07-MIE_departamentos_concreto.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70c2e060",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# funciones desarrolladas\n",
    "from functions.cargar_data import cargar_dd_deptos\n",
    "from functions.modelos import print_scores_simple\n",
    "from functions.graficas import plot_gt\n",
    "\n",
    "pd.set_option('display.max_columns', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fb692f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cod                               int64\n",
       "depto_origen                      int64\n",
       "depto_destino                     int64\n",
       "personas_mig                      int64\n",
       "personas_mig_18_25                int64\n",
       "personas_mig_26_35                int64\n",
       "personas_mig_100k                 int64\n",
       "pob_origen                        int64\n",
       "pob_destino                       int64\n",
       "dist                              int64\n",
       "dist_km                           int64\n",
       "pbi_origen                        int64\n",
       "pbi_porcen_ori                  float64\n",
       "pbi_destino                       int64\n",
       "pbi_porcen_des                  float64\n",
       "pbi_origen_millardos            float64\n",
       "pbi_destino_millardos           float64\n",
       "largo_limite                    float64\n",
       "dummy_limit                        bool\n",
       "empresas_origen                   int64\n",
       "empresas_destino                  int64\n",
       "menores_25_origen                 int64\n",
       "menores_25_destino                int64\n",
       "pob_destino_k                   float64\n",
       "pob_origen_k                    float64\n",
       "nom_depto_orig                   object\n",
       "nom_depto_des                    object\n",
       "edad_prom_ori                   float64\n",
       "porc_ocupados_ori               float64\n",
       "edad_prom_des                   float64\n",
       "porc_ocupados_des               float64\n",
       "tasa_desemp_mujeres_2011_ori    float64\n",
       "tasa_desemp_varones_2011_ori    float64\n",
       "tasa_desemp_total_2011_ori      float64\n",
       "tasa_desemp_mujeres_2011_des    float64\n",
       "tasa_desemp_varones_2011_des    float64\n",
       "tasa_desemp_total_2011_des      float64\n",
       "log_pbi_destino                 float64\n",
       "log_dist_km                     float64\n",
       "log_pbi_origen_millardos        float64\n",
       "log_pbi_destino_millardos       float64\n",
       "log_pob_destino_k               float64\n",
       "log_pob_origen_k                float64\n",
       "log_edad_prom_des               float64\n",
       "log_porc_ocupados_des           float64\n",
       "largo_limite_km                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# carga datos diádicos\n",
    "dd_deptos = cargar_dd_deptos()\n",
    "dd_deptos.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f2af325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\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>cod</th>\n",
       "      <th>depto_origen</th>\n",
       "      <th>depto_destino</th>\n",
       "      <th>personas_mig</th>\n",
       "      <th>personas_mig_18_25</th>\n",
       "      <th>personas_mig_26_35</th>\n",
       "      <th>personas_mig_100k</th>\n",
       "      <th>pob_origen</th>\n",
       "      <th>pob_destino</th>\n",
       "      <th>dist</th>\n",
       "      <th>dist_km</th>\n",
       "      <th>pbi_origen</th>\n",
       "      <th>pbi_porcen_ori</th>\n",
       "      <th>pbi_destino</th>\n",
       "      <th>pbi_porcen_des</th>\n",
       "      <th>pbi_origen_millardos</th>\n",
       "      <th>pbi_destino_millardos</th>\n",
       "      <th>largo_limite</th>\n",
       "      <th>dummy_limit</th>\n",
       "      <th>empresas_origen</th>\n",
       "      <th>empresas_destino</th>\n",
       "      <th>menores_25_origen</th>\n",
       "      <th>menores_25_destino</th>\n",
       "      <th>pob_destino_k</th>\n",
       "      <th>pob_origen_k</th>\n",
       "      <th>nom_depto_orig</th>\n",
       "      <th>nom_depto_des</th>\n",
       "      <th>edad_prom_ori</th>\n",
       "      <th>porc_ocupados_ori</th>\n",
       "      <th>edad_prom_des</th>\n",
       "      <th>porc_ocupados_des</th>\n",
       "      <th>tasa_desemp_mujeres_2011_ori</th>\n",
       "      <th>tasa_desemp_varones_2011_ori</th>\n",
       "      <th>tasa_desemp_total_2011_ori</th>\n",
       "      <th>tasa_desemp_mujeres_2011_des</th>\n",
       "      <th>tasa_desemp_varones_2011_des</th>\n",
       "      <th>tasa_desemp_total_2011_des</th>\n",
       "      <th>log_pbi_destino</th>\n",
       "      <th>log_dist_km</th>\n",
       "      <th>log_pbi_origen_millardos</th>\n",
       "      <th>log_pbi_destino_millardos</th>\n",
       "      <th>log_pob_destino_k</th>\n",
       "      <th>log_pob_origen_k</th>\n",
       "      <th>log_edad_prom_des</th>\n",
       "      <th>log_porc_ocupados_des</th>\n",
       "      <th>largo_limite_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>914</td>\n",
       "      <td>114</td>\n",
       "      <td>247</td>\n",
       "      <td>891</td>\n",
       "      <td>1318755</td>\n",
       "      <td>73377</td>\n",
       "      <td>610726</td>\n",
       "      <td>611</td>\n",
       "      <td>465848031</td>\n",
       "      <td>50.3</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>465.848</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>3124</td>\n",
       "      <td>55</td>\n",
       "      <td>456072</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>1318.755</td>\n",
       "      <td>MONTEVIDEO</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>37.481048</td>\n",
       "      <td>70.395702</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.415097</td>\n",
       "      <td>6.143859</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>7.184443</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>70</td>\n",
       "      <td>520173</td>\n",
       "      <td>73377</td>\n",
       "      <td>601915</td>\n",
       "      <td>602</td>\n",
       "      <td>86306492</td>\n",
       "      <td>9.3</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>86.306</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>563</td>\n",
       "      <td>55</td>\n",
       "      <td>197993</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>520.173</td>\n",
       "      <td>CANELONES</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>35.406640</td>\n",
       "      <td>71.045180</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.400257</td>\n",
       "      <td>4.457899</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>6.254161</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>402</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>84698</td>\n",
       "      <td>73377</td>\n",
       "      <td>406694</td>\n",
       "      <td>407</td>\n",
       "      <td>17048887</td>\n",
       "      <td>1.8</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>17.049</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>88</td>\n",
       "      <td>55</td>\n",
       "      <td>33360</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>84.698</td>\n",
       "      <td>CERRO LARGO</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>35.614749</td>\n",
       "      <td>65.582039</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.008813</td>\n",
       "      <td>2.836092</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>4.439092</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>502</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>123203</td>\n",
       "      <td>73377</td>\n",
       "      <td>556292</td>\n",
       "      <td>556</td>\n",
       "      <td>43788749</td>\n",
       "      <td>4.7</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>43.789</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>43030</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>123.203</td>\n",
       "      <td>COLONIA</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>37.632420</td>\n",
       "      <td>76.058415</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.320768</td>\n",
       "      <td>3.779383</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>4.813833</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>602</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>57084</td>\n",
       "      <td>73377</td>\n",
       "      <td>446123</td>\n",
       "      <td>446</td>\n",
       "      <td>13023942</td>\n",
       "      <td>1.4</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>13.024</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>40</td>\n",
       "      <td>55</td>\n",
       "      <td>22663</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>57.084</td>\n",
       "      <td>DURAZNO</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>35.093021</td>\n",
       "      <td>67.284108</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>13.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.100319</td>\n",
       "      <td>2.566794</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>4.044524</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cod  depto_origen  depto_destino  personas_mig  personas_mig_18_25  \\\n",
       "0  102             1              2           914                 114   \n",
       "1  302             3              2            74                   9   \n",
       "2  402             4              2            15                   0   \n",
       "3  502             5              2            28                   7   \n",
       "4  602             6              2            32                   0   \n",
       "\n",
       "   personas_mig_26_35  personas_mig_100k  pob_origen  pob_destino    dist  \\\n",
       "0                 247                891     1318755        73377  610726   \n",
       "1                  22                 70      520173        73377  601915   \n",
       "2                   6                 14       84698        73377  406694   \n",
       "3                   8                 25      123203        73377  556292   \n",
       "4                  10                 29       57084        73377  446123   \n",
       "\n",
       "   dist_km  pbi_origen  pbi_porcen_ori  pbi_destino  pbi_porcen_des  \\\n",
       "0      611   465848031            50.3     14214980             1.5   \n",
       "1      602    86306492             9.3     14214980             1.5   \n",
       "2      407    17048887             1.8     14214980             1.5   \n",
       "3      556    43788749             4.7     14214980             1.5   \n",
       "4      446    13023942             1.4     14214980             1.5   \n",
       "\n",
       "   pbi_origen_millardos  pbi_destino_millardos  largo_limite  dummy_limit  \\\n",
       "0               465.848                 14.215        0.0001        False   \n",
       "1                86.306                 14.215        0.0001        False   \n",
       "2                17.049                 14.215        0.0001        False   \n",
       "3                43.789                 14.215        0.0001        False   \n",
       "4                13.024                 14.215        0.0001        False   \n",
       "\n",
       "   empresas_origen  empresas_destino  menores_25_origen  menores_25_destino  \\\n",
       "0             3124                55             456072               30880   \n",
       "1              563                55             197993               30880   \n",
       "2               88                55              33360               30880   \n",
       "3               60                55              43030               30880   \n",
       "4               40                55              22663               30880   \n",
       "\n",
       "   pob_destino_k  pob_origen_k nom_depto_orig nom_depto_des  edad_prom_ori  \\\n",
       "0         73.377      1318.755     MONTEVIDEO       ARTIGAS      37.481048   \n",
       "1         73.377       520.173      CANELONES       ARTIGAS      35.406640   \n",
       "2         73.377        84.698    CERRO LARGO       ARTIGAS      35.614749   \n",
       "3         73.377       123.203        COLONIA       ARTIGAS      37.632420   \n",
       "4         73.377        57.084        DURAZNO       ARTIGAS      35.093021   \n",
       "\n",
       "   porc_ocupados_ori  edad_prom_des  porc_ocupados_des  \\\n",
       "0          70.395702      33.966911          63.540489   \n",
       "1          71.045180      33.966911          63.540489   \n",
       "2          65.582039      33.966911          63.540489   \n",
       "3          76.058415      33.966911          63.540489   \n",
       "4          67.284108      33.966911          63.540489   \n",
       "\n",
       "   tasa_desemp_mujeres_2011_ori  tasa_desemp_varones_2011_ori  \\\n",
       "0                           7.4                           5.1   \n",
       "1                           8.1                           4.3   \n",
       "2                           3.8                           1.7   \n",
       "3                           4.1                           2.5   \n",
       "4                          13.1                           5.8   \n",
       "\n",
       "   tasa_desemp_total_2011_ori  tasa_desemp_mujeres_2011_des  \\\n",
       "0                         6.2                          11.5   \n",
       "1                         6.0                          11.5   \n",
       "2                         2.6                          11.5   \n",
       "3                         3.2                          11.5   \n",
       "4                         8.9                          11.5   \n",
       "\n",
       "   tasa_desemp_varones_2011_des  tasa_desemp_total_2011_des  log_pbi_destino  \\\n",
       "0                           5.4                         8.1        16.469807   \n",
       "1                           5.4                         8.1        16.469807   \n",
       "2                           5.4                         8.1        16.469807   \n",
       "3                           5.4                         8.1        16.469807   \n",
       "4                           5.4                         8.1        16.469807   \n",
       "\n",
       "   log_dist_km  log_pbi_origen_millardos  log_pbi_destino_millardos  \\\n",
       "0     6.415097                  6.143859                   2.654298   \n",
       "1     6.400257                  4.457899                   2.654298   \n",
       "2     6.008813                  2.836092                   2.654298   \n",
       "3     6.320768                  3.779383                   2.654298   \n",
       "4     6.100319                  2.566794                   2.654298   \n",
       "\n",
       "   log_pob_destino_k  log_pob_origen_k  log_edad_prom_des  \\\n",
       "0           4.295611          7.184443           3.525387   \n",
       "1           4.295611          6.254161           3.525387   \n",
       "2           4.295611          4.439092           3.525387   \n",
       "3           4.295611          4.813833           3.525387   \n",
       "4           4.295611          4.044524           3.525387   \n",
       "\n",
       "   log_porc_ocupados_des  largo_limite_km  \n",
       "0               4.151677     1.000000e-07  \n",
       "1               4.151677     1.000000e-07  \n",
       "2               4.151677     1.000000e-07  \n",
       "3               4.151677     1.000000e-07  \n",
       "4               4.151677     1.000000e-07  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_deptos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b116021b",
   "metadata": {},
   "source": [
    "## 1. Modelos con Montevideo\n",
    "\n",
    "### 1.1. **Modelo restringido en origen**\n",
    "\n",
    "\n",
    "- Logaritmo de la población en destino\n",
    "\n",
    "- Logaritmo de la edad promedio en destino\n",
    "\n",
    "- Logaritmo del PBI en destino en millardos\n",
    "\n",
    "- Logaritmo del porcentaje de desocupados en destino\n",
    "\n",
    "- Logaritmo de la distancia en kilómetros\n",
    "\n",
    "\n",
    "Devuelve un R-cuadrado superior a las demás combinaciones de variables independientes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91a3c22d",
   "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>cod</th>\n",
       "      <th>depto_origen</th>\n",
       "      <th>depto_destino</th>\n",
       "      <th>personas_mig</th>\n",
       "      <th>personas_mig_18_25</th>\n",
       "      <th>personas_mig_26_35</th>\n",
       "      <th>personas_mig_100k</th>\n",
       "      <th>pob_origen</th>\n",
       "      <th>pob_destino</th>\n",
       "      <th>dist</th>\n",
       "      <th>dist_km</th>\n",
       "      <th>pbi_origen</th>\n",
       "      <th>pbi_porcen_ori</th>\n",
       "      <th>pbi_destino</th>\n",
       "      <th>pbi_porcen_des</th>\n",
       "      <th>pbi_origen_millardos</th>\n",
       "      <th>pbi_destino_millardos</th>\n",
       "      <th>largo_limite</th>\n",
       "      <th>dummy_limit</th>\n",
       "      <th>empresas_origen</th>\n",
       "      <th>empresas_destino</th>\n",
       "      <th>menores_25_origen</th>\n",
       "      <th>menores_25_destino</th>\n",
       "      <th>pob_destino_k</th>\n",
       "      <th>pob_origen_k</th>\n",
       "      <th>nom_depto_orig</th>\n",
       "      <th>nom_depto_des</th>\n",
       "      <th>edad_prom_ori</th>\n",
       "      <th>porc_ocupados_ori</th>\n",
       "      <th>edad_prom_des</th>\n",
       "      <th>porc_ocupados_des</th>\n",
       "      <th>tasa_desemp_mujeres_2011_ori</th>\n",
       "      <th>tasa_desemp_varones_2011_ori</th>\n",
       "      <th>tasa_desemp_total_2011_ori</th>\n",
       "      <th>tasa_desemp_mujeres_2011_des</th>\n",
       "      <th>tasa_desemp_varones_2011_des</th>\n",
       "      <th>tasa_desemp_total_2011_des</th>\n",
       "      <th>log_pbi_destino</th>\n",
       "      <th>log_dist_km</th>\n",
       "      <th>log_pbi_origen_millardos</th>\n",
       "      <th>log_pbi_destino_millardos</th>\n",
       "      <th>log_pob_destino_k</th>\n",
       "      <th>log_pob_origen_k</th>\n",
       "      <th>log_edad_prom_des</th>\n",
       "      <th>log_porc_ocupados_des</th>\n",
       "      <th>largo_limite_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>914</td>\n",
       "      <td>114</td>\n",
       "      <td>247</td>\n",
       "      <td>891</td>\n",
       "      <td>1318755</td>\n",
       "      <td>73377</td>\n",
       "      <td>610726</td>\n",
       "      <td>611</td>\n",
       "      <td>465848031</td>\n",
       "      <td>50.3</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>465.848</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>3124</td>\n",
       "      <td>55</td>\n",
       "      <td>456072</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>1318.755</td>\n",
       "      <td>MONTEVIDEO</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>37.481048</td>\n",
       "      <td>70.395702</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.415097</td>\n",
       "      <td>6.143859</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>7.184443</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>70</td>\n",
       "      <td>520173</td>\n",
       "      <td>73377</td>\n",
       "      <td>601915</td>\n",
       "      <td>602</td>\n",
       "      <td>86306492</td>\n",
       "      <td>9.3</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>86.306</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>563</td>\n",
       "      <td>55</td>\n",
       "      <td>197993</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>520.173</td>\n",
       "      <td>CANELONES</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>35.406640</td>\n",
       "      <td>71.045180</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.400257</td>\n",
       "      <td>4.457899</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>6.254161</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>402</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>84698</td>\n",
       "      <td>73377</td>\n",
       "      <td>406694</td>\n",
       "      <td>407</td>\n",
       "      <td>17048887</td>\n",
       "      <td>1.8</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>17.049</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>88</td>\n",
       "      <td>55</td>\n",
       "      <td>33360</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>84.698</td>\n",
       "      <td>CERRO LARGO</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>35.614749</td>\n",
       "      <td>65.582039</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.008813</td>\n",
       "      <td>2.836092</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>4.439092</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>502</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>123203</td>\n",
       "      <td>73377</td>\n",
       "      <td>556292</td>\n",
       "      <td>556</td>\n",
       "      <td>43788749</td>\n",
       "      <td>4.7</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>43.789</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>43030</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>123.203</td>\n",
       "      <td>COLONIA</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>37.632420</td>\n",
       "      <td>76.058415</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.320768</td>\n",
       "      <td>3.779383</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>4.813833</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>602</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>57084</td>\n",
       "      <td>73377</td>\n",
       "      <td>446123</td>\n",
       "      <td>446</td>\n",
       "      <td>13023942</td>\n",
       "      <td>1.4</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>13.024</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>40</td>\n",
       "      <td>55</td>\n",
       "      <td>22663</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>57.084</td>\n",
       "      <td>DURAZNO</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>35.093021</td>\n",
       "      <td>67.284108</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>13.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.100319</td>\n",
       "      <td>2.566794</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>4.044524</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cod  depto_origen  depto_destino  personas_mig  personas_mig_18_25  \\\n",
       "0  102             1              2           914                 114   \n",
       "1  302             3              2            74                   9   \n",
       "2  402             4              2            15                   0   \n",
       "3  502             5              2            28                   7   \n",
       "4  602             6              2            32                   0   \n",
       "\n",
       "   personas_mig_26_35  personas_mig_100k  pob_origen  pob_destino    dist  \\\n",
       "0                 247                891     1318755        73377  610726   \n",
       "1                  22                 70      520173        73377  601915   \n",
       "2                   6                 14       84698        73377  406694   \n",
       "3                   8                 25      123203        73377  556292   \n",
       "4                  10                 29       57084        73377  446123   \n",
       "\n",
       "   dist_km  pbi_origen  pbi_porcen_ori  pbi_destino  pbi_porcen_des  \\\n",
       "0      611   465848031            50.3     14214980             1.5   \n",
       "1      602    86306492             9.3     14214980             1.5   \n",
       "2      407    17048887             1.8     14214980             1.5   \n",
       "3      556    43788749             4.7     14214980             1.5   \n",
       "4      446    13023942             1.4     14214980             1.5   \n",
       "\n",
       "   pbi_origen_millardos  pbi_destino_millardos  largo_limite  dummy_limit  \\\n",
       "0               465.848                 14.215        0.0001        False   \n",
       "1                86.306                 14.215        0.0001        False   \n",
       "2                17.049                 14.215        0.0001        False   \n",
       "3                43.789                 14.215        0.0001        False   \n",
       "4                13.024                 14.215        0.0001        False   \n",
       "\n",
       "   empresas_origen  empresas_destino  menores_25_origen  menores_25_destino  \\\n",
       "0             3124                55             456072               30880   \n",
       "1              563                55             197993               30880   \n",
       "2               88                55              33360               30880   \n",
       "3               60                55              43030               30880   \n",
       "4               40                55              22663               30880   \n",
       "\n",
       "   pob_destino_k  pob_origen_k nom_depto_orig nom_depto_des  edad_prom_ori  \\\n",
       "0         73.377      1318.755     MONTEVIDEO       ARTIGAS      37.481048   \n",
       "1         73.377       520.173      CANELONES       ARTIGAS      35.406640   \n",
       "2         73.377        84.698    CERRO LARGO       ARTIGAS      35.614749   \n",
       "3         73.377       123.203        COLONIA       ARTIGAS      37.632420   \n",
       "4         73.377        57.084        DURAZNO       ARTIGAS      35.093021   \n",
       "\n",
       "   porc_ocupados_ori  edad_prom_des  porc_ocupados_des  \\\n",
       "0          70.395702      33.966911          63.540489   \n",
       "1          71.045180      33.966911          63.540489   \n",
       "2          65.582039      33.966911          63.540489   \n",
       "3          76.058415      33.966911          63.540489   \n",
       "4          67.284108      33.966911          63.540489   \n",
       "\n",
       "   tasa_desemp_mujeres_2011_ori  tasa_desemp_varones_2011_ori  \\\n",
       "0                           7.4                           5.1   \n",
       "1                           8.1                           4.3   \n",
       "2                           3.8                           1.7   \n",
       "3                           4.1                           2.5   \n",
       "4                          13.1                           5.8   \n",
       "\n",
       "   tasa_desemp_total_2011_ori  tasa_desemp_mujeres_2011_des  \\\n",
       "0                         6.2                          11.5   \n",
       "1                         6.0                          11.5   \n",
       "2                         2.6                          11.5   \n",
       "3                         3.2                          11.5   \n",
       "4                         8.9                          11.5   \n",
       "\n",
       "   tasa_desemp_varones_2011_des  tasa_desemp_total_2011_des  log_pbi_destino  \\\n",
       "0                           5.4                         8.1        16.469807   \n",
       "1                           5.4                         8.1        16.469807   \n",
       "2                           5.4                         8.1        16.469807   \n",
       "3                           5.4                         8.1        16.469807   \n",
       "4                           5.4                         8.1        16.469807   \n",
       "\n",
       "   log_dist_km  log_pbi_origen_millardos  log_pbi_destino_millardos  \\\n",
       "0     6.415097                  6.143859                   2.654298   \n",
       "1     6.400257                  4.457899                   2.654298   \n",
       "2     6.008813                  2.836092                   2.654298   \n",
       "3     6.320768                  3.779383                   2.654298   \n",
       "4     6.100319                  2.566794                   2.654298   \n",
       "\n",
       "   log_pob_destino_k  log_pob_origen_k  log_edad_prom_des  \\\n",
       "0           4.295611          7.184443           3.525387   \n",
       "1           4.295611          6.254161           3.525387   \n",
       "2           4.295611          4.439092           3.525387   \n",
       "3           4.295611          4.813833           3.525387   \n",
       "4           4.295611          4.044524           3.525387   \n",
       "\n",
       "   log_porc_ocupados_des  largo_limite_km  \n",
       "0               4.151677     1.000000e-07  \n",
       "1               4.151677     1.000000e-07  \n",
       "2               4.151677     1.000000e-07  \n",
       "3               4.151677     1.000000e-07  \n",
       "4               4.151677     1.000000e-07  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_deptos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e629ccdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>personas_mig</td>   <th>  No. Observations:  </th>  <td>   342</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   318</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>         <td>Poisson</td>     <th>  Df Model:          </th>  <td>    23</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>          <td>Log</td>       <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -13551.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 24 Aug 2022</td> <th>  Deviance:          </th> <td>  24875.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:42:30</td>     <th>  Pearson chi2:      </th> <td>2.77e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>6</td>        <th>  Pseudo R-squ. (CS):</th>  <td> 1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                   <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[ARTIGAS]</th>        <td>   13.2386</td> <td>    0.470</td> <td>   28.170</td> <td> 0.000</td> <td>   12.318</td> <td>   14.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[CANELONES]</th>      <td>   13.0442</td> <td>    0.468</td> <td>   27.894</td> <td> 0.000</td> <td>   12.128</td> <td>   13.961</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[CERRO LARGO]</th>    <td>   12.9048</td> <td>    0.470</td> <td>   27.446</td> <td> 0.000</td> <td>   11.983</td> <td>   13.826</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[COLONIA]</th>        <td>   12.6024</td> <td>    0.470</td> <td>   26.841</td> <td> 0.000</td> <td>   11.682</td> <td>   13.523</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[DURAZNO]</th>        <td>   12.3588</td> <td>    0.470</td> <td>   26.296</td> <td> 0.000</td> <td>   11.438</td> <td>   13.280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[FLORES]</th>         <td>   11.4094</td> <td>    0.470</td> <td>   24.273</td> <td> 0.000</td> <td>   10.488</td> <td>   12.331</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[FLORIDA]</th>        <td>   12.1343</td> <td>    0.469</td> <td>   25.846</td> <td> 0.000</td> <td>   11.214</td> <td>   13.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[LAVALLEJA]</th>      <td>   12.1102</td> <td>    0.470</td> <td>   25.787</td> <td> 0.000</td> <td>   11.190</td> <td>   13.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[MALDONADO]</th>      <td>   12.6923</td> <td>    0.470</td> <td>   27.014</td> <td> 0.000</td> <td>   11.771</td> <td>   13.613</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[MONTEVIDEO]</th>     <td>   14.9960</td> <td>    0.469</td> <td>   31.942</td> <td> 0.000</td> <td>   14.076</td> <td>   15.916</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[PAYSANDU]</th>       <td>   12.9175</td> <td>    0.469</td> <td>   27.516</td> <td> 0.000</td> <td>   11.997</td> <td>   13.838</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[RIO NEGRO]</th>      <td>   12.4089</td> <td>    0.470</td> <td>   26.406</td> <td> 0.000</td> <td>   11.488</td> <td>   13.330</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[RIVERA]</th>         <td>   13.0778</td> <td>    0.470</td> <td>   27.821</td> <td> 0.000</td> <td>   12.156</td> <td>   13.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[ROCHA]</th>          <td>   12.4558</td> <td>    0.470</td> <td>   26.490</td> <td> 0.000</td> <td>   11.534</td> <td>   13.377</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[SALTO]</th>          <td>   13.1517</td> <td>    0.470</td> <td>   27.987</td> <td> 0.000</td> <td>   12.231</td> <td>   14.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[SAN JOSE]</th>       <td>   12.0249</td> <td>    0.469</td> <td>   25.625</td> <td> 0.000</td> <td>   11.105</td> <td>   12.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[SORIANO]</th>        <td>   12.6904</td> <td>    0.470</td> <td>   27.027</td> <td> 0.000</td> <td>   11.770</td> <td>   13.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[TACUAREMBO]</th>     <td>   13.0446</td> <td>    0.470</td> <td>   27.767</td> <td> 0.000</td> <td>   12.124</td> <td>   13.965</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nom_depto_orig[TREINTA Y TRES]</th> <td>   12.3891</td> <td>    0.470</td> <td>   26.346</td> <td> 0.000</td> <td>   11.467</td> <td>   13.311</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_pob_destino_k</th>              <td>    0.4180</td> <td>    0.013</td> <td>   32.632</td> <td> 0.000</td> <td>    0.393</td> <td>    0.443</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_edad_prom_des</th>              <td>   -3.0930</td> <td>    0.124</td> <td>  -24.857</td> <td> 0.000</td> <td>   -3.337</td> <td>   -2.849</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_pbi_destino_millardos</th>      <td>    0.5221</td> <td>    0.013</td> <td>   41.322</td> <td> 0.000</td> <td>    0.497</td> <td>    0.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_porc_ocupados_des</th>          <td>    0.8514</td> <td>    0.093</td> <td>    9.113</td> <td> 0.000</td> <td>    0.668</td> <td>    1.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_dist_km</th>                    <td>   -0.6906</td> <td>    0.004</td> <td> -167.398</td> <td> 0.000</td> <td>   -0.699</td> <td>   -0.682</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:           personas_mig   No. Observations:                  342\n",
       "Model:                            GLM   Df Residuals:                      318\n",
       "Model Family:                 Poisson   Df Model:                           23\n",
       "Link Function:                    Log   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -13551.\n",
       "Date:                Wed, 24 Aug 2022   Deviance:                       24875.\n",
       "Time:                        20:42:30   Pearson chi2:                 2.77e+04\n",
       "No. Iterations:                     6   Pseudo R-squ. (CS):              1.000\n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================================\n",
       "                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------\n",
       "nom_depto_orig[ARTIGAS]           13.2386      0.470     28.170      0.000      12.318      14.160\n",
       "nom_depto_orig[CANELONES]         13.0442      0.468     27.894      0.000      12.128      13.961\n",
       "nom_depto_orig[CERRO LARGO]       12.9048      0.470     27.446      0.000      11.983      13.826\n",
       "nom_depto_orig[COLONIA]           12.6024      0.470     26.841      0.000      11.682      13.523\n",
       "nom_depto_orig[DURAZNO]           12.3588      0.470     26.296      0.000      11.438      13.280\n",
       "nom_depto_orig[FLORES]            11.4094      0.470     24.273      0.000      10.488      12.331\n",
       "nom_depto_orig[FLORIDA]           12.1343      0.469     25.846      0.000      11.214      13.055\n",
       "nom_depto_orig[LAVALLEJA]         12.1102      0.470     25.787      0.000      11.190      13.031\n",
       "nom_depto_orig[MALDONADO]         12.6923      0.470     27.014      0.000      11.771      13.613\n",
       "nom_depto_orig[MONTEVIDEO]        14.9960      0.469     31.942      0.000      14.076      15.916\n",
       "nom_depto_orig[PAYSANDU]          12.9175      0.469     27.516      0.000      11.997      13.838\n",
       "nom_depto_orig[RIO NEGRO]         12.4089      0.470     26.406      0.000      11.488      13.330\n",
       "nom_depto_orig[RIVERA]            13.0778      0.470     27.821      0.000      12.156      13.999\n",
       "nom_depto_orig[ROCHA]             12.4558      0.470     26.490      0.000      11.534      13.377\n",
       "nom_depto_orig[SALTO]             13.1517      0.470     27.987      0.000      12.231      14.073\n",
       "nom_depto_orig[SAN JOSE]          12.0249      0.469     25.625      0.000      11.105      12.945\n",
       "nom_depto_orig[SORIANO]           12.6904      0.470     27.027      0.000      11.770      13.611\n",
       "nom_depto_orig[TACUAREMBO]        13.0446      0.470     27.767      0.000      12.124      13.965\n",
       "nom_depto_orig[TREINTA Y TRES]    12.3891      0.470     26.346      0.000      11.467      13.311\n",
       "log_pob_destino_k                  0.4180      0.013     32.632      0.000       0.393       0.443\n",
       "log_edad_prom_des                 -3.0930      0.124    -24.857      0.000      -3.337      -2.849\n",
       "log_pbi_destino_millardos          0.5221      0.013     41.322      0.000       0.497       0.547\n",
       "log_porc_ocupados_des              0.8514      0.093      9.113      0.000       0.668       1.035\n",
       "log_dist_km                       -0.6906      0.004   -167.398      0.000      -0.699      -0.682\n",
       "==================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = \"\"\"\n",
    "personas_mig ~\n",
    "nom_depto_orig +\n",
    "log_pob_destino_k +\n",
    "log_edad_prom_des + \n",
    "log_pbi_destino_millardos +\n",
    "log_porc_ocupados_des + \n",
    "log_dist_km\n",
    "-1\n",
    "\"\"\"\n",
    "\n",
    "# entrena y ajusta el modelo\n",
    "f = sm.families.Poisson()\n",
    "prodSim = smf.glm(formula=formula, data=dd_deptos, family=f).fit()\n",
    "\n",
    "# resultados\n",
    "prodSim.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0871ee81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "$R²$ = 0.9854"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "RMSE = 235.1647"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "actual_counts = dd_deptos.personas_mig\n",
    "predicted_counts = prodSim.predict()\n",
    "\n",
    "print_scores_simple(actual_counts, predicted_counts)\n",
    "\n",
    "title = 'Groud truth vs. estimaciones'\n",
    "subtitle = 'MIE restringido en origen con Mvdeo.'\n",
    "\n",
    "plot_gt(actual_counts, predicted_counts, title, subtitle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5bef6029",
   "metadata": {},
   "outputs": [],
   "source": [
    "prodSim.save(\"modelos/restringido_origen_mvo.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f3a797a",
   "metadata": {},
   "source": [
    "### 1.2. **Restringido en origen pero prediciendo migrantes de 18 a 24 años**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "47738ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "$R²$ = 0.8987"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "RMSE = 110.7891"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "formula = \"\"\"\n",
    "personas_mig_18_25 ~ nom_depto_orig + dummy_limit\n",
    "+ largo_limite_km + pbi_destino_millardos + dist_km + -1\n",
    "\"\"\"\n",
    "\n",
    "prodSim = smf.glm(formula=formula, data=dd_deptos, family=f).fit()\n",
    "\n",
    "# resultados\n",
    "actual_counts = dd_deptos.personas_mig_18_25\n",
    "predicted_counts = prodSim.predict()\n",
    "\n",
    "print_scores_simple(actual_counts, predicted_counts)\n",
    "\n",
    "title = 'Groud truth vs. estimaciones'\n",
    "subtitle = 'MIE restringido en origen con Mvdeo.'\n",
    "\n",
    "plot_gt(actual_counts, predicted_counts, title, subtitle)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a9c7182",
   "metadata": {},
   "source": [
    "### 1.3. **Restringido en origen pero prediciendo migrantes de 26 a 35**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30fd64bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "$R²$ = 0.8218"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "RMSE = 196.2055"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HcAO90QqyHwBqxZ2d9Vf4QXF1xR9/ClT7M2WWDt32SbgFqMedHl2J+yDpG2XdL+EOYdTgkkH0sBx6/IKVQ41VmHHcTmK/Q59/P/B14AHcFS+jhxOj78HnfftO//uoxJ3IcDI+gBtbeAE3tvIpH0MVbqzhF7hewRpevaDVSVPVHlWtVNWeUZY34F7vxbjEE23fD3wcl2AacL+f4Pym23AFM/8oIh24E1gu8s99Eff3+g3c7+OtwFv97wn/f3LpRF9bprNCj2bKE5GP4QbnT6aXkPH8WW7PAvkJOARnzIisZ2KmHBFZIiIb/KGmM3CHJX6Z6rgmk4i83R9enIvrIf6fJRKTTJZMzFSUhzstuQPYBvwad6hoOrkeN/fnZdz41MdSG46Z6uwwlzHGmAmznokxxpgJm3bzTObPn6+rV69OdRjGGJNRHn/88WZVXTDa8mmXTFavXs3evSdypqwxxhgROTjWcjvMZYwxZsIsmRhjjJkwSybGGGMmzJKJMcaYCbNkYowxZsIsmRhjjJkwSybGGGMmzJKJMcaYCbNkYowxU1w4EuZY37Gk7sOSiTHGTGHH+o5Re7SWrv7kXmx12pVTMcaY6WAwMkioM0TXQLKv2O1YMjHGmCnmaO9RmrubiWhk0vZpycQYY6aI/nA/oc4QPYM9k75vSybGGDMFtPa00tLdgpKaCx5aMjHGmAzWN9hHqCtE72BvSuOwZGKMMRlIVWnpaaGtpy1lvZEgSybGGJNhegd7OdJ5hP5wf6pDibFkYowxGSKiEZq7mznaezTVoQxjycQYYzJA90A3oc4QA5GBVIcyIksmxhiTxiIaobGrMenlUCbKkokxxqSpzv5OGrsaGYwMpjqU47JkYowxaSYcCdPY1UhHf0eqQxk3SybGGJNGjvUdo6mribCGUx3KCbFkYowxaWAgPEBjV+OkFWZMNEsmxhiTYqkozJholkyMMSZFUlmYMdEsmRhjzCRTVdp621JamDHRLJkYY8wk6hvs40jnEfrCfakOJaEsmRhjzCRIt8KMiZa0a8CLyPdFpFFEng20zRORrSLykv85N7DsRhE5ICIvisjlgfbzReQZv+zrIiK+PV9Efubbd4nI6mS9FmOMmYiegR4Oth+ktad1SiYSSGIyAe4ErhjSdgNQpaqnAVX+MSJyFnA1sNY/51siku2f823gOuA0f4tu81qgTVVPBb4GfCVpr8QYY05CtBRK3bG6tKrwmwxJSyaqugNoHdJ8JXCXv38XcFWg/W5V7VPVGuAAcKGILAFmqepjqqrAD4c8J7qte4HyaK/FGGNSrXugm4NHD6Zlhd9kmOwxk0Wq2gCgqg0istC3LwN2Btar920D/v7Q9uhz6vy2BkWkHTgFaB66UxG5Dte7YeXKlQl7McYYM1Q4EqapuyntCzMmWjIPc52IkXoUOkb7WM8Z3qh6h6qWqWrZggULTjJEY4wZW0dfB7VHa6ddIoHJTyYhf+gK/7PRt9cDKwLrLQcO+/blI7THPUdEcoDZDD+sZowxSTcYGeRwx2EaOhsyrqZWokx2MrkPuMbfvwb4daD9an+GVgluoH23PyTWISLr/HjIB4c8J7qtdwDb/LiKMcZMmmN9xzh49CCd/Z2pDiWlkjZmIiI/BTYB80WkHvhH4FbgHhG5FjgEvBNAVfeLyD3Ac8Ag8HHVWHr/GO7MsELgfn8D+B7wIxE5gOuRXJ2s12KMMUMNhAcIdYXoHuhOdShpQabbl/mysjLdu3dvqsMwxmSwtp42WnpaMqow48y8mSyZueSkny8ij6tq2WjLbQa8McaMU3+4nyOdR+gd7E11KGnHkokxxhzHVCzMmGiWTIwxZgy9g72EOkNTrjBjolkyMcaYEUQLM7b22IyD8bBkYowxQ/QM9BDqCk35elqJZMnEGGO8iEZo7m6eNvW0EsmSiTHGAF39XYS6QgxGBlMdSkayZGKMmdama2HGRLNkYoyZtjr6Omjsapy29bQSyZKJMWbaGYwM0tjVOO3raSWSJRNjzLTS3ttOU3dTRpVCyQSWTIwx04IVZkwuSybGmCmvraeN5u5mK4WSRJZMjDFTlhVmnDyWTIwxU46q0trTSmtPq/VGJoklE2PMlGKFGVPDkokxZkpQVZq7m2nrbUt1KNOSJRNjTMbrHugm1BliIDKQ6lCmLUsmxpiMFdEITV1NtPe1pzqUac+SiTEmI1lhxvRiycQYk1HCkTCNXY109HekOhQTYMnEGJMxrDBj+rJkYoxJe4ORQUKdIboGulIdihmFJRNjTFqzwoyZwZKJMSYtWWHGzJKVip2KyKdFZL+IPCsiPxWRAhGZJyJbReQl/3NuYP0bReSAiLwoIpcH2s8XkWf8sq+LiKTi9RhjEqutp43ao7WWSDLIpCcTEVkGfAIoU9WzgWzgauAGoEpVTwOq/GNE5Cy/fC1wBfAtEcn2m/s2cB1wmr9dMYkvxRiTYH2DfRxqP0RTd5PV1MowKemZ4A6vFYpIDlAEHAauBO7yy+8CrvL3rwTuVtU+Va0BDgAXisgSYJaqPqaqCvww8BxjTAZRVVq6WzjUfsgq/GaoSU8mqvoK8O/AIaABaFfVPwKLVLXBr9MALPRPWQbUBTZR79uW+ftD240xGaR3sJeD7Qdp6Wmx3kgGS8Vhrrm43kYJsBQoFpH3j/WUEdp0jPaR9nmdiOwVkb1NTU0nGrIxJglUlaauJg61H6I/3J/qcMwEpeIwVwVQo6pNqjoA/C9wMRDyh67wPxv9+vXAisDzl+MOi9X7+0Pbh1HVO1S1TFXLFixYkNAXY4w5cd0D3dQerbUKv1NIKpLJIWCdiBT5s6/KgeeB+4Br/DrXAL/29+8DrhaRfBEpwQ207/aHwjpEZJ3fzgcDzzHGpKGIRgh1hqg/Vm8VfqeYSZ9noqq7RORe4AlgEHgSuAOYAdwjItfiEs47/fr7ReQe4Dm//sdVY7UUPgbcCRQC9/ubMSYNdfZ30tjVaIUZpyhxJ0JNH2VlZbp3795Uh2HMtGGFGdPDzLyZLJm55KSfLyKPq2rZaMttBrwxJmmO9R2jqavJCjNOA5ZMjDEJZ4UZpx9LJsaYhDrae5Tm7mYrzDjNWDIxxiREf7ifUGeInsGeVIdiUsCSiTFmwlp7Wmnpthns05klE2PMSesb7CPUFbJ6WsaSiTHmxKkqLT0ttPW0WW/EAJZMjDEnqGegh1BXyOppmTjjSib+GiSrguur6o5kBWWMST8RjdDc3czR3qOpDsWkoeMmExH5CvBuXDmT6MwjBSyZGDNNdA90E+oMWT0tM6rx9EyuAs5Q1b4kx2KMSTPhSJim7iaO9R1LdSgmzY0nmVQDuYAlE2OmESvMaE7EqMlERL6BO5zVDewTkSoCCUVVP5H88Iwxky0cCRPqCtHZ35nqUEwGGatnEi2t+zjumiJBdi6gMVOQFWY0J2vUZKKqdwGIyCdV9bbgMhH5ZLIDM8ZMnoHwAI1djVaY0Zy08Vxp8ZoR2j6U4DiMMSlytPcoB9sPWiIxEzLWmMl7gPcCJSISPMw1E2hJdmDGmOSywowmkcYaM3kUaADmA/8RaO8Ank5mUMaY5FFV2nrbrDCjSaixxkwOAgeB9ZMXjjEmmfoG+zjSeYS+sJ3pbxJrPDPgO3j17K083JyTLlWdlczAjDGJY4UZTbIdN5mo6szgYxG5CrgwWQEZYxLLCjOayTCes7niqOqvgM2JD8UYk0gRjdDY1UjdsTpLJCbpxnOY688DD7OAMmzSojFprau/i1BXyEqhmEkzntpcbw3cHwRqgSuTEo0xZkKsMKNJlTGTiYhkA0+r6tcmKR5jzEnq6OugsavRSqGYlBhzzERVw8DbJikWY8xJGIwMcrjjMA2dDZZITMqMZwD+URH5LxG5VETOi94mslMRmSMi94rICyLyvIisF5F5IrJVRF7yP+cG1r9RRA6IyIsicnmg/XwRecYv+7qIyETiMibTtPe2U3u01ir8mpQbz5jJxf7nLYE2ZWJndN0G/F5V3yEieUAR8AWgSlVvFZEbgBuAz4vIWcDVwFpgKVApIqf7XtO3geuAncDvgCuA+ycQlzEZYSA8QKgrRPdAd6pDMQYYXzK5VlWrgw0iUnqyOxSRWcBl+GKRqtoP9IvIlcAmv9pdwHbg87jB/rv9lR5rROQAcKGI1AKzVPUxv90f4q4KacnETGltPW209LQQ0UiqQzEmZjyHue4doe3nE9hnKdAE/EBEnhSR74pIMbBIVRsA/M+Ffv1lQF3g+fW+bZm/P7TdmCmpP9zPofZDNHU3WSIxaWesqsFn4g4tzR4y12QWUDDBfZ4H/K2q7hKR23CHtEYNZYQ2HaN9+AZErsMdDmPlypUnFq0xKaaqtPa00trTaqVQTNoa6zDXGcBbgDnEzzXpAD46gX3WA/Wquss/vheXTEIiskRVG0RkCdAYWH9F4PnLgcO+ffkI7cOo6h3AHQBlZWX232gyRu9gL6HOkBVmNGlvrKrBvwZ+LSLro+MSiaCqR0SkTkTOUNUXgXLgOX+7BrjV//y1f8p9wE9E5Ku4AfjTgN2qGhaRDhFZB+wCPgh8I1FxGpNKqkpzdzNtvW2pDsWYcRlPoceEJZKAvwV+7M/kqgY+jBu/uUdErgUOAe/0+98vIvfgks0g8HF/JhfAx4A7gULcwLsNvpuMZ4UZTSYaz9lcCaeq+3A1voYqH2X9LwNfHqF9L3B2QoMzJkUiGqGpq4n2vvZUh2LMCUtJMjHGxLPCjCbTjXU212fGeqKqfjXx4RgzvVhhRjMZjvYe5fcHfk/p3FLecvpbkrKPsXom0YtinQFcgBsIB3dm146kRGPMNGKFGU0yHe44TFV1FZU1lex5ZQ9hDbNx1cbJTyaq+iUAEfkjcJ6qdvjHNzOxSYvGTGuDkUEauxqtnpZJKFXlTy1/orKmkqrqKvY37Y9bXpBTwILiBUQ0Qpac8HURj2s8YyYrgeBpJf3A6oRHYsw00N7bbjPYTcKEI2GePPJkrAdyqP1Q3PI5BXPYXLKZitIK/qz0z1gzb03SYhlPMvkRsFtEfombYf524IdJi8iYKcgKM5pE6Rvs49G6R6msqWRbzTZae1rjli+buYyK0goqSis4b8l55GS5j/mi3KKkxjWeeSZfFpH7gUt904dV9cmkRmXMFNLW00Zzd7OVQjEn7VjfMbbXbqeyupKHDj007EvJmfPPZEvpFipKKzjjlDNIxdU4xnMN+JVAM/DLYJuqHhr9WcaYvsE+Ql0hegd7Ux2KyUChzlBs/GPXK7viThvPkizOX3J+rAeyfNbyMbY0OcZzmOu3vFpAsRAoAV7EFYE0xgxhhRnNyVBVqtuqqayupLKmkqdDT8ctz8/O5+IVF7OldAtvKHkD8wrnpSjSkY3nMNdrg4/9VRavT1pExmQwK8xoTkREIzwdepqt1VuprK6k9mht3PLZ+bPZtHoTW0q3sGHlhqSPe0zECc+AV9UnROSCZARjTKaywoxmvPrD/eys30lltRtAb+puilu+ZMYSykvKqVhTQdmSMnKzc1MU6YkZz5hJcCZ8Fu5aJE2jrG7MtNM90E2oM8RAZCDVoZg01dnfyY6DO6isrmR77Xa6Brrilp8+73TKS8vZUrqFsxaclZIB9IkaT89kZuD+IG4M5RfJCceYzGGFGc1Ymrqa2FazjcqaSh6reyzuy4YgnLvkXCpK3AD6qjmrUhhpYoxnzCQ6E36me6g2bddMe1aY0Yykpq0mdgbWviP74k7AyM3K5eIVF1NRWsHmks3ML5o/aXHtqm7hF3v/RPOxIlbMLeL6y0rZdObC4z/xBIznMNfZuImL8/zjZuAaVX02oZEYkwHCkTCNXY109HekOhSTBiIa4dnGZ90ZWNWVvNz2ctzymXkz2bh6I+Ul5Vy26jJm5M2Y1PhysnLYV9vNt7e1UpBdyJzCXBo7ernpvv3cAglNKOM5zHUH8BlVfQBARDb5tosTFoUxGeBY3zGaupqsMOM01x/uZ88re2I9kFBXKG75wuKFbgC9tIILl11IXnbepMUmCIW5hRTlFlGcW0x+Tj5f/PlOCnOKKcrzM+HzcujuH+T2HdWTnkyKo4kEQFW3i0hxwiIwJs0NRgYJdYaGDZqa6aOrv4uHDj0UG0Af2jNdM3cNFaUVlJeU89pFr01KIcXR5GXnxZJHUW7RsMH7urZu5hTGnxFWmJtNfVtiS/uMJ5lUi8g/4A51AbwfqEloFMakKSvMOH21dLewrXYbVdVVPFL3yLDLKJ+z6BzKS10PpHRu6aTFlSVZseRRnFccq701mhVzi2js6I31TAB6BsIsn5vYOSvjSSZ/CXwJ+F//eAfumu3GTFn94X5CnSF6BntSHYqZRIfaD8XGP55oeGLYAPpFyy+ivKSc8pJyFs1YNGlxFeQUxJJHQU7BCT33+stKuem+/XT3D1KYm03PQJiBsHL9ZYlNgGMmExHJBn6uqhUJ3asxaay1p5WW7hYrhTINqCrPNT1HZY1LIH9q+VPc8uLc4tgA+sZVG5mZP3OULSVWTlZO7LBVUW4R2VnZJ72tTWcu5Bbg9h3V1Ld1szwVZ3OpalhEukVktqrayfRmSrPCjNPDYGSQvYf3xnogDZ0NccvnF82P9T7Wr1g/KQPogsQSR3FeccL3uenMhQlPHkON5zBXL/CMiGwFYiOQqvqJpEVlzCRSVVp6WmjrabPeyBTVM9DDI3WPsPXlrWyv3c7RvqNxy1fPXk3FGjeAfs7icyZlAD0/Oz+WPApzCjNy1nvQeKsG/zbZgRiTCr2DvRzpPDJscNVkvtae1tg1QB6pe2RYj/PshWfHrgGyZu6apH+YZ0t2LHkU5RYdd+A804xnBvxdkxGIMZMpohFaulusMOMUU3+snqqaKipfrmRvw964s/BysnK4cNmFVJS4GehLZi5JaiyCuIFznzxOdOA804yaTETkSmC5qn7TP94FLPCLP6eq905CfMYknBVmnDpUlRdbXoyNfzzf/Hzc8qLcIi5deSkVpRVsXLWR2QWzkxpPblZuLHkU5RZN6nyTVBurZ/I54OrA43zgAqAY+AFgycRklIhGaOxq5FjfsVSHYiYgHAnzRMMTsYtI1R+rj1s+r3Aem0s2U1FSwfoV65PaIwjO+SjKLcqYcvHJMFYyyVPVusDjh1W1BWhJxAx4f9rxXuAVVX2LiMwDfgasBmqBd6lqm1/3RuBaIAx8QlX/4NvPB+7EXQHyd8AnVdVGUM0wnf2dNHY1WmHGDNU72MujdY/GrgEy9PDk8lnLY+Mf5y4+d0Kn0h5PQU5BLIEU5BRk/MB5ooyVTOYGH6jq3wQeLmDiPgk8D8zyj28AqlT1VhG5wT/+vIicheshrQWWApUicrqqhoFvA9cBO3HJ5Arg/gTEZqYIK8yYudp723mg9gGqqqt46NBDwyaQvmb+a6gorWBL6RZOP+X0pH2o52TlxPU+kpmoMtlYyWSXiHxUVb8TbBSR64HdE9mpiCwH3gx8GYhefOtKYJO/fxewHfi8b79bVfuAGhE5AFwoIrXALFV9zG/zh8BVWDIxnhVmzDwNHQ1uAL26kt2v7I773WVLNucvPZ8tpVsoLyln2axlSYkh2XM+pqqxksmngV+JyHuBJ3zb+bixk6smuN//xI3JBKeTLlLVBgBVbRCR6AybZbieR1S9bxvw94e2DyMi1+F6MKxcuXKCoZt0Z4UZM4eqcqD1QOwa6Pub9sctL8gp4JKVl1BRUsGm1ZuYWzh3lC1NzFSb85EKoyYTVW0ELhaRzbhDTAC/VdVtE9mhiLwFaFTVx305++M+ZaTwxmgf3qh6B65sPmVlZTamMoUd7T1Kc3ezFWZMYxGNsO/IPrZWb6WquoqD7Qfjls/Jn8Pmks2Ul5azYcUGCnMLEx7DVJ/zkQrjmWeyDZhQAhliA/A2EXkTUADMEpH/AUIissT3SpYAjX79emBF4PnLgcO+ffkI7WYassKM6a0/3M9j9Y9R+XIl22q30dzdHLd86cylVJRWUFFSwflLz0/4h/t0m/ORCpOejlX1RuBGiF1o6+9U9f0i8m/ANcCt/uev/VPuA34iIl/FDcCfBuz2dcM6RGQdsAv4IPCNyXwtJvVUlbbeNivMmIY6+jp48OCDVFZX8uDBB+keiL9+xumnnB47A+s181+T8ENL03nORyqkU9/uVuAeEbkWOAS8E0BV94vIPcBzwCDwcX8mF8DHePXU4PuxwfdppW+wjyOdR+gL96U6FOOFOkNsq91G5cuV7HplV9zE0CzJ4rwl58UG0FfMXjHGlk5cdM5H9Myr6TznIxVkuk3LKCsr071796Y6DDMBVpgxvbzc9jJV1e4MrKdCT8Uty8vOY8OKDVSUVvCG1W/glKJTErpvm/MxeUTkcVUtG215OvVMjDmunoEeQl0hK8yYQhGN8EzoGSqrK9lavZWao/EXXp2VP4uNqzaypXQLl6y8hOK8xF3l2+Z8pC9LJiYjRDRCc3czR3uPpjqUaak/3M/uV3ZTWV1JVU0VjV2NccsXFS+KTSAsW1qWsENMglCYWxhLHvk5+QnZrkk8SyYm7XX1d9HY1WiFGSdZZ38nOw7uoKq6iu0Ht9PZ3xm3/NR5p1JRUkFFaQVnLzw7YYeYbM5HZrJkYtJWOBKmqbvJCjNOoqauJh6ofYDK6koerXs0LoELwjmLz6G8tJyKkgpK5pYkZJ8252NqsN+aSUsdfR00djVaKZRJUHu0NlbCfd+RfXEnNeRm5bJ+xfrYNUAWFE+8LJ/N+ZiaLJmYtDIYGaSxq3HYIRWTOKrKs43PUllTSVV1FS+1vhS3fEbeDDat2kR5aTmXrbqMGXkzJrxPm/Mx9VkyMWkj0YUZd1W3cPeeOo6097B4diFXX7CCi0oTe2pqphgID7D38N7YAHpDZ0Pc8gVFCygvLae8pJx1y9dNuLihzfmYfiyZmJQbCA8Q6goNmyE9EbuqW7it6iVys4WZBbm0dvVxW9VLfBKmTULpHujmoYMPUVlTyfba7cPGnlbPWR2bQPj6xa+fcG/B5nxMb5ZMTEolqzDj3XvqyM0WCnLdPAT3M8zde+qmdDJp7WllW8222AD60OoAr1v0OncG1poK1sxdM6F92ZwPE2TJxKREsgszHmnvYWZB/KGV/JxsQu1TrxBkXXsdVTVVbK3eyhMNT8Ql5pysHNYtW8fmUncZ20UzFp30fmzOhxmLJRMzqSarMOPi2YW0dvXFeiYAfYNhFs1OfDnzyaaqvND8QmwG+ostL8YtL8otYuOqjZSXlLNx9UZm5c8aZUvHl5edF0seRblFdujKjMqSiZk0k1mY8eoLVnBb1UtAmPycbPoGwwyElasvSGxxwckyGBnk8cOPx87AeqXjlbjlpxSeQnlJOeWl5axfvv6kew3ROR/ReR8258OMl/2lmKSLFmZs7WmdtH1eVHoKn8SNnYTae1iUgWdz9Qz08EjdI1RVV7GtdtuwUjIrZ6+korSC8pJyzl187kmNWUTnfESTh835MCfLkolJqlQWZryo9JSMSh4AbT1tbK/dTmVNJQ8fepjewd645WsXrHUXkSqt4LR5p53UYafcrNy4Gec258MkgiUTkxRWmHH8Dnccjs1A33t4b9w8m2zJ5sJlF1JR6magL5259IS3b3M+zGSwZGISrqu/i1BXiMHIYKpDSUuqyp9a/hQb/9jftD9ueWFOIZeuvJTy0nI2rd7EnII5J7yP/Ox8ivOKbc6HmTSWTEzCWGHG0YUjYZ488qS7iFRNJYfaD8Utn1Mwh80lm6korWDDig0nPHZhcz5MqlkyMQlhhRmH6xvs47H6x9havZVtNduGnYCwbOay2DXQz11y7gmdOWVzPky6sWSSwba/0MjtO6qpa+tmxdwirr+slE1nLpzUGKwwY7z23nYePPggldWVPHTooWElYs6cf2YsgZxxyhkndPjJ5nyYdGbJJENtf6GRm+7bT262MKcwl8aOXm66bz+3wKQllPbedpq6mxJeCiXThDpDVNa4AfTdr+yOGyvKkizOX3J+7Ays5bOWj3u7NufDZBL768xQt++oJjdbKMpzv8KivBy6+we5fUd10pNJMgozZhJVpbqtOjYD/ZnGZ+KW52fns2HlBraUbmHT6k3MK5w3ru3anA+TySyZZKi6tm7mFMaf4lmYm019W3I/4Nt62mjubk5qKZR0FNEITx15KtYDqT1aG7d8dv5sNq3exJbSLWxYuYGi3KJxbdfmfJipwpJJhloxt4jGjt5YzwSgZyDM8rnj+xA7Uf3hfo50Hhk2iW4q6w/3s7N+J5XVlWyr2UZTd1Pc8iUzlrgZ6KXllC0pG9f8jSzJojCnMJY8JnrdEGPShSWTDHX9ZaXcdN9+uvsHKczNpmfA1Z66/rLShO5HVWntaaW1p3Va9EY6+jrYcXAHlTWVPFj7IF0DXXHLT5t3GhWlFWwp3cJZC84a1yB4dM5HUW4RhTmFNnBupiRLJhlq05kLuQU3dlLf1s3yJJzN1TvYS6gzNCmFGVOpsasxdg2QnfU7GYgMxJYJwrlLzo1dRGrVnFXH3V50zkd03ofN+TDTgSWTDLbpzIVJGWxXVZq7m2nrbUv4ttNFTVtNbAb6viP74npduVm5XLziYraUbuENJW9gftH8Mbdlcz6MSUEyEZEVwA+BxUAEuENVbxORecDPgNVALfAuVW3zz7kRuBYIA59Q1T/49vOBO4FC4HfAJ1V16h+LSaLugW5CnaG4b+dTQUQjPNv4bKwG1sttL8ctn5k3k42rNlKxpoJLV17KjLwZY27P5nwYEy8VPZNB4LOq+oSIzAQeF5GtwIeAKlW9VURuAG4APi8iZwFXA2uBpUCliJyuqmHg28B1wE5cMrkCuH/SX9EUENEITV1NtPe1pzqUhOkP97PnlT2xHkioKxS3fGHxQspLytlSuoULll0w5mC4zfkwZmyT/h+hqg1Ag7/fISLPA8uAK4FNfrW7gO3A53373araB9SIyAHgQhGpBWap6mMAIvJD4CosmZywzv5OGrsap0Rhxs7+Th469BCV1W4AvaO/I275mrlrXAJZs4WzF5495qm4hTmFNufDmHFK6dcrEVkNnAvsAhb5RIOqNohIdDBgGa7nEVXv2wb8/aHtI+3nOlwPhpUrVybwFWS2cCRMY1fjsA/cTNPS3RIbQH+0/tFh1045Z9E5lJeWU1FaQenc0c92szkfxpy8lCUTEZkB/AL4lKoeG+OY80gLdIz24Y2qdwB3AJSVldmYCnCs7xhNXU0ZW5jxUPuh2PjHEw1PDBtAv2j5RbGrEC4sHvkkBZvzYUzipCSZiEguLpH8WFX/1zeHRGSJ75UsARp9ez0QvHD3cuCwb18+QrsZQ6aWQlFVnmt6jq3VW6mqruJPrX+KW16cW8zG1RspLyln46qNzMyfOeJ2bM6HMcmRirO5BPge8LyqfjWw6D7gGuBW//PXgfafiMhXcQPwpwG7VTUsIh0isg53mOyDwDcm6WVkpLaeNlp6WjKmMONAeIC9DXupqq6iqqaKwx3x3xXmF82nvKSc8tJy1i9fP2LPIluyY8nD5nwYkzyp6JlsAD4APCMi+3zbF3BJ5B4RuRY4BLwTQFX3i8g9wHO4M8E+7s/kAvgYr54afD82+D6iTCqF0j3QzSOHHqGyupIHah8YdnbZ6tmrqVjjDl+ds/icYeMa0Tkf0eRhcz6MmRwy3aZllJWV6d69e1MdxqTIlFIorT2tbK/dTmV1JQ8fenjYjPvXLnxtrIRJ6dzSYYembM6HMcknIo+ratloy+1k+Skq3Uuh1B+rp6qmisrqSvYe3ht36C0nK4cLl11IRYkrorh4xuK452ZJVix52JwPY9KD/RdOMelaCkVVebHlxdgZWM83Px+3vCi3iEtXXkp5aTmbVm1idsHsuOUFOQUU5xbbnA9j0pQlkykk3UqhhCNhnmh4wiWQmkrqj9XHLZ9XOI/NJZupKKng4hUXx41v5GTlxPU+bM6HMenNkskUkE6lUHoHe3mkzg+g1zwwrIe0YtYKV4G3tJxzF58bO7tKkLhyJTbnw5jMYskkw3X1dxHqCqW0FEp7bzsP1D5AVXUVDx16iJ7BnrjlaxesdTPQSyo4/ZTTYwPk+dn5seRhcz6MyWyWTDJUqkuhNHQ0xAbQd7+yO24mfbZkU7a0LDYDfdmsZbH2YLkSGzg3Zuqw/+YMlIpSKKrKgdYDbK3eSmV1Jfub9sctL8gp4JKVl1BRUsGm1ZuYWzgXQdzAuU8eNnBuzNRlySSDDEYGCXWGhl1KNlnCkTD7QvuorHYl3A+2H4xbPid/DptLNlNeWs6GFRsozC0kNys3ljysWKIx04clkwxxtPcozd3NSS+F0jfYx876nWyt3sq2mm209LTELV82c1ls/OP8peeTl50Xd4na3OzcpMZnjElPlkzSXH+4n1BnaNigdiId6zvGg7UPUllTyY6DO4YVgTz9lNPZUrqFLaVbOHP+mXHlSgpyCmzg3BhjySRdqSptvW20dLckpRRKqDNEVU0VVdVV7HxlZ9zZYIJw3pLz2FK6hYrSCkrmlsSSR1FukRVLNMYMY8kkDfUN9nGk80jCS6G83PYyVdXuDKynQk/FLcvLzmPDig1UlFawefVmls9ebsUSjTHjZskkjagqLT0ttPW0nXBvZFd1C3fvqeNIew+LZxdy9QUruKBkLk+Hno6VMKk5WhP3nFn5s9i0ehMVJRVsLtnMguIFFOcWU5hbaAPnxpgTYskkTfQO9nKk88iwS86Ox67qFm6reoncbKE4X6g+tofP/P6/Gcx9hqN9zXHrLp6xmIqSCras2cLGVRuZUzCHotwiGzg3xkyIJZMUG1qYcaQexkWlp4y5jR/tepF23cOxgSdp6d5HGD9Y74+SnTrvVCpKK3jzaW/momUXMSNvhg2cG2MSypJJAm1/oZGv/P4Fqpu7CEci5GVnU5SfzWkLZ3L9ZaVsOjP+WuRDCzMGexgzC3Jp7erjtqqX+CQMSyhNXU1sq9lGZU0lD7U8ihIspyLMyj6VmZzPPdf8La9d+FobODfGJJUlkwTZ/kIjf3/vU7R1D6CqhBUGI2G6B8Ic62nl7+/t4APrVvFYdSuHWjtZOLufPy+bG5ck7t5TR262UJDrPvTdzzB376njotJTqD1aGxv/2HdkX9y4ipDD3Jy1LMxdx6LcS5DwQhbPmsXFK9ZN9lthjJmGLJmcpO0vNHL7jmrq2rpZMbeIutYuWrr6iYwwbt4fVpo6+7lt2wFWzBPy849xpLOf26qa43odR9p7mFnw6tiFqtIvB3miZRdv/skXOdB6IG67M/JmsGn1Js6YtYmHnllFcc48ivJy6RkIMxBRrr+sNJlvgTHGxFgyOQnbX2jkpvv2k5stzCnMpaa5kyPHxj6NVwnTHznGgZZu5hXlcsrMfIK9DoDFswtp7uyiN+slmgf20jLwOH3a6jbgfywsXsjlay7nqjOv4vI1l1OcV+xiWuOSW31bN8vnFo14WM0YY5LFkslJuH1HNbnZQlGee/s6eoMT/hh2Um+EbsLSDrhSKC3dA7R0D5CXJXT0DtLV38XDhx7maO5veLznYcLEz0BfUrySd6y9kr94zV9wycpLRhz72HTmQksexpiUsWRyEurauplT+OrhqP5whNwsGIjEJxIlTJijqPQO20aYDo7K0xwZ3MdF332BgUj8KcHFchqriy/lsxuu4cPrLkvWSzHGmISwZHISVswtorGjN9Yz0YgyOKQ7MrQ3AjAoTfRmPUVv9pP0y8sgGl2ZnKwcLl15KW8/8+38xVl/wdKZSyfp1RhjzMRZMjkJ60vn8fVtLzE4QgFfZdD3RvpQlEGpoyd7H71ZTzGYFX8NdNECiiLnsSx/I7s++znmFMyZnBdgjDEJZsnkBF3yL5XUt4882B6mk7C00Z/1Er1Z++jNfoqwxJdwz9LZFIYvpDh8MQWRcyjMzefcBXMtkRhjMpolk3F6z+2P8lhN24jLwnTSnf0wPdmP05v1NCrxF6/KiSymMLyeosh68iNnILgBdAFm5OfYKbzGmIyX8clERK4AbgOyge+q6q2J3sfqG347rC3MMbqzd9Od/TB9WU+hMhC3PC9yKoXhdRSF15GrqxCEbIGcbKEv7MZKls3O55/e/jo7C8sYk/EyOpmISDbwTWALUA/sEZH7VPW5RO0jmEgGpZHurJ10Z++kL+tZkMCgiWZREHmtSyCRdeTogrjtFOQIJfNn0Nk3aPNAjDFTTkYnE+BC4ICqVgOIyN3AlUDCkgmAMsCR/L+nPyt+BrpoPoWR8ykMX0Rh+EKymTni8wtzs/j2+8635GGMmbIyPZksA+oCj+uBixK9EyEXUfdWZenM2OGrgsg5ZDHyhaMEKF1QzEBYueVtay2RGGOmtExPJiPVUB9WHUtErgOuA1i5cuVJ7Wj24AeQwWzyI6+JDaCPJSdbWDizwA5nGWOmhUxPJvXAisDj5cDhoSup6h3AHQBlZWUndUH1wsjrx73u7MIcbnv3uZZEjDHTRqZfm3UPcJqIlIhIHnA1cF+qgsnNEl6zeKYlEmPMtJPRPRNVHRSRvwH+gDs1+Puquj+R+6i99c0jnhocXWaMMSbDkwmAqv4O+F0y92FJwxhjxpbph7mMMcakAUsmxhhjJsySiTHGmAmzZGKMMWbCLJkYY4yZMFE9qTl8GUtEmoCDJ/n0+UBzAsNJpHSNLV3jgvSNLV3jgvSNLV3jgvSN7UTjWqU6pIJtwLRLJhMhIntVtSzVcYwkXWNL17ggfWNL17ggfWNL17ggfWNLdFx2mMsYY8yEWTIxxhgzYZZMTswdqQ5gDOkaW7rGBekbW7rGBekbW7rGBekbW0LjsjETY4wxE2Y9E2OMMRNmyWScROQKEXlRRA6IyA2TsL/vi0ijiDwbaJsnIltF5CX/c25g2Y0+thdF5PJA+/ki8oxf9nURGemCYicS1woReUBEnheR/SLyyTSKrUBEdovIUz62L6VLbH6b2SLypIj8Js3iqvXb3Ccie9MlNhGZIyL3isgL/u9tfZrEdYZ/r6K3YyLyqTSJ7dP+b/9ZEfmp/5+YnLhU1W7HueHK278MlAJ5wFPAWUne52XAecCzgbZ/BW7w928AvuLvn+VjygdKfKzZftluYD3uqpT3A2+cYFxLgPP8/ZnAn/z+0yE2AWb4+7nALmBdOsTmt/kZ4CfAb9Ll9+m3WQvMH9KW8tiAu4CP+Pt5wJx0iGtIjNnAEWBVqmPDXca8Bij0j+8BPjRZcSXkDZ3qN/+m/iHw+EbgxknY72rik8mLwBJ/fwnw4kjx4K7vst6v80Kg/T3A7QmO8dfAlnSLDSgCngAuSofYcFcBrQI282oySXlcfju1DE8mKY0NmIX7YJR0imuEOP8MeCQdYsMlkzpgHu7yIr/x8U1KXHaYa3yiv6Soet822RapagOA/xm9nONo8S3z94e2J4SIrAbOxfUA0iI2fyhpH9AIbFXVdIntP4HPAZFAWzrEBaDAH0XkcRG5Lk1iKwWagB/4Q4PfFZHiNIhrqKuBn/r7KY1NVV8B/h04BDQA7ar6x8mKy5LJ+Ix0vDCdToMbLb6kxS0iM4BfAJ9S1WPpEpuqhlX1HFxP4EIROTvVsYnIW4BGVX18vE+ZjLgCNqjqecAbgY+LyGVpEFsO7jDvt1X1XKALd4gm1XG9ukN3qfC3AT8/3qqjxJDov7O5wJW4Q1ZLgWIRef9kxWXJZHzqgRWBx8uBwymIIyQiSwD8z0bfPlp89f7+0PYJEZFcXCL5sar+bzrFFqWqR4HtwBVpENsG4G0iUgvcDWwWkf9Jg7gAUNXD/mcj8EvgwjSIrR6o9z1LgHtxySXVcQW9EXhCVUP+capjqwBqVLVJVQeA/wUunqy4LJmMzx7gNBEp8d9GrgbuS0Ec9wHX+PvX4MYrou1Xi0i+iJQApwG7fZe2Q0TW+bMxPhh4zknx2/ke8LyqfjXNYlsgInP8/ULcP9cLqY5NVW9U1eWquhr3t7NNVd+f6rgARKRYRGZG7+OOsT+b6thU9QhQJyJn+KZy4LlUxzXEe3j1EFc0hlTGdghYJyJFfnvlwPOTFleiBqKm+g14E+7MpZeBL07C/n6KO+45gPumcC1wCm4Q9yX/c15g/S/62F4kcOYFUIb7cHgZ+C+GDGieRFyX4Lq8TwP7/O1NaRLb64AnfWzPAjf59pTHFtjuJl4dgE95XLixiaf8bX/0bztNYjsH2Ot/n78C5qZDXH6bRUALMDvQlvLYgC/hvkA9C/wId6bWpMRlM+CNMcZMmB3mMsYYM2GWTIwxxkyYJRNjjDETZsnEGGPMhFkyMcYYM2GWTMyUJCLbg1VQfdunRORbx3lOOl6re7WIvDfwuExEvp6k/Tx7/DWNGc6SiZmqfoqbIBgUrKM0YSKSnahtHcdqIJZMVHWvqn5ikvZtzLhYMjFT1b3AW0QkH2JFKZcCD4vIt0VkrwSueTKUiLzHX8/hWRH5SqC9U0RuEZFdwHoReb+4a6jsE5HbfaHJbBG50z/3GRH59AjbXyAivxCRPf62wbdvlFevk/Gkn51+K3Cpb/u0iGySV6+JcrOI3CUifxR3XZI/F5F/9fv9vbjSN4jITX4/z4rIHX5mc/S6FU+JyGPAxwPxFYjID/x2nhSRN/j2tYHX+7SInDbxX5WZEhIxq9dudkvHG/Bb4Ep//wbg3/z9ef5nNq5+1+v84+24mb9LcaUpFuAKDm4DrvLrKPAuf/81wP8Buf7xt3ClJ87HVSyOxjFnhNh+Alzi76/ElafBb2+Dvz/D738Tfta8b489Bm4GHsZdv+X1QDd+JjOuztZVwdfs7/8IeKu//zSw0d//N/wlD4DPAj/w98/070cB8A3gfb49D3/tDLvZzXomZioLHuoKHuJ6l4g8gSu9shZ3kaCgC4Dt6grmDQI/xl2sDCCMK3IJrvbR+cAecWXvy3HlSaqBUhH5hohcAYxUVbkC+C//vPuAWb4X8gjwVRH5BC4JDY7jdd6vrrDfM7gE+Xvf/gzuEBnAG0Rkl4g8g7umyloRme338aBf50eBbV4SfayqLwAHgdOBx4AviMjngVWq2jOO+Mw0YMnETGW/AspF5DzcN+gnfEG7vwPKVfV1uN5LwZDnjXWJ0l5VDQfWu0tVz/G3M1T1ZlVtw/UStuMOHX13hO1kAesDz12mqh2qeivwEaAQ2CkiZ47jdfYBqGoEGFDVaI2kCJAjIgW4XtM7VPW1wHf8axZGLy0+4nugqj/BlV3vAf4gIpvHEZ+ZBiyZmClLVTtxH+jf59VeySzctTHaRWQRroz4ULuAjSIy3w+yvwd4cIT1qoB3iMhCiF03fZWIzAeyVPUXwD/gSqcP9Ufgb6IPROQc/3ONqj6jql/BFTk8E+jAXSL5ZEWTZbO469C8A2Jl+ttF5BK//H2B5+yIPhaR03GH4l4UkVKgWlW/jutRvW4CcZkpJCfVARiTZD/FXdfhagBVfUpEnsRVyK3GHVaKo6oNInIj8ADuG/rvVHVYCW5VfU5E/h/uKoVZuArPH8d9a/+BbwN3edShPgF8U0Sexv0f7gD+CviUH+wO40qu34/rYQyKyFPAnbjDc+OmqkdF5Du4w161uEsqRH0Y+L6IdOMu2xr1LeC//WGxQeBDqtonIu8G3i8iA7hrn99yIrGYqcuqBhtjjJkwO8xljDFmwiyZGGOMmTBLJsYYYybMkokxxpgJs2RijDFmwiyZGGOMmTBLJsYYYybMkokxxpgJ+/8B+iPB1Q3jJjEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "formula = \"\"\"\n",
    "personas_mig_26_35 ~ nom_depto_orig + dummy_limit\n",
    "+ largo_limite_km + pbi_destino_millardos + dist_km + -1\n",
    "\"\"\"\n",
    "\n",
    "prodSim = smf.glm(formula=formula, data=dd_deptos, family=f).fit()\n",
    "\n",
    "# resultados\n",
    "actual_counts = dd_deptos.personas_mig_26_35\n",
    "predicted_counts = prodSim.predict()\n",
    "\n",
    "print_scores_simple(actual_counts, predicted_counts)\n",
    "\n",
    "title = 'Groud truth vs. estimaciones'\n",
    "subtitle = 'MIE restringido en origen con Mvdeo.'\n",
    "\n",
    "plot_gt(actual_counts, predicted_counts, title, subtitle)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f95f6a",
   "metadata": {},
   "source": [
    "## 2. Modelos sin Montevideo\n",
    "\n",
    "### 2.1. **Restringido en origen con mismas var. indep. pero sin Montevideo**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6944c566",
   "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>cod</th>\n",
       "      <th>depto_origen</th>\n",
       "      <th>depto_destino</th>\n",
       "      <th>personas_mig</th>\n",
       "      <th>personas_mig_18_25</th>\n",
       "      <th>personas_mig_26_35</th>\n",
       "      <th>personas_mig_100k</th>\n",
       "      <th>pob_origen</th>\n",
       "      <th>pob_destino</th>\n",
       "      <th>dist</th>\n",
       "      <th>dist_km</th>\n",
       "      <th>pbi_origen</th>\n",
       "      <th>pbi_porcen_ori</th>\n",
       "      <th>pbi_destino</th>\n",
       "      <th>pbi_porcen_des</th>\n",
       "      <th>pbi_origen_millardos</th>\n",
       "      <th>pbi_destino_millardos</th>\n",
       "      <th>largo_limite</th>\n",
       "      <th>dummy_limit</th>\n",
       "      <th>empresas_origen</th>\n",
       "      <th>empresas_destino</th>\n",
       "      <th>menores_25_origen</th>\n",
       "      <th>menores_25_destino</th>\n",
       "      <th>pob_destino_k</th>\n",
       "      <th>pob_origen_k</th>\n",
       "      <th>nom_depto_des</th>\n",
       "      <th>edad_prom_ori</th>\n",
       "      <th>porc_ocupados_ori</th>\n",
       "      <th>edad_prom_des</th>\n",
       "      <th>porc_ocupados_des</th>\n",
       "      <th>tasa_desemp_mujeres_2011_ori</th>\n",
       "      <th>tasa_desemp_varones_2011_ori</th>\n",
       "      <th>tasa_desemp_total_2011_ori</th>\n",
       "      <th>tasa_desemp_mujeres_2011_des</th>\n",
       "      <th>tasa_desemp_varones_2011_des</th>\n",
       "      <th>tasa_desemp_total_2011_des</th>\n",
       "      <th>log_pbi_destino</th>\n",
       "      <th>log_dist_km</th>\n",
       "      <th>log_pbi_origen_millardos</th>\n",
       "      <th>log_pbi_destino_millardos</th>\n",
       "      <th>log_pob_destino_k</th>\n",
       "      <th>log_pob_origen_k</th>\n",
       "      <th>log_edad_prom_des</th>\n",
       "      <th>log_porc_ocupados_des</th>\n",
       "      <th>largo_limite_km</th>\n",
       "      <th>ARTIGAS</th>\n",
       "      <th>CANELONES</th>\n",
       "      <th>CERRO LARGO</th>\n",
       "      <th>COLONIA</th>\n",
       "      <th>DURAZNO</th>\n",
       "      <th>FLORES</th>\n",
       "      <th>FLORIDA</th>\n",
       "      <th>LAVALLEJA</th>\n",
       "      <th>MALDONADO</th>\n",
       "      <th>PAYSANDU</th>\n",
       "      <th>RIO NEGRO</th>\n",
       "      <th>RIVERA</th>\n",
       "      <th>ROCHA</th>\n",
       "      <th>SALTO</th>\n",
       "      <th>SAN JOSE</th>\n",
       "      <th>SORIANO</th>\n",
       "      <th>TACUAREMBO</th>\n",
       "      <th>TREINTA Y TRES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>914</td>\n",
       "      <td>114</td>\n",
       "      <td>247</td>\n",
       "      <td>891</td>\n",
       "      <td>1318755</td>\n",
       "      <td>73377</td>\n",
       "      <td>610726</td>\n",
       "      <td>611</td>\n",
       "      <td>465848031</td>\n",
       "      <td>50.3</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>465.848</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>3124</td>\n",
       "      <td>55</td>\n",
       "      <td>456072</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>1318.755</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>37.481048</td>\n",
       "      <td>70.395702</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.415097</td>\n",
       "      <td>6.143859</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>7.184443</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>70</td>\n",
       "      <td>520173</td>\n",
       "      <td>73377</td>\n",
       "      <td>601915</td>\n",
       "      <td>602</td>\n",
       "      <td>86306492</td>\n",
       "      <td>9.3</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>86.306</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>563</td>\n",
       "      <td>55</td>\n",
       "      <td>197993</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>520.173</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>35.406640</td>\n",
       "      <td>71.045180</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.400257</td>\n",
       "      <td>4.457899</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>6.254161</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>402</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>84698</td>\n",
       "      <td>73377</td>\n",
       "      <td>406694</td>\n",
       "      <td>407</td>\n",
       "      <td>17048887</td>\n",
       "      <td>1.8</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>17.049</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>88</td>\n",
       "      <td>55</td>\n",
       "      <td>33360</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>84.698</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>35.614749</td>\n",
       "      <td>65.582039</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.008813</td>\n",
       "      <td>2.836092</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>4.439092</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>502</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>123203</td>\n",
       "      <td>73377</td>\n",
       "      <td>556292</td>\n",
       "      <td>556</td>\n",
       "      <td>43788749</td>\n",
       "      <td>4.7</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>43.789</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>43030</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>123.203</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>37.632420</td>\n",
       "      <td>76.058415</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.320768</td>\n",
       "      <td>3.779383</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>4.813833</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>602</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>57084</td>\n",
       "      <td>73377</td>\n",
       "      <td>446123</td>\n",
       "      <td>446</td>\n",
       "      <td>13023942</td>\n",
       "      <td>1.4</td>\n",
       "      <td>14214980</td>\n",
       "      <td>1.5</td>\n",
       "      <td>13.024</td>\n",
       "      <td>14.215</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>False</td>\n",
       "      <td>40</td>\n",
       "      <td>55</td>\n",
       "      <td>22663</td>\n",
       "      <td>30880</td>\n",
       "      <td>73.377</td>\n",
       "      <td>57.084</td>\n",
       "      <td>ARTIGAS</td>\n",
       "      <td>35.093021</td>\n",
       "      <td>67.284108</td>\n",
       "      <td>33.966911</td>\n",
       "      <td>63.540489</td>\n",
       "      <td>13.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.469807</td>\n",
       "      <td>6.100319</td>\n",
       "      <td>2.566794</td>\n",
       "      <td>2.654298</td>\n",
       "      <td>4.295611</td>\n",
       "      <td>4.044524</td>\n",
       "      <td>3.525387</td>\n",
       "      <td>4.151677</td>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cod  depto_origen  depto_destino  personas_mig  personas_mig_18_25  \\\n",
       "0  102             1              2           914                 114   \n",
       "1  302             3              2            74                   9   \n",
       "2  402             4              2            15                   0   \n",
       "3  502             5              2            28                   7   \n",
       "4  602             6              2            32                   0   \n",
       "\n",
       "   personas_mig_26_35  personas_mig_100k  pob_origen  pob_destino    dist  \\\n",
       "0                 247                891     1318755        73377  610726   \n",
       "1                  22                 70      520173        73377  601915   \n",
       "2                   6                 14       84698        73377  406694   \n",
       "3                   8                 25      123203        73377  556292   \n",
       "4                  10                 29       57084        73377  446123   \n",
       "\n",
       "   dist_km  pbi_origen  pbi_porcen_ori  pbi_destino  pbi_porcen_des  \\\n",
       "0      611   465848031            50.3     14214980             1.5   \n",
       "1      602    86306492             9.3     14214980             1.5   \n",
       "2      407    17048887             1.8     14214980             1.5   \n",
       "3      556    43788749             4.7     14214980             1.5   \n",
       "4      446    13023942             1.4     14214980             1.5   \n",
       "\n",
       "   pbi_origen_millardos  pbi_destino_millardos  largo_limite  dummy_limit  \\\n",
       "0               465.848                 14.215        0.0001        False   \n",
       "1                86.306                 14.215        0.0001        False   \n",
       "2                17.049                 14.215        0.0001        False   \n",
       "3                43.789                 14.215        0.0001        False   \n",
       "4                13.024                 14.215        0.0001        False   \n",
       "\n",
       "   empresas_origen  empresas_destino  menores_25_origen  menores_25_destino  \\\n",
       "0             3124                55             456072               30880   \n",
       "1              563                55             197993               30880   \n",
       "2               88                55              33360               30880   \n",
       "3               60                55              43030               30880   \n",
       "4               40                55              22663               30880   \n",
       "\n",
       "   pob_destino_k  pob_origen_k nom_depto_des  edad_prom_ori  \\\n",
       "0         73.377      1318.755       ARTIGAS      37.481048   \n",
       "1         73.377       520.173       ARTIGAS      35.406640   \n",
       "2         73.377        84.698       ARTIGAS      35.614749   \n",
       "3         73.377       123.203       ARTIGAS      37.632420   \n",
       "4         73.377        57.084       ARTIGAS      35.093021   \n",
       "\n",
       "   porc_ocupados_ori  edad_prom_des  porc_ocupados_des  \\\n",
       "0          70.395702      33.966911          63.540489   \n",
       "1          71.045180      33.966911          63.540489   \n",
       "2          65.582039      33.966911          63.540489   \n",
       "3          76.058415      33.966911          63.540489   \n",
       "4          67.284108      33.966911          63.540489   \n",
       "\n",
       "   tasa_desemp_mujeres_2011_ori  tasa_desemp_varones_2011_ori  \\\n",
       "0                           7.4                           5.1   \n",
       "1                           8.1                           4.3   \n",
       "2                           3.8                           1.7   \n",
       "3                           4.1                           2.5   \n",
       "4                          13.1                           5.8   \n",
       "\n",
       "   tasa_desemp_total_2011_ori  tasa_desemp_mujeres_2011_des  \\\n",
       "0                         6.2                          11.5   \n",
       "1                         6.0                          11.5   \n",
       "2                         2.6                          11.5   \n",
       "3                         3.2                          11.5   \n",
       "4                         8.9                          11.5   \n",
       "\n",
       "   tasa_desemp_varones_2011_des  tasa_desemp_total_2011_des  log_pbi_destino  \\\n",
       "0                           5.4                         8.1        16.469807   \n",
       "1                           5.4                         8.1        16.469807   \n",
       "2                           5.4                         8.1        16.469807   \n",
       "3                           5.4                         8.1        16.469807   \n",
       "4                           5.4                         8.1        16.469807   \n",
       "\n",
       "   log_dist_km  log_pbi_origen_millardos  log_pbi_destino_millardos  \\\n",
       "0     6.415097                  6.143859                   2.654298   \n",
       "1     6.400257                  4.457899                   2.654298   \n",
       "2     6.008813                  2.836092                   2.654298   \n",
       "3     6.320768                  3.779383                   2.654298   \n",
       "4     6.100319                  2.566794                   2.654298   \n",
       "\n",
       "   log_pob_destino_k  log_pob_origen_k  log_edad_prom_des  \\\n",
       "0           4.295611          7.184443           3.525387   \n",
       "1           4.295611          6.254161           3.525387   \n",
       "2           4.295611          4.439092           3.525387   \n",
       "3           4.295611          4.813833           3.525387   \n",
       "4           4.295611          4.044524           3.525387   \n",
       "\n",
       "   log_porc_ocupados_des  largo_limite_km  ARTIGAS  CANELONES  CERRO LARGO  \\\n",
       "0               4.151677     1.000000e-07        0          0            0   \n",
       "1               4.151677     1.000000e-07        0          1            0   \n",
       "2               4.151677     1.000000e-07        0          0            1   \n",
       "3               4.151677     1.000000e-07        0          0            0   \n",
       "4               4.151677     1.000000e-07        0          0            0   \n",
       "\n",
       "   COLONIA  DURAZNO  FLORES  FLORIDA  LAVALLEJA  MALDONADO  PAYSANDU  \\\n",
       "0        0        0       0        0          0          0         0   \n",
       "1        0        0       0        0          0          0         0   \n",
       "2        0        0       0        0          0          0         0   \n",
       "3        1        0       0        0          0          0         0   \n",
       "4        0        1       0        0          0          0         0   \n",
       "\n",
       "   RIO NEGRO  RIVERA  ROCHA  SALTO  SAN JOSE  SORIANO  TACUAREMBO  \\\n",
       "0          0       0      0      0         0        0           0   \n",
       "1          0       0      0      0         0        0           0   \n",
       "2          0       0      0      0         0        0           0   \n",
       "3          0       0      0      0         0        0           0   \n",
       "4          0       0      0      0         0        0           0   \n",
       "\n",
       "   TREINTA Y TRES  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies = pd.get_dummies(dd_deptos.nom_depto_orig)\n",
    "\n",
    "df = pd.concat([dd_deptos, dummies], axis=1)\n",
    "df = df.drop(['nom_depto_orig', 'MONTEVIDEO'], axis=1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "76d90cc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cod', 'depto_origen', 'depto_destino', 'personas_mig',\n",
       "       'personas_mig_18_25', 'personas_mig_26_35', 'personas_mig_100k',\n",
       "       'pob_origen', 'pob_destino', 'dist', 'dist_km', 'pbi_origen',\n",
       "       'pbi_porcen_ori', 'pbi_destino', 'pbi_porcen_des',\n",
       "       'pbi_origen_millardos', 'pbi_destino_millardos', 'largo_limite',\n",
       "       'dummy_limit', 'empresas_origen', 'empresas_destino',\n",
       "       'menores_25_origen', 'menores_25_destino', 'pob_destino_k',\n",
       "       'pob_origen_k', 'nom_depto_des', 'edad_prom_ori', 'porc_ocupados_ori',\n",
       "       'edad_prom_des', 'porc_ocupados_des', 'tasa_desemp_mujeres_2011_ori',\n",
       "       'tasa_desemp_varones_2011_ori', 'tasa_desemp_total_2011_ori',\n",
       "       'tasa_desemp_mujeres_2011_des', 'tasa_desemp_varones_2011_des',\n",
       "       'tasa_desemp_total_2011_des', 'log_pbi_destino', 'log_dist_km',\n",
       "       'log_pbi_origen_millardos', 'log_pbi_destino_millardos',\n",
       "       'log_pob_destino_k', 'log_pob_origen_k', 'log_edad_prom_des',\n",
       "       'log_porc_ocupados_des', 'largo_limite_km', 'ARTIGAS', 'CANELONES',\n",
       "       'CERRO_LARGO', 'COLONIA', 'DURAZNO', 'FLORES', 'FLORIDA', 'LAVALLEJA',\n",
       "       'MALDONADO', 'PAYSANDU', 'RIO_NEGRO', 'RIVERA', 'ROCHA', 'SALTO',\n",
       "       'SAN_JOSE', 'SORIANO', 'TACUAREMBO', 'TREINTA_Y_TRES',\n",
       "       'log_largo_limite_km'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = df.columns.str.replace(' ', '_')\n",
    "\n",
    "df['log_largo_limite_km'] = np.log(df.largo_limite_km)\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e9d2070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>personas_mig</td>   <th>  No. Observations:  </th>  <td>   342</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   320</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>         <td>Poisson</td>     <th>  Df Model:          </th>  <td>    21</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>          <td>Log</td>       <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -10761.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 24 Aug 2022</td> <th>  Deviance:          </th> <td>  19294.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:42:47</td>     <th>  Pearson chi2:      </th> <td>2.16e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>6</td>        <th>  Pseudo R-squ. (CS):</th>  <td> 1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                 <td>    6.6007</td> <td>    0.033</td> <td>  197.301</td> <td> 0.000</td> <td>    6.535</td> <td>    6.666</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ARTIGAS</th>                   <td>   -2.2654</td> <td>    0.018</td> <td> -127.042</td> <td> 0.000</td> <td>   -2.300</td> <td>   -2.230</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CANELONES</th>                 <td>   -2.4347</td> <td>    0.010</td> <td> -245.563</td> <td> 0.000</td> <td>   -2.454</td> <td>   -2.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CERRO_LARGO</th>               <td>   -2.5773</td> <td>    0.018</td> <td> -144.130</td> <td> 0.000</td> <td>   -2.612</td> <td>   -2.542</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>COLONIA</th>                   <td>   -2.6361</td> <td>    0.016</td> <td> -164.380</td> <td> 0.000</td> <td>   -2.668</td> <td>   -2.605</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DURAZNO</th>                   <td>   -2.9602</td> <td>    0.018</td> <td> -165.442</td> <td> 0.000</td> <td>   -2.995</td> <td>   -2.925</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FLORES</th>                    <td>   -3.9279</td> <td>    0.026</td> <td> -151.472</td> <td> 0.000</td> <td>   -3.979</td> <td>   -3.877</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FLORIDA</th>                   <td>   -3.1215</td> <td>    0.017</td> <td> -185.169</td> <td> 0.000</td> <td>   -3.155</td> <td>   -3.088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LAVALLEJA</th>                 <td>   -3.2609</td> <td>    0.018</td> <td> -177.738</td> <td> 0.000</td> <td>   -3.297</td> <td>   -3.225</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MALDONADO</th>                 <td>   -2.5884</td> <td>    0.015</td> <td> -176.013</td> <td> 0.000</td> <td>   -2.617</td> <td>   -2.560</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAYSANDU</th>                  <td>   -2.5120</td> <td>    0.016</td> <td> -152.409</td> <td> 0.000</td> <td>   -2.544</td> <td>   -2.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RIO_NEGRO</th>                 <td>   -2.9960</td> <td>    0.019</td> <td> -157.826</td> <td> 0.000</td> <td>   -3.033</td> <td>   -2.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RIVERA</th>                    <td>   -2.4501</td> <td>    0.018</td> <td> -136.934</td> <td> 0.000</td> <td>   -2.485</td> <td>   -2.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ROCHA</th>                     <td>   -2.9486</td> <td>    0.019</td> <td> -155.361</td> <td> 0.000</td> <td>   -2.986</td> <td>   -2.911</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SALTO</th>                     <td>   -2.4030</td> <td>    0.017</td> <td> -140.301</td> <td> 0.000</td> <td>   -2.437</td> <td>   -2.369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SAN_JOSE</th>                  <td>   -3.6204</td> <td>    0.018</td> <td> -204.170</td> <td> 0.000</td> <td>   -3.655</td> <td>   -3.586</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SORIANO</th>                   <td>   -2.6696</td> <td>    0.016</td> <td> -163.559</td> <td> 0.000</td> <td>   -2.702</td> <td>   -2.638</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TACUAREMBO</th>                <td>   -2.4997</td> <td>    0.017</td> <td> -150.328</td> <td> 0.000</td> <td>   -2.532</td> <td>   -2.467</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TREINTA_Y_TRES</th>            <td>   -3.0280</td> <td>    0.020</td> <td> -153.917</td> <td> 0.000</td> <td>   -3.067</td> <td>   -2.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_largo_limite_km</th>       <td>    0.0517</td> <td>    0.001</td> <td>   91.334</td> <td> 0.000</td> <td>    0.051</td> <td>    0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_pbi_destino_millardos</th> <td>    0.9785</td> <td>    0.003</td> <td>  342.362</td> <td> 0.000</td> <td>    0.973</td> <td>    0.984</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_dist_km</th>               <td>   -0.2784</td> <td>    0.007</td> <td>  -42.424</td> <td> 0.000</td> <td>   -0.291</td> <td>   -0.266</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:           personas_mig   No. Observations:                  342\n",
       "Model:                            GLM   Df Residuals:                      320\n",
       "Model Family:                 Poisson   Df Model:                           21\n",
       "Link Function:                    Log   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -10761.\n",
       "Date:                Wed, 24 Aug 2022   Deviance:                       19294.\n",
       "Time:                        20:42:47   Pearson chi2:                 2.16e+04\n",
       "No. Iterations:                     6   Pseudo R-squ. (CS):              1.000\n",
       "Covariance Type:            nonrobust                                         \n",
       "=============================================================================================\n",
       "                                coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Intercept                     6.6007      0.033    197.301      0.000       6.535       6.666\n",
       "ARTIGAS                      -2.2654      0.018   -127.042      0.000      -2.300      -2.230\n",
       "CANELONES                    -2.4347      0.010   -245.563      0.000      -2.454      -2.415\n",
       "CERRO_LARGO                  -2.5773      0.018   -144.130      0.000      -2.612      -2.542\n",
       "COLONIA                      -2.6361      0.016   -164.380      0.000      -2.668      -2.605\n",
       "DURAZNO                      -2.9602      0.018   -165.442      0.000      -2.995      -2.925\n",
       "FLORES                       -3.9279      0.026   -151.472      0.000      -3.979      -3.877\n",
       "FLORIDA                      -3.1215      0.017   -185.169      0.000      -3.155      -3.088\n",
       "LAVALLEJA                    -3.2609      0.018   -177.738      0.000      -3.297      -3.225\n",
       "MALDONADO                    -2.5884      0.015   -176.013      0.000      -2.617      -2.560\n",
       "PAYSANDU                     -2.5120      0.016   -152.409      0.000      -2.544      -2.480\n",
       "RIO_NEGRO                    -2.9960      0.019   -157.826      0.000      -3.033      -2.959\n",
       "RIVERA                       -2.4501      0.018   -136.934      0.000      -2.485      -2.415\n",
       "ROCHA                        -2.9486      0.019   -155.361      0.000      -2.986      -2.911\n",
       "SALTO                        -2.4030      0.017   -140.301      0.000      -2.437      -2.369\n",
       "SAN_JOSE                     -3.6204      0.018   -204.170      0.000      -3.655      -3.586\n",
       "SORIANO                      -2.6696      0.016   -163.559      0.000      -2.702      -2.638\n",
       "TACUAREMBO                   -2.4997      0.017   -150.328      0.000      -2.532      -2.467\n",
       "TREINTA_Y_TRES               -3.0280      0.020   -153.917      0.000      -3.067      -2.989\n",
       "log_largo_limite_km           0.0517      0.001     91.334      0.000       0.051       0.053\n",
       "log_pbi_destino_millardos     0.9785      0.003    342.362      0.000       0.973       0.984\n",
       "log_dist_km                  -0.2784      0.007    -42.424      0.000      -0.291      -0.266\n",
       "=============================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = \"\"\"\n",
    "personas_mig ~\n",
    "ARTIGAS + \n",
    "CANELONES +\n",
    "CERRO_LARGO + \n",
    "COLONIA + \n",
    "DURAZNO + \n",
    "FLORES + \n",
    "FLORIDA + \n",
    "LAVALLEJA +\n",
    "MALDONADO + \n",
    "PAYSANDU + \n",
    "RIO_NEGRO + \n",
    "RIVERA + \n",
    "ROCHA + \n",
    "SALTO +\n",
    "SAN_JOSE + \n",
    "SORIANO + \n",
    "TACUAREMBO + \n",
    "TREINTA_Y_TRES +\n",
    "log_largo_limite_km +\n",
    "log_pbi_destino_millardos +\n",
    "log_dist_km\n",
    "\"\"\"\n",
    "              \n",
    "# entrena y ajusta el modelo\n",
    "f = sm.families.Poisson()\n",
    "prodSim = smf.glm(formula=formula, data=df, family=f).fit()\n",
    "\n",
    "# resultados\n",
    "prodSim.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c9aafa4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bbd2137",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e18b43e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83631974",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c6731d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0a598dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4340953",
   "metadata": {},
   "outputs": [],
   "source": [
    "dd_deptos_no_mvo = dd_deptos.loc[(dd_deptos.depto_origen!=1) & (dd_deptos.depto_destino!=1)]\n",
    "\n",
    "formula = \"\"\"\n",
    "personas_mig ~ nom_depto_orig + dummy_limit + menores_25_destino\n",
    "+ largo_limite_km + pbi_destino_millardos + dist_km -1\n",
    "\"\"\"\n",
    "\n",
    "prodSim = smf.glm(formula=formula, data=dd_deptos_no_mvo, family=f).fit()\n",
    "\n",
    "prodSim.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6e43507",
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_counts = dd_deptos_no_mvo.personas_mig\n",
    "predicted_counts = prodSim.predict()\n",
    "\n",
    "print_scores_simple(actual_counts, predicted_counts)\n",
    "\n",
    "\n",
    "subtitle = 'MIE restringido en origen sin Mvdeo.'\n",
    "plot_gt(actual_counts, predicted_counts, title, subtitle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4008336b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# matriz de los valores estimados\n",
    "dd_deptos['prodsimest'] = np.round(predicted_counts)\n",
    "\n",
    "pd.pivot_table(dd_deptos,\n",
    "values='prodsimest',\n",
    "index ='depto_origen',\n",
    "columns='depto_destino',\n",
    "fill_value=0,\n",
    "aggfunc=sum,\n",
    "margins=True,\n",
    "margins_name='Total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4ee0f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "prodSim.save(\"modelos/restringido_origen_noMVO.pickle\")"
   ]
  }
 ],
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