UMAP-GMM.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "03c51be9-9961-4b72-af5c-5135fca95a98",
   "metadata": {},
   "source": [
    "### Outline\n",
    "\n",
    "1) Introdução\n",
    "    - Descrição dos procedimentos\n",
    "2) Análise Descritiva e Limpeza    \n",
    "    - Histogramas   \n",
    "    - Normalizacao   \n",
    "3) Modelagem\n",
    "    * Redução de dimensionalidade\n",
    "        - UMAP\n",
    "    * Clusterização\n",
    "        - GMM\n",
    "    * Classificação  \n",
    "        - glmnet       \n",
    "        - Random Forest      \n",
    "5) Resultados e conclusões"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea843eae-ca24-4c19-86fa-02ea5ad676b4",
   "metadata": {},
   "source": [
    "# 1. Introdução"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11c697af-d5f1-4cf0-926d-407a5dc8664d",
   "metadata": {},
   "source": [
    "Uma grande multinacional varejista do ramo de supermercados deseja ingressar no mercado brasileiro. Você é o responsável pela análise que servirá de base para a estratégia (tomada de decisão) de entrada dessa empresa no Brasil.\n",
    "A escolha do local é uma das decisoes mais importantes que deve-se levar em consideração durante a implementação de uma nova franquia de varejo, pois determina a vantagem completitiva sobre outras franquias.\n",
    "\n",
    "O objetivo desde notebook é ELABORAR e IMPLEMENTAR os seguintes itens:\n",
    "\n",
    "\n",
    " *  **Realizar uma clusterização dos municípios brasileiros com base nas informações disponíveis e fazer uma caracterização dos           municípios em grupos.**\n",
    " \n",
    " *  **Elaborar uma forma de classificar um novo município entre os grupos já criados na etapa anterior**\n",
    " \n",
    " * **Responder quais grupos de municípios deveriam ser a porta de entrada para empresa no país e porque.**\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bc85396-a05e-462a-bde2-3757b6f0166d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/projects'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "\n",
    "import sklearn as sk\n",
    "import shap\n",
    "from umap import UMAP\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a8b72fc-8799-404f-bf3d-938366b5ad91",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 2. Análise descritiva e limpeza dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84456bad-f32f-4ee1-af29-4641b35541be",
   "metadata": {},
   "source": [
    "Antes de começarmos a modelagem dos dados, devemos selecionar somente as columas numéricas que representam as nossas variáveis onde queremos realizar a clusterização das observações. Como as variáveis estão em diferentes escalas, e iremos utilizar técnicas de redução de dimensionalidade que sao baseados em KNN, é de grande ajuda converter as features em z-scores ou normalizá-las em seu range (minmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "45a8ed8f-0128-48a8-99ed-a8fbd0233245",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## leitura do dataset\n",
    "diretorio = \"/projects/data/raw/dados_municipais.csv\"\n",
    "dados_municipais = pd.read_csv(diretorio, sep = \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "188b9520-45da-4dca-8607-05b961c72108",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Selecionando somente as colunas numéricas\n",
    "columns = [ \n",
    "\t   'Densidade_demográfica_2000', 'Distância_à_capital_km_',\n",
    "       'Esperança_de_vida_ao_nascer_2000',\n",
    "       'Mortalidade_até_um_ano_de_idade_2000',\n",
    "       'Taxa_de_fecundidade_total_2000',\n",
    "       'Percentual_de_pessoas_de_25_anos_ou_mais_analfabetas_2000',\n",
    "       'Renda_per_Capita_2000', 'Índice_de_Gini_2000',\n",
    "       'Intensidade_da_indigência_2000', 'Intensidade_da_pobreza_2000',\n",
    "       'Índice_de_Desenvolvimento_Humano_Municipal_2000',\n",
    "       'Taxa_bruta_de_freqüência_à_escola_2000', 'Taxa_de_alfabetização_2000',\n",
    "       'Média_de_anos_de_estudo_das_pessoas_de_25_anos_ou_mais_de_idade_2000',\n",
    "       'População_de_25_anos_ou_mais_de_idade_1991',\n",
    "       'População_de_25_anos_ou_mais_de_idade_2000',\n",
    "       'População_de_65_anos_ou_mais_de_idade_1991',\n",
    "       'População_de_65_anos_ou_mais_de_idade_2000', 'População_total_1991',\n",
    "       'População_total_2000', 'População_urbana_2000',\n",
    "       'População_rural_2000'\n",
    "    ]\n",
    "\n",
    "dados_municipais_numericFeatures = dados_municipais[columns].values\n",
    "dados_municipais_numericFeatures.shape\n",
    "\n",
    "## normalizacao\n",
    "scale = sk.preprocessing.MinMaxScaler()\n",
    "minmax = scale.fit_transform(dados_municipais_numericFeatures)\n",
    "df_std = pd.DataFrame(minmax)\n",
    "df_std.columns = columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d07511-bb1a-4a0f-a370-55b3a4f027bb",
   "metadata": {},
   "source": [
    "------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71370a39-d6a0-4d5b-9f3a-b41cddd99c3e",
   "metadata": {},
   "source": [
    "### 2.1 - Análise Exploratória"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a762d501-37cc-4c27-b72f-b835f3c8f2b4",
   "metadata": {},
   "source": [
    "A seguinte tabela mostra as estatisticas descritivas das variáveis numéricas do dataset.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba258946-d50a-46da-a7ff-4447c44668a2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5360, 22)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Densidade_demográfica_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.007417</td>\n",
       "      <td>0.040881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000869</td>\n",
       "      <td>0.001824</td>\n",
       "      <td>0.003689</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distância_à_capital_km_</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.171785</td>\n",
       "      <td>0.110823</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082624</td>\n",
       "      <td>0.154661</td>\n",
       "      <td>0.242701</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Esperança_de_vida_ao_nascer_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.561566</td>\n",
       "      <td>0.203722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.426353</td>\n",
       "      <td>0.582459</td>\n",
       "      <td>0.715904</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mortalidade_até_um_ano_de_idade_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.275640</td>\n",
       "      <td>0.177007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.128008</td>\n",
       "      <td>0.231374</td>\n",
       "      <td>0.391600</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taxa_de_fecundidade_total_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.209861</td>\n",
       "      <td>0.119769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.123596</td>\n",
       "      <td>0.178170</td>\n",
       "      <td>0.268058</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percentual_de_pessoas_de_25_anos_ou_mais_analfabetas_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.361973</td>\n",
       "      <td>0.222020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.175996</td>\n",
       "      <td>0.302682</td>\n",
       "      <td>0.554404</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renda_per_Capita_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.153251</td>\n",
       "      <td>0.103588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.062606</td>\n",
       "      <td>0.140526</td>\n",
       "      <td>0.220017</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Índice_de_Gini_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.437143</td>\n",
       "      <td>0.127735</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intensidade_da_indigência_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.563686</td>\n",
       "      <td>0.119236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.485056</td>\n",
       "      <td>0.560512</td>\n",
       "      <td>0.639052</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intensidade_da_pobreza_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.466455</td>\n",
       "      <td>0.159645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333841</td>\n",
       "      <td>0.454289</td>\n",
       "      <td>0.590159</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Índice_de_Desenvolvimento_Humano_Municipal_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.513235</td>\n",
       "      <td>0.184507</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.360619</td>\n",
       "      <td>0.544248</td>\n",
       "      <td>0.665929</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taxa_bruta_de_freqüência_à_escola_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.529316</td>\n",
       "      <td>0.106247</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.466371</td>\n",
       "      <td>0.535844</td>\n",
       "      <td>0.599343</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taxa_de_alfabetização_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.650109</td>\n",
       "      <td>0.208385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.478243</td>\n",
       "      <td>0.713640</td>\n",
       "      <td>0.818912</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Média_de_anos_de_estudo_das_pessoas_de_25_anos_ou_mais_de_idade_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.364827</td>\n",
       "      <td>0.145312</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.248869</td>\n",
       "      <td>0.367647</td>\n",
       "      <td>0.463801</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_de_25_anos_ou_mais_de_idade_1991</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.002352</td>\n",
       "      <td>0.017702</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000369</td>\n",
       "      <td>0.000755</td>\n",
       "      <td>0.001535</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_de_25_anos_ou_mais_de_idade_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.002597</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000775</td>\n",
       "      <td>0.001642</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_de_65_anos_ou_mais_de_idade_1991</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.002552</td>\n",
       "      <td>0.018829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000452</td>\n",
       "      <td>0.000936</td>\n",
       "      <td>0.001937</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_de_65_anos_ou_mais_de_idade_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.002658</td>\n",
       "      <td>0.019003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>0.000933</td>\n",
       "      <td>0.001924</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_total_1991</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.002706</td>\n",
       "      <td>0.017737</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000451</td>\n",
       "      <td>0.000950</td>\n",
       "      <td>0.001902</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_total_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.002884</td>\n",
       "      <td>0.018122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000924</td>\n",
       "      <td>0.001965</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_urbana_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.002557</td>\n",
       "      <td>0.018572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.000542</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_rural_2000</th>\n",
       "      <td>5360.0</td>\n",
       "      <td>0.009327</td>\n",
       "      <td>0.016955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002956</td>\n",
       "      <td>0.005930</td>\n",
       "      <td>0.011679</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     count      mean  \\\n",
       "Densidade_demográfica_2000                          5360.0  0.007417   \n",
       "Distância_à_capital_km_                             5360.0  0.171785   \n",
       "Esperança_de_vida_ao_nascer_2000                    5360.0  0.561566   \n",
       "Mortalidade_até_um_ano_de_idade_2000                5360.0  0.275640   \n",
       "Taxa_de_fecundidade_total_2000                      5360.0  0.209861   \n",
       "Percentual_de_pessoas_de_25_anos_ou_mais_analfa...  5360.0  0.361973   \n",
       "Renda_per_Capita_2000                               5360.0  0.153251   \n",
       "Índice_de_Gini_2000                                 5360.0  0.437143   \n",
       "Intensidade_da_indigência_2000                      5360.0  0.563686   \n",
       "Intensidade_da_pobreza_2000                         5360.0  0.466455   \n",
       "Índice_de_Desenvolvimento_Humano_Municipal_2000     5360.0  0.513235   \n",
       "Taxa_bruta_de_freqüência_à_escola_2000              5360.0  0.529316   \n",
       "Taxa_de_alfabetização_2000                          5360.0  0.650109   \n",
       "Média_de_anos_de_estudo_das_pessoas_de_25_anos_...  5360.0  0.364827   \n",
       "População_de_25_anos_ou_mais_de_idade_1991          5360.0  0.002352   \n",
       "População_de_25_anos_ou_mais_de_idade_2000          5360.0  0.002597   \n",
       "População_de_65_anos_ou_mais_de_idade_1991          5360.0  0.002552   \n",
       "População_de_65_anos_ou_mais_de_idade_2000          5360.0  0.002658   \n",
       "População_total_1991                                5360.0  0.002706   \n",
       "População_total_2000                                5360.0  0.002884   \n",
       "População_urbana_2000                               5360.0  0.002557   \n",
       "População_rural_2000                                5360.0  0.009327   \n",
       "\n",
       "                                                         std  min       25%  \\\n",
       "Densidade_demográfica_2000                          0.040881  0.0  0.000869   \n",
       "Distância_à_capital_km_                             0.110823  0.0  0.082624   \n",
       "Esperança_de_vida_ao_nascer_2000                    0.203722  0.0  0.426353   \n",
       "Mortalidade_até_um_ano_de_idade_2000                0.177007  0.0  0.128008   \n",
       "Taxa_de_fecundidade_total_2000                      0.119769  0.0  0.123596   \n",
       "Percentual_de_pessoas_de_25_anos_ou_mais_analfa...  0.222020  0.0  0.175996   \n",
       "Renda_per_Capita_2000                               0.103588  0.0  0.062606   \n",
       "Índice_de_Gini_2000                                 0.127735  0.0  0.347826   \n",
       "Intensidade_da_indigência_2000                      0.119236  0.0  0.485056   \n",
       "Intensidade_da_pobreza_2000                         0.159645  0.0  0.333841   \n",
       "Índice_de_Desenvolvimento_Humano_Municipal_2000     0.184507  0.0  0.360619   \n",
       "Taxa_bruta_de_freqüência_à_escola_2000              0.106247  0.0  0.466371   \n",
       "Taxa_de_alfabetização_2000                          0.208385  0.0  0.478243   \n",
       "Média_de_anos_de_estudo_das_pessoas_de_25_anos_...  0.145312  0.0  0.248869   \n",
       "População_de_25_anos_ou_mais_de_idade_1991          0.017702  0.0  0.000369   \n",
       "População_de_25_anos_ou_mais_de_idade_2000          0.018061  0.0  0.000359   \n",
       "População_de_65_anos_ou_mais_de_idade_1991          0.018829  0.0  0.000452   \n",
       "População_de_65_anos_ou_mais_de_idade_2000          0.019003  0.0  0.000456   \n",
       "População_total_1991                                0.017737  0.0  0.000451   \n",
       "População_total_2000                                0.018122  0.0  0.000418   \n",
       "População_urbana_2000                               0.018572  0.0  0.000246   \n",
       "População_rural_2000                                0.016955  0.0  0.002956   \n",
       "\n",
       "                                                         50%       75%  max  \n",
       "Densidade_demográfica_2000                          0.001824  0.003689  1.0  \n",
       "Distância_à_capital_km_                             0.154661  0.242701  1.0  \n",
       "Esperança_de_vida_ao_nascer_2000                    0.582459  0.715904  1.0  \n",
       "Mortalidade_até_um_ano_de_idade_2000                0.231374  0.391600  1.0  \n",
       "Taxa_de_fecundidade_total_2000                      0.178170  0.268058  1.0  \n",
       "Percentual_de_pessoas_de_25_anos_ou_mais_analfa...  0.302682  0.554404  1.0  \n",
       "Renda_per_Capita_2000                               0.140526  0.220017  1.0  \n",
       "Índice_de_Gini_2000                                 0.434783  0.521739  1.0  \n",
       "Intensidade_da_indigência_2000                      0.560512  0.639052  1.0  \n",
       "Intensidade_da_pobreza_2000                         0.454289  0.590159  1.0  \n",
       "Índice_de_Desenvolvimento_Humano_Municipal_2000     0.544248  0.665929  1.0  \n",
       "Taxa_bruta_de_freqüência_à_escola_2000              0.535844  0.599343  1.0  \n",
       "Taxa_de_alfabetização_2000                          0.713640  0.818912  1.0  \n",
       "Média_de_anos_de_estudo_das_pessoas_de_25_anos_...  0.367647  0.463801  1.0  \n",
       "População_de_25_anos_ou_mais_de_idade_1991          0.000755  0.001535  1.0  \n",
       "População_de_25_anos_ou_mais_de_idade_2000          0.000775  0.001642  1.0  \n",
       "População_de_65_anos_ou_mais_de_idade_1991          0.000936  0.001937  1.0  \n",
       "População_de_65_anos_ou_mais_de_idade_2000          0.000933  0.001924  1.0  \n",
       "População_total_1991                                0.000950  0.001902  1.0  \n",
       "População_total_2000                                0.000924  0.001965  1.0  \n",
       "População_urbana_2000                               0.000542  0.001338  1.0  \n",
       "População_rural_2000                                0.005930  0.011679  1.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## estatisticas descritivas do dataset\n",
    "print(df_std.shape)\n",
    "#dados_municipais.describe().transpose()\n",
    "df_std.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60ded95d-3e0c-48f9-889e-cb4461a118dd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) \n",
    "seaborn.set(style = \"darkgrid\")\n",
    "fig, axes = plt.subplots(7,2, figsize = (12,40))\n",
    "for i, j in zip(df_std.columns[1:15], range(0, 13)):\n",
    "    df_std.hist(\"{0}\".format(i), bins = 22 , ax = axes[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38c56e8c-947b-4915-bd87-7b555b14c2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_treino = df_std.columns[1:14]\n",
    "df_treino = df_std[features_treino]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "248a4679-eb11-4094-996b-ca0d3860da3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Distância_à_capital_km_', 'Esperança_de_vida_ao_nascer_2000',\n",
       "       'Mortalidade_até_um_ano_de_idade_2000',\n",
       "       'Taxa_de_fecundidade_total_2000',\n",
       "       'Percentual_de_pessoas_de_25_anos_ou_mais_analfabetas_2000',\n",
       "       'Renda_per_Capita_2000', 'Índice_de_Gini_2000',\n",
       "       'Intensidade_da_indigência_2000', 'Intensidade_da_pobreza_2000',\n",
       "       'Índice_de_Desenvolvimento_Humano_Municipal_2000',\n",
       "       'Taxa_bruta_de_freqüência_à_escola_2000', 'Taxa_de_alfabetização_2000',\n",
       "       'Média_de_anos_de_estudo_das_pessoas_de_25_anos_ou_mais_de_idade_2000'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inserir aqui o motivo da escolha das variaveis 1:14\n",
    "df_treino.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef0def8-ad83-416c-84c1-e8185e33974f",
   "metadata": {},
   "source": [
    "----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c9bb0d-2526-42a3-b481-426772b7b1b5",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 3. Modelagem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f38916-3616-4a4b-a033-1ab8ce9b183e",
   "metadata": {},
   "source": [
    "## 3.1 - Redução de dimensionalidade"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe648b5-5852-4d08-8be6-6b7e5416c737",
   "metadata": {},
   "source": [
    "No âmbido do big data, métodos de redução de dimensionalidade são ubíquos. Essas técnicas nos permitem visualizar e sumarizar os dados em espaços topológicos com dimensão inferior àquelas dos dados originais"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afb22693-dc77-4369-afbf-d3706d08891c",
   "metadata": {},
   "source": [
    "### UMAP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "628a4470-58a8-49c4-b28e-ad0c20dfac5f",
   "metadata": {},
   "source": [
    "\n",
    "UMAP é um acrônimo para **Uniform Manifold Aproximation and Projection**. UMAP é utilizado como uma técnica de redução de dimensionalidade. Assim, quando temos um conjunto de dados com um número de variáveis elevado (k > 3), utiliza-se UMAP para projetar essas variáveis num espaço topológico de menor dimensão onde a estrutura original dos dados é preservada.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "519b240d-ac15-4126-8670-1f90cab6943a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#fig, axes =  plt.subplots(9, figsize = (15, 100))\n",
    "for i, j in zip(itertools.product([25, 50, 100, 200, 300, 400, 500, 600], [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]), range(48)):\n",
    "    redim = UMAP(n_neighbors = i[0], min_dist = i[1], n_components = 5)\n",
    "    emb = redim.fit_transform(minmax)\n",
    "    plt.scatter(emb[:,2], emb[:,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f43e15bc-5ce9-4d3a-95da-f37a017c26ac",
   "metadata": {},
   "source": [
    "Após uma busca intensiva no espaço hiperparamétrico, os melhores valores para os hiperparâmetros do UMAP são:\n",
    "\n",
    " - n_neighbors = 300\n",
    " - min_dist = 0.155\n",
    " - n_components = 6\n",
    " - metric = 'chebyshev'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "74832577-de72-470c-96ea-4b27c27556e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "umap = UMAP(random_state = 124104, n_neighbors = 300,  min_dist = 0.155, n_components = 6, metric = 'chebyshev')\n",
    "UMAP_embedding = umap.fit_transform(df_treino)\n",
    "embedding = pd.DataFrame(UMAP_embedding)\n",
    "embedding.columns = [\"emb0\",\"emb1\",\"emb2\",\"emb3\",\"emb4\",\"emb5\"]\n",
    "#chebyshev performs better than mahalanobis\n",
    "embedding.to_csv(\"data/transformed/UMAP_embeddings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d82eabad-d236-485c-8976-90a7051320bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.set_theme()\n",
    "fig, (s,s1) = plt.subplots(1,2, figsize = (16,6),dpi=50)\n",
    "s.scatter(UMAP_embedding[:,2], UMAP_embedding[:,5])\n",
    "heatmap, xedges, yedges = np.histogram2d(UMAP_embedding[:,2], UMAP_embedding[:,5], bins=20)\n",
    "extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n",
    "s1.imshow(heatmap.T, extent=extent, origin='lower',cmap=\"viridis\",aspect=\"auto\", interpolation=\"catrom\",interpolation_stage=\"rgba\")\n",
    "s1.grid(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da998403-554b-4067-996b-b9759f8bb505",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3.2. Clusterização"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4c7b71-532d-4908-9aea-ebba7153ce87",
   "metadata": {
    "tags": []
   },
   "source": [
    "O Mapa de calor a seguir mostra os pontos de maior densidade, indicando os possíveis clusters.\n",
    "Devido à anisotropia presente na projeção dos dados obtidos pelo processo de redução de dimensionalidade e nas multiplas vizinhanças onde a densidade da distribuição é elevada, temos indícios de que um modelo gaussiano misto (GMM) possa ser adequado para realizar a clusterizacao dos municipios\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153adeb6-8eeb-47f9-ba84-3a8491fe704b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 3.2.1. - GMM "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "949d46cd-1bfb-436d-b29b-571fc9921779",
   "metadata": {},
   "source": [
    "Gaussian Mixture Models ou Modelo de Misturas Gaussianas é um modelo probabilístico paramétrico onde ajustamos uma combinação linear convexa de componentes probabilisticas com distribuição normal.\n",
    "Um GMM com K componentes é dado pela expressão:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "120f2235-9f7f-4bf8-815c-14c17e813ee6",
   "metadata": {},
   "source": [
    "$$f(x) = \\sum_{i=1}^K \\pi_i N(\\pmb{x}| \\pmb{\\mu}_i, \\Sigma_i)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "add1e647-1aeb-4d7e-9216-73b1d4261bc2",
   "metadata": {},
   "source": [
    "Em que  \\\n",
    "$\\mu_i$: vetor de médias da i-ésima componente \\\n",
    "$\\Sigma_i$: matriz de variância-covariância da i-ésima componente \\\n",
    "$ 0 \\leq \\pi_i \\leq 1;   \\sum_{i=1}^K \\pi_i = 1$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eac5a535-7a1d-41ac-953d-7c39b5e33744",
   "metadata": {},
   "source": [
    "No GMM é necessário estimar $3K$ parâmetros:\n",
    "\n",
    "$$(\\pi_1 \\ldots \\pi_K,\\mu_K, \\ldots, \\mu_K, \\Sigma_1, \\ldots, \\Sigma_K)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57365ae9-a61f-4e1b-8251-70f09744b767",
   "metadata": {},
   "source": [
    "#### Definindo a quantidade ideal de componentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "4d07da01-d92b-4414-ae03-e2b87962640b",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(151561)\n",
    "\n",
    "S = []\n",
    "\n",
    "BIC = []\n",
    "\n",
    "K = range(2,30)\n",
    "\n",
    "X = df_c[['emb2', 'emb5']]\n",
    "\n",
    "for k in K:\n",
    "    model = GaussianMixture(n_components = k, init_params = \"kmeans\", max_iter = 100, covariance_type = 'full' )\n",
    "\n",
    "    labels = model.fit_predict(X)\n",
    "    \n",
    "    S.append(sk.metrics.silhouette_score(X, labels, metric = 'euclidean'))\n",
    "    \n",
    "    BIC.append(model.bic(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "a76a8541-d51e-4bce-90f5-c34a851d90a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'BIC')"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.set_theme()\n",
    "fig, (sub,sub1) = plt.subplots(1,2, figsize = (16,4),dpi=80)\n",
    "sub.plot(K, S, 'bo-')\n",
    "sub.set_ylabel(\"Silhouete score\")\n",
    "sub.set_title(\"Identificando o número de clusters utilizando Silhueta\")\n",
    "sub1.plot(K, BIC, 'bo-')\n",
    "sub1.set_title(\"Identificando o número de clusters utilizando BIC\")\n",
    "sub1.set_ylabel('BIC')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e5d087-a3b3-484c-ba67-73768f08abc1",
   "metadata": {},
   "source": [
    "#### GMM com 5 componentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "2bccfe69-e29f-4ee4-9b1d-8b80d751a815",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f265582fa60>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(561)\n",
    "#np.random.seed(6114)\n",
    "gmm = GaussianMixture(n_components = 5, init_params = \"kmeans\", max_iter = 100, covariance_type = 'diag')\n",
    "\n",
    "gmm = gmm.fit(embedding[[\"emb2\",\"emb5\"]])\n",
    "\n",
    "mixtures = pd.DataFrame({\"model\" : gmm.predict(embedding[[\"emb2\",\"emb5\"]])})\n",
    "\n",
    "df_c = pd.concat([embedding[[\"emb2\",\"emb5\"]],  mixtures.reset_index(drop = True)], axis = 1)\n",
    "\n",
    "\n",
    "df_c.columns = ['emb2','emb5',\"cluster\"]\n",
    "\n",
    "#plotting clusters\n",
    "fg = seaborn.FacetGrid(data=df_c, hue = 'cluster', aspect=1.8,height =5)\n",
    "fg.map(plt.scatter, 'emb2',\"emb5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "f18baf68-73b6-46eb-8391-0ea1df1fcce9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantidade de observações por cluster:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "model\n",
       "0     899\n",
       "1    1291\n",
       "2     736\n",
       "3    1173\n",
       "4    1261\n",
       "Name: model, dtype: int64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_clusterizados = pd.DataFrame(df_std)\n",
    "dados_clusterizados.columns = columns\n",
    "dados_clusterizados = pd.concat([dados_clusterizados, mixtures.reset_index(drop = True)], axis = 1)\n",
    "print(\"Quantidade de observações por cluster:\")\n",
    "dados_clusterizados.groupby(['model'])['model'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "ce2ef5dc-6470-42aa-9317-9b821ab43eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_clusterizados.to_csv(\"clustered_dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecdbb94a-c4ed-47ca-94b5-c88f4ac853a3",
   "metadata": {
    "tags": []
   },
   "source": [
    "# EAD nos clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56eaa933-367a-4d65-9a21-6f749491af4e",
   "metadata": {},
   "source": [
    "Nesta sessão iremos analisar os clusters obtidos por meio do GMM para extrair o maximo de informações e caracterizá-los."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "id": "e60b6a78-fce6-4d21-9592-638ad19eb0b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Taxa_bruta_de_freqüência_à_escola_2000')"
      ]
     },
     "execution_count": 751,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x1740 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (linha_b_1 , linha_b_2, linha_b_3,linha_b_4,linha_b_5) = plt.subplots(5,2, figsize = (15,29), dpi = 60)\n",
    "seaborn.boxplot(ax= linha_b_1[0], x = \"model\", y = \"Intensidade_da_pobreza_2000\", data = dados_clusterizados,)\n",
    "linha_b_1[0].set_title('Intensidade_da_pobreza_2000')\n",
    "\n",
    "seaborn.boxplot(ax= linha_b_1[1], x = \"model\", y = \"Índice_de_Gini_2000\", data = dados_clusterizados )\n",
    "linha_b_1[1].set_title('Indice de Gini 2000')\n",
    "\n",
    "seaborn.boxplot(ax= linha_b_2[0], x = \"model\", y = \"Renda_per_Capita_2000\", data = dados_clusterizados )\n",
    "linha_b_2[0].set_title('Renda_per_Capita_2000')\n",
    "\n",
    "seaborn.boxplot(ax= linha_b_2[1], x = \"model\", y = \"Esperança_de_vida_ao_nascer_2000\", data = dados_clusterizados )\n",
    "linha_b_2[1].set_title('Esperança_de_vida_ao_nascer_2000')\n",
    "\n",
    "seaborn.boxplot(ax= linha_b_3[0], x = \"model\", y = \"Média_de_anos_de_estudo_das_pessoas_de_25_anos_ou_mais_de_idade_2000\", data =dados_clusterizados, )\n",
    "linha_b_3[0].set_title('Media Anos de estudo Pessoas >= 25 anos')\n",
    "\n",
    "seaborn.boxplot(ax= linha_b_3[1], x = \"model\", y = \"Taxa_de_fecundidade_total_2000\", data = dados_clusterizados)\n",
    "linha_b_3[1].set_title('Taxa_de_fecundidade_total_2000')\n",
    "\n",
    "seaborn.boxplot(ax= linha_b_4[0], x = \"model\", y = \"Percentual_de_pessoas_de_25_anos_ou_mais_analfabetas_2000\", data = dados_clusterizados )\n",
    "linha_b_4[0].set_title('Pessoas >25 anos analfabetas')\n",
    "\n",
    "seaborn.boxplot(ax= linha_b_4[1], x = \"model\", y = \"Distância_à_capital_km_\", data = dados_clusterizados )\n",
    "linha_b_4[1].set_title('Distância_à_capital_km_')\n",
    "\n",
    "seaborn.boxplot(ax= linha_b_5[0], x = \"model\", y = \"Mortalidade_até_um_ano_de_idade_2000\", data = dados_clusterizados )\n",
    "linha_b_5[0].set_title('Mortalidade_até_um_ano_de_idade_2000')\n",
    "\n",
    "\n",
    "seaborn.boxplot(ax= linha_b_5[1], x = \"model\", y = \"Taxa_bruta_de_freqüência_à_escola_2000\", data = dados_clusterizados )\n",
    "linha_b_5[1].set_title('Taxa_bruta_de_freqüência_à_escola_2000')\n",
    "\n",
    "\n",
    "#seaborn.boxplot(x = \"model\", y= \"Intensidade_da_pobreza_2000\", data =dados_clusterizados )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e49a99-48d1-4033-8152-42d0975c626b",
   "metadata": {},
   "source": [
    "### Cluster 0\n",
    "Os municípios do cluster 0 possuem elevados índices de pobreza e desigualdade com índices de renda per capita muito baixos\n",
    "Esse cluster possui os municipios com as menores expectativas de vida\n",
    "E elevados indices de analfabetismo em adultos.\n",
    "o que pode indicar falta de saneamento básico e infra-estrutura em geral.\n",
    "Essas condições implicam em uma menor expectativa de vida em relação aos demais clusters.\n",
    " \n",
    " * CARACTERÍSTICAS\n",
    "  - Rendas per capita muito baixas\n",
    "  - Maior intensidade de pobreza\n",
    "  - Elevadas taxas de mortalidade infantil\n",
    "  - Expectativa de vida muito baixa\n",
    "  - Elevadas taxas de adultos analfabetos \n",
    "  - Municipios subdesenvolvidos\n",
    "\n",
    "### Cluster 1\n",
    "Já o cluster 1 engloba os municípios com os menores indices de Gini (maior igualdade) e rendas per-capita razoáveis.\n",
    "Observa-se o segundo menor indice de analfabetismo em adultos com mais de 25 anos, menos mortalidade infantil e elevada expectativa de vida\n",
    "As taxas de mortalidade infantil nesse cluster é bem baixa em relação aos outros clusters.\n",
    "Elevada expectativa de vida e baixa taxa de fecundidade\n",
    "\n",
    "* CARACTERÍSTICAS\n",
    "  - Municípios com distribuição de renda mais igualitária\n",
    "  - menor intensidade de pobreza\n",
    "  - baixa mortalidade infantil\n",
    "    \n",
    "### Cluster 2\n",
    "Esse é o cluster com maiores indices de riqueza per capita, no entanto observa-se  municipios com indices de gini acima de 0.8, o que indica que alguns municipios nesse cluster possuem elevado indice de desigualdade.\n",
    "As pessoas tiveram uma quantidade elevada de anos de estudo e observa-se menor indice de analfabetismo.\n",
    "Nele, também observamos melhores condiçoes de expectativa de vida, o que pode indicar melhores condiçoes sanitarias, menor violência e menor indice de desigualdade. \n",
    "Esse cluster também abriga os municípios mais próximos de suas respectivas capitais.\n",
    "\n",
    " * CARACTERÍSTICAS\n",
    "     - Municipios mais desenvolvidos e com melhor infra estrutura\n",
    "     - maiores indices de alfabetização em adultos\n",
    "     - distribuição de renda assimpétrica à direita: concentra rendas mais elevadas em poucos municipios\n",
    "\n",
    "### Cluster 3\n",
    "   * CARACTERÍSTICAS:\n",
    "     - Municipios em desenvolvimento\n",
    "     - Pobreza moderada\n",
    "     - Municípios desiguais assim como no cluster 0\n",
    "     - Expectativa de vida razoável\n",
    "\n",
    "### Cluster 4\n",
    "  Semelhante ao cluster 1, porém com maiores indices de expectativa de vida e menor mortalidade infantil\n",
    "  - Compreende muniicípios com maiores distâncias até suas respectivas capitais\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b81d232-8663-4513-a68b-3dd674f9fec4",
   "metadata": {},
   "source": [
    "# 4. Classificação\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "id": "62a6a539-dec4-4e7e-9fc8-2998acb25c71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.experimental import enable_halving_search_cv  # noqa\n",
    "from sklearn.model_selection import HalvingGridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "856b2e66-29a4-4ce5-b0f1-2347bfa86374",
   "metadata": {},
   "source": [
    "### Split do dataset em Treino e validação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "1cf131a5-7529-4d4e-a7b6-2325aa11185f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_treino\n",
    "\n",
    "y = mixtures['model']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.35, random_state = 35)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3589657d-5ecc-4b8e-806d-d490f755cb9b",
   "metadata": {},
   "source": [
    "### Random Forest\n",
    "##### A seguir vamos implementar um RandomForest para classificação multinomial utilizando a heurística de busca em grade e cross validation com 10 estratos (10-fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "id": "5339d8b9-72e3-489f-b23e-cee5863d1160",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_depth=15,\n",
       "                       min_samples_split=4, n_estimators=256, random_state=234)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_depth=15,\n",
       "                       min_samples_split=4, n_estimators=256, random_state=234)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(bootstrap=False, criterion='entropy', max_depth=15,\n",
       "                       min_samples_split=4, n_estimators=256, random_state=234)"
      ]
     },
     "execution_count": 651,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_classifier = RandomForestClassifier(random_state = 234)\n",
    "\n",
    "grid_ = {'max_depth': [3, 5, 10, 15, 20, 21, 23, 25],\n",
    "         \n",
    "         'min_samples_split': [2, 5, 10,13,15,16,17,19,20,30],       \n",
    "         \n",
    "         'min_samples_split':[2,3,4,6,10, 40, 70, 90, 120, 160, 200],\n",
    "         \n",
    "         \"bootstrap\": [True, False],\n",
    "         \n",
    "         \"criterion\": [\"gini\", \"entropy\"]\n",
    "        }\n",
    "\n",
    "sh =  HalvingGridSearchCV(rf_classifier, grid_, cv = 10,\n",
    "                         factor = 2, resource = 'n_estimators',\n",
    "                         max_resources = 300) \\\n",
    "                        .fit(X_train, y_train)\n",
    "sh.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "id": "66cdf4a6-8842-4efb-b98b-9432deb38161",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_best = sh.best_estimator_.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 803,
   "id": "c849a3fb-dab5-4496-b6ca-1b6a9d1eb6b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 macro: 0.9242233552378376\n",
      "F1 micro: 0.923773987206823\n",
      "Acurácia: 0.923773987206823\n",
      "\n",
      "  Matriz de Confusão:\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "      <td>320</td>\n",
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       "      <td>10</td>\n",
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       "      <td>446</td>\n",
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       "  </tbody>\n",
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       "     0    1    2    3    4\n",
       "0  320    0    0    0   10\n",
       "1    0  405    5   24    0\n",
       "2    0   15  223    8    0\n",
       "3    0   31    6  339   11\n",
       "4   23    0    0   10  446"
      ]
     },
     "execution_count": 803,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"F1 macro: {0}\".format(sk.metrics.f1_score(y_test, rf_best, average = \"macro\")))\n",
    "\n",
    "print(\"F1 micro: {0}\".format(sk.metrics.f1_score(y_test, rf_best, average =\"micro\")))\n",
    "\n",
    "print(\"Acurácia: {0}\\n\".format(sk.metrics.accuracy_score(y_pred = rf_best, y_true = y_test)))\n",
    "\n",
    "print(\"  Matriz de Confusão:\")\n",
    "\n",
    "cm = sk.metrics.confusion_matrix(y_test, rf_best)\n",
    "\n",
    "pd.DataFrame(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 786,
   "id": "17e4a28d-6ba1-4791-bbd7-88979e9c55ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.920482165793894"
      ]
     },
     "execution_count": 786,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db58b880-5ef6-4a12-afe5-1ad1445ee7d4",
   "metadata": {},
   "source": [
    "### Escolha dos municípios"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "597acccf-b6a7-4668-b67e-9ba3bb3751e0",
   "metadata": {},
   "source": [
    "##### Partindo do pressuposto que municípios com melhores condicoes de infraestrutura são mais adequados para iniciar um negocio no ramo de varejo, temos que o cluster 2 seria o mais adequado, pois nele observamos as maiores rendas per-capita, menores taxas de mortalidade infantil. Ainda, este cluster se torna ideal, pois é sábido que o nivel de consumo consumo é diretamente proporcional a renda de uma pessoa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ef4d0c9-a8f1-40a3-8122-e41bc67ce37b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_municipais[[\"Município\",\"Estado\",\"0\"]] = dados_municipais.Município.str.split(\"(\", expand = True)\n",
    "dados_municipais.drop(['0'], axis=1,inplace=True)\n",
    "municipios = pd.concat([dados_municipais, mixtures], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 743,
   "id": "7efc34cc-a979-4f24-bcae-f60467545e2e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Estado</th>\n",
       "      <td>SP)</td>\n",
       "      <td>RS)</td>\n",
       "      <td>SC)</td>\n",
       "      <td>MG)</td>\n",
       "      <td>RJ)</td>\n",
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       "      <td>MT)</td>\n",
       "      <td>MS)</td>\n",
       "      <td>ES)</td>\n",
       "      <td>...</td>\n",
       "      <td>AL)</td>\n",
       "      <td>PI)</td>\n",
       "      <td>DF)</td>\n",
       "      <td>CE)</td>\n",
       "      <td>RR)</td>\n",
       "      <td>BA</td>\n",
       "      <td>AP)</td>\n",
       "      <td>SE)</td>\n",
       "      <td>AM)</td>\n",
       "      <td>AC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>247</td>\n",
       "      <td>118</td>\n",
       "      <td>85</td>\n",
       "      <td>77</td>\n",
       "      <td>52</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         26   23   24   11   19   18   9    13   12   8   ...   1    17   7   \\\n",
       "Estado  SP)  RS)  SC)  MG)  RJ)  PR)  GO)  MT)  MS)  ES)  ...  AL)  PI)  DF)   \n",
       "n       247  118   85   77   52   50   31   19   16    8  ...    1    1    1   \n",
       "\n",
       "         6    22  4    3    25   2    0   \n",
       "Estado  CE)  RR)  BA  AP)  SE)  AM)  AC)  \n",
       "n         1    1   1    1    1    1    1  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 743,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 726x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_2 = municipios[municipios['model'] == 2]\n",
    "cluster_2.reset_index(inplace=True)\n",
    "estados =cluster_2.groupby(['Estado'])[\"Estado\"].count().reset_index(name=\"n\").sort_values(['n'], ascending=False)\n",
    "plt.figure(figsize=(22,12), dpi=33)\n",
    "plt.bar(estados['Estado'], estados['n'])\n",
    "(estados.transpose())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c9789f-38b8-4432-9890-9b5b6cfc8082",
   "metadata": {},
   "source": [
    "Política de seleção:\n",
    "O recomendado é realizar uma amostra aleatória simples sem reposição, tendo em vista a homogeneidade das observcações no cluster.\n",
    "Caso se faça necessário diversificar por região, podedmos implementar uma amostragem aleatória cujas probabilidades de seleçãoo de \n",
    "cada elemento seja inversamente proporcional a frequencia das observações por região"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "id": "64f7e2df-cafd-457c-abcb-0a4d9ba42b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([719, 560,  98,  64,  34, 219,  78, 190,  16, 598, 194, 608, 124,\n",
       "       488, 267, 369, 396, 670, 274, 185])"
      ]
     },
     "execution_count": 745,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "#cluster_2.drop([\"index\"],axis=1,inplace=True)\n",
    "amostra  = random.sample(range(1, cluster_2.shape[0]), 20)\n",
    "np.transpose(amostra )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b801ec2-f1b0-40a6-aa18-7d9c34888ff7",
   "metadata": {},
   "source": [
    "### Municípios Selecionados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 788,
   "id": "704ff2ca-816a-416e-af98-8350b630a5af",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <td>20.0</td>\n",
       "      <td>2166.850000</td>\n",
       "      <td>1651.813111</td>\n",
       "      <td>188.000</td>\n",
       "      <td>793.000000</td>\n",
       "      <td>1643.000000</td>\n",
       "      <td>3606.750000</td>\n",
       "      <td>5231.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Código</th>\n",
       "      <td>20.0</td>\n",
       "      <td>403700.200000</td>\n",
       "      <td>68174.813826</td>\n",
       "      <td>250750.000</td>\n",
       "      <td>351060.000000</td>\n",
       "      <td>420312.500000</td>\n",
       "      <td>431125.000000</td>\n",
       "      <td>520910.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Área_km_</th>\n",
       "      <td>20.0</td>\n",
       "      <td>934.180000</td>\n",
       "      <td>1291.949204</td>\n",
       "      <td>110.600</td>\n",
       "      <td>258.850000</td>\n",
       "      <td>366.600000</td>\n",
       "      <td>1026.000000</td>\n",
       "      <td>5491.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Densidade_demográfica_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>277.700000</td>\n",
       "      <td>607.099565</td>\n",
       "      <td>7.200</td>\n",
       "      <td>50.100000</td>\n",
       "      <td>148.700000</td>\n",
       "      <td>240.350000</td>\n",
       "      <td>2822.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distância_à_capital_km_</th>\n",
       "      <td>20.0</td>\n",
       "      <td>193.984443</td>\n",
       "      <td>148.270966</td>\n",
       "      <td>0.000</td>\n",
       "      <td>84.932389</td>\n",
       "      <td>137.788635</td>\n",
       "      <td>318.555053</td>\n",
       "      <td>455.787054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Esperança_de_vida_ao_nascer_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>73.165000</td>\n",
       "      <td>2.386357</td>\n",
       "      <td>68.220</td>\n",
       "      <td>71.770000</td>\n",
       "      <td>73.575000</td>\n",
       "      <td>74.900000</td>\n",
       "      <td>76.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mortalidade_até_um_ano_de_idade_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>15.267500</td>\n",
       "      <td>7.033064</td>\n",
       "      <td>8.720</td>\n",
       "      <td>10.870000</td>\n",
       "      <td>14.145000</td>\n",
       "      <td>15.920000</td>\n",
       "      <td>40.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taxa_de_fecundidade_total_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>2.354500</td>\n",
       "      <td>0.407747</td>\n",
       "      <td>1.900</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>2.225000</td>\n",
       "      <td>2.437500</td>\n",
       "      <td>3.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percentual_de_pessoas_de_25_anos_ou_mais_analfabetas_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>9.291500</td>\n",
       "      <td>3.043651</td>\n",
       "      <td>5.710</td>\n",
       "      <td>7.312500</td>\n",
       "      <td>8.165000</td>\n",
       "      <td>11.622500</td>\n",
       "      <td>15.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renda_per_Capita_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>325.438000</td>\n",
       "      <td>55.821114</td>\n",
       "      <td>235.770</td>\n",
       "      <td>289.717500</td>\n",
       "      <td>319.200000</td>\n",
       "      <td>356.360000</td>\n",
       "      <td>441.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Índice_de_Gini_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.552500</td>\n",
       "      <td>0.043995</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.527500</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.572500</td>\n",
       "      <td>0.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intensidade_da_indigência_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>54.982000</td>\n",
       "      <td>9.184228</td>\n",
       "      <td>42.230</td>\n",
       "      <td>48.452500</td>\n",
       "      <td>54.745000</td>\n",
       "      <td>61.177500</td>\n",
       "      <td>71.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intensidade_da_pobreza_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>40.511500</td>\n",
       "      <td>4.648196</td>\n",
       "      <td>31.200</td>\n",
       "      <td>38.207500</td>\n",
       "      <td>40.600000</td>\n",
       "      <td>44.647500</td>\n",
       "      <td>47.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Índice_de_Desenvolvimento_Humano_Municipal_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.812700</td>\n",
       "      <td>0.022569</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.804500</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>0.829250</td>\n",
       "      <td>0.848000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taxa_bruta_de_freqüência_à_escola_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>85.233500</td>\n",
       "      <td>5.991933</td>\n",
       "      <td>74.990</td>\n",
       "      <td>81.655000</td>\n",
       "      <td>85.350000</td>\n",
       "      <td>88.035000</td>\n",
       "      <td>97.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taxa_de_alfabetização_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>92.251500</td>\n",
       "      <td>2.266997</td>\n",
       "      <td>87.640</td>\n",
       "      <td>90.967500</td>\n",
       "      <td>93.050000</td>\n",
       "      <td>93.545000</td>\n",
       "      <td>95.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Média_de_anos_de_estudo_das_pessoas_de_25_anos_ou_mais_de_idade_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>6.233000</td>\n",
       "      <td>0.707338</td>\n",
       "      <td>4.860</td>\n",
       "      <td>5.912500</td>\n",
       "      <td>6.245000</td>\n",
       "      <td>6.622500</td>\n",
       "      <td>7.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_de_25_anos_ou_mais_de_idade_1991</th>\n",
       "      <td>20.0</td>\n",
       "      <td>48567.850000</td>\n",
       "      <td>62683.394820</td>\n",
       "      <td>1975.000</td>\n",
       "      <td>11239.750000</td>\n",
       "      <td>24123.500000</td>\n",
       "      <td>57132.000000</td>\n",
       "      <td>226433.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_de_25_anos_ou_mais_de_idade_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>64362.450000</td>\n",
       "      <td>82610.238436</td>\n",
       "      <td>3290.000</td>\n",
       "      <td>14585.250000</td>\n",
       "      <td>30639.500000</td>\n",
       "      <td>79172.750000</td>\n",
       "      <td>306142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_de_65_anos_ou_mais_de_idade_1991</th>\n",
       "      <td>20.0</td>\n",
       "      <td>4696.450000</td>\n",
       "      <td>6294.938196</td>\n",
       "      <td>137.000</td>\n",
       "      <td>1079.000000</td>\n",
       "      <td>1957.000000</td>\n",
       "      <td>4949.750000</td>\n",
       "      <td>23896.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_de_65_anos_ou_mais_de_idade_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>6990.900000</td>\n",
       "      <td>9153.657859</td>\n",
       "      <td>416.000</td>\n",
       "      <td>1623.250000</td>\n",
       "      <td>3017.500000</td>\n",
       "      <td>7118.250000</td>\n",
       "      <td>33275.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_total_1991</th>\n",
       "      <td>20.0</td>\n",
       "      <td>100748.100000</td>\n",
       "      <td>132538.151838</td>\n",
       "      <td>4188.000</td>\n",
       "      <td>22782.750000</td>\n",
       "      <td>52016.500000</td>\n",
       "      <td>122352.750000</td>\n",
       "      <td>497600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_total_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>121012.100000</td>\n",
       "      <td>157187.905872</td>\n",
       "      <td>6045.000</td>\n",
       "      <td>26233.250000</td>\n",
       "      <td>57497.000000</td>\n",
       "      <td>152913.500000</td>\n",
       "      <td>597934.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_urbana_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>115230.050000</td>\n",
       "      <td>156517.770310</td>\n",
       "      <td>4980.000</td>\n",
       "      <td>19860.750000</td>\n",
       "      <td>50953.000000</td>\n",
       "      <td>142227.500000</td>\n",
       "      <td>597934.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>População_rural_2000</th>\n",
       "      <td>20.0</td>\n",
       "      <td>5782.050000</td>\n",
       "      <td>3962.144176</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3309.000000</td>\n",
       "      <td>5753.000000</td>\n",
       "      <td>8166.250000</td>\n",
       "      <td>13696.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>20.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    count           mean  \\\n",
       "index                                                20.0    2166.850000   \n",
       "Código                                               20.0  403700.200000   \n",
       "Área_km_                                             20.0     934.180000   \n",
       "Densidade_demográfica_2000                           20.0     277.700000   \n",
       "Distância_à_capital_km_                              20.0     193.984443   \n",
       "Esperança_de_vida_ao_nascer_2000                     20.0      73.165000   \n",
       "Mortalidade_até_um_ano_de_idade_2000                 20.0      15.267500   \n",
       "Taxa_de_fecundidade_total_2000                       20.0       2.354500   \n",
       "Percentual_de_pessoas_de_25_anos_ou_mais_analfa...   20.0       9.291500   \n",
       "Renda_per_Capita_2000                                20.0     325.438000   \n",
       "Índice_de_Gini_2000                                  20.0       0.552500   \n",
       "Intensidade_da_indigência_2000                       20.0      54.982000   \n",
       "Intensidade_da_pobreza_2000                          20.0      40.511500   \n",
       "Índice_de_Desenvolvimento_Humano_Municipal_2000      20.0       0.812700   \n",
       "Taxa_bruta_de_freqüência_à_escola_2000               20.0      85.233500   \n",
       "Taxa_de_alfabetização_2000                           20.0      92.251500   \n",
       "Média_de_anos_de_estudo_das_pessoas_de_25_anos_...   20.0       6.233000   \n",
       "População_de_25_anos_ou_mais_de_idade_1991           20.0   48567.850000   \n",
       "População_de_25_anos_ou_mais_de_idade_2000           20.0   64362.450000   \n",
       "População_de_65_anos_ou_mais_de_idade_1991           20.0    4696.450000   \n",
       "População_de_65_anos_ou_mais_de_idade_2000           20.0    6990.900000   \n",
       "População_total_1991                                 20.0  100748.100000   \n",
       "População_total_2000                                 20.0  121012.100000   \n",
       "População_urbana_2000                                20.0  115230.050000   \n",
       "População_rural_2000                                 20.0    5782.050000   \n",
       "model                                                20.0       2.000000   \n",
       "\n",
       "                                                              std         min  \\\n",
       "index                                                 1651.813111     188.000   \n",
       "Código                                               68174.813826  250750.000   \n",
       "Área_km_                                              1291.949204     110.600   \n",
       "Densidade_demográfica_2000                             607.099565       7.200   \n",
       "Distância_à_capital_km_                                148.270966       0.000   \n",
       "Esperança_de_vida_ao_nascer_2000                         2.386357      68.220   \n",
       "Mortalidade_até_um_ano_de_idade_2000                     7.033064       8.720   \n",
       "Taxa_de_fecundidade_total_2000                           0.407747       1.900   \n",
       "Percentual_de_pessoas_de_25_anos_ou_mais_analfa...       3.043651       5.710   \n",
       "Renda_per_Capita_2000                                   55.821114     235.770   \n",
       "Índice_de_Gini_2000                                      0.043995       0.490   \n",
       "Intensidade_da_indigência_2000                           9.184228      42.230   \n",
       "Intensidade_da_pobreza_2000                              4.648196      31.200   \n",
       "Índice_de_Desenvolvimento_Humano_Municipal_2000          0.022569       0.766   \n",
       "Taxa_bruta_de_freqüência_à_escola_2000                   5.991933      74.990   \n",
       "Taxa_de_alfabetização_2000                               2.266997      87.640   \n",
       "Média_de_anos_de_estudo_das_pessoas_de_25_anos_...       0.707338       4.860   \n",
       "População_de_25_anos_ou_mais_de_idade_1991           62683.394820    1975.000   \n",
       "População_de_25_anos_ou_mais_de_idade_2000           82610.238436    3290.000   \n",
       "População_de_65_anos_ou_mais_de_idade_1991            6294.938196     137.000   \n",
       "População_de_65_anos_ou_mais_de_idade_2000            9153.657859     416.000   \n",
       "População_total_1991                                132538.151838    4188.000   \n",
       "População_total_2000                                157187.905872    6045.000   \n",
       "População_urbana_2000                               156517.770310    4980.000   \n",
       "População_rural_2000                                  3962.144176       0.000   \n",
       "model                                                    0.000000       2.000   \n",
       "\n",
       "                                                              25%  \\\n",
       "index                                                  793.000000   \n",
       "Código                                              351060.000000   \n",
       "Área_km_                                               258.850000   \n",
       "Densidade_demográfica_2000                              50.100000   \n",
       "Distância_à_capital_km_                                 84.932389   \n",
       "Esperança_de_vida_ao_nascer_2000                        71.770000   \n",
       "Mortalidade_até_um_ano_de_idade_2000                    10.870000   \n",
       "Taxa_de_fecundidade_total_2000                           2.100000   \n",
       "Percentual_de_pessoas_de_25_anos_ou_mais_analfa...       7.312500   \n",
       "Renda_per_Capita_2000                                  289.717500   \n",
       "Índice_de_Gini_2000                                      0.527500   \n",
       "Intensidade_da_indigência_2000                          48.452500   \n",
       "Intensidade_da_pobreza_2000                             38.207500   \n",
       "Índice_de_Desenvolvimento_Humano_Municipal_2000          0.804500   \n",
       "Taxa_bruta_de_freqüência_à_escola_2000                  81.655000   \n",
       "Taxa_de_alfabetização_2000                              90.967500   \n",
       "Média_de_anos_de_estudo_das_pessoas_de_25_anos_...       5.912500   \n",
       "População_de_25_anos_ou_mais_de_idade_1991           11239.750000   \n",
       "População_de_25_anos_ou_mais_de_idade_2000           14585.250000   \n",
       "População_de_65_anos_ou_mais_de_idade_1991            1079.000000   \n",
       "População_de_65_anos_ou_mais_de_idade_2000            1623.250000   \n",
       "População_total_1991                                 22782.750000   \n",
       "População_total_2000                                 26233.250000   \n",
       "População_urbana_2000                                19860.750000   \n",
       "População_rural_2000                                  3309.000000   \n",
       "model                                                    2.000000   \n",
       "\n",
       "                                                              50%  \\\n",
       "index                                                 1643.000000   \n",
       "Código                                              420312.500000   \n",
       "Área_km_                                               366.600000   \n",
       "Densidade_demográfica_2000                             148.700000   \n",
       "Distância_à_capital_km_                                137.788635   \n",
       "Esperança_de_vida_ao_nascer_2000                        73.575000   \n",
       "Mortalidade_até_um_ano_de_idade_2000                    14.145000   \n",
       "Taxa_de_fecundidade_total_2000                           2.225000   \n",
       "Percentual_de_pessoas_de_25_anos_ou_mais_analfa...       8.165000   \n",
       "Renda_per_Capita_2000                                  319.200000   \n",
       "Índice_de_Gini_2000                                      0.550000   \n",
       "Intensidade_da_indigência_2000                          54.745000   \n",
       "Intensidade_da_pobreza_2000                             40.600000   \n",
       "Índice_de_Desenvolvimento_Humano_Municipal_2000          0.814000   \n",
       "Taxa_bruta_de_freqüência_à_escola_2000                  85.350000   \n",
       "Taxa_de_alfabetização_2000                              93.050000   \n",
       "Média_de_anos_de_estudo_das_pessoas_de_25_anos_...       6.245000   \n",
       "População_de_25_anos_ou_mais_de_idade_1991           24123.500000   \n",
       "População_de_25_anos_ou_mais_de_idade_2000           30639.500000   \n",
       "População_de_65_anos_ou_mais_de_idade_1991            1957.000000   \n",
       "População_de_65_anos_ou_mais_de_idade_2000            3017.500000   \n",
       "População_total_1991                                 52016.500000   \n",
       "População_total_2000                                 57497.000000   \n",
       "População_urbana_2000                                50953.000000   \n",
       "População_rural_2000                                  5753.000000   \n",
       "model                                                    2.000000   \n",
       "\n",
       "                                                              75%  \\\n",
       "index                                                 3606.750000   \n",
       "Código                                              431125.000000   \n",
       "Área_km_                                              1026.000000   \n",
       "Densidade_demográfica_2000                             240.350000   \n",
       "Distância_à_capital_km_                                318.555053   \n",
       "Esperança_de_vida_ao_nascer_2000                        74.900000   \n",
       "Mortalidade_até_um_ano_de_idade_2000                    15.920000   \n",
       "Taxa_de_fecundidade_total_2000                           2.437500   \n",
       "Percentual_de_pessoas_de_25_anos_ou_mais_analfa...      11.622500   \n",
       "Renda_per_Capita_2000                                  356.360000   \n",
       "Índice_de_Gini_2000                                      0.572500   \n",
       "Intensidade_da_indigência_2000                          61.177500   \n",
       "Intensidade_da_pobreza_2000                             44.647500   \n",
       "Índice_de_Desenvolvimento_Humano_Municipal_2000          0.829250   \n",
       "Taxa_bruta_de_freqüência_à_escola_2000                  88.035000   \n",
       "Taxa_de_alfabetização_2000                              93.545000   \n",
       "Média_de_anos_de_estudo_das_pessoas_de_25_anos_...       6.622500   \n",
       "População_de_25_anos_ou_mais_de_idade_1991           57132.000000   \n",
       "População_de_25_anos_ou_mais_de_idade_2000           79172.750000   \n",
       "População_de_65_anos_ou_mais_de_idade_1991            4949.750000   \n",
       "População_de_65_anos_ou_mais_de_idade_2000            7118.250000   \n",
       "População_total_1991                                122352.750000   \n",
       "População_total_2000                                152913.500000   \n",
       "População_urbana_2000                               142227.500000   \n",
       "População_rural_2000                                  8166.250000   \n",
       "model                                                    2.000000   \n",
       "\n",
       "                                                              max  \n",
       "index                                                 5231.000000  \n",
       "Código                                              520910.000000  \n",
       "Área_km_                                              5491.800000  \n",
       "Densidade_demográfica_2000                            2822.200000  \n",
       "Distância_à_capital_km_                                455.787054  \n",
       "Esperança_de_vida_ao_nascer_2000                        76.590000  \n",
       "Mortalidade_até_um_ano_de_idade_2000                    40.400000  \n",
       "Taxa_de_fecundidade_total_2000                           3.350000  \n",
       "Percentual_de_pessoas_de_25_anos_ou_mais_analfa...      15.460000  \n",
       "Renda_per_Capita_2000                                  441.880000  \n",
       "Índice_de_Gini_2000                                      0.660000  \n",
       "Intensidade_da_indigência_2000                          71.690000  \n",
       "Intensidade_da_pobreza_2000                             47.060000  \n",
       "Índice_de_Desenvolvimento_Humano_Municipal_2000          0.848000  \n",
       "Taxa_bruta_de_freqüência_à_escola_2000                  97.090000  \n",
       "Taxa_de_alfabetização_2000                              95.080000  \n",
       "Média_de_anos_de_estudo_das_pessoas_de_25_anos_...       7.530000  \n",
       "População_de_25_anos_ou_mais_de_idade_1991          226433.000000  \n",
       "População_de_25_anos_ou_mais_de_idade_2000          306142.000000  \n",
       "População_de_65_anos_ou_mais_de_idade_1991           23896.000000  \n",
       "População_de_65_anos_ou_mais_de_idade_2000           33275.000000  \n",
       "População_total_1991                                497600.000000  \n",
       "População_total_2000                                597934.000000  \n",
       "População_urbana_2000                               597934.000000  \n",
       "População_rural_2000                                 13696.000000  \n",
       "model                                                    2.000000  "
      ]
     },
     "execution_count": 788,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_2.iloc[amostra].sort_values('Renda_per_Capita_2000',ascending=False).describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 798,
   "id": "e4455125-5dec-4b58-b2b1-990d46eb9113",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>719</th>\n",
       "      <th>560</th>\n",
       "      <th>98</th>\n",
       "      <th>64</th>\n",
       "      <th>34</th>\n",
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       "      <th>185</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Municipio</th>\n",
       "      <td>Três Passos</td>\n",
       "      <td>Primavera do Leste</td>\n",
       "      <td>Birigui</td>\n",
       "      <td>Balneário Barra do Sul</td>\n",
       "      <td>Araraquara</td>\n",
       "      <td>Cruz Alta</td>\n",
       "      <td>Barra Mansa</td>\n",
       "      <td>Chapecó</td>\n",
       "      <td>Anápolis</td>\n",
       "      <td>Salto de Pirapora</td>\n",
       "      <td>Cidreira</td>\n",
       "      <td>Santa Isabel</td>\n",
       "      <td>Caçapava</td>\n",
       "      <td>Palhoça</td>\n",
       "      <td>Frederico Westphalen</td>\n",
       "      <td>João Pessoa</td>\n",
       "      <td>Londrina</td>\n",
       "      <td>São Sebastião do Caí</td>\n",
       "      <td>Goiatuba</td>\n",
       "      <td>Cedral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estado</th>\n",
       "      <td>RS)</td>\n",
       "      <td>MT)</td>\n",
       "      <td>SP)</td>\n",
       "      <td>SC)</td>\n",
       "      <td>SP)</td>\n",
       "      <td>RS)</td>\n",
       "      <td>RJ)</td>\n",
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       "      <td>SP)</td>\n",
       "      <td>RS)</td>\n",
       "      <td>SP)</td>\n",
       "      <td>SP)</td>\n",
       "      <td>SC)</td>\n",
       "      <td>RS)</td>\n",
       "      <td>PB)</td>\n",
       "      <td>PR)</td>\n",
       "      <td>RS)</td>\n",
       "      <td>GO)</td>\n",
       "      <td>SP)</td>\n",
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      "text/plain": [
       "                   719                 560      98                      64   \\\n",
       "Municipio  Três Passos  Primavera do Leste  Birigui  Balneário Barra do Sul   \n",
       "Estado             RS)                 MT)      SP)                     SC)   \n",
       "\n",
       "                  34         219          78       190       16   \\\n",
       "Municipio  Araraquara  Cruz Alta  Barra Mansa  Chapecó  Anápolis   \n",
       "Estado            SP)        RS)          RJ)      SC)       GO)   \n",
       "\n",
       "                         598       194           608       124      488  \\\n",
       "Municipio  Salto de Pirapora  Cidreira  Santa Isabel  Caçapava  Palhoça   \n",
       "Estado                   SP)       RS)           SP)       SP)      SC)   \n",
       "\n",
       "                            267          369       396                   670  \\\n",
       "Municipio  Frederico Westphalen  João Pessoa  Londrina  São Sebastião do Caí   \n",
       "Estado                      RS)          PB)       PR)                   RS)   \n",
       "\n",
       "                274     185  \n",
       "Municipio  Goiatuba  Cedral  \n",
       "Estado          GO)     SP)  "
      ]
     },
     "execution_count": 798,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_2.iloc[amostra][['Municipio',\"Estado\"]].transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf406e5d-e7bc-46ab-a488-7327f985ba06",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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