx+dT2zsKYoUCeXFFzvw8MPNCA62ebFN4MmqKBwVkW7AHpz7FPpBapfUQrmQzX/Fx8DmmbBtLnR42wpCHvbUUwt54YVfAOjY8XImT+5O9eolXE5ljHuyKgoPAuNwRkh9TFXPnv/oAHzr62B+a+9yWPkKHFoHDfvZcNh5XJ8+jXjvvdW88EIH7r23kd2RbAKe+FtHosjISF25cqXbMTL3w0NwbIczv3LV69xOY9LZvPkw06ev5vnn26cWgNOnkyhQwLoIm/xNRFapamR27ez/hJxwbAeciXMenzoMNXpYQchjkpJSeP31ZYwe/RMJCUnUq1eWe+65EsAKgjFp2P8NlyIlGebcBlu/glJ1/74h7coB7uYy51izZj/33z+bP/7YBzj3HXTtWsvlVMbkTZkWBRFpCSy3G9WycCjKKQi3fg/VO7qdxqSTkJDEc88tYezYX0lKSqFq1eJMntyNzp1ruh3NmDwrqz539wKrRGSGiNwnIhc8JaeIdBGRTSKyVURGZtGumYgki0ivC12Hq3b9ACVrWUHIoyZMWMHzz/9McnIKjzzSnKioh6wgGJONTI8UVHUggIjUAW4ApnvubF4EzAd+VdXkzN4vIsHAeJx7HKKBFSIyW1U3ZNBuLPDdJW5L7ls93ibJyWNUNfUC8qBBzfj5510MG9aS1q2rupzMGP+Q7d05qvqnqr6hql2A9sAvwG3Ab9m8tTmwVVW3qeoZYAaQ0R70EeAL4OAFJXfTse3w01A4cxzq3+t2GuPx/fd/0bLlNGJjTwHOBeSvvrrdCoIxF+CCbtlU1VOqOldVH/Gia1NlYHea59Ge11KJSGXgZmBSVh8kIgNEZKWIrIyJibmQyL6x4lXYuQBajoZSddxOE/COHDlF376z6Nz5Q377bQ9vvbXc7UjG+C1f9j7K6C6g9Bet3wRGqGpyVjcNqepkYDI49ynkWMKLdSoGavWCJoPdThLwvvxyI4MGzWX//hMUKBDMmDHtGDq0pduxjPFbviwK0cBlaZ5XAfamaxMJzPAUhDJAVxFJUtW8PQf0sW1Q82a3UwS0/ftPMHjwXL74YiMAbdpUZerU7lxxhY1masyluOCi4LkwfIeqfpRN0xVALRGJwBk/6Q7grrQNVDUizedOB77J8wVh+zw4sApK1XY7SUDbsCGGL77YSHh4GGPHXs/AgZEEBdkQFcZcqqzuUygGDMK5DjAbWAAMBobhzK2QZVFQ1SQRGYzTqygYeFdV14vIQM/yLK8j5EkLHnRmT2vYH8o1cTtNwDl6NIESJQoC0L59BG+/fQPdutWmWjUbwM6YnJLp2EciMgs4AizDGQSvJBAGPKqqq3MtYTqujH10+hgsfx5Wvw3t3oBGD+bu+gNcSooyfvzvPPnkQr799i7atrXpMI25UDkx9tHlnkl1EJGpwCGgqqoez6GMeV/8QednRhunMFw3Dur1cTtVQPnzz0P07z+bX391OrLNmbPZioIxPpRVUUg8+8DTO2h7QBUEgK97OkWhVF24fQkEh7qdKGAkJibzyitLGTNmMWfOJFOhQjgTJ97ITTdZF2BjfCmrotBIROL4u2tpoTTPVVWL+Tydm86cgH3L4R//g3KN3U4TULZsOUzv3jNZvdqZwuP++xvz6qudKFnS5nYyxteyGuYiODeD5Dmnj4EEQ9kr3U4ScEqUKEh0dBzVq5dgypTuXH/95W5HMiZgZNX7qCAwEKgJrMXpPZSUW8HyhCLlQWye3tzw22/RNGlSkbCwYMqWLcK8eXdTp04ZwsPD3I5mTEDJao/3Ps7NZeuArsBruZIoz1BISnA7RL53/PhpBg+eS4sW03jppV9SX4+MrGQFwRgXZHVNoV6a3kfTgN9zJ1IecGQLfHotJJ50O0m+Nn/+Vh588Bt27TpGSEgQNj2yMe7ztvdRUkBNaH5wNYRXht6L3E6SLx0+HM/Qod/zwQdrAGjatCLTpvWgUaMLnrLDGJPDsioKjT29jcDpcRRYvY+KV4dSV7idIt/ZseMoV189lYMHT1KwYAj/+U87/vnPloSE2LUbY/KCrIrCGlUNvLEcEk86cyWUqe92knypWrXiNGxYjsTEFKZM6U7t2qXdjmSMSSOrouD+ENVu2Pc7nIiGm/L2uHz+QlWZPn01bdtWo2bNUogIM2f2plixAjaAnTF5UFZFoZyIDM1soaq+7oM8ecNl7aB8U7dT+L3t248wYMA3/PDDNtq1q86PP/YhKEhSB7UzxuQ9WRWFYCCcjCfLMSZTyckpvP327zzxxELi4xMpXboQ/fs3sd5FxviBrIrCPlX9T64lMfnChg0x9O8/m2XLogG4444GvPVWF8qVK+JyMmOMN7IqCva9zlyQY8cSaNFiKsePn6FSpaJMnHgjPXpYDy5j/ElWRaFDrqXISxYNgRAbeO1iFC9ekJEj27Bjx1FeeaUjxYvbtQNj/E1WA+LF5maQPOHwRjgUBffk8iQ+furUqUSeeeYnGjeuwJ13NgRg1Kg2BNSNjsbkMxc8R3O+pAo/j4IVY51eR6UbuJ0oz1u8eAf9+89h69ZYypUrwk031aFQoVArCMb4OSsKAEc2OwXhxhlQ53a30+RpcXGnGTFiAZMmrQKgfv2yTJvWg0KFbAIiY/IDKwrgnDIqVccKQjbmzt3Cgw9+Q3R0HKGhQTz5ZFtGjWpLWFhgT71hTH5iReHoNvj131DGJtPJSmJiMkOHfkd0dBzNm1dm2rQeNGhQzu1YxpgcZkXh+G4IDoPr3nQ7SZ6jqiQmphAWFkxoaDDTpvXgt9/28OijVxMcbAPYGZMfWVE4vMEpCuEV3U6Sp+zZE8fDD8+lbNnCTJ3aA4DWravSunVVl5MZY3zJvu4tGgIlarqdIs9QVaZMWUW9ehOYPXsTM2du4MCBE27HMsbkEjtSKFYNWtloHgB//RXLAw/MYdGiHQB0716biRNvpHz5cHeDGWNyjRUFg6ry5pvLefLJhZw6lUSZMoX5739v4Pbb69t9B8YEGCsKBhEhKuogp04lcdddDXnrrS6UKVPY7VjGGBdYUQhQZ84ks2dPHBERJQF49dVO3HprPbp2reVyMmOMmwL7QnP0Ejj6V8ANgLdixR6aNp3MDTd8REJCEgAlSxaygmCMCfCisPNHqNgSilZ2O0muiI9PZNiw72nRYhpRUQdJTlZ27z7mdixjTB7i06IgIl1EZJOIbBWRkRksv1tE1np+lopII1/mOc//xkHV9rm6SrcsWrSdhg0n8tprywAYPrwVa9YMpFat0i4nM8bkJT67piAiwcB4oCMQDawQkdmquiFNs+3Atap6RERuACYDV/sq0zlOxcLpo9CwX66szk0jR/7A2LG/AtCwYTnefbcnkZGVXE5ljMmLfHmhuTmwVVW3AYjIDKAnkFoUVHVpmvbLgSo+zHOulEQoXA6KR+TaKt3SoEE5QkOD+Pe/r2HEiDY2gJ0xJlO+LAqVgd1pnkeT9VFAP2BeRgtEZAAwAKBqVRtmITsxMSdZunQ3PXvWAeDuuxvSuvVlqT2NjDEmM768ppDRXU+aYUOR63CKwoiMlqvqZFWNVNXIsmXL5mDE/EVV+fjjddStO57evWeycWMM4NyHYAXBGOMNXx4pRAOXpXleBdibvpGIXAlMBW5Q1cM+zHOu5c9C/MFcW52v7d59jIce+pZvv90CQIcOETbxjTHmgvmyKKwAaolIBLAHuAO4K20DEakKfAn8Q1U3+zDL+favgDbP5+oqfSElxRnAbvjwBRw/fobixQvw+uud6du3sQ1RYYy5YD4rCqqaJCKDge+AYOBdVV0vIgM9yycBTwOlgQmeHViSqkb6KtPf4VJg/+9w/SSfr8rXRoxYwKuvOt1Mb7qpDuPHd6VSpaIupzLG+CtRzfA0f54VGRmpK1euvLQPSUmGN8NgaHLOhHLR1q2xdOz4f7z88vX06lXPjg6MMRkSkVXefOkO7Dua/dDatQd47LH5nC3mNWuWYsuWR7jtNhvR1Bhz6WxAPD9x+nQSzz//My+++AtJSSk0bVqRf/zDuQE8JMRquzEmZwRmUTix17mu4CeWL4+mX7/ZbNjgdDEdNKgZN91Ux+VUxpj8KDCLwvf9oEjen5P55MkzPPXUQt566zdUoXbt0kyd2p22bau5Hc0Yk08FZlHYuQB6LXA7RbbeeWcVb775G8HBwvDhrRg9uh0FCwbmr8wYkzsCcw9ToASUb+p2igypauoF48GDm7Nq1T4ef7wlV12V949sjDH+L/CuUCaeckZHzYO+/vpPmjR5h0OH4gEICwvmo49usYJgjMk1gVcUDnjucQjLOzd4HThwgt69P+fmmz9lzZoDTJiwwu1IxpgAFZinjyq3gSD3N11V+fDDtTz22HfExp6iSJFQXnrpeh5+uJnb0YwxAcr9PWOA2rXrGAMHfsO8eVsB6NSpBu+8043q1Uu4nMwYE8gCryjsXQpnTridgh07jjJv3lZKlCjIG2905t57G9kdycYY1wVeUYg/CBWbu7LqmJiTlC1bBIBrrqnGtGk96Nq1FhUqhLuSxxhj0gu8C80IlKiVq2tMSkph7NhfqFr1TRYu3J76+v33N7GCYIzJUwKwKOSu1av3c/XVUxk58kcSEpLOKQrGGJPXBN7po1ySkJDEs88uZuzYX0lOVqpVK87kyd3p1KmG29GMMSZTVhR8YMOGGG699TP+/PMQIvDII8154YUOhIeHuR3NGGOyZEXBBypUCCc29hR16pRh6tTutG5d1e1IxhjjFSsKOeSnn3bQsmUVChQIoVSpQixY8A9q1y5tA9gZY/yKXWi+RLGxp+jbdxbXXfc+zz//c+rrV15Z3gqCMcbv2F7rEnzxxQYGDZrLgQMnKVAgmOLFC7gdyRhjLkngHSmsfvuSP2L//hP06vUZvXp9zoEDJ2nbtipr1gzk8cdb5UBAY4xxT2AdKRzbDqFFoMngi/6IbduOEBk5mSNHEggPD2Ps2OsZODCSoCAbosIY4/8CqygkJUDhchBS8KI/IiKiBM2bV0ZEeOedblStWjwHAxpjjLsCqyhchJQUZfz43+nUqQZXXFEGEWHmzN4UKRJqA9gZY/KdwLumcAE2boyhbdv3GDJkPv37z0FVAQgPD7OCYIzJlwLrSGHn93BiT7bNEhOTeeWVpYwZs5gzZ5KpWDGcxx9vaYXAGJPvBVZRSDwF9e/Lsskff+yjX7/ZrF69H4B+/Zrw6qudKFHi4q9DGGOMvwisogAQUjjTRUePJnDttdM5ceIMERElmDKlOx06XJ6L4Ywxxl2BVRR2L4KyV2a6uESJgowefS179sTx3HPtKVLEBrAzxgSWwCoK8QegQrPUp8ePn2bkyB+4+uoq9OnTCIBhw+wGNGNM4Aqs3kchhSG8CgDz5m2hfv0JTJiwkuHDF5CQkORyOGOMcZ9Pi4KIdBGRTSKyVURGZrBcRGScZ/laEbnKl3kAjsUl0KfPV3Tt+jG7d8cRGVmJBQv+YYPXGWMMPjx9JCLBwHigIxANrBCR2aq6IU2zG4Banp+rgYmef3Ocnj6O7FtGz+e/ZfGGYhQsGMKzz17HY4+1ICQksA6YjDEmM778etwc2Kqq2wBEZAbQE0hbFHoCH6hzV9hyESkhIhVVdV9Oh0lKOEn86cIs3lCMa6+txtSpPahZs1ROr8YYY/yaL78iVwZ2p3ke7XntQtsgIgNEZKWIrIyJibmoMKGFi3O4/hgmTbqRhQvvtYJgjDEZ8OWRQka3/+pFtEFVJwOTASIjI89b7pXQQlzecxgPXtSbjTEmMPjySCEauCzN8yrA3otoY4wxJpf4siisAGqJSISIhAF3ALPTtZkN9PH0QmoBHPPF9QRjjDHe8dnpI1VNEpHBwHdAMPCuqq4XkYGe5ZOAuUBXYCsQD/T1VR5jjDHZ82nnfFWdi7PjT/vapDSPFRjkywzGGGO8Zx30jTHGpLKiYIwxJpUVBWOMMamsKBhjjEklZ+cd9hciEgPsvMi3lwEO5WAcf2DbHBhsmwPDpWxzNVUtm10jvysKl0JEVqpqpNs5cpNtc2CwbQ4MubHNdvrIGGNMKisKxhhjUgVaUZjsdgAX2DYHBtvmwODzbQ6oawrGGGOyFmhHCsYYY7JgRcEYY0yqfFkURKSLiGwSka0iMjKD5SIi4zzL14rIVW7kzElebPPdnm1dKyJLRaSRGzlzUnbbnKZdMxFJFpFeuZnPF7zZZhFpJyKrRWS9iCzO7Yw5zYu/7eIiMkdE1ni22a9HWxaRd0XkoIhEZbLct/svVc1XPzjDdP8FXA6EAWuAeunadAX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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba[:,1])\n",
    "auc_score = auc(fpr, tpr)\n",
    "\n",
    "plt.plot([0, 1], [0, 1], color='navy', linestyle='--', lw = 2)\n",
    "plt.plot(fpr, tpr, color = 'darkorange', lw = 1)\n",
    "plt.xlabel('1 - Specificty')\n",
    "plt.ylabel('TPR / Sensitivity')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(['Baseline', f'ROC AUC = {auc_score:.2f}'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
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         "name": "",
         "orientation": "v",
         "showlegend": false,
         "stackgroup": "1",
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "fig = px.area(\n",
    "    x = fpr,\n",
    "    y = tpr,\n",
    "    title='ROC Curve',\n",
    "    labels=dict(x='False Positive Rate', y='True Positive Rate'),\n",
    "    width=700,\n",
    "    height=500,\n",
    ")\n",
    "fig.add_shape(\n",
    "    type='line', line=dict(dash='dash'),\n",
    "    x0=0, x1=1, y0=0, y1=1\n",
    ")\n",
    "fig.update_yaxes(scaleanchor=\"x\", scaleratio=1)\n",
    "fig.update_xaxes(constrain='domain')\n",
    "fig.show()\n",
    "\n",
    "fig.write_image(\"roc-auc-final.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {},
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   ],
   "source": [
    "import plotly.express as px\n",
    "df = pd.DataFrame({\n",
    "    'False Positive Rate': fpr,\n",
    "    'True Positive Rate': tpr\n",
    "}, index=thresholds)\n",
    "df.index.name = \"Thresholds\"\n",
    "df.columns.name = \"Rate\"\n",
    "\n",
    "fig = px.line(\n",
    "    df,\n",
    "    title='TPR and FPR at every threshold',\n",
    "    width=500,\n",
    "    height=500,\n",
    ")\n",
    "fig.update_yaxes(scaleanchor=\"x\", scaleratio=1)\n",
    "fig.update_xaxes(range=[0, 1], constrain='domain')\n",
    "\n",
    "fig.show()\n",
    "\n",
    "fig.write_image(\"tpr-vs-fpr.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.63988724, 0.0584919 ],\n",
       "       [0.17054264, 0.13107822]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred, normalize = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30162085976039466"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.sum() / y_test.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e53c786648>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test, y_pred, normalize = 'all'), annot = True, cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e500049848>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "clf_report  = classification_report(y_pred, y_test, output_dict=True)\n",
    "sns.heatmap(pd.DataFrame(clf_report).iloc[:-1, :].T, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47503936720816287"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_loss(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4831879939382996"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_loss(y_train, model.predict_proba(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}