Projeto.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQz8l0WLVEI1"
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      "source": [
        "# 1. Introdução"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JUV6r44XVMw1"
      },
      "source": [
        "## 1.1 Resumo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZdyKVyTleHjk"
      },
      "source": [
        "Quando falamos em ciência de dados, pensamos em super computadores, máquinas trabalhando de maneira autonoma, várias fórmulas matemáticas assustadoras, etc. Porém acabamos esquecendo que ela está o tempo todo em nosso dia a dia e que podemos usar em diferentes setores da indústria, como logística, finanças, entretenimento e até mesmo na medicina.\n",
        "\n",
        "Um exemplo nessa área que os algoritmos de Machine Learning são muito utilizados é a parte de Drug discovery. Essa é uma das práticas mais antigas da nossa sociedade, com aproximadamente 5000 anos [1]. Atualmente, o processo de descobrimento é dividido em 3 partes: pesquisa, desenvolvimento e aprovação [2], e os algoritmos são usados na parte de pesquisa como forma de automatizar os processos de identificação de novos compostos. \n",
        "\n",
        "Nesse projeto será utilizado uma amostra de dados sobre testes de drogas em células, que foi fornecido pela Alura para realizar sua terceira imersão de dados. Os dados foram utilizados no desafio do laboratório de inovação e ciência de Harvard e fornecem informações genéricas como quantidade de droga dosada, tempo de dosagem, tipo de droga ou até mesmo informações mais específicas como genes presentes em cada célula, que precisam de um certo dominio na área para tratá-los."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vkrJ7M9nVPfv"
      },
      "source": [
        "## 1.2 Objetivo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xxR87VjpfZ3"
      },
      "source": [
        "A tuberculose é uma doença infecciosa e transmissível que afeta prioritariamente os pulmões, é causada pelo Mycobacterium tuberculosis ou bacilo de Koch e cerca de 10 milhões de pessoas adoecem por tuberculose no mundo, e a doença leva mais de um milhão de pessoas a óbito anualmente [3]. Seu tratamento é feito principalmente com a Rifampicina e seu custo é por volta de U$10 [4]. Por esse motivo é sempre bom descobrir novas substâncias que possam ser utilizados como forma de tratamento alternativo.\n",
        "\n",
        "O objetivo desse projeto é trabalhar em cima dos dados de testes, aplicando técnicas de limpeza, exploração e manipulação de dados para tentar descobrir relações não triviais entre parâmetros, e ao final realizar um modelo de Machine Learning que consiga identificar novas possíveis substâncias que possam ser utilizadas para o tratamento da tuberculose."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AqYVUGPpVZxm"
      },
      "source": [
        "# 2. Conhecendo os dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GESqQTzbx6Ip"
      },
      "source": [
        "Como comentado na sessão anterior, a amostra de dados apresentam desde informações mais gerais até as mais específicas. Sendo assim, essa sessão tem como intuito a apresentação e um melhor entendimento dos dados, mostrando os números de linha e colunas, tipo de features(categórica ou númerica), cardinalidade das features categóricas e suas distribuições. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vkYhKAeDVeXp"
      },
      "source": [
        "## 2.1 Importando bibliotecas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8SR1WybzXm2"
      },
      "source": [
        "Começando pelas bibliotecas, nessa sessão usaremos a Pandas e a Numpy, duas bibliotecas famosas por sua capacidade de trabalhar com dados. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOz_7sVAdk2z"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tYbAkuQ7Vj97"
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      "source": [
        "## 2.2 Importando os dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qBI9r_jM0aBp"
      },
      "source": [
        "Os dados estão disponíveis na página do Github da Alura e podem ser acesados [neste link](https://github.com/alura-cursos/imersaodados3). Como podemos ver, há dois datasets, um referente aos testes e outro aos resultados, que estão conectados pela coluna \"id\". Para importa-los, usaremos a função *read_csv()* do Pandas, colocando a URL com os dados e alterando o parâmetro *compression* para 'zip' no dataset dos testes, informando o tipo de arquivo da URL. Utilizando a função *head()*, é possível ver as 5 primeiras linhas do Dataframe e com *shape* é possível ver o número de linhas e o números de colunas."
      ]
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      "source": [
        "dados_teste = pd.read_csv('https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true', compression = 'zip')\n",
        "dados_teste.head()"
      ],
      "execution_count": null,
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              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.1220</td>\n",
              "      <td>-0.3752</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2274</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...    c-96    c-97    c-98    c-99\n",
              "0  id_000644bb2  com_droga     24   D1  ... -0.3981  0.2139  0.3801  0.4176\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.1522  0.1241  0.6077  0.7371\n",
              "2  id_000a6266a  com_droga     48   D1  ... -0.6417 -0.2187 -1.4080  0.6931\n",
              "3  id_0015fd391  com_droga     48   D1  ... -1.6210 -0.8784 -0.3876 -0.8154\n",
              "4  id_001626bd3  com_droga     72   D2  ...  0.1094  0.2885 -0.3786  0.7125\n",
              "\n",
              "[5 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XG2i07co26zO",
        "outputId": "82a4c68c-e2ae-44e7-cda9-91efc13510cc"
      },
      "source": [
        "dados_teste.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23814, 877)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fNEDRPwmtnFW"
      },
      "source": [
        "Como python nem sempre consegue interpretar o \"-\" como uma string, para evitar problemas posteriores, vamos mudar por \"_\" utilizando a função *replace()*."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "JCNvNT9OuMfN",
        "outputId": "9da686f1-cb88-4048-ec9c-823b4b3dd7c4"
      },
      "source": [
        "nome_das_colunas = dados_teste.columns\n",
        "\n",
        "novo_nome_coluna = []\n",
        "for coluna in nome_das_colunas:\n",
        "  coluna = coluna.replace('-', '_')\n",
        "  novo_nome_coluna.append(coluna)\n",
        "dados_teste.columns = novo_nome_coluna \n",
        "dados_teste.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>droga</th>\n",
              "      <th>g_0</th>\n",
              "      <th>g_1</th>\n",
              "      <th>g_2</th>\n",
              "      <th>g_3</th>\n",
              "      <th>g_4</th>\n",
              "      <th>g_5</th>\n",
              "      <th>g_6</th>\n",
              "      <th>g_7</th>\n",
              "      <th>g_8</th>\n",
              "      <th>g_9</th>\n",
              "      <th>g_10</th>\n",
              "      <th>g_11</th>\n",
              "      <th>g_12</th>\n",
              "      <th>g_13</th>\n",
              "      <th>g_14</th>\n",
              "      <th>g_15</th>\n",
              "      <th>g_16</th>\n",
              "      <th>g_17</th>\n",
              "      <th>g_18</th>\n",
              "      <th>g_19</th>\n",
              "      <th>g_20</th>\n",
              "      <th>g_21</th>\n",
              "      <th>g_22</th>\n",
              "      <th>g_23</th>\n",
              "      <th>g_24</th>\n",
              "      <th>g_25</th>\n",
              "      <th>g_26</th>\n",
              "      <th>g_27</th>\n",
              "      <th>g_28</th>\n",
              "      <th>g_29</th>\n",
              "      <th>g_30</th>\n",
              "      <th>g_31</th>\n",
              "      <th>g_32</th>\n",
              "      <th>g_33</th>\n",
              "      <th>g_34</th>\n",
              "      <th>...</th>\n",
              "      <th>c_60</th>\n",
              "      <th>c_61</th>\n",
              "      <th>c_62</th>\n",
              "      <th>c_63</th>\n",
              "      <th>c_64</th>\n",
              "      <th>c_65</th>\n",
              "      <th>c_66</th>\n",
              "      <th>c_67</th>\n",
              "      <th>c_68</th>\n",
              "      <th>c_69</th>\n",
              "      <th>c_70</th>\n",
              "      <th>c_71</th>\n",
              "      <th>c_72</th>\n",
              "      <th>c_73</th>\n",
              "      <th>c_74</th>\n",
              "      <th>c_75</th>\n",
              "      <th>c_76</th>\n",
              "      <th>c_77</th>\n",
              "      <th>c_78</th>\n",
              "      <th>c_79</th>\n",
              "      <th>c_80</th>\n",
              "      <th>c_81</th>\n",
              "      <th>c_82</th>\n",
              "      <th>c_83</th>\n",
              "      <th>c_84</th>\n",
              "      <th>c_85</th>\n",
              "      <th>c_86</th>\n",
              "      <th>c_87</th>\n",
              "      <th>c_88</th>\n",
              "      <th>c_89</th>\n",
              "      <th>c_90</th>\n",
              "      <th>c_91</th>\n",
              "      <th>c_92</th>\n",
              "      <th>c_93</th>\n",
              "      <th>c_94</th>\n",
              "      <th>c_95</th>\n",
              "      <th>c_96</th>\n",
              "      <th>c_97</th>\n",
              "      <th>c_98</th>\n",
              "      <th>c_99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4805</td>\n",
              "      <td>0.4965</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4083</td>\n",
              "      <td>0.0319</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.5477</td>\n",
              "      <td>-0.7576</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.1220</td>\n",
              "      <td>-0.3752</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2274</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...    c_96    c_97    c_98    c_99\n",
              "0  id_000644bb2  com_droga     24   D1  ... -0.3981  0.2139  0.3801  0.4176\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.1522  0.1241  0.6077  0.7371\n",
              "2  id_000a6266a  com_droga     48   D1  ... -0.6417 -0.2187 -1.4080  0.6931\n",
              "3  id_0015fd391  com_droga     48   D1  ... -1.6210 -0.8784 -0.3876 -0.8154\n",
              "4  id_001626bd3  com_droga     72   D2  ...  0.1094  0.2885 -0.3786  0.7125\n",
              "\n",
              "[5 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "czchxK3UPwAw",
        "outputId": "77a4e422-9df5-4ab2-f5e4-c7c6b0157eb2"
      },
      "source": [
        "dados_resultados = pd.read_csv('https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_resultados.csv?raw=true')\n",
        "dados_resultados.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>5-alpha_reductase_inhibitor</th>\n",
              "      <th>11-beta-hsd1_inhibitor</th>\n",
              "      <th>acat_inhibitor</th>\n",
              "      <th>acetylcholine_receptor_agonist</th>\n",
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              "      <th>alk_inhibitor</th>\n",
              "      <th>ampk_activator</th>\n",
              "      <th>analgesic</th>\n",
              "      <th>androgen_receptor_agonist</th>\n",
              "      <th>androgen_receptor_antagonist</th>\n",
              "      <th>anesthetic_-_local</th>\n",
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              "      <th>angiotensin_receptor_antagonist</th>\n",
              "      <th>anti-inflammatory</th>\n",
              "      <th>antiarrhythmic</th>\n",
              "      <th>antibiotic</th>\n",
              "      <th>anticonvulsant</th>\n",
              "      <th>antifungal</th>\n",
              "      <th>antihistamine</th>\n",
              "      <th>antimalarial</th>\n",
              "      <th>antioxidant</th>\n",
              "      <th>antiprotozoal</th>\n",
              "      <th>antiviral</th>\n",
              "      <th>apoptosis_stimulant</th>\n",
              "      <th>aromatase_inhibitor</th>\n",
              "      <th>atm_kinase_inhibitor</th>\n",
              "      <th>atp-sensitive_potassium_channel_antagonist</th>\n",
              "      <th>atp_synthase_inhibitor</th>\n",
              "      <th>atpase_inhibitor</th>\n",
              "      <th>atr_kinase_inhibitor</th>\n",
              "      <th>aurora_kinase_inhibitor</th>\n",
              "      <th>...</th>\n",
              "      <th>protein_synthesis_inhibitor</th>\n",
              "      <th>protein_tyrosine_kinase_inhibitor</th>\n",
              "      <th>radiopaque_medium</th>\n",
              "      <th>raf_inhibitor</th>\n",
              "      <th>ras_gtpase_inhibitor</th>\n",
              "      <th>retinoid_receptor_agonist</th>\n",
              "      <th>retinoid_receptor_antagonist</th>\n",
              "      <th>rho_associated_kinase_inhibitor</th>\n",
              "      <th>ribonucleoside_reductase_inhibitor</th>\n",
              "      <th>rna_polymerase_inhibitor</th>\n",
              "      <th>serotonin_receptor_agonist</th>\n",
              "      <th>serotonin_receptor_antagonist</th>\n",
              "      <th>serotonin_reuptake_inhibitor</th>\n",
              "      <th>sigma_receptor_agonist</th>\n",
              "      <th>sigma_receptor_antagonist</th>\n",
              "      <th>smoothened_receptor_antagonist</th>\n",
              "      <th>sodium_channel_inhibitor</th>\n",
              "      <th>sphingosine_receptor_agonist</th>\n",
              "      <th>src_inhibitor</th>\n",
              "      <th>steroid</th>\n",
              "      <th>syk_inhibitor</th>\n",
              "      <th>tachykinin_antagonist</th>\n",
              "      <th>tgf-beta_receptor_inhibitor</th>\n",
              "      <th>thrombin_inhibitor</th>\n",
              "      <th>thymidylate_synthase_inhibitor</th>\n",
              "      <th>tlr_agonist</th>\n",
              "      <th>tlr_antagonist</th>\n",
              "      <th>tnf_inhibitor</th>\n",
              "      <th>topoisomerase_inhibitor</th>\n",
              "      <th>transient_receptor_potential_channel_antagonist</th>\n",
              "      <th>tropomyosin_receptor_kinase_inhibitor</th>\n",
              "      <th>trpv_agonist</th>\n",
              "      <th>trpv_antagonist</th>\n",
              "      <th>tubulin_inhibitor</th>\n",
              "      <th>tyrosine_kinase_inhibitor</th>\n",
              "      <th>ubiquitin_specific_protease_inhibitor</th>\n",
              "      <th>vegfr_inhibitor</th>\n",
              "      <th>vitamin_b</th>\n",
              "      <th>vitamin_d_receptor_agonist</th>\n",
              "      <th>wnt_inhibitor</th>\n",
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              "</table>\n",
              "<p>5 rows × 207 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ...  wnt_inhibitor\n",
              "0  id_000644bb2  ...              0\n",
              "1  id_000779bfc  ...              0\n",
              "2  id_000a6266a  ...              0\n",
              "3  id_0015fd391  ...              0\n",
              "4  id_001626bd3  ...              0\n",
              "\n",
              "[5 rows x 207 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNMAc6U5QRzd",
        "outputId": "5197ef3f-3c54-4002-bc94-a1a244899d6f"
      },
      "source": [
        "dados_resultados.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23814, 207)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iTwfOov3QWH8"
      },
      "source": [
        "## 2.3 Construindo o Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "73K08oOpQbXU"
      },
      "source": [
        "A Rifampicina é utilizada como tratamento como tuberculose pois ela iníbe o RNA polimerase, impedindo que a bactéria se replique. Sendo assim, olharemos somente para a coluna \"rna_polymerase_inhibitor\" dos dados de resultado. Utilizando a função *join()* do pandas, juntaremos as tabelas seguindo a coluna \"id\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "UmCMDWDcTb0B",
        "outputId": "31897211-625a-4a27-8e38-cc27a2af25f3"
      },
      "source": [
        "data = dados_teste.join(dados_resultados['rna_polymerase_inhibitor'])\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0319</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.7576</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.3752</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 878 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo  ...    c_98    c_99  rna_polymerase_inhibitor\n",
              "0  id_000644bb2  com_droga     24  ...  0.3801  0.4176                         0\n",
              "1  id_000779bfc  com_droga     72  ...  0.6077  0.7371                         0\n",
              "2  id_000a6266a  com_droga     48  ... -1.4080  0.6931                         0\n",
              "3  id_0015fd391  com_droga     48  ... -0.3876 -0.8154                         0\n",
              "4  id_001626bd3  com_droga     72  ... -0.3786  0.7125                         0\n",
              "\n",
              "[5 rows x 878 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnSfAsHMUpQe",
        "outputId": "5ed33010-ce69-444e-e31d-24eb5049aaa5"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23814, 878)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ImMnFol9xLA2"
      },
      "source": [
        "## 2.4 Informações adicionais"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u0F4yH1s3XAc"
      },
      "source": [
        "Podemos ver que temos um grande número de colunas e se fomos um pouco mais a fundo, podemos ver que existem 772 colunas com nome 'g-n' e 100 colunas com 'c-n', isso porque as colunas 'g-n' representam cada gene presente na célula e o tipo de célula que a droga foi testada (saudável, cancerígena, célula óssea, célula tronco etc), respectivamente. Sendo assim, podemos ver que somente as colunas tratamento, tempo, dose e droga são categóricas, enquanto as demais são colunas númericas contínuas (Float).\n",
        "\n",
        "Agora que ja vimos como os dados organizados, vamos a uma breve legenda para que descrevam as colunas:\n",
        "*   id: Código de identificação do teste;\n",
        "*   tratamento: Teste foi realizado com ou sem droga;\n",
        "*   tempo: Tempo de observação do teste em horas;\n",
        "*   dose: Quantidade da droga utilizada;\n",
        "*   droga: Droga utilizada no tratamento;\n",
        "*   g-n's: Quantidade do gene n produzido com o estimulo da droga;\n",
        "*   c-n's: Resposta da célula n à droga.\n",
        "*   rna_polymerase_inhibitor: Booleano de drogas conseguem inibir o RNA polimerase.\n",
        "\n",
        "Vamos agora observar o tipo de dados dessas categorias, excluindo as \"g-n's\"  e as \"c-n's\" que são contínuas, e a quantidade de valores distintos em cada.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hW7WS5yFqFhL",
        "outputId": "07ada686-b87c-48bd-ccc4-d21aaf106d2e"
      },
      "source": [
        "data[['id','tratamento', 'tempo', 'dose', 'droga', 'rna_polymerase_inhibitor']].dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                          object\n",
              "tratamento                  object\n",
              "tempo                        int64\n",
              "dose                        object\n",
              "droga                       object\n",
              "rna_polymerase_inhibitor     int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6uZckf_73ePL",
        "outputId": "c8aa3a30-ebb0-4515-abce-4c7d94ad5834"
      },
      "source": [
        "for i in ['tratamento', 'tempo', 'dose', 'droga', 'rna_polymerase_inhibitor']:\n",
        "  print('A categoria \"{}\" possui {} valores distintos'.format(i, len(data[i].unique())))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A categoria \"tratamento\" possui 2 valores distintos\n",
            "A categoria \"tempo\" possui 3 valores distintos\n",
            "A categoria \"dose\" possui 2 valores distintos\n",
            "A categoria \"droga\" possui 3289 valores distintos\n",
            "A categoria \"rna_polymerase_inhibitor\" possui 2 valores distintos\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-qYk5HIp61D"
      },
      "source": [
        "Como podemos ver, as categorias \"tratamento\", \"tempo\" e \"dose\" são colunas de baixa cardinalidade, ou seja, poucos valores distintos, enquanto a coluna \"droga\" é de alta cardinalidade. Sendo assim, podemos ver como estão distribuidos nessas categorias. Para isso, usaremos a função *value_counts()*, com o parâmetro *normalize* como True, para olhar a porcentagem e como a coluna \"droga\" possui muitas categorias, olharemos somente as 10 mais presentes. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sW6_gp3LpZbA",
        "outputId": "bede7cb9-5ea8-4689-c785-de9661f25368"
      },
      "source": [
        "data['tratamento'].value_counts(normalize = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "com_droga       0.921643\n",
              "com_controle    0.078357\n",
              "Name: tratamento, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N12q0ENku7qA",
        "outputId": "f8385cb2-df56-4f31-d201-3524d6b4508d"
      },
      "source": [
        "data['tempo'].value_counts(normalize = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48    0.346435\n",
              "72    0.327202\n",
              "24    0.326363\n",
              "Name: tempo, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWjC5H71u7ZS",
        "outputId": "c7070577-68db-44f1-f9e1-ba805410d740"
      },
      "source": [
        "data['dose'].value_counts(normalize = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "D1    0.510078\n",
              "D2    0.489922\n",
              "Name: dose, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bz3w1C4TVUZE",
        "outputId": "96b96380-55bd-4bf6-ca79-f5553ac03a65"
      },
      "source": [
        "data['rna_polymerase_inhibitor'].value_counts(normalize = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.99895\n",
              "1    0.00105\n",
              "Name: rna_polymerase_inhibitor, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l0AaesvRv7Y1",
        "outputId": "2fa5c114-f88b-4109-b76b-20a8d61ec910"
      },
      "source": [
        "data['droga'].value_counts(normalize = True).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cacb2b860    0.078357\n",
              "87d714366    0.030150\n",
              "9f80f3f77    0.010330\n",
              "8b87a7a83    0.008524\n",
              "5628cb3ee    0.008482\n",
              "d08af5d4b    0.008230\n",
              "292ab2c28    0.008146\n",
              "d50f18348    0.007811\n",
              "d1b47f29d    0.007475\n",
              "67c879e79    0.000798\n",
              "Name: droga, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_HYvom1823a4"
      },
      "source": [
        "Um ponto a ser observado é o fato dass colunas \"tempo\" e \"dose\" estão bem distribuidas, enquanto as categorias \"tratamento\" e \"rna_polymerase_inhibitor\" não estão. Isso se da ao fato de que testes que não são aplicados nenhuma droga são chamados de controle e um mesmo teste de controle pode ser usado para vários outros testes e que é normal somente algumas drogas possuírem uma característica específica. Outra observação é que a droga 'cacb2b860' apresenta exatamente a mesma proporção dos testes que foram realizados como controle. Vamos ver se é pura coincidência ou se realmente esse composto foi a base de controle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uiX8H94yhVb",
        "outputId": "52e3abba-077a-4ff5-d962-87a07c114bdc"
      },
      "source": [
        "data.query('tratamento == \"com_controle\"')['droga'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cacb2b860    1866\n",
              "Name: droga, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YeEQjD_YzBlh"
      },
      "source": [
        "Como podemos ver, realmente esse composto é o que foi utilizado para ser como controle"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9i3FDMr5ZND"
      },
      "source": [
        "Agora que já entendemos melhor esses dados mais gerais, vamos olhar as colunas g-n's e c-n's. Como são muitas colunas, faremos de uma maneira diferente. Ao invés de olharmos individualmente, iremos olhar como elas se comportam em grupo, analizando sua média, seu desvio padrão, valores mínimos e máximos. Para isso, usaremos a função *describe()* do pandas e as bibliotecas **Matplotlib.pyplot** e **Seaborn** para a vizualização desses dados atraves de gráficos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ox4eZsW88Lcj",
        "outputId": "6b252b6b-4e1b-4d81-d7d6-39f98a2286d5"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "g_describe = data.loc[:, 'g_0': 'g_771'].describe().T\n",
        "g_describe.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>g_0</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.248366</td>\n",
              "      <td>1.393399</td>\n",
              "      <td>-5.513</td>\n",
              "      <td>-0.473075</td>\n",
              "      <td>-0.00885</td>\n",
              "      <td>0.525700</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_1</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.095684</td>\n",
              "      <td>0.812363</td>\n",
              "      <td>-5.737</td>\n",
              "      <td>-0.562200</td>\n",
              "      <td>-0.04660</td>\n",
              "      <td>0.403075</td>\n",
              "      <td>5.039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_2</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.152253</td>\n",
              "      <td>1.035731</td>\n",
              "      <td>-9.104</td>\n",
              "      <td>-0.437750</td>\n",
              "      <td>0.07520</td>\n",
              "      <td>0.663925</td>\n",
              "      <td>8.257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_3</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.081971</td>\n",
              "      <td>0.950012</td>\n",
              "      <td>-5.998</td>\n",
              "      <td>-0.429575</td>\n",
              "      <td>0.00805</td>\n",
              "      <td>0.463400</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_4</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.057347</td>\n",
              "      <td>1.032091</td>\n",
              "      <td>-6.369</td>\n",
              "      <td>-0.470925</td>\n",
              "      <td>-0.02690</td>\n",
              "      <td>0.465375</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       count      mean       std    min       25%      50%       75%     max\n",
              "g_0  23814.0  0.248366  1.393399 -5.513 -0.473075 -0.00885  0.525700  10.000\n",
              "g_1  23814.0 -0.095684  0.812363 -5.737 -0.562200 -0.04660  0.403075   5.039\n",
              "g_2  23814.0  0.152253  1.035731 -9.104 -0.437750  0.07520  0.663925   8.257\n",
              "g_3  23814.0  0.081971  0.950012 -5.998 -0.429575  0.00805  0.463400  10.000\n",
              "g_4  23814.0  0.057347  1.032091 -6.369 -0.470925 -0.02690  0.465375  10.000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nt1J7uhK9OF0",
        "outputId": "df45de13-0c1e-4633-c18f-233ec1fdc8e6"
      },
      "source": [
        "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n",
        "fig.suptitle('Comportamento das colunas \"g-n\"', fontsize = 16)\n",
        "plt.subplots_adjust(hspace = 0.4)\n",
        "sns.set()\n",
        "\n",
        "sns.histplot(ax=axes[0][0], x = g_describe['mean'], bins = 100)\n",
        "axes[0][0].set_title('Média', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[0][1], x = g_describe['std'], bins = 100)\n",
        "axes[0][1].set_title('Desvio Padrão', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[1][0], x = g_describe['min'])\n",
        "axes[1][0].set_title('Valor Mínimo', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[1][1], x = g_describe['max'])\n",
        "axes[1][1].set_title('Valor Máximo', fontsize = 12)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V9V/ANcAeSZ7cXjVYm+SnSQ7p2/7z2m1cleRNfdMOSXJsko8kWQu8IMleSU5t9+2yJO9NskXPMo9M8u0k17X/mrSNmQmZunIasF2SX0+yKXAA8JGe6W8Hfg3YE7gfsAvwtwBJngT8JfBE4P7AXP0mNgH+A7gPsBtwA/DeJd0TSROrqr4FXAz8Xls0a90CvLaddxWwE3AwUMAngH2SbAvQ1ln7Ax8bsMkXtK/HAvcFtmGIOqdN5J4BbA+cA6ynOWndHngy8JIkT2/n3QM4DHgecC/gHkD/iea+wLHt8h8FbgFeTXPZ8hHA44GXtuvbAfgs8C/tut4JfDbJPTYWt5aOCZm6NNNK9kTgXOCStjzAQcCrq+rqqloH/ANN0gZNRfgfVfXdqloPHDLbBqrqqqo6rqp+0a7nbTSXMSStHJcCO7SXBeeqW24CdgbuU1U3VdXX25ari4CzgGe08z0O+EVVnTZgW88B3llVP66q64E3Agckma2L0L2SXAv8HHgz8LyqOq+qTqmqc6rq1qo6G/g4t9dd+wEnVNXXqmoD8DfArX3rPbWq/qtd/oaqOrOqTmsvXV4I/HvP+p4MnF9VH26nfxz4AfDUjR1YLR37kKlLHwa+BuxOz+VKmrPTuwJnNvUn0CRpm7bv7wWc2TP/RbNtIMldgUOBJwF3b4u3TbJpVd2y2B2QNBV2Aa5m43XLP9Kc4H2pnX54Vb29nfYxmn6vRwF/zODWMWjqp9466SKa/2t34vaTzl6XVtWdulEkeRhNa95vAlsAWwKf7NnGT2fmrar1Sa7qW8VPez8k+TWalq81NMdgM26vR/tjnol7l4F7qJGwhUydac86fwLsA3yqZ9LPaS4t/kZVbd++7lZV27TTLwN27Zl/tzk281rgAcDDqmo74NFteWZfRNJykeR3aBKLb7CRuqWq1lXVa6vqvsDTgNckeXy7qk8Ce7d9UJ/B7AnZpTRdJGbsBtwMXD7P0D8GHA/sWlV3A97H7fXWHerA9sSz//Ji9X0+jKbV6/5tXXhwz/r6Y56Je1ACqRExIVPXXgQ8rr30OONW4P3AoUl+BSDJLkn+oJ1+DE0n1T3aiujNc6x/W5oK+Nq2n8Rc80paJpJsl+QpNP2/PjJz+Y856pYkT0lyv/bS5nU0/a5uBaiqK4FTaPqk/qSqzp1l0x8HXp1k9yTb0FwSPbqqbp7nLmwLXF1Vv0yyF02r3Ixjgack+d22Y/7fs/H/z7cF1gLXJ3kg8JKeaZ8Dfi3JHyfZLMmzgD2AE+YZsxbBhEydqqofVdUZAya9HrgAOK0dJfRlmpYuqurzwLuAr7TzfGWOTbwL2IrmzPg04AtLF72kCfSZJOtoLtm9ieYyXe8tL2atW2gGCX0ZuB44Ffi3qjq5Z9mP0Qwimq11DOBD3N4d4yfAL4FXLGA/Xgr8fbsvf0vPfcOq6nvAy9o4LqMZmXnxRtb3lzRJ3TqapPTonvVdBTyF5orCVcBfAU+pqp8vIG4tUKr6WzUlSZI0TraQSZIkdcyETJIkqWMmZJIkSR0zIZMkSerYVN8Ydscdd6zVq1d3HYakMTrzzDN/XlWruo5jsay/pJVnrvprqhOy1atXc8YZg+6YIGm5SjLrkxmmifWXtPLMVX95yVKSJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkKlThx13Nocdd3bXYUiaQtYfWk6m+sawmn5r12/oOgRJU8r6Q8uJLWSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCponjyClJ0kpjQqaJs3b9BkdPaWIluUuSbyX53yTfS/J3bfnuSU5PckGSo5Ns0XWskqaHCZkkzc8G4HFV9WBgT+BJSR4OvAM4tKruB1wDvKjDGCVNGRMySZqHalzffty8fRXwOODYtvxI4OkdhCdpSpmQSdI8Jdk0yXeAK4ATgR8B11bVze0sFwO7DFjuoCRnJDnjyiuvHF/AkiaeCZkkzVNV3VJVewL3BvYCHjjkcodX1ZqqWrNq1aqRxihpuvjoJC3KzGjIlzzzQfNebputNh9FSNLYVNW1SU4GHgFsn2SztpXs3sAl3UYnaZrYQqZFWeiIyLXrN7DuhhtHEJE0WklWJdm+fb8V8ETgXOBkYL92tgOBT3cToaRpZAuZJM3PzsCRSTalOak9pqpOSPJ94BNJ3gr8D/DBLoOUNF3GnpAl2RU4CtiJZmTS4VX17iSHAH8KzPR0PbiqPjfu+CRpLlV1NvCQAeU/pulPJknz1kUL2c3Aa6vqrCTbAmcmObGddmhV/VMHMUmSJHVm7AlZVV0GXNa+X5fkXAYMD5ckSVopOu1DlmQ1TdP/6cCjgJcneT5wBk0r2jUDljkIOAhgt912G1usWrzDjjubtes3cK8dt+k6FEmSJkpnoyyTbAMcB7yqqtYChwG/SvMoksuAfx60nPfxmV5r12/guutvdHSlJEl9OknIkmxOk4x9tKo+BVBVl7c3W7wVeD92jpUkSSvE2BOyJKEZDn5uVb2zp3znntmeAXx33LFJkiR1oYs+ZI8Cngec0z4LDuBg4NlJ9qS5FcaFwJ91EJskSdLYdTHK8htABkzynmOSJGlF8tFJkiRJHTMhkyRJ6pgJmSRJUsd8uLgkaWptu/UWt910erutt+Qlz3xQ1yFJC2JCJkmaajM3nZammZcsJUmSOmYLmRbNSwaSJC2OCZmWhJcMJElaOC9ZSpImzmHHnc1hx53ddRjS2NhCJkmaOGvXb+g6BGmsbCGTpCEl2TXJyUm+n+R7SV7Zlh+S5JIk32lf+3Qdq6TpYguZJA3vZuC1VXVWkm2BM5Oc2E47tKr+qcPYJE0xEzJJGlJVXQZc1r5fl+RcYJduo5K0HHjJUpIWIMlq4CHA6W3Ry5OcneRDSe4+yzIHJTkjyRlXXnnlmCKVNA1MyCRpnpJsAxwHvKqq1gKHAb8K7EnTgvbPg5arqsOrak1VrVm1atXY4pU0+UzIJGkekmxOk4x9tKo+BVBVl1fVLVV1K/B+YK8uY5Q0fUzIJGlISQJ8EDi3qt7ZU75zz2zPAL477tgkTTc79UvS8B4FPA84J8l32rKDgWcn2RMo4ELgz7oJT9K0MiGTpCFV1TeADJj0uXHHIml5MSHTyBx23Nlss9XmXH/DTWyz1eZdhyNpQh123NmsXb+Be+24zZz1xcyjlF7yzAfNe93bbb3lnZaba5o0biZkGpm16zdQFOvW30hRXYcjaUKtXb+B666/kW23vnHO+mIhj1OaWfd8p0njZqd+SZKkjo09IZvjWXA7JDkxyfntvwNvrChJkrTcdNFCNvMsuD2AhwMvS7IH8AbgpKq6P3BS+1mSJGnZG3sfsjmeBbcvsHc725HAKcDrxx2fJGkybLv1Fnfo8L9UZgYcSZOk0079fc+C26lN1gB+Buw0yzIHAQcB7LbbbqMPUp2YqYhh+BFVCxmBJWmy9Xb4X8p1OtBIk6azTv0DngV3m6oqGPzX4rPgVo616zfMa1TVfOeXJGlSdJKQDXoWHHD5zONH2n+v6CI2SZKkcetilOXAZ8EBxwMHtu8PBD497tgkSZK60EUfstmeBfd24JgkLwIuAvbvIDZJ0pgspHP9TP9SO+VruelilOVsz4IDePw4Y5EkdWehnevtlK/lyEcnaUmN+uy19/mYcOcRlRsbnelITEnSJDIh05Ib5dlr7/Mx55pnIdMkSeqKz7KUJEnqmAmZJElSx0zIJEkjc9hxZ9/Wd1PS7OxDJkkaGfttSsMxIdMdzDzId7utt7zTSMS5po3KXA8XXuoHDzsCU8NIsitwFM3zdgs4vKrenWQH4GhgNXAhsH9VXdNVnJKmi5csdQczD/IddFY717RxxLTuhjuPrJxr2kK249m8hnAz8Nqq2gN4OPCyJHsAbwBOqqr7Aye1nyVpKCZkkjQPVXVZVZ3Vvl8HnAvsAuwLHNnOdiTw9G4ilDSNvGQpSQuUZDXwEOB0YKequqyd9DOaS5r98x8EHASw2267jSfIFWRjN4aejy66aGhls4VMkhYgyTbAccCrqmpt77SqKrjz3ZGr6vCqWlNVa1atWjWmSFeWpep60FUXDa1cJmSSNE9JNqdJxj5aVZ9qiy9PsnM7fWfgiq7ikzR9vGSpzi31aMl+o1y3Vp4kAT4InFtV7+yZdDxwIPD29t9PdxCepCllQqaJMHN5YNutFz9acpzr1or0KOB5wDlJvtOWHUyTiB2T5EXARcD+HcUnaQotKiFL8qiq+ubGyiRp0iy0/qqqbwCZZfLjlyo+SSvLYvuQvWfIMkmaNNZfK8hM14gPf+7cWaf5iCd1aUEtZEkeATwSWJXkNT2TtgM2XYrAJGkUrL9WrrXrN1B3Hvx62zSpSwu9ZLkFsE27/LY95WuB/RYblCSNkPWXpImzoISsqr4KfDXJEVV10RLHJEkjY/0laRItdpTllkkOp3mY7m3rqqrHLXK9Uidm+pJss9XmXH/DTd6pe3mz/pI0MRabkH0SeB/wAeCWxYcjdW+mn8m69Tdy3fXeKmMZs/7qyMxJz7RaqsczSb0Wm5DdXFWHLUkkkjRe1l8dmatz/TRwAIBGYbG3vfhMkpcm2TnJDjOvuRZI8qEkVyT5bk/ZIUkuSfKd9rXPIuOSpI2Zd/0lSaOy2BayA9t/X9dTVsB951jmCOC9wFF95YdW1T8tMh5JGtZC6i9JGolFJWRVtfsClvlaktWL2a4kLdZC6i9JGpXFPjrp+YPKq6q/9WsYL2/Xdwbw2qq6ZpZtHgQcBLDbbrstYDNarN6RiNNoJn4YrlPufOfXdFji+ktzmPkbWrt+A/facZuuw5nVtNdtmm6LvWT5Oz3v70LzHLezuPPlyI05DHgLzeWCtwD/DPzJoBmr6nDgcIA1a9ZMb6/QKbfSOuXaiXdZWqr6S0NYu34D111/I9tuPdkjl6e9btP0Wuwly1f0fk6yPfCJBazn8p51vB84YTFxSdLGLFX9JUlLYbGjLPutB+bdLyPJzj0fnwF8d7Z5JWlEFlR/SdJSWGwfss/AbW27mwK/DhyzkWU+DuwN7JjkYuDNwN5J9mzXdSHwZ4uJS5I2ZiH1lySNymL7kPXepuJm4KKquniuBarq2QOKP7jIOCRpvuZdf0nSqCy2D9lXk+zE7Z1jz198SJI0etZfmi9HYGqUFtWHLMn+wLeAPwL2B05Pst9SBCZJo2T9pflau34D626Y7FGiml6LvWT5JuB3quoKgCSrgC8Dxy42MEkasQXVX0k+BDwFuKKqfrMtOwT4U+DKdraDq+pzI4pb0jK02FGWm8xUZq2rlmCdkjQOC62/jgCeNKD80Kras32ZjEmal8W2kH0hyReBj7efnwVYEUmaBguqv3z8m6RRWFBCluR+wE5V9bok/x/wu+2kU4GPLlVwkrTURlh/bfTxb8v90W+9j0e6/oab7AAvzcNCW8jeBbwRoKo+BXwKIMlvtdOeuiTRSQs0zDPp5vt8vUHPtJxZfrutt/Q5l9NjFPXXUI9/W+6Pfut9PNK69Tf6CCJpHhaakO1UVef0F1bVOTbla1IM80y6+T5fr/+ZljPLa6osef3l498kLdZCO+BvP8e0rRa4TkkahyWvv3z8m6TFWmhCdkaSP+0vTPJi4MzFhSRJI7Wo+qt9/NupwAOSXJzkRcD/TXJOkrOBxwKvXuqgJS1vC71k+SrgP5M8h9srsDXAFjRnh5I0qRZVf/n4N0mjsKCErO0v8cgkjwV+sy3+bFV9Zckik6QRsP5a/oYZ1DNO/YOBpEEW+yzLk4GTlygWaSpMWmWvhbH+Wt6GGdQzLv2DgaRBFntjWGlFmqTKXpI0/XzMkSRJUsdMyCRJkjpmQiZJktQxEzJJkqSOmZCtcIcdd/ZtQ7K1cDMjLz2WkqSFcJTlCudw7KXjsZQkLZQtZJIkSR3rpIUsyYeApwBXVNVvtmU7AEcDq4ELgf2r6pou4pMkaS4z3RTWrt/Adltv6V34tWhdtZAdATypr+wNwElVdX/gpPazJEkTae36DVx3/Y12V9CS6CQhq6qvAVf3Fe8LHNm+PxJ4+liDkiRJ6sgkderfqaoua9//DNhp0ExJDgIOAthtt93GFNryNujZjDNl4ANxJb5UAjAAACAASURBVEkatYns1F9VBYMfFFhVh1fVmqpas2rVqjFHtnytXb+BdTfceKcym+IlSRq9SUrILk+yM0D77xUdxyNJkjQWk5SQHQ8c2L4/EPh0h7FIkjbCmyFLS6er2158HNgb2DHJxcCbgbcDxyR5EXARsH8XsUmShmOXBmnpdJKQVdWzZ5n0+LEGIknz5H0UJY3CJF2ylKRpcATeR1HSEpuk215oAg26JYa0klXV15Ks7ivel6YbBjT3UTwFeP3YgpI09UzItFFr12+gBt+FRFLD+yiuYIPu2zjzWKV77bjNneb3kUsaxEuWkrSEvI/iytR/38aZxyr139+xd5qDItTLhEySFs/7KEpaFBMySVo876MoaVFMyCRpHtr7KJ4KPCDJxe29E98OPDHJ+cAT2s+SNDQ79S9jM6Mjr7/hJsCHhI+DD2Vf/ryPoqRRMCFbxmZGR65bf+dOpRodO+pKy8d8bv3jbYK0GCZkkiTNYT63/vE2QVoo+5BJkiR1zIRMkiSpYyZkkiRJHbMP2Qrh6L/xmjneM49OGWakq9+PJpmP+5FGy4RsBXH033jNPB5l261vHGqkq9+PJtnM71nSaHjJUpIkqWMmZJIkSR0zIZMkSeqYCZkkSRPisOPOvm2Aj1YWO/V3qItRS/2j/yRJk8PBPSuXCVmHuhq11Dv6T5Ikdc9LlpIkSR2buBayJBcC64BbgJurak23EUmSJI3WxCVkrcdW1c+7DkKSJGkcJjUhkyRNoJmBQdtstfnAz1p6PlZtZZjEhKyALyUp4N+r6vDeiUkOAg4C2G233ToIb27+4Uha7tau30BRs37W0nLk5cowiQnZ71bVJUl+BTgxyQ+q6mszE9sE7XCANWvWTFwN4B+OtHLZB1bSQk1cQlZVl7T/XpHkP4G9gK/NvZQkTQz7wEqat4m67UWSrZNsO/Me+H3gu91GJUmSNFqT1kK2E/CfSaCJ7WNV9YVuQ5KkoU11H1iNV++AiOtvuOlOAyV6n6gyUwZ37KPcu3z/tLn0r8v+z92bqISsqn4MPLjrOCRpgaa6D6zGb2ZAxLr1N95poET/E1UG9VHuXX6+253rs8ZvohIy3W7Qcy49g5luGzvD1fSzD6ykhTIhm1CDnnPpGcz0m+sMV9Ot7fe6SVWt6+kD+/cdhyVpSpiQSdLSsA+spAUzIZOkJWAfWEmLYUImSdKIdfGIqUEjKXtHbi50PUvJvtG3MyGTJGkMxt1fdNBIyv6RmwtZz1Kyb/TtJurGsJIkSSuRLWQdmE+zdX8zd+8NA3tviSFJkqaXCVkH5tts3T//oFtiSJKk6WVCJkkCBt+QWtNjrkcuDboqs5iBBuPsjL9SfpcmZJIkwNb35WC2Ry7NdlVmoQMNxtkZf6X8Lu3UL0mS1DETMkmSpI6tqEuW47rmPdtIyKW+KeBsD6vWZBvUz2M2g27suM1Wm3P9DTctuk+Fvx1JmhwrKiHr+pr3KG4K6E31ptOwN2gcdGPHoli3/sZF96nwtzP9BiXsvZ/nmneu+T3Zm1zzOaFb7Lqvv+GmgbdcGjRgYK6TxP51wh1/V7Ntt3f7vfP1L9+FUQw0WFEJmSQtJ4MS9mHnXcj8mgwLveP+fNe9bv2NA2+5NGjAwFwnif3rHHa7/dufmW8SjGKggX3IJEmSOmZCJkmS1DETMkmSpI6t6D5ks42EhPl3GFzMCMr+jpGLGYk5qs6eGo/53mm71zC/3dnWvbHlRmFSOudK0iRY0QnZbCMhF7quxYyg7O0Yudj1jKqzp8Zjvnfa7p1noevuwqR0zp0W/aPZZkvYFzNSrt9iHq2jlWkpGxiG3cZcozth+JPU/hGg435k04pOyCRpWvSPZpsrYV/oSLm5tisNa6kaGIbdxlyjO4dZz2wjQMf9yKaJ6kOW5ElJzktyQZI3dB2PJM2HdZikhZqYhCzJpsC/An8I7AE8O8ke3UYlScOxDpO0GJN0yXIv4IKq+jFAkk8A+wLfX6oNbLf1lgM/95b3zzOfdW+z1eaE3PbvoHUPmmfbrba4Q9l8pw3ap9mWW8rtzmfactzuJOzTfH+7s/0+urDQv7UJNtI6rLf+gMn+XbrdyV33JGx3Ib/rjU0bVJ8spE4cNG2udS9lPZaqyegbkGQ/4ElV9eL28/OAh1XVy/vmOwg4qP34AOC8sQY6tx2Bn3cdxBCmIU5jXDrTEOd8YrxPVa0aZTALMUwdNqD+uorJ/27GbRp+r+PmMbmjaT4es9Zfk9RCNpSqOhw4vOs4BklyRlWt6TqOjZmGOI1x6UxDnNMQ41Lor79Wyn7Ph8fkzjwmd7Rcj8fE9CEDLgF27fl877ZMkqaBdZikBZukhOzbwP2T7J5kC+AA4PiOY5KkYVmHSVqwiblkWVU3J3k58EVgU+BDVfW9jsOar4m8lDrANMRpjEtnGuKchhjntMA6bOr3ewQ8JnfmMbmjZXk8JqZTvyRJ0ko1SZcsJUmSViQTMkmSpI6ZkM1Tkh2SnJjk/Pbfu88y3y1JvtO+ju8p3z3J6e2jVY5uO/+OPcYkeyY5Ncn3kpyd5Fk9045I8pOe+Pdc4vjmfLxMki3bY3NBe6xW90x7Y1t+XpI/WMq45hnja5J8vz12JyW5T8+0gd99BzG+IMmVPbG8uGfage3v4/wkB44qxiHjPLQnxh8mubZn2liO5agt5rtajpJ8KMkVSb47y/Qk+Zf2eJ2d5KHjjnHchjgmeye5ruc38rfjjnGckuya5OS2nv1eklcOmGd5/U6qytc8XsD/Bd7Qvn8D8I5Z5rt+lvJjgAPa9+8DXtJFjMCvAfdv398LuAzYvv18BLDfiI7fpsCPgPsCWwD/C+zRN89Lgfe17w8Ajm7f79HOvyWwe7ueTTuK8bHAXdv3L5mJca7vvoMYXwC8d8CyOwA/bv+9e/v+7l3F2Tf/K2g6w4/tWE7yd7VcX8CjgYcC351l+j7A54EADwdO7zrmCTgmewMndB3nGI/HzsBD2/fbAj8c8HezrH4ntpDN377Ake37I4GnD7tgkgCPA45dyPLzsNEYq+qHVXV++/5S4ApgHHc/v+3xMlV1IzDzeJlevfEfCzy+PXb7Ap+oqg1V9RPggnZ9Y4+xqk6uql+0H0+juefUOA1zHGfzB8CJVXV1VV0DnAg8aULifDbw8RHF0pXFfFfLUlV9Dbh6jln2BY6qxmnA9kl2Hk903RjimKwoVXVZVZ3Vvl8HnAvs0jfbsvqdmJDN305VdVn7/mfATrPMd5ckZyQ5LclMQnQP4Nqqurn9fDF3/oGNM0YAkuxFc+b+o57it7VNwIcmWcqHDu4C/LTn86BjcNs87bG6jubYDbPsuGLs9SKas7QZg777pTZsjM9sv8djk8zctHRcx3Fe22ov++4OfKWneBzHctQW812tVOP8jU6TRyT53ySfT/IbXQczLm23lYcAp/dNWla/k4m5D9kkSfJl4J4DJr2p90NVVZLZ7htyn6q6JMl9ga8kOYcmsZikGGnPJj4MHFhVt7bFb6RJ5Lagud/L64G/X4q4l5skzwXWAI/pKb7Td19VPxq8hpH6DPDxqtqQ5M9oWh0f10EcwzoAOLaqbukpm5RjOWrT9l1p/M6i+Xu4Psk+wH8B9+84ppFLsg1wHPCqqlrbdTyjZEI2QFU9YbZpSS5PsnNVXdYmM1fMso5L2n9/nOQUmuz+OJom1c3alp8FP1plKWJMsh3wWeBNbXPvzLpnWtc2JPkP4C8XEuMshnm8zMw8FyfZDLgbzUOYx/VomqG2k+QJNAnwY6pqw0z5LN/9UicRG42xqq7q+fgBmr6FM8vu3bfsKUsc34z5fGcHAC/rLRjTsRy1xXxXK5WPoerTm4xU1eeS/FuSHatqWh+yvVFJNqf5f/OjVfWpAbMsq9+Jlyzn73hgZlTagcCn+2dIcveZy3xJdgQeBXy/qgo4GdhvruXHFOMWwH/SXH8/tm/azu2/oel/NnDUzwIN83iZ3vj3A77SHrvjgQPSjMLcnebs8FtLGNvQMSZ5CPDvwNOq6oqe8oHffUcx9valeBpNHwxo7iT/+22sdwd+vy0bhaEeJ5TkgTQDDE7tKRvXsRy1xXxXK9XxwPPbUXQPB67rOVFckZLcs62TZ7qZbEJzorostfv6QeDcqnrnLLMtr99J16MKpu1F05fpJOB84MvADm35GuAD7ftHAufQjKY6B3hRz/L3pUkiLgA+CWzZUYzPBW4CvtPz2rOd9pU27u8CHwG2WeL49qEZMfMjmtY5aC6JPq19f5f22FzQHqv79iz7pna584A/HOH3vLEYvwxc3nPsjt/Yd99BjP8H+F4by8nAA3uW/ZP2+F4AvHDEfzNzxtl+PgR4e99yYzuWo34t5rtaji+agRuXtXXQxTT9MP8c+PN2eoB/bY/XOcCarmOegGPy8p7fyGnAI7uOecTH43eBAs7uqWf3Wc6/Ex+dJEmS1DEvWUqSJHXMhEySJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEkagySvSnLXWaa9IMl7xx2TJocJmSRJ4/EqYGBCJpmQqXNJVif5QZIjkvwwyUeTPCHJN5Ocn2SvJFsn+VCSbyX5nyT79iz79SRnta9HtuV7JzmlfVDzD9p1pts9lbRStHXWZ9uHgX83yZuBewEnJzm5neeFbZ33LZonUWgF81mWmhT3A/6I5g7y3wb+mOZOzU8DDqZ5ZM5XqupPkmwPfKt9wPoVwBOr6pdJ7k9zt+s17TofAvwGcCnwTZoK7xvj2yVJK9iTgEur6skASe4GvBB4bFX9vH1c1t8Bvw1cR/OEhv/pKlh1zxYyTYqfVNU5VXUrzeNBTqrmMRLnAKtpnrf4hiTfoXkQ9l2A3YDNgfcnOYfmcUt79KzzW1V1cbvO77TrkaRxOAd4YpJ3JPm9qrqub/rDgFOq6sqquhE4evwhapLYQqZJsaHn/a09n2+l+Z3eAjyzqs7rXSjJITTPlHwwzQnGL2dZ5y34e5c0JlX1wyQPpXn+4luTnNR1TJpstpBpWnwReMVMP7AkD2nL7wZc1raCPQ/YtKP4JOk2Se4F/KKqPgL8I/BQYB2wbTvL6cBjktwjyeY0XTa0gtlioGnxFuBdwNlJNgF+AjwF+DfguCTPB74ArO8uREm6zW8B/5jkVuAm4CXAI4AvJLm0qh7btvCfClxL061CK1iabjqSJEnqipcsJUmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMo1Mkkpyvw62+9AkP0xy943M93tJzhtXXJImX1f1Vs/2n5XkG0k2G3L+9yX5m1HHpdEzIdOsknwhyd8PKN83yc+GrTCWKJZD2orylX3lr2zLD2k/bwn8O3BAVV0z1zqr6utV9YCRBS1p7Kax3uopvwfw18D+VXXzMNuoqj+vqrcsWdDqjAmZ5nIk8Nwk6St/HvDRYSuM+Zqjwvwh8Py+sgPb8hkPAN5cVWeNIjZJE28a660ZewB/WlWXLmVsmg4mZJrLfwH3AH5vpqC9DPgU4KgkeyU5Ncm1SS5L8t4kWwxaUZK7JTkqyZVJLkry10k2aae9IMk3kxya5CrgkFni+TZw1yS/0S73G8Bd2vIZOwCH92z3wiR/meTsJNclOTrJXdppeye5uG/e17Xzrk/ywSQ7Jfl8knVJvtx7GTTJ05J8r93/U5L8+vCHVtKITF29leTuSU4APgV8PskJSe7dTtshycVJntp+3ibJBUme334+Islb2/d7t/P+VZIr2v17epJ92m4cVyc5uGe7WyZ5V5JL29e72qsM6oAJmWZVVTcAx3DHs7v9gR9U1f8CtwCvBnYEHgE8HnjpLKt7D3A34L7AY9p1vrBn+sOAHwM7AW+bI6wP98RzYPt5Y/YHngTsDjwIeMEc8z4TeCLwa8BTgc8DBwOraP5e/gIgya8BHwde1U77HPCZ2Sp2SeMxpfXWJsARwH2A3YAbgPe2+3M18CfA+5P8CnAo8J2qOmqWbd2TJuHbBfhb4P3Ac4HfpklS/ybJ7u28bwIeDuwJPBjYi+aSqTpgQqaNORLYb6ZViaZSORKgqs6sqtOq6uaqupCm79Zj+leQZFPgAOCNVbWunfefaS4hzLi0qt7TruuGOeL5CPDsJJu36/zIEPvwL1V1aVuxfYam8pnNe6rq8qq6BPg6cHpV/U9V/RL4T+Ah7XzPAj5bVSdW1U3APwFbAY8cIh5JozVV9VZVXVVVx1bVL6pqHU1y95ie6V8CPgmcBOwD/Nkc27oJeFtbL32CJvF8d7sP3wO+T5N8ATwH+PuquqKqrgT+rm//NEYmZJpTVX0D+Dnw9CS/SnMG9TFoWonapvWfJVkL/APNH3+/HYHNgYt6yi6iOYOb8dMh4/l/wAXtts6vqmGW+1nP+18A28wx7+U9728Y8Hlm2XvRsz9VdSvNPvTuk6QOTFu9leQuSd6d5LwkPwVOALZvk8IZhwO/CRxRVVfNsbmrquqW9v1MkjhUPda+v9cw+6SlZ0KmYRxFc4b5XOCLVTXzx30Y8APg/lW1Hc2lvf6OtNBUjDfRNMfP2A24pOdzzTOe17b/duVSevan7UC8K3fcJ0ndmaZ667U0ydbDq2pXmu4SzMTVJmaHt8u+NEt3W4471GM0++eAgo6YkGkYRwFPAP6Uttm/tS2wFrg+yQOBlwxauD1bOwZ4W5Jtk9wHeA3DXW4c5Gjg99t1duUY4MlJHt9ehngtsAH47w5jknS7aaq3tgduBn6ZZAeavl+9DqZJ/v4E+EeawQmbsngfB/46yaokO7bbXej+aZFMyLRRbd+J/wa2Bo7vmfSXwB8D62g6jh49x2peAayn6QD7DZrLBx9aYDw3VNWXN9JnY6Sq6jyaM+/30JxJPxV4alXd2FVMkm43ZfXWocCWwJXAacAXZiYk+W2aRPD5bZL4Dprk7A0LiaPPW4EzgLOBc4Cz2jJ1IFXzaXGVJEnSUrOFTJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6NtvT6afCjjvuWKtXr+46DEljdOaZZ/68qlZ1HcdiWX9JK89c9ddUJ2SrV6/mjDPO6DoMSWOU5KKNzzX5rL+klWeu+stLlpIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMRMySZKkjq2ohOymm2+dyHVJkqTRG8X/3Uu1zqm+D9l8bb7ZJhz8b99cknX9w0sftSTrkSRJ47GUecCMpcoHVlQLmSTNR5JNk/xPkhPaz7snOT3JBUmOTrJFW75l+/mCdvrqLuOWNH1MyCRpdq8Ezu35/A7g0Kq6H3AN8KK2/EXANW35oe18kjQ0EzJJGiDJvYEnAx9oPwd4HHBsO8uRwNPb9/u2n2mnP76dX5KGYkImSYO9C/grYKbH7j2Aa6vq5vbzxcAu7ftdgJ8CtNOva+e/gyQHJTkjyRlXXnnlKGOXNGVGnpDZB0PStEnyFOCKqjpzKddbVYdX1ZqqWrNq1aqlXLWkKTeOFjL7YEiaNo8CnpbkQuATNJcq3w1sn2RmdPq9gUva95cAuwK00+8GXDXOgCVNt5EmZPbBkDSNquqNVXXvqloNHAB8paqeA5wM7NfOdiDw6fb98e1n2ulfqaoaY8iSptyoW8jsgyFpOXk98JokF9DUTx9syz8I3KMtfw3who7ikzSlRnZj2N4+GEn2Xqr1VtXhwOEAa9as8QxU0khV1SnAKe37HwN7DZjnl8AfjTUwScvKKO/UP9MHYx/gLsB29PTBaFvBBvXBuNg+GJIkaSUZ2SVL+2BIkiQNp4v7kNkHQ5IkqcdYHi5uHwxJkqTZead+SZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMRMySZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMRMySZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMRMySZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkk9UlylyTfSvK/Sb6X5O/a8t2TnJ7kgiRHJ9miLd+y/XxBO311l/FLmj4mZJJ0ZxuAx1XVg4E9gScleTjwDuDQqrofcA3wonb+FwHXtOWHtvNJ0tBMyCSpTzWubz9u3r4KeBxwbFt+JPD09v2+7Wfa6Y9PkjGFK2kZGFlCZpO/pGmWZNMk3wGuAE4EfgRcW1U3t7NcDOzSvt8F+ClAO/064B4D1nlQkjOSnHHllVeOehckTZFRtpDZ5C9palXVLVW1J3BvYC/ggUuwzsOrak1VrVm1atWiY5S0fIwsIbPJX9JyUFXXAicDjwC2T7JZO+newCXt+0uAXQHa6XcDrhpzqJKm2Ej7kNnkL2kaJVmVZPv2/VbAE4FzaRKz/drZDgQ+3b4/vv1MO/0rVVXji1jStBtpQmaTv6QptTNwcpKzgW8DJ1bVCcDrgdckuYDmhPGD7fwfBO7Rlr8GeEMHMUuaYpttfJbFq6prk9yhyb9tBRvU5H+xTf6SulRVZwMPGVD+Y5qTy/7yXwJ/NIbQJC1ToxxlaZO/JEnSEEbZQrYzcGSSTWkSv2Oq6oQk3wc+keStwP9wxyb/D7dN/lcDB4wwNkmSpIkxsoTMJn9JkqTheKd+SZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMRMySZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHhkrIkjxqmDJJmjTWX5KmwbAtZO8ZskySJo31l6SJt9lcE5M8AngksCrJa3ombQdsOsrAJGkxrL8kTZM5EzJgC2Cbdr5te8rXAvuNKihJWgLWX5KmxpwJWVV9FfhqkiOq6qIxxSRJi2b9JWmabKyFbMaWSQ4HVvcuU1WPG0VQkrSErL8kTbxhE7JPAu8DPgDcMrpwJGnJWX9JmnjDJmQ3V9VhI41EkkbD+kvSxBv2thefSfLSJDsn2WHmNdLIJGlpWH9JmnjDtpAd2P77up6yAu67tOFI0pKz/pI08YZKyKpq91EHIkmjYP0laRoMlZAlef6g8qo6amnDkaSlZf0laRoMe8nyd3re3wV4PHAWYIUmadJZf0maeMNesnxF7+ck2wOfGElEkrSErL8kTYNhR1n2Ww/YL0PSNLL+kjRxhu1D9hmaUUnQPJT314FjRhWUJC0V6y9J02DYPmT/1PP+ZuCiqrp4BPFI0lKbd/2VZFeaPmY70SRzh1fVu9v7lx1N8ximC4H9q+qaJAHeDewD/AJ4QVWdtdQ7Imn5GuqSZfuQ3h8A2wJ3B24cZVCStFQWWH/dDLy2qvYAHg68LMkewBuAk6rq/sBJ7WeAPwTu374OAnwygKR5GSohS7I/8C3gj4D9gdOT7LeRZXZNcnKS7yf5XpJXtuU7JDkxyfntv3dvy5PkX5JckOTsJA9d3K5J0sLqr6q6bKaFq6rWAecCuwD7Ake2sx0JPL19vy9wVDVOA7ZPsvOS74ykZWvYS5ZvAn6nqq4ASLIK+DJw7BzLzJxhnpVkW+DMJCcCL6A5w3x7kjfQnGG+njueYT6M5gzzYfPfJUm6g4XUX7dJshp4CHA6sFNVXdZO+hnNJU1okrWf9ix2cVt2WU8ZSQ6iaUFjt912m/+eSFq2hh1luclMZda6amPLeoYpaULMu/6akWQb4DjgVVW1tndaVRW3DxYYSlUdXlVrqmrNqlWr5rOopGVu2BayLyT5IvDx9vOzgM8NuxHPMCV1aEH1V5LNaZKxj1bVp9riy5PsXFWXtSeMM4neJcCuPYvfuy2TpKHMeZaY5H5JHlVVrwP+HXhQ+zoVOHyYDXiGKakLi6m/2lGTHwTOrap39kw6ntsfVn4g8Ome8ue3fWEfDlzXc+IpSRu1sRaydwFvBGjPED8FkOS32mlPnWthzzAldWgx9dejgOcB5yT5Tlt2MPB24JgkLwIuohkkAE2L2z7ABTS3vXjhku6JpGVvYwnZTlV1Tn9hVZ3TXoac1RBnmG/nzmeYL0/yCZrO/J5hSlqMBddfVfUNILNMfvyA+Qt42QJilCRg4wnZ9nNM22ojy3qGKalLi6m/JGmsNpaQnZHkT6vq/b2FSV4MnDnXgp5hSurYgusvSRq3jSVkrwL+M8lzuL0CW/P/t3f/sXbX9R3Hn69RkLHEIqN2DMhgsWIUpyO1Q50OVjarY1QXR+riVhxbN3+wjWxZQDONWeIIbnEwdRlztTUSDDKnZLpNVslIUHBMFIr8aAMRWoHWGE0WM6D2vT/Ol+RSW3p77vecz/nxfCQ39/v93nO/9/05557PeZ3P53u+X+AY4E2jLEySlsj+S9LUeNZAVlWPA69Kci5wZrf581X1pZFXJklLYP8laZos6jxkVXUzcPOIa5Gk3tl/SZoGiz1TvyRJkkbEQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJOogkm5PsSbJ9wbYTktyUZEf3/Xnd9iS5OsnOJHclOatd5ZKm0cgCmZ2ZpCm3BVh3wLbLgG1VtQrY1q0DvB5Y1X1t0eKysgAACflJREFUAv5+TDVKmhGjHCHbgp2ZpClVVbcA3z1g83pga7e8FXjjgu2fqIHbgOOTnDSeSiXNgpEFMjszSTNoZVU92i0/Bqzslk8GHllwu13dtmdIsinJHUnu2Lt372grlTRVxn0M2ZI6M7BDkzQZqqqAOsLfuaaqVlfV6hUrVoyoMknTqNlB/cN0Zt3v2aFJauXxp0fvu+97uu27gVMX3O6UbpskLcq4A5mdmaRpdiOwsVveCHxuwfbf6T6gdDbw/QWzAZJ0WOMOZHZmkqZCkuuArwBnJNmV5GLgCuBXkuwAzuvWAb4APAjsBP4ReEeDkiVNsWWj2nHXmZ0DnJhkF/A+Bp3X9V3H9i3gwu7mXwDewKAz+wHwtlHVJUmLUVVvOcSP1h7ktgW8c7QVSZplIwtkdmaSJEmL45n6JUmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkzZ2n9u2fin1qfozsWpaSJE2qo5f9GO/+6K297vMD73h1r/vTfHGETJIkqTEDmaZSn1MDTjNIklpzylJj89S+/Ry9rJ/3AH1ONzjNIE22PvsOaVIZyDQ2hihJw/B4L80D33JIkiQ1ZiCbMR5bJUnS9HHKckh9HtPw1L4fcvSyo3rZl9OCktTGKI51m+d9zhsD2ZD6Dj6TGKLm5QnWb7iej/tM0o8axbFu79/0yl73B9NT57z1pwYyHVLfT9pJHXFzVFHSpJqWDzRMS52TbH6ipyRJmhrzdhyzI2SSNKc8lkiTbN5G3QxkkjSn5u0FT5pkvo2RJElqzEAmSerNvB33I/XFKUtJUm+cBpWG4wiZ1KO+RwccbZCk+eAImdSjeTl3mySpX46QSRPMa5NK0nxwhEyaYH2OuPV5aRPPNTV+3ufSbDOQSXPCS0RNNw+Wl2bbRL3dSrIuyf1Jdia5rHU9knQk7MMkDWtiAlmSo4CPAK8HXgy8JcmL21YlSYtjHyZpKSYmkAFrgJ1V9WBVPQl8CljfuCZJB+GHDQ7KPkzS0FJVrWsAIMmbgXVV9Xvd+m8Dv1BV7zrgdpuATd3qGcD9R/BnTgS+00O5k8i2Ta9Zbt8o2vYzVbWi530u2WL6sCX2X5Nqlv9/F8P2z3f74cjug0P2X1N3UH9VXQNcM8zvJrmjqlb3XNJEsG3Ta5bbN8ttG8ZS+q9JNe+Pse2f7/ZDf/fBJE1Z7gZOXbB+SrdNkqaBfZikoU1SIPtvYFWS05McA2wAbmxckyQtln2YpKFNzJRlVe1L8i7gP4CjgM1VdU/Pf2ampgoOYNum1yy3b5bb9gxj6sMm0dw8xodg+9XLfTAxB/VLkiTNq0maspQkSZpLBjJJkqTGZj6QJfnNJPck2Z9k9QE/u7y7xMn9SV7Xqsa+JHl5ktuSfD3JHUnWtK6pT0kuSXJf93he2bqeUUjyp0kqyYmta+lLkg92j9tdSf4lyfGta1I/khyV5M4k/9q6lhaSHJ/khu7/+94kr2xd0zglubTrj7cnuS7Jsa1rGqUkm5PsSbJ9wbYTktyUZEf3/XnD7n/mAxmwHfgN4JaFG7tLmmwAXgKsAz7aXfpkml0JvL+qXg68t1ufCUnOZXDW85dV1UuAv25cUu+SnAr8KvBw61p6dhNwZlX9HPAAcHnjetSfPwbubV1EQ1cB/15VLwJexhzdF0lOBv4IWF1VZzL4IMuGtlWN3BYGeWGhy4BtVbUK2NatD2XmA1lV3VtVBzsb9nrgU1X1RFU9BOxkcOmTaVbAc7vl5cC3G9bSt7cDV1TVEwBVtadxPaPwIeDPGTyOM6OqvlhV+7rV2xicn0tTLskpwK8BH2tdSwtJlgOvBf4JoKqerKrvta1q7JYBP55kGXAcs/Wa8yOq6hbguwdsXg9s7Za3Am8cdv8zH8iexcnAIwvWd3XbptmfAB9M8giDEaRZGol4IfCaJLcn+a8kr2hdUJ+SrAd2V9U3WtcyYr8L/FvrItSLv2XwBmJmLkZ6hE4H9gIf76ZtP5bkJ1oXNS5VtZvB68zDwKPA96vqi22ramJlVT3aLT8GrBx2RxNzHrKlSPKfwE8d5EfvqarPjbueUXq2tgJrgUur6p+TXMjgndt546xvKQ7TtmXACcDZwCuA65P8bE3ReVsO0753M5iunEqLeQ4meQ+wD7h2nLWpf0nOB/ZU1f8kOad1PY0sA84CLqmq25NcxWC66i/aljUe3bFS6xkE0+8Bn07y1qr6ZNvK2qmqSjL0a9JMBLKqGiZ0TOVlTp6trUk+weCYDoBPM2VTCYdp29uBz3QB7KtJ9jO4oOvecdW3VIdqX5KXMujUvpEEBv+LX0uypqoeG2OJQzvcczDJRcD5wNppCtE6pFcDFyR5A3As8Nwkn6yqtzaua5x2Abuq6vZu/QaWcPzQFDoPeKiq9gIk+QzwKmDeAtnjSU6qqkeTnAQMfTjNPE9Z3ghsSPKcJKcDq4CvNq5pqb4N/FK3/MvAjoa19O2zwLkASV4IHAN8p2lFPamqu6vq+VV1WlWdxqCjP2tawtjhJFnHYGrrgqr6Qet6tHRVdXlVndL9v24AvjRnYYzu+flIkjO6TWuBbzYsadweBs5OclwG7yTXMkcfaljgRmBjt7wRGHpWbiZGyJ5NkjcBfwesAD6f5OtV9bqquifJ9QyeQPuAd1bVD1vW2oPfB67qDrD8P2BT43r6tBnY3H3c+ElgoyMtU+PDwHOAm7oRwNuq6g/bliT14hLg2u7apQ8Cb2tcz9h007Q3AF9j8Bp6JzN+GaUk1wHnACcm2QW8D7iCwSE0FwPfAi4cev++pkmSJLU1z1OWkiRJE8FAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJpKSS5IMk8nYZQkzTBPeyFJktSYI2SaOElOS3Jfki1JHkhybZLzktyaZEeSNUkuSvLh7vZbklyd5MtJHkzy5tZtkDR/Ftl3rUnyle6C5F9++kz/SS5NsrlbfmmS7UmOa9sijZOBTJPqBcDfAC/qvn4L+EXgzxhciPtAJ3U/P5/BmZMlqYXD9V33Aa+pqp8H3gt8oPu9q4AXdFeX+TjwB15qbL7M/KWTNLUeqqq7AZLcA2yrqkpyN3DaQW7/2araD3wzycox1ilJCx2u71oObE2yCijgaICq2p/kIuAu4B+q6tYWxasdR8g0qZ5YsLx/wfp+Dv5GYuHtM6qiJOkwDtd3/SVwc1WdCfw6cOyC268C/hf46THUqQljIJMkaXyWA7u75Yue3phkOXA18FrgJz0Wdv4YyCRJGp8rgb9KcifPHO3/EPCRqnoAuBi4IsnzWxSoNjzthSRJUmOOkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmN/T+URGAIZiuXrAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jkwFoBPrHGuQ"
      },
      "source": [
        "Como podemos ver, a médias e o desvio padrão estão aparentanda ser curvas normais e os valores mínimos e máximos estão em -10 e 10, respectivamente. Logo podemos imaginar que as colunas 'g-n' apresentam um comportamento de curva normal, isto é, apresentam média 0 e desvio padrão -1. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OIYA79clJyI7"
      },
      "source": [
        "Vamos agora realizar o mesmo processo com as colunas c-n's."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5DJYkulxcaK",
        "outputId": "17a062cc-3928-4922-86d3-13732827cd55"
      },
      "source": [
        "c_describe = data.loc[:, 'c_0': 'c_99'].describe().T\n",
        "c_describe.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>c_0</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.355156</td>\n",
              "      <td>1.752565</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>-0.549300</td>\n",
              "      <td>-0.00900</td>\n",
              "      <td>0.448975</td>\n",
              "      <td>3.365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c_1</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.455359</td>\n",
              "      <td>2.004962</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>-0.538425</td>\n",
              "      <td>0.00980</td>\n",
              "      <td>0.476875</td>\n",
              "      <td>3.915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c_2</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.480542</td>\n",
              "      <td>2.039346</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>-0.550375</td>\n",
              "      <td>-0.00400</td>\n",
              "      <td>0.460575</td>\n",
              "      <td>2.970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c_3</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.377666</td>\n",
              "      <td>1.554583</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>-0.601975</td>\n",
              "      <td>-0.03905</td>\n",
              "      <td>0.413100</td>\n",
              "      <td>2.408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c_4</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.493552</td>\n",
              "      <td>2.091888</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>-0.559975</td>\n",
              "      <td>0.01140</td>\n",
              "      <td>0.470300</td>\n",
              "      <td>4.555</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       count      mean       std   min       25%      50%       75%    max\n",
              "c_0  23814.0 -0.355156  1.752565 -10.0 -0.549300 -0.00900  0.448975  3.365\n",
              "c_1  23814.0 -0.455359  2.004962 -10.0 -0.538425  0.00980  0.476875  3.915\n",
              "c_2  23814.0 -0.480542  2.039346 -10.0 -0.550375 -0.00400  0.460575  2.970\n",
              "c_3  23814.0 -0.377666  1.554583 -10.0 -0.601975 -0.03905  0.413100  2.408\n",
              "c_4  23814.0 -0.493552  2.091888 -10.0 -0.559975  0.01140  0.470300  4.555"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1M4-Nbmnxcsk",
        "outputId": "37227f08-94e4-41f7-82a7-be9d38551d20"
      },
      "source": [
        "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n",
        "fig.suptitle('Comportamento das colunas \"c-n\"', fontsize = 16)\n",
        "plt.subplots_adjust(hspace = 0.4)\n",
        "sns.set()\n",
        "\n",
        "sns.histplot(ax=axes[0][0], x = c_describe['mean'], bins = 100)\n",
        "axes[0][0].set_title('Média', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[0][1], x = c_describe['std'], bins = 100)\n",
        "axes[0][1].set_title('Desvio Padrão', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[1][0], x = c_describe['min'])\n",
        "axes[1][0].set_title('Valor Mínimo', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[1][1], x = c_describe['max'])\n",
        "axes[1][1].set_title('Valor Máximo', fontsize = 12)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5MUkjMBUzE76"
      },
      "source": [
        "Como podemos ver, diferente das colunas g-n's, não podemos falar que as colunas c-n' são uma curva normal, já que suas médias não se encontram em 0 e seus divios padrão em 1. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mb4frzvmWHuR"
      },
      "source": [
        "# 3. Limpeza dos dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6wZjd7RsZd_z"
      },
      "source": [
        "Alguns modelos de Machine Learning são extremamente sensíveis às impurezas (outliers, dados nulos, dados duplicados), então precisamos limpa-los para que nosso modelo tenha uma melhor performance sem que haja um super adequamento (overfitting) aos dados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7msLQhGsZT5s"
      },
      "source": [
        "## 3.1 Dados Duplicados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jjDE6BKkdaLe"
      },
      "source": [
        "A primeira técnica de limpeza é a remoção, caso haja, de dados duplicados. Precisamos retirá-los, pois eles podem ser responsáveis por alterar estatísticas como média, mediana etc, para isso, basta usar a função *drop_duplicates()* do pandas e ela removerá todas as  linhas em que todas as colunas são iguais."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8OrA8Egid0u",
        "outputId": "2659ff58-2c74-4bc5-b966-54c4ccff170a"
      },
      "source": [
        "data_sem_duplicados = data.drop_duplicates(keep='last')\n",
        "print('Número de linhas deletadas: {}'.format(len(data)-len(data_sem_duplicados)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Número de linhas deletadas: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJ67x3FRWL9J"
      },
      "source": [
        "## 3.2 Dados Nulos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNH8EXGIjQrO"
      },
      "source": [
        "Dados nulos são outros tipos de dados que precisam ser removidos ou manipulados, caso haja. Para isso, cada coluna deve ser tratada de uma forma, então vamos ver se há alguma coluna com dados faltando."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NkdpjyNwkzbO"
      },
      "source": [
        "for i in data_sem_duplicados.isnull().sum():\n",
        "  if(i != 0):\n",
        "    print('{}'.format(i.index))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvmoP_wznlei"
      },
      "source": [
        "Portanto não há colunas sem dados nulo e podemos seguir"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "olVM1-4hWfLv"
      },
      "source": [
        "## 3.3 Outliers\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "paeUnzfZoWnm"
      },
      "source": [
        "Por fim, precisamos limpar as anomalias, ou outliers, que podem tanto serem causados por errors humanos tanto por causa natural, como é o caso dos nossos dados, já que são resultados de testes de drogas em células. Podemos ver que nossos dados apresentam outliers usando o *boxplot()* da biblioteca **Seaborn** na coluna \"g-0\",por exemplo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cR5y-QPh1cAy",
        "outputId": "9fcce83f-9d0d-4090-b760-fb886db1edc9"
      },
      "source": [
        "plt.figure(figsize = (10,6))\n",
        "plt.title('Boxplot g_0', fontsize = 16)\n",
        "sns.boxplot(data = data, x = 'g_0')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QwixZT03iNW"
      },
      "source": [
        "Nesse gráfico, todos os pontos que estão acima de 2 e abaixo de -2 são considerados outliers. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TNa8FyyW7cB9"
      },
      "source": [
        "Para achar e tentar diminuir esses outliers, vamos usar o algoritmo *IsolationForest()* da biblioteca **Sklearn**. Apesar de ser baseado no modelo *DecisionTree()*, esse algoritmo é não supervisionado e divide os dados aleatoriamente e detecta como outliers aqueles pontos que tiverem menor caminho, porém como não queremos retirar nenhum ponto que tenha reultado positivo, vamos aplicar somente nos pontos em que o resultado foi negativo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZjxWgUSn8u5m",
        "outputId": "c8b31301-458e-4ccd-9691-7fcf0c77bdd4"
      },
      "source": [
        "from sklearn.ensemble import IsolationForest\n",
        "\n",
        "outlier_detector = IsolationForest(n_estimators = 70, max_features = 872, random_state = 0)\n",
        "outlier_detector.fit(data.loc[:, 'g_0': 'c_99'])\n",
        "\n",
        "data['outliers'] = outlier_detector.predict(data.loc[:, 'g_0': 'c_99'])\n",
        "\n",
        "data.drop(data.query('rna_polymerase_inhibitor == 0')[data.outliers == -1].index, inplace = True)\n",
        "data.drop(columns = 'outliers', inplace = True)\n",
        "data.reset_index(drop = True, inplace = True)\n",
        "\n",
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22122, 878)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ff0DoIy0_yUg"
      },
      "source": [
        "Como podemos ver 1692 outliers foram detectados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fA1DKISWv2O"
      },
      "source": [
        "# 4. Análise de dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBrpVY_sz4b2"
      },
      "source": [
        "Saber como suas variáveis se comportam entre elas e com a variável alvo é de extrema importância em qualquer ciência, inclusive na ciência de dados. Por isso nessa sessão vamos analisar como essas variáveis se relacionam uma com as outras."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GpfQ5rpE58lE"
      },
      "source": [
        "## 4.1 Relação entre variavéis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHwNvJrSt0Gi"
      },
      "source": [
        "###4.1.1 Dose x Tempo x Tratamento"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bPX_ZOAG7WgG"
      },
      "source": [
        "Utilizando a função *crosstab()* do pandas, é possível gerar uma tabela mostre como os dados estão distribuidos entre as váriaveis \"tempo\", \"dose\" e \"tratamento\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nARX7dAbotBq",
        "outputId": "e4e337e6-5248-4a87-a1e2-1b76f16c45c7"
      },
      "source": [
        "pd.crosstab([data['dose'], data['tempo']],  data['tratamento'], normalize = 'columns')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>tratamento</th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D1</th>\n",
              "      <th>24</th>\n",
              "      <td>0.162162</td>\n",
              "      <td>0.164315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.182162</td>\n",
              "      <td>0.180939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.164324</td>\n",
              "      <td>0.160221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D2</th>\n",
              "      <th>24</th>\n",
              "      <td>0.164324</td>\n",
              "      <td>0.165401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.162703</td>\n",
              "      <td>0.165499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.164324</td>\n",
              "      <td>0.163625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tratamento  com_controle  com_droga\n",
              "dose tempo                         \n",
              "D1   24         0.162162   0.164315\n",
              "     48         0.182162   0.180939\n",
              "     72         0.164324   0.160221\n",
              "D2   24         0.164324   0.165401\n",
              "     48         0.162703   0.165499\n",
              "     72         0.164324   0.163625"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hGCz8Rm5t7Rp"
      },
      "source": [
        "Podemos ver que a proporção entre controle e com droga está distribuida igualmente entre as categorias"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PTRuXZcnt8PM"
      },
      "source": [
        "###4.1.2 G-n's"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yIJ0b96jRp9o"
      },
      "source": [
        "Vimos na sessão 2.4 que as distribuição das g-n's se distribuião de forma parecida, mas será que elas são dependente uma das outras? Para descobrir isso, por serem variáveis continuas, calcularemos a carrelação entre essas variáveis e vizualizaremos utilizando o *heatmap()*. A correlação é um número no intervalo de -1 a 1 calculado a partir da covariância entre as variáveis e seus respectivos desvios padrão, sendo que números próximos dos extremos são variávies inversamente (extremo negativo) e diretamente(extremo positivo) proporcionais enquanto próximos de 0 são variáveis independentes. O *heatmap()*, por sua vez, é uma tabela que representa seus valores através de uma paleta de cores. Por se tratar de muitas variaveis, plotaremos somente as 100 primeiras colunas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PhZS2p_X10R"
      },
      "source": [
        "corr = data.loc[:,'g_0':'g_99'].corr()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H-c-qr_sZJiZ",
        "outputId": "a4eb3536-cde4-43af-feb6-03aa475de234"
      },
      "source": [
        "# Generate a mask for the upper triangle\n",
        "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(20, 20))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr, mask=mask, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fef806c3bd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ug4sF4m-ZWw_"
      },
      "source": [
        "Podemos ver então, que apesar de estarem padronizadas, a correlação do conjunto não passa de 0.7 nem de -0.5."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MutvhWOUuC1k"
      },
      "source": [
        "###4.1.3 C-n's"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "52eAqN83Z8RG"
      },
      "source": [
        "Vamos fazer o mesmo para para as categorias c-n's, calcular a correlação e depois plotar o *headtmap()* "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5eG8ErSbimw"
      },
      "source": [
        "corr_celular = data.loc[:,'c_0':'c_99'].corr()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yx0zXPe5bufo",
        "outputId": "de8c1f22-9fc0-4d01-a82d-55c62f57c629"
      },
      "source": [
        "# Generate a mask for the upper triangle\n",
        "mask = np.triu(np.ones_like(corr_celular, dtype=bool))\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(20, 20))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr_celular, mask=mask, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fef8f543550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "siwF4hmJb_Sl"
      },
      "source": [
        "Como podemos ver, há uma bela diferença em relação as g-n's, já que não há nenhuma dupla de variáveis com correlação abaixo de 0.1. \n",
        "\n",
        "\n",
        "> *Um ponto a ser lembrado é correlação não implica causualidade, como se trata de uma fórmula matemática, ela apenas mostra o quanto uma variável varia com a outra, porém os motivos podem ser variados e precisariamos de mais informações sobre cada gene/célula para sabe se o motivo da correlação alta vem da mesma causa*\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3uNsc2d0uHrT"
      },
      "source": [
        "###4.1.4 G-n's x C-n's"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "upDpvKu8dkSQ"
      },
      "source": [
        "Tendo em vista que as variáveis g-n's se comportam de uma forma entre elas e as c-n's se comportam de outra, como será que uma se comporta em relação a outra? para fazer isso, utilizaremos as 50 últimas colunas dos g-n's e as 50 primeiras dos c-n's e usaremos a função *loc[]* para selecionar as linhas e as colunas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZIFNaFNfdsP"
      },
      "source": [
        "corr = data.loc[:,'g_721':'c_50'].corr()\n",
        "corr_celular_genes = corr.loc['g_721':'g_771','c_0':'c_50']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdUQPgDGfl9s",
        "outputId": "d230da03-ca84-4f15-feb2-81fd2d503064"
      },
      "source": [
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(15, 15))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr_celular_genes, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fef71200050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmwIK7Tkh42m"
      },
      "source": [
        "Se olharmos para essa figura, podemos ver algo curioso: um gene possui uma correlação parecida com todas as células. Isso porque genes são segmentos de DNA que contém o código de uma proteína em um ou mais tipos de células do organismos, fazendo com que a resposta do gene à droga seja parecida em grande parte das células."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yUipouQvWiTG"
      },
      "source": [
        "## 4.2 Relação com a variável alvo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tFWNla0PWoX8"
      },
      "source": [
        "Saber quais variáveis  e como se relacionam com seu alvo é muito importânte para entender seus dados e na hora de escolher quais entregar as variáveis para o modelo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eFkwUS4q-ut"
      },
      "source": [
        "###4.2.1 Dose X Tempo X Rna polymerase inhibitor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0nJFv0ZsBWD"
      },
      "source": [
        "Como se tratam de variáveis categóricas de baixa cardinalidade, podemos utilizar o *crosstab()* novamente para analisar de maneira única. Usaremos somente os dados que não foram de controle, já que testes de controle não devem possui essa característica"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GTc8DbJntCFB",
        "outputId": "3b75c114-b950-48e6-e575-70b796c85602"
      },
      "source": [
        "pd.crosstab([data.query('tratamento == \"com_droga\"')['dose'], data.query('tratamento == \"com_droga\"')['tempo']],  data['rna_polymerase_inhibitor'], normalize='columns')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>rna_polymerase_inhibitor</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D1</th>\n",
              "      <th>24</th>\n",
              "      <td>0.164321</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.180916</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.160221</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D2</th>\n",
              "      <th>24</th>\n",
              "      <td>0.165407</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.165506</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.163629</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "rna_polymerase_inhibitor         0     1\n",
              "dose tempo                              \n",
              "D1   24                   0.164321  0.16\n",
              "     48                   0.180916  0.20\n",
              "     72                   0.160221  0.16\n",
              "D2   24                   0.165407  0.16\n",
              "     48                   0.165506  0.16\n",
              "     72                   0.163629  0.16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z38p60J0v8Jf"
      },
      "source": [
        "Apesar de aparentar ter uma leve alteração nas coluna de resultado positivo, vale lembrar que há uma amostra muito pequena para afirmar que há alguma relação. De resto, estão mto bem distribuidas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IOJWzWj5uoaQ"
      },
      "source": [
        "###4.2.2 G-n's x Rna polymerase inhibitor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afdV0klDltvG"
      },
      "source": [
        "Para análisar a relação entre os g-n's e nossa variável alvo, usaremos o *describe()* novamente, porem dessa vez separando de acordo com o resultado."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gKIpoSPlpCrn",
        "outputId": "63687df3-7654-44f8-80ab-ac5d7147c9fb"
      },
      "source": [
        "g_describe_0 = data.query('rna_polymerase_inhibitor == 0').loc[:, 'g_0': 'g_771'].describe().T\n",
        "g_describe_0.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>g_0</th>\n",
              "      <td>22097.0</td>\n",
              "      <td>0.005433</td>\n",
              "      <td>0.837145</td>\n",
              "      <td>-5.513</td>\n",
              "      <td>-0.4993</td>\n",
              "      <td>-0.0596</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>7.095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_1</th>\n",
              "      <td>22097.0</td>\n",
              "      <td>-0.096499</td>\n",
              "      <td>0.743965</td>\n",
              "      <td>-5.596</td>\n",
              "      <td>-0.5408</td>\n",
              "      <td>-0.0477</td>\n",
              "      <td>0.3839</td>\n",
              "      <td>5.039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_2</th>\n",
              "      <td>22097.0</td>\n",
              "      <td>0.104498</td>\n",
              "      <td>1.000252</td>\n",
              "      <td>-9.104</td>\n",
              "      <td>-0.4626</td>\n",
              "      <td>0.0340</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>6.226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_3</th>\n",
              "      <td>22097.0</td>\n",
              "      <td>0.037508</td>\n",
              "      <td>0.767408</td>\n",
              "      <td>-4.927</td>\n",
              "      <td>-0.4180</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.4274</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_4</th>\n",
              "      <td>22097.0</td>\n",
              "      <td>-0.034774</td>\n",
              "      <td>0.857879</td>\n",
              "      <td>-6.369</td>\n",
              "      <td>-0.4863</td>\n",
              "      <td>-0.0575</td>\n",
              "      <td>0.3872</td>\n",
              "      <td>6.511</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       count      mean       std    min     25%     50%     75%     max\n",
              "g_0  22097.0  0.005433  0.837145 -5.513 -0.4993 -0.0596  0.4107   7.095\n",
              "g_1  22097.0 -0.096499  0.743965 -5.596 -0.5408 -0.0477  0.3839   5.039\n",
              "g_2  22097.0  0.104498  1.000252 -9.104 -0.4626  0.0340  0.6091   6.226\n",
              "g_3  22097.0  0.037508  0.767408 -4.927 -0.4180  0.0000  0.4274  10.000\n",
              "g_4  22097.0 -0.034774  0.857879 -6.369 -0.4863 -0.0575  0.3872   6.511"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P1s-BOBbpLep",
        "outputId": "a1a825ba-39df-4683-e320-2da3292d7651"
      },
      "source": [
        "g_describe_1 = data.query('rna_polymerase_inhibitor == 1').loc[:, 'g_0': 'g_771'].describe().T\n",
        "g_describe_1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>g_0</th>\n",
              "      <td>25.0</td>\n",
              "      <td>1.398504</td>\n",
              "      <td>2.794183</td>\n",
              "      <td>-0.6905</td>\n",
              "      <td>-0.2296</td>\n",
              "      <td>0.0967</td>\n",
              "      <td>0.8771</td>\n",
              "      <td>9.1370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_1</th>\n",
              "      <td>25.0</td>\n",
              "      <td>-0.012664</td>\n",
              "      <td>0.752926</td>\n",
              "      <td>-2.1510</td>\n",
              "      <td>-0.4663</td>\n",
              "      <td>0.1544</td>\n",
              "      <td>0.4511</td>\n",
              "      <td>0.9564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_2</th>\n",
              "      <td>25.0</td>\n",
              "      <td>0.719024</td>\n",
              "      <td>1.267041</td>\n",
              "      <td>-1.3430</td>\n",
              "      <td>-0.0721</td>\n",
              "      <td>0.6622</td>\n",
              "      <td>1.0300</td>\n",
              "      <td>4.1510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_3</th>\n",
              "      <td>25.0</td>\n",
              "      <td>-0.213656</td>\n",
              "      <td>1.005590</td>\n",
              "      <td>-3.6590</td>\n",
              "      <td>-0.4939</td>\n",
              "      <td>0.0071</td>\n",
              "      <td>0.4131</td>\n",
              "      <td>1.0580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_4</th>\n",
              "      <td>25.0</td>\n",
              "      <td>0.943116</td>\n",
              "      <td>1.618908</td>\n",
              "      <td>-1.0310</td>\n",
              "      <td>-0.0544</td>\n",
              "      <td>0.5175</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>5.3850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     count      mean       std     min     25%     50%     75%     max\n",
              "g_0   25.0  1.398504  2.794183 -0.6905 -0.2296  0.0967  0.8771  9.1370\n",
              "g_1   25.0 -0.012664  0.752926 -2.1510 -0.4663  0.1544  0.4511  0.9564\n",
              "g_2   25.0  0.719024  1.267041 -1.3430 -0.0721  0.6622  1.0300  4.1510\n",
              "g_3   25.0 -0.213656  1.005590 -3.6590 -0.4939  0.0071  0.4131  1.0580\n",
              "g_4   25.0  0.943116  1.618908 -1.0310 -0.0544  0.5175  1.0940  5.3850"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdSQFDkepdsA",
        "outputId": "e35d3368-8ebc-407c-e255-8be0d3e788ec"
      },
      "source": [
        "fig, axes = plt.subplots(2, 4, figsize=(20, 8))\n",
        "fig.suptitle('Comportamento das colunas \"g-n\" em relação ao inibidor do RNA polimerase ', fontsize = 16)\n",
        "plt.subplots_adjust(hspace = 0.4)\n",
        "sns.set()\n",
        "\n",
        "sns.histplot(ax=axes[0][0], x = g_describe_0['mean'], bins = 100)\n",
        "axes[0][0].set_title('Média 0', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[0][1], x = g_describe_0['std'], bins = 100)\n",
        "axes[0][1].set_title('Desvio Padrão 0', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[0][2], x = g_describe_0['min'])\n",
        "axes[0][2].set_title('Valor Mínimo 0', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[0][3], x = g_describe_0['max'])\n",
        "axes[0][3].set_title('Valor Máximo 0', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[1][0], x = g_describe_1['mean'], bins = 100)\n",
        "axes[1][0].set_title('Média 1', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[1][1], x = g_describe_1['std'], bins = 100)\n",
        "axes[1][1].set_title('Desvio Padrão 1', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[1][2], x = g_describe_1['min'])\n",
        "axes[1][2].set_title('Valor Mínimo 1', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[1][3], x = g_describe_1['max'])\n",
        "axes[1][3].set_title('Valor Máximo 1', fontsize = 12)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yEhITEwVB+L/jM3PmTJnp3N3dBTMzM+HixYviMGmZsm/fvhLXLT1uW7durVBMRa8fqYKCAiE3N1cIDw8X7O3thfz8fEEQBCE+Pl6wsLAQFi1aVGJ8heXl5Qm5ublCv379hPnz54vDlyxZIrRp00amrMrLyxP69esnuLu7K7186XxPnz4VzMzMhKNHj4rDBw0aJPTt21em/Pjnn38ES0vLUrdhxowZQq9evcTP0rLR09NTZrqNGzcKZmZmwrNnz8RhRa8/abk8e/ZsmXnDw8MFCwsL4e+//5ZZh/Saz8/PF3r06CGMGjVKZr5Dhw4JZmZmMuso673s2LFjJW6/IPzf9Vc07nXr1sldfxXZ19LzOTs7W8jNzRWSkpKEiIgIoU2bNnJxFr7HvXr1SujQoYN4beXm5iosF6T7KyYmRhAEQXjw4IFgZmYmBAcHl7oPpNdK0WMQHR0tmJmZCb/++qsgCBUrrz09PWXOq4qWGX369BFGjBghM29aWprg4OAgLFiwQGa95ubmws2bN0vcB+W99z1+/FiwsLCQ+Y4gCIJw6dIlpc9BIiJlsGobEVW5ixcv4qOPPkK9evXEYXp6enBycsLFixdlptXR0UHHjh3Fz61atQIAdO3aVeZXtVatWiEvLw8vX76Umb9nz57Q0dERPzdr1gw2NjZilY9r164hNzcXbm5uMvN9/PHH0NDQkIune/fuJf6SXNSNGzfg6+uLrl27wtLSElZWVjh37hwePnwoN23RN5Sk29q9e3dxmIGBAYyMjMQ3EV6/fo2LFy/CxcVFfDsiLy8PgiCga9eucq+7161bF507dxY/a2lp4b333sPTp09L3ZaLFy+icePG6NSpk8xwNzc3JCcn4/79+8XOe+XKFdjY2Mj8Kqujo6OwhyZl9lnbtm1x+vRprFixApcuXUJOTk6p8V++fBmZmZklth9z8eJFSCQSuLq6ym1jbm6uwqpC5fXRRx/JfG7durXccTh8+DAGDx4Me3t7WFpawtbWFpmZmTJVV6ytrbFp0yZs27YNd+7cqXC7XXfv3kVmZiZcXFxkhjs7O8t8FgRBPN+k/wpr2bIl3nvvPfFzgwYN0KBBA6XOteKcPXsWpqamsLOzk1lvt27dFB4fRdeUjo4O7O3tZYYBKLY6T1F9+/atUEyFvXjxAoGBgejVqxesra1hZWWFlStX4r///hOrJv76668oKCgotd2jX3/9FV5eXujUqZN43cTHx8tcNxcvXoSNjY34RhgAqKurw9XVFbdu3ZKrzltUZGQk3NzcYGdnB0tLS/Eclq4jMzMTN2/eRP/+/cV2dwCgefPmaN++vVx5qqyix9HMzAyAcsesf//+Mp8//vhjFBQUyFU/k3r27BmePXsmN1+/fv1ktgko271MU1MTvXr1KjVe6fWnKO7CVLWv27ZtCysrK3Tp0gXLly/H5MmTS+wUw9DQED4+PoiOji6xCl1UVBT09PTEZbVq1Qo2NjY4cOCA0j2CFt0H0vvclStXAFROeV2eMiM+Ph7//PMPBg4cKFMG1KlTB3Z2dnL3YVNTU7Rp00Zu3aq490nLCzc3N5lYbGxsoKurW+5rkIioKFZtI6IKMzQ0RJ06dZR+QExNTRWr5RTWsGFDuapJ+vr6Mp+lDVIW/uIOvPmSDrxpV6awBg0aKFyPtL0JaZsBRePR0NCAoaGhXDzSKmvKSEhIgLe3Nz788EMEBASgadOmUFdXx6pVqxR+AZe+3l50m4puq5aWlridqampyM/PR3h4OMLDwxXGUVBQIFbPK7os6fKUScSUdNyk44vz8uVLsSpeYUWPj7L7zNfXF1paWoiJiUFERAR0dHTg4uKCadOmFVs9SXqspdUPi9tGAwMDuYZPldnGsip6vIseh7i4OEyaNAmDBg3CV199hfr160NNTQ1jx46VmW7lypVYs2YNNm7ciEWLFsHY2BhDhw6Fn59fuXrdkyZjix6boo3r/v7773I9bBWuIld0+xRtY1klJyfjyZMnsLKyUji+aBsgiq6p4sqUomVHcYpeA2WNSaqgoADjx4/Hixcv4O/vj1atWkFbWxvHjx9HRESEGI8y5+1ff/2FsWPHwtHREQsXLoSxsTEkEgkCAgJk9ndqaqrCB9iGDRtCEASkpqYW2/OZtFqoj48PHB0dUa9ePQiCgC+++EKM9b///oMgCArLyYYNG4pVSMuqaG+YZTlmRc9b6XmtqG0n4P/O/6LzSe8JhZXlXla/fn2lqhQVt/6in1W1r/fs2QM1NTW8ePEC4eHh+O6778R2dYrj7e2NHTt2YPXq1QgJCVG4Db/88gv69++PnJwc8Rzs168fgoODcf78eXTr1q3U2Ipus5aWFurVqyceu8oor8tTZkiTvt9++y2+/fZbuWU2bdpU5rOic0ZV9z5pLEUT3lJsJ4mIVIWJJCKqMA0NDTg4OODcuXNK9T5iYGCAxMREueGJiYkKHz4rQlGDw4mJiWLbEdIHg8TERJlER15eHlJSUioUz9mzZ5GWloaVK1fKPAQWbui1ovT19SGRSDB8+HB88sknCqcpTzJBEQMDA4VvUkmPZUn7ytjYWOGxKDpM2X2mqamJsWPHYuzYsXj58qXYLkZWVhZWrlypMAZpt9PPnz8X32hQtI2pqaly57Ey26ilpYXc3FyZYUUbhi2LQ4cOoWXLljJtueTm5so9HDVo0ABBQUEICgrC33//jaioKISGhsLIyAgeHh5lXq/0IScpKUnmmih6zVpZWWHv3r1lXn5FGBoaolmzZsUe45LabFEVNTU1lcT0zz//4MaNG1i2bJnMtXvy5EmZ6Qqft9I3IYo6evQo1NXVERoaKiaggTfJhsLJ45LKXjU1tRLP70OHDqFLly4ybT49fvxYZpp69epBTU1N7s1Q6TqKJmKqQtGyXVrmFNd+kPT8L7qfpPeEwspyLyt63hSn8PpLuv5Uta+trKzEN5rat2+P/v37Y8GCBYiOji723qGrqwtfX18sWbIEo0ePlhsfExOD/Px8HDx4EAcPHpQbv3//fqUSSUW3OScnB//995947CpSXquSdF9PmTIFXbp0kRtf+JoEFJ8Lqrr3SWPZvHmzwh+OquMaJKK3E6u2EZFKjB07FikpKcX2RPP48WPcvn0bANCxY0ecOXNGphpFeno6Tp48CQcHB5XGdfr0aWRmZoqf//33X1y7dk3s7tnGxgaampo4dOiQzHyHDx9GXl6eUvFIv8AW/cInbey4cLWDhw8fyvS0VFHSV+5v374NKysrtG3bVu5fWRW3PQ4ODnj27Bn++OMPmeEHDx5EgwYNSuxO2c7ODteuXZOpipKZmYm4uDiZ6cqzz4yNjTF48GB07dq12J6NpDHo6Ohgz549xU7j4OCAgoICHDlyRGZ4TEwMNDU1S+wm3NTUVK6HntOnTxc7fWlev34t9wZDdHS0wh69pFq1aoXJkyfDwMCgxH1REjMzM+jo6Mjtg6Kf9fT0KnyulVX37t3x7Nkz6OjoKDzXlWksu6bEJL2+Cj9k5ubmIiYmRma6rl27QiKRlHjeZmVlQSKRyDygnj9/Xu4t0Y4dO+LatWtir5LAm84SDh8+DEtLy2LfRpLGW7RqV9GeznR0dGBlZYUjR47InKdPnjzBlStXVF6+K+Pnn3+W+Xzo0CFIJBLY2NgonL5JkyYwMTGRm+/o0aNy1Tcr415mbm4OHR0dhXEXVhn72sjICBMmTMDdu3cRGxtb4rQeHh5o3LixwgTq/v37YWpqiu+//17uX/fu3XH8+PFSq1EC8sfuyJEjKCgogJ2dHYCKldeq1KpVK5iamuLevXsKy4DCja4XR1X3vm7dukEikeDp06cKY2nevHkFt5aI6A2+kUREKtGxY0fMnDlT7HFs0KBBaNq0KVJTU3H+/Hns3bsXISEhsLCwgJ+fH06dOgVvb298+eWXUFNTw4YNG5CVlYUJEyaoNK46depg1KhRGDNmDHJycrB69Wro6enB29sbwJtf50aNGoV169ahbt266NmzJx48eICVK1eiQ4cOcu3YKCJNoOzcuRODBg2ChoYGzM3N0bVrV2hoaGDGjBnw8fHBy5cvxZ65KtqOTWEzZ86Ep6cnRo8ejc8//xzGxsZ49eoVbt68ifz8fJnen5Qh7VFny5Yt6NGjByQSCdq2bYtBgwbh+++/h7+/PyZNmoTGjRsjJiYG586dw7x580qstjFy5EhERkZi1KhR8Pf3F3ttK9relLL7bPz48bCwsICVlRXq1auHmzdv4uzZsyX2HKenp4cpU6Zg/vz58Pf3x8CBA6Grq4tbt25BW1sbXl5e6NGjBzp06ICgoCAkJyejdevWOH36NH788Uf4+vqWmKgYMGAAZs2ahUWLFqFXr164ffu23IN2WUgfuKTL+/PPP7Fjxw6ZX5nT0tLg7e2NgQMHolWrVtDU1MSJEyeQmpqq1C/+ihgYGGDEiBFYt24ddHV1xV7bfvrpJwDKv1lRGQYOHIh9+/bB29sbo0aNgoWFBXJycvD48WPExcUhLCwMdevWrRUxSR8+V6xYAYlEAg0NDWzbtk1uuhYtWsDb2xtbtmxBRkYGnJycIJFI8Oeff6JVq1YYMGAAunfvjm3btmHmzJn47LPP8PDhQ4SHh8u9dePt7Y39+/eL16Genh4iIyMRHx+PdevWlbid3bt3x4YNGxAREYF27drht99+U5hsmDhxInx9feHr6wsPDw9kZmYiNDQUenp68PHxKePerbgzZ85g6dKlcHR0xPXr1xEWFgZ3d3eZ9rsKk0gkmDBhAgICAvDNN99gwIAB+Oeff7B+/Xq5RFtl3Mvq1auHkSNHIiIiArq6unB0dMSff/6p8O2/ytjXQ4cOxaZNm7B27Vq4uLgUe71raWlhwoQJmD17tszwmzdv4u7du/D391dYPS47Oxtnz55FbGys2ENlce7fvy8eg/j4eKxYsQIODg7iWz8VKa9VSU1NDUFBQfDz80Nubi769++P+vXrIzExEVeuXEHTpk1LPR6quve1aNECX375JebPn4+HDx/CwcEB2traSEhIwLlz5zB48GCZdhKJiMqLiSQiUhlvb2+0a9cOW7duxbJly/Dq1Svo6urC2toac+fOFRtWtrCwwPbt27FixQrMnDkTgiDAxsYGO3bsUOqXu7Jwd3dH3bp1MW/ePLx69Qpt27bFihUrZF7vnjRpEoyMjLBr1y7s2rULhoaGcHd3x5QpU5SqFmZhYQF/f3/88MMP+PHHH1FQUIATJ06gdevWCA4OxurVqzF+/Hi0aNECU6ZMwdmzZ/H777+rbBulVYzWrFmDBQsWIC0tDUZGRrC0tMSwYcPKvLxevXrBw8MDkZGRCAsLgyAIuHPnDnR0dLB9+3YEBwcjJCQEGRkZeP/99+Wq5ihiZGSErVu3YuHChZgxYwYMDQ0xdOhQ5OfnIywsTJxO2X3WsWNHHDlyBJGRkcjKyoKJiQnGjBmDcePGlRiHp6cnGjZsiE2bNmHq1KnQ0NDABx98IHZtL5FIsH79eixfvhwbN25ESkoKTE1N8c0332DkyJElLnvQoEFISEjATz/9hB9++AH29vYICwsrtq2K0nzxxRcyy2vbti0iIiLw1VdfidNoa2vDysoKP/74I54+fQo1NTW8//77CAkJKbHB3NJ8/fXXAIC9e/di+/btsLGxweLFizFs2DC59kKqkqamJjZt2oT169fjhx9+wL///gsdHR00b94cH330kVwVkpock5aWFsLCwjBv3jzMmDEDBgYG+Oyzz9C0aVMEBATITDtjxgy0aNECkZGR4ptJDg4OYrKwe/fuCAgIwJYtW3D06FG0bt0ay5Ytw9q1a2WW07hxY0RGRiIkJARz5sxBTk4O2rRpg3Xr1sk1MlzUhAkT8N9//2Hr1q3Izs6Gg4MDNm7cKHee9ejRA+vWrUNYWBj+97//QVNTEw4ODpg2bVq5uqOvqODgYGzevBm7d++GpqYmBg8ejBkzZpQ4z+DBg5GZmYmtW7fi4MGDaN26Nb777jtMnz5dZrrKupf5+/tDEATs3bsXO3fuhI2NDSIiIuQa3K6Mfa2lpQU/Pz8EBgbi+PHjJZZfn376KTZt2oT4+Hhx2P79+yGRSDBo0CCF8zg6OsLExARRUVGlJpK+/cdOlscAACAASURBVPZbsa24/Px8ODk5ybRBVJHyWtV69uyJHTt2ICIiAgEBAXj9+jWMjY1hY2ODAQMGlDq/Ku99kydPRqtWrRAZGYnIyEioqamhSZMm6NKlS7EJVCKislITVPmzOBFRDWJubo5x48Zh0qRJ1R0KUa135MgRTJw4ETt37pTpwYiq1uPHj+Hn54d9+/ZVS+KMqLJduHABI0aMwJYtW9C1a9fqDoeIiBTgG0lEREQk49q1azh16hRsbGygra2NGzduYMOGDbC1tUWHDh2qO7x31vHjxwG86W783r17sLS0rOaIiIiI6F3ERBIRERHJ0NHRwaVLlxAZGYn09HQYGRnBxcUFU6ZMqdY2kt51CxYsQGJiItq3b19sD25ERERElY1V24iIiIiIiIiISCmltyJLREREREREREQEJpKIiIiIiIiIiEhJTCQREREREREREZFSmEgiIiIiIiIiIiKlMJFERERERERERERKYSKJiIiIiIiIiIiUwkQSEREREREREREphYkkIiIiIiIiIiJSChNJRERERERERESkFCaSiIiIiIiIiIhIKUwkERERERERERGRUphIIiIiIiIiIiIipTCRRERERERERERESmEiiYiIiIiIiIiIlMJEEhERERERERERKYWJJCIiIiIiIiIiUgoTSUREREREREREpBQmkoiIiIiIiIiISClMJBERERERERERkVKYSCIiIiIiIiIiIqUwkUREREREREREREphIomIiIiIiIiIiJTCRBIRERERERERESmFiSQiIiIiIiIiIlIKE0lERERERERERKQUJpKIiIiIiIiIiEgpTCRRtZk7dy5WrFhR4jTm5uZ49OgRACAwMBBhYWFVERoRvUXGjBmD/fv3V2sMTk5O+PXXX0uc5ttvv8WAAQOQkJCAkSNHVlFkRFRRhb+rVKW//voLzs7OSE1NLXG6S5cuwdnZuYqiIqKqVl1lkNThw4cxbNgw5OXlKTU9n+neDkwkkUo4OTnB2toaycnJMsPd3d1hbm6Of//9V2b4Dz/8AE1NTUyaNEnpdcybNw8TJkwoV3wpKSmYMGECbG1t0atXL8TExJRrOURUfk5OTmjXrh3s7Oxgb2+PoUOHYteuXSgoKKjU9W7cuBGDBg0q83wXLlyAhYUF7OzsYGdnB2dnZ/z000+VEOEbr169QkhICCZNmoT+/fuXezks74jKZvTo0Vi1apXc8OPHj6Nbt25KPxypQmhoKMzNzbFt2zaZ4du2bYO5uTlCQ0MBADk5OQgMDMTy5cthYGBQ4jLt7e0RGxtbaTEX9u+//8LLyws2NjZwcXEpNYFORLWzDJJ69eoV1q5di5UrV0JDQ0OpdVTkma6sYmJi0KtXL9ja2sLPzw8pKSlVst53ARNJpDKmpqY4dOiQ+PnOnTvIyspSOO2QIUMwa9asqgoN8+bNg6amJs6dO4fg4GDMmTMH9+7dq7L1E9EbERERuHLlCk6ePIkvv/wSGzZswLffflvdYRWrUaNGuHLlCi5fvoxp06Zh9uzZuH//vkrXIf2CGB4eDktLS+zevRtDhw4t9/JY3hGVzaBBg3DgwAEIgiAz/MCBAxg4cKDSD0dlVdzD4XvvvYfo6GiZYVFRUXjvvffEz3///Te+/vprWFlZVUps5TVlyhRYWlriwoULmDRpEr7++mu5HxmJSFZtLIOkHjx4gPnz56Nx48aVEWKF3Lt3D4GBgVi2bBnOnTuHunXrYu7cudUd1luDiSRSmU8++QRRUVHi56ioKLi7u8tMk5OTg6VLl+Kjjz5C165dERgYiNevX4vjN27cCEdHRzg6OmLv3r0y886cOVOsCpeamgpfX1907twZHTt2hK+vL549e6YwrszMTBw9ehQTJ06Erq4u7O3t4eTkJFdAElHV0dfXR+/evbFy5Urs378fd+/eBVByGZGcnAxfX1/Y29vDwcEBHh4eKCgowPr16/H111/LLH/BggVYsGABAMDLyws//vgjAKCgoADh4eHo1asXunTpgunTpyMtLa3UeNXU1NCnTx/Uq1cP9+/fx6lTp+Du7o727dujZ8+ecr/QRUVFoVevXujUqRPWrl0rMy40NBRff/01pk6divbt22P//v24fv06hgwZAnt7ezg6OmLevHnIyckR57l8+TI+++wzdOjQAZ999hkuX76sME6Wd0Rl16dPH6SkpODSpUvisNTUVJw8eRLu7u6lXp+FpaWlYfr06ejcuTN69eqF8PBw8a3Lffv2YejQoVi0aBE6deokV25ItW3bFllZWWIC+N69e8jOzkbbtm1l4ps9e7b42cnJCZs2bcLAgQPRoUMH/O9//0N2djaAN29X9ujRQ2bajRs3YuDAgbC1tcWsWbOQmJiIMWPGwM7ODt7e3jLV5U6cOIGPP/4Y9vb28PLywoMHDxTG/fDhQ/z111/w9/dHnTp14OzsDDMzsyp7G4qotqqtZZCvry+++uorfPnllzLPYikpKejRowfi4uIAABkZGejbt6/4nFj4mU5aPm3YsAFdunSBo6Mjjh8/jtOnT8PZ2RkODg6IiIgQ15uTk4OFCxeKz4sLFy4sdl/ExMTAyckJHTt2hK6uLiZOnIhjx44hPT299INCpWIiiVTG1tYW6enpePDgAfLz83Ho0CG4ubnJTBMSEoKHDx8iKioKR48exYsXL8Q6smfOnMHmzZuxefNmHD16FOfPny92XQUFBfj0009x8uRJnDx5Etra2pg3b57CaePj46Guro73339fHGZhYaHytwqIqOzatWuHJk2aiF+eSiojtmzZgsaNG+P8+fM4d+4cJk+eDDU1NXz88cc4ffq0+MUgPz8fR44cgaurq9z69u3bh/379+P777/H8ePHkZmZWWzZUVhBQQGOHTuGtLQ0mJmZoW7duli6dCkuXbqEdevWYdeuXTh+/DgA4P79+5g7dy6WLVuGs2fPIiUlRS7RfeLECbi4uODSpUsYOHAgJBIJvvnmG/z222/YvXs3zp8/j8jISABvvpD5+vrCy8sLFy5cgI+PD3x9ffHq1Su5OFneEZVdnTp10L9/f5kfw37++We0atUKFhYWJV6fRc2fPx9paWk4fvw4tm/fjujoaJkqsdevX0fz5s1x7tw5jB8/vtiYCv84t3//fnzyySelbsfPP/+MjRs34sSJE7hz5w727dtX7LRHjx7Fli1bEBsbK74hOnnyZPz2228oKCjA9u3bAbxJDk2ZMgWzZs3C+fPn0aNHD4wbN07hg9v9+/fRvHlz6OnpicNY/hCVrjaWQQUFBRg0aJDCZzFDQ0MsWrQIs2fPRlJSEhYvXow2bdrIvWAglZiYiOzsbJw5cwZff/01AgICcODAAfz000/YuXMnwsPD8fjxYwDA2rVrce3aNURHR+PAgQP4888/ER4ernC59+7dg7m5ufi5RYsW0NTURHx8fLHbTcpjIolUSlronDt3Dh988IHMa46CIGDPnj2YNWsWDA0NoaenB19fX7E63M8//4xPP/0UZmZm0NHRwVdffVXseurXrw9nZ2fUrVsXenp6GD9+PC5evKhw2szMTJkvNcCbtyEyMjJUsMVEVFGNGjVCampqqWWEhoYGXr58iadPn0JTUxP29vZQU1ODqakpLC0txUTOb7/9hjp16sDW1lZuXTExMfD29kbz5s2hq6uLyZMn4/Dhw8W+3v3ixQvY29ujc+fOWLNmDZYtW4ZWrVqhU6dOMDc3h0QigYWFBT7++GP8/vvvAIAjR47go48+QseOHaGlpYWJEydCIpG93dra2qJPnz6QSCSoU6cOrK2tYWtrCw0NDTRr1gxDhgwRy7RTp06hZcuWcHd3h4aGBlxdXdGqVSucPHlSLl6Wd0Tl4+7ujtjYWPEtnqioKLFttZKuz8Ly8/Nx+PBhTJkyBXp6emjWrBl8fHxw4MABcZpGjRrBy8sLGhoaqFOnTrHxuLm54dChQ8jNzcXhw4flfphTxMvLC40bN4ahoSF69eqFW7duFTutp6cnGjZsiMaNG8Pe3h7t2rWDpaUltLW10bdvX9y8eRPAm0Z0e/bsiW7dukFTUxOjR4/G69evceXKFbllZmRkQF9fX2YYyx8i5dS2Mqh+/fpwcXEp9lnM0dERLi4u8Pb2xunTp0usUqahoYHx48dDU1MTAwYMwKtXrzBixAjo6emhdevW+PDDD3Hnzh0Ab77HTZgwAQ0aNICRkREmTJggs32FZWZmypVJenp6LJNUpHIqXNI765NPPoGnpyf+/fdfucx1cnIysrKy8Omnn4rDBEEQX7d88eIFrK2txXGmpqbFricrKwuLFy/G2bNnxdevMzIykJ+fD3V1dZlpdXR05F5hTE9Ph66ubvk2kohU6vnz5zAwMCi1jBg9ejTWrFmDUaNGAXjT1trYsWMBAK6urjh48CDc3d1x8OBBhW8jAW/KmcJli6mpKfLy8pCUlKSwfn+jRo1w5swZueHXrl1DSEgI7t27h9zcXOTk5MDFxUVcR5MmTcRpdXR0YGhoKDN/4fHAm1/9lyxZghs3biArKwv5+fli2ycvXrxA06ZNZaZv2rQpnj9/LhcXyzui8rG3t0f9+vVx/PhxtG3bFn/++SfWrFkDoOTrs7BXr14hNzdX5noteq0WvfaL07RpU7Ro0QLLly9Hy5YtYWJiUuo8xsbG4t9169bFixcvip22YcOG4t/a2toyn+vUqYPMzEwA8uWPRCKBiYmJwvJHV1eX5Q9ROdW2Mig7OxvBwcE4e/YsXr9+DUEQ8N9//8k8i33xxRfYsWMHxo0bh/r16xe7LkNDQ3EeaXKrQYMG4nhtbW0x+VO0TGratGmxZR2/E1UuvpFEKmVqaopmzZrh9OnT6Nevn8y4+vXro06dOjh06BAuXbqES5cu4Y8//hB/1WrUqBESEhLE6Z8+fVrsejZv3oyHDx9iz549uHz5Mnbu3AkAco3UAW8ajMvPz5d5jfH27dv48MMPK7KpRKQC169fx/Pnz9GhQ4dSywg9PT3MnDkTJ06cwNq1a7FlyxaxCmz//v3x+++/49mzZzh27BgGDhyocH2NGjXCkydPxM9Pnz6FhoaGzBcWZUyZMgW9e/fG6dOn8ccff2Do0KFi+dOoUSOZqmxZWVlyvYSoqanJfJ4zZw5atWqF2NhYXL58GZMmTZJZXtHyMCEhQWHii+UdUflJ36o+cOAAHB0dxeRKSddnYfXr14empqbM9Vr0Wi167ZfE3d0dW7ZsKbY6SFUoWv4IglBs+fPhhx/i8ePHMg9uLH+IlFebyqDNmzfj3r172LNnD06fPi22YySNKz8/H4GBgXB3d0dkZCQePXqk9HpLUrRMSkhIQKNGjRRO27p1a9y+fVv8/PjxY+Tm5ipsNJzKjokkUrmFCxdi27Zt0NHRkRkukUgwePBgLFq0CElJSQDevIlw9uxZAICLiwv279+P+/fvIysrS8zCK5KRkQFtbW3Uq1cPKSkpJU6ro6ODvn37YvXq1cjMzMQff/yBEydOKNXeABFVjvT0dJw8eRKTJ0+Gm5ubWE2spDLi5MmTePToEQRBgL6+PtTV1cUvREZGRnBwcMA333yDZs2a4YMPPlC4XldXV2zbtg2PHz9GRkYGVqxYgf79+5e5R5SMjAwYGBhAW1sb169fx8GDB8Vxzs7OOHXqFC5duoScnBysXr1afKuqpOXp6upCV1cXDx48wK5du8RxPXv2RHx8PGJiYpCXl4fDhw/j/v37+Oijj+SWw/KOqPzc3d1x/vx57NmzR+bBqaTrszB1dXW4uLhgxYoVSE9Px5MnT7BlyxalqqUpMmDAAGzevBn9+/cv1/yq0L9/f5w+fRrnz59Hbm4uNm/eDC0tLdjZ2clN+/7776NNmzYICwtDdnY2jh07hjt37sDZ2bkaIieqfWpTGfTff/9BQ0MD2traSElJEduzlIqIiICamhoWLVqE0aNHY8aMGcjPzy9XHIV9/PHHWLt2LZKTk5GcnIywsLBifzwcOHAgTp48iUuXLiEzMxOrVq1C37595ZoAoPJhIolUrkWLFjKt+hc2bdo0tGzZEl988QXat28Pb29vPHz4EMCbh6WRI0di5MiR6Nu3Lzp37lzsOkaOHIns7Gx07twZQ4YMQffu3UuMKSgoCK9fv0bXrl0xZcoUzJkzB61bty7/RhJRuYwbNw52dnbo2bMnIiIi4OPjg8WLF4vjSyojHj16BB8fH9jZ2WHIkCEYNmyYTDnh6uqKX3/9tdhqbQDw2Wefwc3NDZ6enujduze0tLRkej5SVlBQEFavXg07OzuEhYXJfMlq3bo1AgMDMXXqVHTv3h316tUr9VXyGTNm4ODBg2jfvj1mz56NAQMGiOPq16+PiIgIbNmyBZ06dcLGjRsREREBIyOjYmNjeUdUds2aNYOdnR2ysrLQu3dvcXhJ12dRs2fPRt26ddGnTx94eHjA1dUVn332WbniqVOnDrp27VpiOyaVrVWrVggODsb8+fPRuXNnnDx5EhEREdDS0lI4/fLly3Hjxg107NgRISEhWL16dbFlFRHJqk1lkLe3N3JyctClSxe5Z7EbN25g69atWLp0KdTV1fHll18CANavX1+uOArz8/ODtbU13Nzc4ObmBisrK/j5+SmctnXr1pg7dy6mTp2Krl27IiMjA0FBQRWOgd5QExS9F0dERERERERERFQE30giIiIiIiIiIiKlMJFERERERERERERKYSKJiIiIiIiIiIiUwkQSEREREREREREphYkkIiIiIiIiIiJSikZ1B6AKr15loKBA9Z3PNWigh6SkdJUvV9UYp2oxTtUqLU6JRA316+tWYUSVR5myqCYft5ocG1Cz46vJsQE1O76aEtu7VhYBNWffqxK3qXbgNpXsbSmPKusZraZ4G89jRbidbx9lt7WksuitSCQVFAiVVkjVlsKPcaoW41St2hJnRSlbFtXk/VGTYwNqdnw1OTagZsdXk2Orjcryveht3PfcptqB2/T2q8xntJribd8+KW7n26ei28qqbUREREREREREpBQmkoiIiIiIiIiISClMJBERERERERERkVKYSCIiIiIiIiIiIqUwkUREREREREREREphIolUTiJRg0SiVuZxRESlYflCRERERFS9NKo7AHq7SCRq2HHsLgDAs6+ZXLeCJY0jIipNcWVIaWUPEcnLzSuAsbF+hZeTnZOH/1KzVBARERERFVXPoC60tVSXusnNK6jwMphIIpVLy8gu1zgiotKwfCFSHU0NCaatOl3h5QRP7KmCaIiIiEgRbS0NldyvpVRx32YiiYiIiIiI6C21dOlSxMbG4smTJ4iJiYGZmRkAwMnJCVpaWtDW1gYATJ06Fd27dwcAXL16FYGBgcjOzoapqSmCg4PRoEGDatsGIqpZ2EYSERERERHRW6p3797YuXMnTE1N5catXr0a0dHRiI6OFpNIBQUFmDZtGgIDAxEbGwt7e3uEhIRUddhEVIMxkURERERERPSWsre3h4mJidLT37hxA9ra2rC3twcADB06FEeOHKms8IioFmIiiYjeSWvWrIG5uTnu3n3TQPPVq1fh5uYGZ2dnjBo1CklJSdUcIREREVHlmjp1KgYOHIg5c+bgv//+AwAkJCSgadOm4jRGRkYoKChASkpKdYVJRDUM20gionfOX3/9hatXr4qveEtf4V68eDHs7e0RHh6OkJAQLF68uJojJSIiIqocO3fuhImJCXJycrBw4ULMmzdPpVXYGjTQU9myaipV9HxZG3A7q5+mpmpTNxXdViaSiOidkpOTg3nz5uG7777DiBEjACh+hbt3795MJNUSEokaJBK16g6DiIioVpFWd9PS0oKHhwfGjx8vDn/69Kk4XXJyMiQSCQwNDcu0/KSkdBQUCKoLuIYxNtbHy5dp1R1GpeN2Vj9jY33k5uapdJnKbKtEolZsQpiJJCJ6p6xatQpubm5o1qyZOKykV7jL8qVJ2V/eavKvHTU5NgDQ0Hhz2yq8r8P2XoNuXU2F46pSTd93NTm+mhwbEdHbKDMzE/n5+dDX14cgCDh8+DDatGkDALC2tsbr169x6dIl2NvbY/fu3XBxcanmiImoJmEiiYjeGVeuXMGNGzcwderUSlm+Mr+81fRfO2pqbMCb+PLy3vwaI93XEokaXqVmIi9XS25cVcdW0/ddTY2vpsRW0q9uRES12YIFC3D06FEkJibCx8cHhoaGiIiIgL+/P/Lz81FQUIAPPvgAQUFBAACJRIJly5YhKCgI2dnZMDU1RXBwcDVvBRHVJEwkEdE74+LFi3jw4AF69+4NAHj27BlGjx4NLy8vlbzCTURU2NKlSxEbG4snT54gJiYGZmZmAAAnJydoaWlBW1sbwJvGbqXdbl+9ehWBgYEyD28NGjSotm0gotovICAAAQEBcsOjoqKKnad9+/aIiYmpzLCIqBZjr21E9M4YO3YsfvnlF8TFxSEuLg5NmjTBpk2bMGbMGPEVbgB8hZuIVKJ3797YuXOn2LB/YatXr0Z0dDSio6PFJJK04f/AwEDExsbC3t5epQ3fEhEREakCE0lE9M6TvsI9d+5c9OvXDxcvXsSUKVOqOywiquXs7e3FxmyVoajh/yNHjlRWeERERETlwqptRPTOiouLE//mK9xEVJWmTp0KQRDQoUMHTJ48GfXq1VNZw/9ERERElYmJJCIiIqIqtHPnTpiYmCAnJwcLFy7EvHnzVFqFrSyNhmtqquarYE3qea8mxaIqhbcpN68AmhqqqVSgymWV1dt+nIiI3mZVlkgqrmFJNipJRERE7xJpdTctLS14eHhg/Pjx4nBVNPyvbM+Fxsb6yM3NK9Oyi1MTet4Dak4vgKpUdJuMjfUxbdVplSw7eGLPatlf78Jxqgj2IklENV2V/gRRtGFJNipJRERE75LMzEykpb152BQEAYcPH0abNm0AANbW1mz4n4iIiGq8aq3apqhRyd69e2Px4sXVGRYRERFRhS1YsABHjx5FYmIifHx8YGhoiIiICPj7+yM/Px8FBQX44IMPEBQUBOD/Gv4PCgqSeVObiIiIqCap0kRS0YYl2ajku0siUQMApV69JyIiqo0CAgIQEBAgNzwqKqrYedjwPxEREdV0VZZIUtSwZN++fVWy7MqsQ1xbGs2rSXFqaLw5rRQdFw0NDejraCLyxH0AwITPbao0NmXVpP1ZEsZJREREREREVanKEkmKGpYcMWJElTYqWVa1pSHAmhSnRKKGvLw3DXcWPS7GxvrIy8tDXp4EaZk5CqepCWrS/izJ2xInG5QkIiIiIiKqPaqkse3iGpZko5JERERERERERLVHlbyRlJSUpLBhSTYqSURERERERERUe1RJIql58+bFNizJRiWJiIiIiIiIiGqHKqnaRkREREREREREtR8TSUREREREREREpBQmkoiIiIiIiIiISClV0kYSvZskErXqDoGIiIiIiIiIVIiJJKoU+jpa+D72DtIysmHSUA/pWbkw0K9T3WERERERERERUQUwkUSVJi0jG6npOdDXyUFaZg401FmTkoiIiIiIiKg2YyKJiIiIiIjoLbV06VLExsbiyZMniImJgZmZGV69eoXp06fjn3/+gZaWFlq2bIl58+bByMgIAGBubg4zMzNIJG9+CF62bBnMzc2rczOIqAbhKyJERERERERvqd69e2Pnzp0wNTUVh6mpqWHMmDGIjY1FTEwMmjdvjpCQEJn5du/ejejoaERHRzOJREQymEgiIqJaRyJRE/8RERFR8ezt7WFiYiIzzNDQEJ06dRI/29ra4unTp1UdGhHVUqzaRjVC4YfBggKhGiMhopquaGP+REREVH4FBQXYtWsXnJycZIZ7eXkhPz8fPXr0gL+/P7S0tMq03AYN3v57tLGxfnWHUCW4ndVPU1O1qZuKbisTSVTtJBI17Dh2F2kZ2dDX1YZnXzMmk4ioRIUb8yciIqLymz9/PnR0dODp6SkOO3XqFExMTJCeno5p06YhLCwMkyZNKtNyk5LS3+rv9MbG+nj5Mq26w6h03M7qZ2ysj9zcPJUuU5ltlUjUik0Is2ob1QjSh8K0jOzqDoWIiIiI6J2wdOlSPHr0CCtXrhQb1gYgVoXT09PD4MGDcfny5eoKkYhqICaSiIiICmHbS0RE9C5Yvnw5bty4gbCwMJlqa6mpqXj9+jUAIC8vD7GxsWjTpk11hUlENRCrthEREf1/0qq2AFjNliqMXW4TUU2wYMECHD16FImJifDx8YGhoSFWrlyJdevW4b333sPQoUMBAM2aNUNYWBj+/vtvBAYGQk1NDXl5ebCzs8PEiROreSuIqCZhIomIiKgQVrElVenduzdGjBiB4cOHi8OkXW5Le0taunQpQkJCsGjRInGa3bt3Q1dXt8rjJaK3U0BAAAICAuSG37lzR+H0dnZ2iImJqeywiKgWY9U2qlasQkJERG8rdrlNREREbyO+kUTVRtqFt15dzeoOhYiIqMrVhC63VdWdcE3qMrkmxaIqRbdJld1AV9f+eheOExHR24qJJKpWaRnZgMA2SIiI6N1T3V1uq7I74ZrSZXJN7r65vIpuk6q7ga6O/fUuHKeKKKnLbSKimoBV24iIiIiqGLvcJiIiotqKiSQiIiKiKsQut4mIiKg2Y9U2IiIiokrALreJiIjobcREEhEREVElYJfbRERE9Daq8qpta9asgbm5Oe7evQsAuHr1Ktzc3ODs7IxRo0YhKSmpqkMioneIn58f3Nzc4O7uDg8PD9y6dQsA8PDhQwwZMgTOzs4YMmQI4uPjqzdQIiIiIiKiGqhKE0l//fUXrl69ClNTUwBvur2dNm0aAgMDERsbC3t7e4SEhFRlSET0jlm6dCkOHDiAqKgojBo1CrNmzQIABAUFwcPDA7GxsfDw8EBgYGA1R0pERERERFTzVFkiKScnB/PmzcOcOXPEYTdu3IC2tjbs7e0BAEOHDsWRI0eqKiQiegfp6+uL7J62JAAAIABJREFUf6enp0NNTQ1JSUm4efMmXF1dAQCurq64efMmkpOTqytMIiIiIiKiGqnK2khatWoV3Nzc0KxZM3FYQkICmjZtKn42MjJCQUEBUlJSYGhoqPSyGzTQU2mshRkb65c+UQ1Qk+LU0NCAhoY6NDQ0oKlZIP5ddFzR/99Mq1Gpx1NZNWl/loRxls+3336Lc+fOQRAEbNy4EQkJCWjcuDHU1dUBAOrq6mjUqBESEhJgZGSk9HKVPXdr2v4orCbHBhRfvhQuZypahpR3OTV939Xk+GpybEREREQkq0oSSVeuXMGNGzcwderUSll+UlI6CgoElS/X2FgfL1+mqXy5qlaT4pRI3vQ0k5cnQV5eHnJz85CXl4+8vDwAmjLjiv7/ZlpJpR1PZdWk/VmStyVOiUStypOHCxcuBABERUVh2bJlKusVSZlztyYft5ocG/AmvuLKF+kwoGL3BGkZVtbl1IZ9V1PjqymxVUdZRERERFQbVUnVtosXL+LBgwfo3bs3nJyc8OzZM4wePRqPHj3C06dPxemSk5MhkUjK9DYSEVF5ubu748KFC2jSpAmeP3+O/Px8AEB+fj5evHgBExOTao6QiIiIiIioZqmSRNLYsWPxyy+/IC4uDnFxcWjSpAk2bdqEMWPG4PXr17h06RIAYPfu3XBxcamKkIjoHZSRkYGEhATxc1xcHAwMDNCgQQO0adMGBw8eBAAcPHgQbdq0KVO1NqocEokaJBK16g6DiIiIiIj+vyprI0kRiUSCZcuWISgoCNnZ2TA1NUVwcHB1hkREb7GsrCxMnDgRWVlZkEgkMDAwQEREBNTU1DBnzhzMnDkT4eHhqFevHpYuXVrd4b7zJBI17Dh2FwDg2desmqMhIqqYegZ1oa1Vvq/ebEeMiIhqkmpJJMXFxYl/t2/fHjExMdURBhG9Yxo2bIg9e/YoHPfBBx/gxx9/rOKIqDRpGdnVHQIRkUpoa2lg2qrTZZ5PU1MDubl54ufgiT1VGRYREVGZVUnVNiIiIiIiIiIiqv2YSCIiIiIiIiIiIqUwkUREREREREREREphIomIiIiIiOgttXTpUjg5OcHc3Bx3794Vhz98+BBDhgyBs7MzhgwZgvj4eKXGERExkURERERERPSW6t27N3bu3AlTU1OZ4UFBQfDw8EBsbCw8PDwQGBio1DgiIiaSiIiIiIiI3lL29vYwMTGRGZaUlISbN2/C1dUVAODq6oqbN28iOTm5xHFERACgUd0BEBERlUYiUavuEIiIiN4aCQkJaNy4MdTV1QEA6urqaNSoERISEiAIQrHjjIyMlF5HgwZ6lRJ7TWJsrF/dISA3rwCaGqp7P0TR8mrCdlaF/8fevcfHdOf/A3/NZCYREiKRkGBZtjRoKxXVi2tQUYkEtbIuXYoqita65IvGvdlgaYu4bCtbl9WtqiBU3C+1SoNSZd2atioh5MLkIpOZOb8/8ss0k0wmJ5kzMyeT1/Px8Ig558xn3mcu7znnPefz+ch5P9VqaUs31u4rC0lERCRrnnVdsTn5Ohp41nF0KERERCRSZmYuDAbB0WHYjK+vJx480Dg6DPj6emLmRycka2/5tB4m+yWX/bQ1Oe+nr68niop0krYpZl+VSkWFBWF2bSMiItnT5BUiL1/r6DCIqoQD3BKRXPn7++P+/fvQ6/UAAL1ej4yMDPj7+1tcR0QEsJBEElAqFex2QkREVAYHuCUiufLx8UFgYCCSkpIAAElJSQgMDIS3t7fFdUREAAtJZCWlUoGth25g66EbLCYRERGVwgFuiUgOlixZgu7du+PevXsYM2YMBgwYAABYsGABtm7din79+mHr1q1YuHCh8T6W1hERiR4j6euvv0b//v3LLT9w4ABCQ0MlDYpqFk1eoaNDoFqEuYiI7MUW+cYeA9wSkXOxNhfNmzcP8+bNK7e8devW2LFjh9n7WFpHzq1IZyg3ELM1AzMXanV4/KjA2rBIZkQXkubOnWs2gcXExPDkjYjshrmIiOylpuabqsyUJNUsMHKa6UZOsZRV3ee77P2knL3HUc+XnF+n6rLVPtXUXEQ1k1qlNBm8W61WWTXQ8/JpPaQIi2Sm0m+hO3fuAAAEQTD+v/Q6V1dX20RGRFQKcxER2Yst803pQWxdXFxMBrEVBKHCdVUhdqYkKWeBkctMN8446465kzgpZ+9xxPMl59epuqTcp5KZknjsQ0RyVWkhqW/fvlAoFBAEAX379jVZ16hRI0yZMsVmwRERlWAuql2sGcS/5H7OPOUw2ZYt803pQWwjIiLKDWJraR0R1S489iEiuaq0kPS///0PADBy5Ehs3brV5gEREZnDXFR7lAzi7+GurvJ9Peu6YnPydQDAyL5tWEyiapEq3yxZsgQHDx7Ew4cPMWbMGHh5eWHfvn1YsGABoqOjER8fj/r16yMuLs54H0vriKh24bEPEcmV6A7WTF5EJAfMRbWDJq8QEKpXBOIEACQVa/MNB7glIinw2IeI5EZ0IenOnTv48MMPce3aNeTn55usO378uNRxERGZxVxERPbCfENEcsBcRERyI7qQNGPGDDRv3hyzZ8+Gu7u7LWMiIqoQcxGJxbGSyFrMN0QkB8xFRCQ3ogtJN2/exPbt26FUKm0ZDxGRRcxFJAbHSiIpMN8QkRwwFxGR3IjORp07d8bVq1dtGQsRUaWYi0gsTV4hx0siqzDfEJEcMBcRkdyIviKpadOmGDduHPr27YtGjRqZrJs2bZrkgRGVYPcUKo25iIjshfmGiOSAuYiI5EZ0IamgoAC9evWCTqfDvXv3qvxAkyZNwm+//QalUom6devi/fffR2BgIFJTUxEdHY2cnBx4eXkhLi4OLVu2rHL75JxKpgEH2D2Filmbi6j2KSlGAyxIU9Uw3xCRHDAXEZHciC4kxcbGWvVAcXFx8PT0BAAcPnwYc+bMwa5duzB//nwMHz4cERER2L17N2JiYrB582arHoucC7umUGnW5iKqXUrGStLkFcKznhsL0lQlzDdEJAfMRUQkN6ILSXfu3KlwXfPmzSu9f0kRCQByc3OhUCiQmZmJq1evIiEhAQAQFhaGxYsXIysrC97e3mJDIzuy9Mt+6XVEtmJtLqLaR5NXiEe5WkeHQTUQ8w0RyQFzERHJjehCUt++faFQKCAIvxcPFIriwsG1a9dEtTF37lycPn0agiDgk08+QXp6Oho3bgwXFxcAgIuLC/z8/JCenl6lQpKPj4fobavK19ez8o1kwF5xrv3yEjR5WnjWc8Xk158DAKhUKnjWVePfR26hnrsaKpUKKpULVCoV1GqD8f8l25bcLvu3eFtVudez5L62fJ3L4usuLSnjlCIXERGJwXxDRHLAXEREciO6kPS///3P5PaDBw+wZs0aBAcHi36wpUuXAgASExOxbNkyyQaHy8zMtUlXBV9fTzx4oJG8XanZK06lUoHsR/l4lKuFTqdDZmYuAECn00GnU0KTr4WuyNV4W6fToahIB51OD51OB0Btsq7s3+JtlSavp1Kp+P/3td3rXBZfd2lVFqdSqahSkVCKXEREJAbzDRHJAXORY9Vv4A43V/OnzdX5sbRQq8PjRwXWhkXkUKILSWX5+vpi7ty56NevH8LDw6t038jISMTExKBJkya4f/8+9Ho9XFxcoNfrkZGRAX9//+qGRUS1jDW5iIioKphviEgOmIvsy81VhZkfnSi3XK1WoahIV+X2lk/rIUVYRA6ltObOP/30EwoKKq+m5uXlIT093Xj76NGjaNCgAXx8fBAYGIikpCQAQFJSEgIDAzk+EhFVidhcRERkLeYbIpID5iIiciTRVyQNHz7c2BcXKJ6G8tatW5g8eXKl9y0oKMC0adNQUFAApVKJBg0aYP369VAoFFiwYAGio6MRHx+P+vXrIy4urnp7QkS1gjW5iIioKphviEgOmIuISG5EF5KGDh1qctvd3R1PP/00WrZsWel9GzVqhC+++MLsutatW2PHjh1iwyAZsdUsbSXtcopuMseaXETyUjaH8DNPcsN8Q0RyYMtc9Ntvv5kUpDQaDXJzc3Hu3DmEhITA1dUVbm5uAIAZM2agW7duVj8mEdV8ogtJgwYNsmUcVMN41nXF5uTr8HBX26RdABjZt42kbZNzYC5yDkqlAlsP3YCHuxq5BUUAij/zLCaRnDDfEJEc2DIXNWvWDLt37zbeXrp0KfR6vfH2xx9/jDZteExORKZEF5KKioqwbt067N69GxkZGfDz80NERATefvttuLq62jJGkilNXiEgSH/Sp8krlLxNch7W5KLs7GzMmjULv/76K1xdXdGiRQssWrQI3t7e+P777xETE4PCwkI0bdoUy5cvh4+Pj532qnYqySGafK2jQyEyi8c+RCQH9spFWq0We/fuxaeffipZm1Rekc5QrdneiOREdCFp+fLluHz5MhYuXIiAgACkpaUhPj4eubm5mDNnji1jJCIysiYXKRQKjBs3Dl26dAEAxMXFYcWKFViyZAlmzpyJ2NhYBAcHIz4+HitWrEBsbKw9domIZIrHPkQkB/bKRUePHkXjxo3Rvn1747IZM2ZAEAR06tQJ06dPR/369SV7vNpKrVKanQWuujgLHDmC6ELSgQMHsHv3bjRs2BAA0KpVK7Rr1w4RERE8mCIiu7EmF3l5eRmLSADQsWNHbN++HVeuXIGbmxuCg4MBAFFRUejduzcLSU6MY7GRGDz2IWcn1ZURhVodHj/iDGK2Yq9ctHPnTgwZMsR4e9u2bfD394dWq8XSpUuxaNEirFixQnR7Pj4eksXmaGq1+dPmipZXt73qsnV71rZfU67AknOcUr/G1u6r6GiECrowVbSciMgWpMpFBoMB27dvR0hICNLT0xEQEGBc5+3tDYPBgJycHHh5eYluU+wBk5y/pOwZm0qlgkrlApWq+Kuo9PNXel3pbUrfVqsN5bap6G/xtirjY6z98hIAYPLrz5mNq2w8Ysj5dQXkHZ9cY7PVsQ8HtyW5kOrKCF4RYVv2OA+7f/8+vvvuOyxbtsy4zN/fHwDg6uqK4cOHY+LEiVVqMzMz1yl+sPH19URRka7ccrVaZXa5GNW9nyPas2Y/Szx4oLE2JJvz9fWUbZwVvQetIWZflUpFhcfDogtJoaGhmDhxIiZPnoyAgADcvXsX69atQ2hoqPhoiYisJFUuWrx4MerWrYuRI0fi0KFDksQm5oBJ7l9S9opNqVRAp9NBp1NCpyv+Yix5/squ+30btcntoiIddDq92W3L/i3eVonMzFwAQPajfJPHLBuXuXWWyPl1BeQdn1xiM3ewZKtjHw5uS0RVYY/zsF27dqFHjx7Gq57y8/Oh1+vh6ekJQRCwf/9+BAYGSvZ4RFSziS4kzZw5E+vWrcOiRYuQkZGBxo0bY8CAAVWuTBMRWUOKXBQXF4dffvkF69evh1KphL+/P9LS0ozrs7KyoFQqq3Q1EhE5H3sc+3BwWyKqjD1y0a5duzB37lzj7czMTEyZMgV6vR4GgwGtW7fG/PnzJXs8qj2kHlycXWnlodJC0vnz53H06FHMnDkT06ZNw7Rp04zrli9fjqtXr6Jjx442DZKISKpctHLlSly5cgUbN240znTSoUMHPHnyBCkpKQgODsbnn3/Oqy2JajF7HvvYYnDbqnTLlGrMBTl1T5RTLGVJNZ6KlGNlOOo9IOfXqbqk3id75qLk5GST282bN0diYqIkbVPtxsHFnVOl3xwbNmzA8OHDza7r0qUL1q9fj/Xr10seGBFRaVLkops3b2LDhg1o2bIloqKiABR3MVm7di2WLVuG+fPno7CwEE2bNsXy5csl3weqmTgwd+1jz2MfqQe3BcR3y5RyzAU5dE8E5NNV0pzqPt/mxieRcqwMR7wH5Pw6VZeU+1TS1ZbnYUQkV5UWkq5du1bhII8vv/wyZy0hIruQIhc99dRTuH79utl1zz//PPbu3WtVjOR8lEoFth66AQAY2bcNi0m1hL2OfWwxuC0ROQ+ehxGRXCkr2yA3NxdFRUVm1+l0OuTl5UkeFBFRWcxF5CiavEJo8godHQbZkb3yjbnBbTWa4isaOLgtEfHYh4jkqtJCUqtWrfDNN9+YXffNN9+gVatWkgdFRFQWcxER2Yu98s2uXbtMurVlZmZi1KhRCA8PR1hYGFJTUzm4LVEtxmMfIpKrSru2jR49GvPnz4fBYECfPn2gVCphMBhw+PBhLFq0CNHR0faIk4hqOeYikkLJeEdEltgr33BwWyKyhMc+RCRXlRaSwsPD8fDhQ8yePRtFRUXw8vJCTk4O1Go1pk6dirCwMHvESUS1HHMRWcuzris2J1+Hh7va0aGQzDHfEJEcMBcRkVyJmu9zzJgxGDp0KC5evIicnBx4eXkhKCgIHh7ip5clIrIWcxFZS5NXCAgcMJsqx3xDRHLAXEREciSqkAQAHh4eFc4aQM5PqVSwSwjJAnMREdkL8w0RyQFzERHJjehCEtVeJdNfszsIETmbkgK5wSD+KqXSRfWq3I+IiIiIyBmwkESisDsIETmbkjGTAGBk3zaiikIlhXVNXiE867mJvh8RERERkbNgIYlkq2xXuupcOUBEZIkmr7Ba93mUq7VBNERERERE8sdCEslS2dmVqnPlABERERERERFJi4Ukkq2y3emqc+UAEdUcJVcdcmB/IiIiIiL5skshKTs7G7NmzcKvv/4KV1dXtGjRAosWLYK3tze+//57xMTEoLCwEE2bNsXy5cvh4+Njj7CIiEgmSq461OQVwr8RpzQmIiIiIpIrpT0eRKFQYNy4cUhOTsbevXvRvHlzrFixAgaDATNnzkRMTAySk5MRHByMFStW2CMkIiKSmZKxh3LzOf4QEREREZFc2eWKJC8vL3Tp0sV4u2PHjti+fTuuXLkCNzc3BAcHAwCioqLQu3dvxMbG2iMsIiIiIiKL6jdwh5srR4MgIiIqYfdvRYPBgO3btyMkJATp6ekICAgwrvP29obBYEBOTg68vLzsHRoREdVS5sZnEjNWE8dzInJ+bq4qzPzohNXtLJ/WQ4JoiIiIHM/uhaTFixejbt26GDlyJA4dOiRJmz4+thtPw9fX02ZtS8nWcapUKqhULlCpVFCrDcb/V/a39LZl27G0bUV/Adu+3iX4ukurpsRJtZO58ZnKzhxp6X4AMC3qebvESkRERETkaHYtJMXFxeGXX37B+vXroVQq4e/vj7S0NOP6rKwsKJXKKl+NlJmZa5Pp4H19PfHggUbydqVm6ziVSgV0Oh10OiV0Oh2KinTQ6fQmyyr6W3pbQC1624q2AWz3epfg6y6tyuJUKhV2KQ4SWVIyPpNnXa3JstIzR1Z0PyIiIpIPdkclsj27fcJWrlyJK1euYOPGjXB1dQUAdOjQAU+ePEFKSgqCg4Px+eefIzQ01F4hERERERER1WohISFwdXWFm5sbAGDGjBno1q1bjZ1dW6ruqCXYLZWoPLsUkm7evIkNGzagZcuWiIqKAgA0a9YMa9euxbJlyzB//nyTBEVERERERDVDkc5Q5W7s5rYv1Orw+FGBVGFRFXz88cdo06aN8XbJ7NqxsbEIDg5GfHw8VqxYwUmRiAiAnQpJTz31FK5fv2523fPPP4+9e/faIwwiIiIiIpKYWqWs0hUgarUKRUW6cst55Yd8cHZtIrKEnUeJiIiI7MzZupIQUc02Y8YMCIKATp06Yfr06Zxdm4gsYiGJiIiIyAHYlYSI5GDbtm3w9/eHVqvF0qVLsWjRIvTt29fqdh05mYpaLe1pbkXtVfdx7BWfVO1Z277U8dlqRmg5zzQtt+eQhSQiIiKJKJUKALDpzJLkvNiVhIgcwd/fHwDg6uqK4cOHY+LEiXjjjTesnl3b1jMtV8TX19Ns10lrmGuvoi6a1W3PGrZsz5r9NNeeFGwxc7WcZ8S2xXtazL5aml2bhSQiIiIJKJUKbD10AwAwsm8bFpOoUuxKQkSOlp+fD71eD09PTwiCgP379yMwMJCza5NsVWdw/8oUaqUt0tQGLCQRERFJRJNX6OgQqIawVVcSoGrdSaS6VF5O3QFsEYtUz5NU3WCk7OLgqH2raHs5vZeqqibGnpmZiSlTpkCv18NgMKB169aYP38+lEolZ9cmWarq4P5icKD/qmMhiYiIiMjObNWVBBDfnUTKS+Xl0h3AFl0TpHyeqtOOuW4lUnZxcMS+WeoqI5f3UlVJ+d6z1J1Eas2bN0diYqLZdZxdm4gqonR0AEREVLsolQrjWEJEtVF+fj40muITzoq6kgBgVxIiIiKSJV6RREREdlMyjpCHu9rRoRA5DLuSEBERUU3GQhIREdmVJq8QEDgQNdVe7EpCRERENRm7thERERERERERkSi8IomIiEgEjutERERERMRCEhERUaU867pic/J1ju1ERERERLUeC0lEVKvExcUhOTkZd+/exd69e9GmTRsAQGpqKqKjo5GTkwMvLy/ExcWhZcuWjg2WZIVjOxERERE5nyKdAWqVEr6+npK0V6jV4fGjAknakisWkoioVunduzfeeOMNjBgxwmT5/PnzMXz4cERERGD37t2IiYnB5s2bHRQlERERERHZg1qlxJz40ygq0knS3vJpPSRpR85YSCKiWiU4OLjcsszMTFy9ehUJCQkAgLCwMCxevBhZWVnw9va2d4hERLVW/QbucHO17vDU19ezVvwaTERE5CgsJBFRrZeeno7GjRvDxcUFAODi4gI/Pz+kp6ezkGSlsgNUO/OA1eb2rfQyg4Hd4ogq4+aqwsyPTlT7/mq1CkVFulrxazAREZGjsJBEFimVCqc+8SOSko+Ph6jtpOp/bQtSx7b2y0vQ5GnRpFE95BUUoZ67GiqVCiqVi9m/arWhwnUARG9blXatbc+zrhprv7xk3Dfg9/dCyf571nPF5Nefk/S5rara9L4jIiIiItthIYkqpFQqsPXQDc5SRE7P398f9+/fh16vh4uLC/R6PTIyMuDv71+ldjIzcyu96sTX1xMPHmisCddmpI5NqVQg+1E+HuVq4e6qhCZfC12RK3Q6HXQ6pdm/RUU66HR6s+sAtehtq9KuFO0VaA3QFen+f5zF7wUAxv3X6XSi3h+2Upved9WlVCpEF4OJiIiIajOlowMgedPkFSI3X+voMIhsysfHB4GBgUhKSgIAJCUlITAwkN3aiIiIiIiIyuAVSWSidDc2dmkjZ7RkyRIcPHgQDx8+xJgxY+Dl5YV9+/ZhwYIFiI6ORnx8POrXr4+4uDhHh1qjlOSLkitu2C22vLLPUWXLiYiIiIjkiIUkMirdlS23oIhd2sgpzZs3D/PmzSu3vHXr1tixY4cDIqr5SnIHAIzs2wYA2C22DM+6rticfB1A8XNUuuBW+rljMYmIarMinUGyMdM4cx8Rke2wkEQmNHmFgCBAk68FBJ7QEJE4mrzC8reZQ0yUfY4qW05EVNuoVUqrZu0rjTP3ERHZjl3GSIqLi0NISAjatm2LGzduGJenpqZi2LBh6NevH4YNG4aff/7ZHuGQEynpPlNRNxpL64iIiIiIiIioauxyRVLv3r3xxhtvYMSIESbL58+fj+HDhyMiIgK7d+9GTEwMNm/ebI+QyAmU7YoHmO8yoskrhGc9N3YbIaIao7Jxk0oXx5nXiIiIiMie7FJICg4OLrcsMzMTV69eRUJCAgAgLCwMixcvRlZWFmdKItFMuuJVsP5RLmedI6Kao7Jxk1gkJyIiIpIvKcd7kyuHjZGUnp6Oxo0bw8XFBQDg4uICPz8/pKens5BkJzV1pqCSuNldjYicVWXjJrFITmRZbTiIJ5JCdnY2Zs2ahV9//RWurq5o0aIFFi1aBG9vb7Rt2xZt2rSBUlk8GsqyZcvQtm1bB0dMJH9SjvcGyHPMN6cYbNvHx8NmbdeUg5DqxLn2y0sAgMmvP2dcplKpoFK5WPyrVhsq3cbctpW1L6Zdz7pq/PvILWjytGjSqJ7JOqD8e+H3dlVVep848+vuCDUlTiIicg4ctJlIHIVCgXHjxqFLly4Aise2XbFiBT744AMAwOeff4569eo5MkQikiGHFZL8/f1x//596PV6uLi4QK/XIyMjA/7+/lVuKzMz1yZX1fj6euLBA43k7UqtOnEqlQpkP8oH8Pvzp1QqoNPpoNMpLf4tKtJBp9NXeVtALUm7mnwtHuVq4e5quq70vpTs4+/tKkW/T5z5dXeEyuJUKhU2LQYTyU1NvRqUpMWrAIhIDry8vIxFJADo2LEjtm/f7sCIiKgmcFghycfHB4GBgUhKSkJERASSkpIQGBjIbm1kFZ6gEZGcedZ1xebk6wCKxz+i2otXARCR3BgMBmzfvh0hISHGZaNGjYJer0f37t0xZcoUuLq6OjBCIpILuxSSlixZgoMHD+Lhw4cYM2YMvLy8sG/fPixYsADR0dGIj49H/fr1ERcXZ49wyEnxBI2IaoLKxj+i2oFXARCR3CxevBh169bFyJEjAQDHjx+Hv78/cnNzMXPmTKxduxbvvfee6PYcecW5Wi3taW5F7VX3cewVn1TtWdu+3PfXFu3KfZ+tHXrELoWkefPmYd68eeWWt27dGjt27LBHCFRL8ASNiIhqGqmvAqjKyZtUB6ZSjoUn1QmLHE8IpDrpdIZ9k/rE3Bx7j9FYk8eEjIuLwy+//IL169cbu9WWDDni4eGBoUOHGmfbFstWw49UxtfXE0VFOknbNNeeWq2q9uPYIz6p2rNmP821JwWp27NFu3LfZzFDpFgagsQpBtsm63AWNCKqLqVSwdxRTWWfN3O5mN11aweprwKoypiAUhyYFukMUKuUVrdjbM+KmEqf8MjxhKA67Zg7iavp+2bpxFTKfbPnWJJSjl1p7/EjV65ciStXrmDjxo3GovWjR4/g5uaGOnXqQKfTITk5GYGBgXaLiYjkjYWkWq6kO5gmrxD+jTjgMRGJp1QqsPXQDXi4qx0dSo1TkntLnjtzubhsd10Wk5yTLa4dqPRKAAAgAElEQVQCsDfOkEZUc928eRMbNmxAy5YtERUVBQBo1qwZxo0bh5iYGCgUxZPXBAUFYdq0aQ6OlojkgoUkgiavEI9ytfCsq3V0KERUw2jyCgGBBY7qKPvcmcvF7K7r3HgVABE52lNPPYXr16+bXbd37147R0NENQULSVRrscsIERE5Cq8CICIiopqKhSSqlUq65ADsMkIkJY6XVLOVfv2YF22LVwEQERWr38Adbq48LSWqSfiJpVqLXUaIpFV23B+qWUoK7Jq8QnjWc2ORnYiI7MLNVSXZOGsAx1ojsgcWkoiISDIcM6lmKxmniYiopivSGeDr62l1O4VaHR4/KpAgIiIi58FCEhERERERORWpZhPk1S1EROWxkERERCQxqceKqsrkALbaloiIiIgIYCGpVuEJAxGR7Uk9VlRJe0Dx5ACWVGUigbLbEhERERGJwUJSLcETBiIi+5F6rKiqTA5gq22JiIiIiAAWkmoVnjAQERERERERkTVYSHISZbutlR2fQ+rxOpyJuS5/7AZI9LuK8gvziv1Zes6VSkWlrwlzGxERERFZi4UkJ1G229rWQzfg4a5GbkERNHmF8G/k4cjwZKvs2CMlJ1dixxghcnbmusVuPXSDecUBLI29VPI6WRqXiV2ciYiIiEgKLCQ5ibLd1krG59Dka/EoVwvPuloHRSZ/5rr8sRsg0e/M5RfmFcewNPaSmHGZmNuIiIiIyFosJDkZMV1NakN3FFvto5hucOw6QkRERERERM6KhSQnImbKaamnpZYjW+2juWm1K+r2U3obImtIVZisqB1L7YsZc4ccr6LXqCrvHXu+ztV5T5eNj7mViIiIyHFYSHIyors2SDgttRzZah/FdINj1xGSirnipZTtWBozR8yYO+R4FRXOy47/Vp02bKE67+nS78XcgqIq3ZeIiIiIpMdCUg1Q2RUDJE7p58rc82btbEiW2uMJD1WXVIXJitqx1H5tKDo7g4pep6q8d+z5WlfnPV163D8iIiIiciwWkmTO0q+3JesaeNZxVHg1Rulf3HMLiir89V6TV4hmTRqYrKvOlRml2/Os58Zfz4mIiIiIiMgpsJBUA1R2xYDKRWnHaGouk1+0K/j1/lGuFg3N/OJdnV/rS9ojIiIiIiIichYsJBER2ZmjB9C2dJ+yf81tQzVfVWb4tDQjZXXeg1WJwRqcQZOIpFCkM8DX11PUtpVtV6jV4fGjAinCIiJyKFkUklJTUxEdHY2cnBx4eXkhLi4OLVu2lPxxHHlQyRmTaqbKZkOqbNyl2qgmn7zZKxfZcwDt0u1XNABzyfKCQj18vepY7P7JwbdrvqrM8AlUPCNldd6D5tqfFvW8RHv2O6kGqncUe+UiIqqcWqXEzI9OVL6dWoWiIp3FbZZP6yFVWHbBXEREFZFFIWn+/PkYPnw4IiIisHv3bsTExGDz5s2SPoYjDyqrc7DNGZMcr7LZkDR5hfBv5GFx29qmpp+82SMXATIYQLuC5fmFBri7Ki12/+Tg285B9AyfFm5XtMzS8qpuY42aPIOmvXIREZElzEVEVBGHD66TmZmJq1evIiwsDAAQFhaGq1evIisrS/LH0uQVOuzAsqLHthSTJq8QuZyhxqEqeg1Kxj8qvY6vVzFHfs6sYc9cRERUEeYiIpID5iIissThVySlp6ejcePGcHFxAQC4uLjAz88P6enp8Pb2FtWG2LEeAvyK+y2rVErRV0qoVNbX2ip6bEsxlayr56aCp4ebxW3q13ODex2Vcduyfxt4aOHXsK7ZdVXZxtptpYjTHts29qlnMc6qPq/16rpW6T1XFVK8P6VU+j0tttufXLoE2isXARCdi8TkiLLrzC2vKJ+UxFKyvLBQD+/6bg7/DFa0bdkcInUM1rRXqDPATaWUzXNl7rVWqZQmr7eYdsu+V8y9r8S8B8u+b0vHV/oxLH0OLH0fWvrsiP1c1sZcBAANPd0q38iO7Vjblkqtgq7Ixep2ynLk81R6n6SOR8q2qtKOuX2SOh4p2xLTjqV9Kq0qY9Q5Uk3NRfZsT+xrLrY9a9iyPWv201x7UpC6PQDw8nSzej9Lk/s+W5uLFILg2H4KV65cwezZs7Fv3z7jstdeew3Lly9H+/btHRgZEdUmzEVEJAfMRUQkB8xFRGSJwy9n8Pf3x/3796HX6wEAer0eGRkZ8Pf3d3BkRFSbMBcRkRwwFxGRHDAXEZElDi8k+fj4IDAwEElJSQCApKQkBAYGir5kkohICsxFRCQHzEVEJAfMRURkicO7tgHA7du3ER0djcePH6N+/fqIi4tDq1atHB0WEdUyzEVEJAfMRUQkB8xFRFQRWRSSiIiIiIiIiIhI/hzetY2IiIiIiIiIiGoGFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEiUWl1IKigowLvvvou+ffsiNDQUx44dM7vd2bNn8dxzzyEiIgIREREYOnSoyfq1a9eiT58+6NOnD9auXeuwOA8fPozBgwcjLCwMAwYMwKZNm4zrvvrqKwQHBxv3YfLkyZLElpqaimHDhqFfv34YNmwYfv7553Lb6PV6LFy4EH369EHfvn2xY8cOUeukJibWtWvXYsCAAQgPD8fgwYNx6tQp47ro6Gh0797d+ByuW7fOITGuXr0aL730kjGOhQsXGteJfa/YI85Zs2YZY4yIiMDTTz+NI0eOVLoPzkDM81Pip59+wnPPPYe4uDjZxbd//36Eh4cjLCwM4eHhePjwoSxiy8zMxFtvvYXw8HD0798fCxYsgE6ns3lscXFxCAkJQdu2bXHjxg2z29gzp1U1Nkv5TQ7xlXDEZ6I22b17N8LDw9GuXTts3brVZJ29vkNsLTU1FaNGjUJERAT69++P1atXOzokSWzZsgWhoaEIDw9HRESEo8ORzNmzZxEYGFju/VgTLVy4EKGhoRg4cCCioqLwww8/ODoksqE1a9aI+l6rqQoLCzF//ny8+uqrCA8Px/vvv+/okGzm2LFjiIyMREREBAYOHIiDBw86OiRJVHT8VZVzlQoJtdjq1auFuXPnCoIgCKmpqcLLL78s5Obmltvu22+/FQYNGmS2jXPnzglhYWFCQUGBUFBQIISFhQnnzp1zSJzff/+9cO/ePUEQBOHx48dCnz59hO+++04QBEHYuXOnMGXKFEnjEgRBGDVqlJCYmCgIgiAkJiYKo0aNKrfNrl27hDfffFPQ6/VCZmam0K1bN+HOnTuVrnNErCdPnhTy8/MFQRCEa9euCZ06dRIKCgoEQRCE2bNnC1u2bLFJbFWJ8eOPPxb+/ve/m72/2PeKPeIs7dq1a8ILL7wgFBYWCoJgeR+cgdjnR6fTCSNHjhSmT59u1+dDTHyXL18W+vfvL2RkZAiCUJxTnjx5IovYlixZYny+tFqt8Prrrwv79u2zeWzfffedkJaWJvTq1Uu4fv262W3smdOqGpul/CaH+ATBcZ+J2uT69evCzZs3hZkzZ5b7TrPXd4itTZw40bhvubm5Qs+ePYVLly45OCrrJCcnC8OHDxc0Go0gCILw4MEDB0ckDY1GI7z++uvCW2+9ZfNjLHs4evSooNVqjf/v3bu3gyMiW7ly5YowduzYSr/XarLFixcLS5cuFQwGgyAIzpN3yjIYDEJwcLDxdbx27ZrQsWNHQa/XOzgy61V0/FXVczlzavUVSV9//TWGDRsGAGjZsiU6dOiAkydPVqmN/fv3IzIyEnXq1EGdOnUQGRmJ/fv3OyTO5557Do0bNwYAeHp6onXr1rh7966ksZSWmZmJq1evIiwsDAAQFhaGq1evIisry2S7/fv3Y+jQoVAqlfD29kafPn1w4MCBStc5ItZu3brB3d0dANC2bVsIgoCcnBzJ47EmRkukeE/bIs4vv/wS4eHhcHV1lTQWOarK87Nx40b07NkTLVu2lF18//rXv/Dmm2/C19cXQHFOcXNzk0VsCoUCeXl5MBgM0Gq1KCoqMuY+WwoODoa/v7/FbeyV06oTmyPzm5j4AMd8JmqbNm3a4E9/+hOUyvKHgPb4DrEHhUIBjUYDAHjy5AkUCgW8vb0dHJV1Nm3ahHfeeQceHh4AgEaNGjk4Imn8/e9/x9ixY9GwYUNHhyKJXr16Qa1WAwA6duyIe/fuwWAwODgqkppWq8WiRYuwYMECR4diM3l5eUhMTMS0adOgUCgAOE/eMUepVBq/NzQaDfz8/Mx+T9Y05o6/pDjnBGp517a0tDQ0bdrUeNvf3x/37t0zu+3PP/+MQYMGYejQodi1a5dxeXp6OgICAkzaSE9Pd1icJW7fvo3vv/8eL774onHZuXPnEBERgREjRuD48eNWx5Weno7GjRvDxcUFAODi4gI/P79y+2/uOSqJ39I6KYmNtbTExET84Q9/QJMmTYzLEhISEB4ejkmTJuH27dsOi3Hfvn0IDw/Hm2++iYsXLxqXV+e9Yss4geIv271792LIkCGi9qGmE/v8/O9//8M333yD0aNHyzK+27dv486dOxgxYgQGDRqE+Ph4CIIgi9gmTZqE1NRUdO3a1fivU6dONo1NLHvlNGuZy2+O5qjPBP3OHt8h9jBnzhzs378f3bp1Q0hICMaOHYtmzZo5Oiyr3L59G5cuXUJUVBQGDx6ML774wtEhWe3EiRPQaDQIDQ11dCg2sW3bNvTs2dMpTkbJ1EcffYSBAwfW+LxiyZ07d+Dl5YU1a9Zg8ODBGDVqFFJSUhwdlk0oFAp8+OGHmDRpEnr16oXJkyc7dff66pwXm6OyRXByMWjQIKSlpZld99///ld0O+3bt8eJEyfg6emJO3fuYMyYMWjcuDFefvllWcVZIiMjA5MmTcL8+fONv9L37NkTr732GurUqYOrV69i/Pjx2Lx5M1q3bm1V7M7q3Llz+Oijj0zGmXrvvffg6+sLpVKJxMREjBs3DocPHzZ+CO0lKioKb7/9NtRqNU6fPo1JkyZh//79sv017/DhwwgICEBgYKBxWU3bB6kVFRXh/fffR2xsrN3fP2Lp9Xpcv34dCQkJ0Gq1GDduHAICAhAZGeno0HDgwAG0bdsWn332GfLy8jB+/HgcOHDAaU9GpGYuvzlaTfhM1BSVHVM4w/Nb2T7+5z//QUREBMaNG4eMjAyMGjUKHTp0wHPPPWfnSMWrbJ/0ej3S09Px73//G9nZ2fjLX/6CP/7xj+jcubOdIxXP0j4dOHAA//jHP5CQkGDnqKwj9vO1b98+7N27F9u2bbNneGQHFy9exJUrVzBjxgxHh2JTer0ed+7cQbt27TB79mxcunQJb7/9Ng4dOmS8MtJZ6HQ6bNiwAfHx8ejUqRPOnz+Pd999F/v27UO9evUcHZ5sOXUhqfSVQ+YEBATg7t27xsud09PT0aVLl3Lblf6wNG/eHH369MGFCxfw8ssvw9/f3+QLJT09XdTl+7aIEyi+VG3MmDEYN24c+vfvb1xe+pLudu3a4fnnn8fly5etKiT5+/vj/v370Ov1cHFxgV6vR0ZGRrn9L3mOnn32WWP8Jb/YW1onJbGxAsVfEDNnzkR8fDxatWplXF6660xkZCRiY2Nx7949k19v7RFjSVcjAHjllVfg7++Pmzdv4oUXXqjSe8XWcZbYuXNnuauRLO1DTSfm+Xnw4AF+/fVXvPXWWwCAx48fQxAE5ObmYvHixQ6PDyjOO6GhoXB1dYWrqyt69+6Ny5cv27SQJDa2rVu34oMPPoBSqYSnpydCQkJw9uxZWRSS7JXTqqui/OZojvxMOJvKjiksscd3iBQq28ctW7bg8OHDAAA/Pz+8+OKL+O6772RdSBJzLBgWFgalUgkfHx+8/PLLuHz5sqwLSZb2KSUlBQ8ePDBOYJOdnY1jx44hJycH77zzjr1CrDIxn69Dhw5h1apV+Ne//uXUXYFqq++++w63b99G7969AQD37t3D2LFjERsbi65duzo4Oun4+/tDpVIZuz8999xzaNiwIVJTU/HMM884ODppXbt2DRkZGcar2zt16gR3d3fcvn3beDznTKp6LleRWn2tZWhoKP7zn/8AKO669sMPP6Bbt27ltsvIyDB26cjJycHp06fx9NNPG9tITEzEkydP8OTJEyQmJpoUcOwZZ3Z2NsaMGYMRI0aUm1nu/v37xv/fvXsX33//Pdq2bWtVXD4+PggMDERSUhIAICkpCYGBgeXGIQgNDcWOHTtgMBiQlZWFw4cPo1+/fpWuk5LYWC9fvoz33nsPH3/8Mdq3b2+yrvRzeOrUKSiVSknHZREbY+k4rl27hrt37+KPf/wjAPHvFXvECRR/uZ4/fx7h4eGi96GmE/P8BAQE4OzZszh69CiOHj2Kv/71r/jzn/9slxNmsa9fWFgYvvnmGwiCgKKiInz77bfGvOfo2Jo1a2Yct0Wr1eLMmTN46qmnbBqbWPbKadVhKb85miM/E/Q7e3yH2EOzZs2MsxLm5ubi/PnzsskR1RUWFmbcp/z8fJw/f97mOdmWgoODcebMGeNnvl+/fpgyZYqsi0hiHDt2DLGxsfj000+duttTbfbWW2/hm2++Mb53mzRpgk8//dSpikhA8UUIXbp0wenTpwEUz/KVmZmJFi1aODgy6TVp0gT37t3DTz/9BKC4K3FmZib+8Ic/ODgy26jKuZwlCsHWg17IWH5+PqKjo3Ht2jUolUrMnDkTffr0AVDc99XPzw9/+ctfsHXrVmzfvh0qlQp6vR6RkZEYN26csZ3Vq1cjMTERQPGVKlOmTHFInHFxcdi2bZvJCfkbb7yBIUOGYOXKlThy5IjxktsxY8Zg0KBBVsd2+/ZtREdH4/Hjx6hfvz7i4uLQqlUrjB8/HlOnTsUzzzwDvV6PRYsWGRPR+PHjjYN5WlonNTGxDhkyBHfv3jUpEC1btgxt27bF6NGjkZmZCYVCAQ8PD8yaNQsdO3a0e4yzZ8/Gjz/+CKVSCbVajalTp6JHjx4ALL9X7B0nAKxbtw43btzAqlWrTO5vaR+cgdjnp8Tq1auRn5+P2bNnyyY+g8GAuLg4nDx5EkqlEl27dsXs2bNtPtaDmNh+/fVXzJ8/Hw8fPoRer0eXLl0wd+5cqFS2vch2yZIlOHjwIB4+fIiGDRvCy8sL+/btE53vHB2bpfwmh/hKs/dnojZJSkrCsmXL8PjxY6jVari7u2PTpk3405/+ZLfvEFu7cuUKlixZgvz8fOh0Orz22ms1vkDx5MkTvP/++7h69SoAICIiwngFnzOIjo5Ghw4dMHLkSEeHYpUXX3wRarXa5ITsX//6V63pul8bhYSEYP369WjTpo2jQ5HcnTt3MGfOHOTk5EClUuHdd991quP10vbs2YN//vOfxoHFp06dWiO//8qq6PirouPtqqjVhSQiIiIiIiIiIhKvVndtIyIiIiIiIiIi8VhIIiIiIiIiIiIiUVhIIiIiIiIiIiIiUVhIIiIiIiIiIiIiUVhIIiIiIiIiIiIiUVhIIiIissLq1asxY8YMR4dBRLVEWloagoKCoNfrHR0KERHVUiwkERERVYBFIiKSm4CAAFy8eBEuLi6ODoWIiGopFpKIiIiIiIiIiEgUFpLI5kJCQvDJJ58gPDwcHTt2xJw5c/Dw4UOMGzcOQUFBGD16NB49egQA+P777xEVFYXg4GAMHDgQZ8+eNbazc+dO9O/fH0FBQejduzc+//xz47qzZ8+ie/fu2LRpE1566SV07doVO3futPu+ElHNtXHjRnTr1g1BQUHo168fjh8/jg0bNuDrr79GUFAQBg4cCAC4c+cORo4ciaCgIIwZMwbZ2dkOjpyInIHY46XffvsNbdu2hU6nAwCMGjUKH374IaKiohAUFIQ333wTWVlZDt4bIqppqnLONnXqVLzyyivo1KkTRowYgZs3bwIAtFotIiIisGXLFgCAXq9HVFQU1qxZ47D9IttgIYns4uDBg0hISEBycjKOHTuG8ePHY/r06fj2229hMBiwZcsW3L9/HxMmTMDEiRNx7tw5zJ49G1OnTjUeDPn4+GDDhg24cOECYmNjERsbix9//NH4GA8fPoRGo8HJkyexdOlSLFq0yJjsiIgs+emnn7Bt2zZ8+eWXuHjxIj799FO0atUKEyZMQP/+/XHx4kXs2bMHADBjxgy0b98eZ8+exaRJk7Br1y4HR09EzkLM8ZI5SUlJiI2NxZkzZ1BUVIRNmzbZOXIicgZic1D37t2RnJyMM2fOoF27dsZhAFxdXbF8+XJ8/PHHuH37NjZu3AiDwYCJEyc6crfIBlSODoBqh5EjR6JRo0YAgODgYHh7e6Ndu3YAgL59++LMmTPYvXs3unfvjh49egAAXnnlFXTo0AEnTpzAoEGD0LNnT2N7L7zwAl555RWkpKSgffv2AACVSoXJkydDpVKhR48eqFu3LlJTU9GxY0f77iwR1TguLi7QarW4ffs2vL290axZM7PbpaWl4YcffkBCQgJcXV3RuXNnhISE2DlaInJWYo6XIiMjy91v8ODB+OMf/wgACA0NxdGjR+0XNBE5DTE5CABef/11432mTJmCzp07Q6PRwNPTE23atMHEiRMxadIkZGVlYceOHRzTzQmxkER2UZKQAMDNzc3kdp06dZCfn4+0tDQcOHAAx44dM67T6XTo0qULAODEiRNYu3Ytfv75ZxgMBjx58gRt2rQxbuvl5QWV6ve3tLu7O/Lz8225W0TkJFq0aIE5c+Zg9erVuHXrFrp27Yro6Ohy22VkZKB+/fqoW7eucVlAQADS09PtGS4ROSkxx0vm+Pr6Gv/P4x8iqi4xOUiv12PVqlU4cOAAsrKyoFQWd3LKzs6Gp6cnACAyMhKrVq3Cq6++ipYtW9p1H8g+WEgi2fD390dERASWLFlSbp1Wq8XUqVMRFxeH3r17Q61WY9KkSRAEwQGREpEzCg8PR3h4OHJzcxETE4MVK1agRYsWJtv4+vri8ePHyM/PNxaT0tLSoFAoHBEyERERkV3t3bsXR44cQUJCApo1awaNRoPOnTubnJctXLgQvXr1wjfffIOUlBQEBwc7MGKyBY6RRLIxcOBAHDt2DKdOnYJer0dhYSHOnj2Le/fuQavVQqvVwtvbGyqVCidOnMDp06cdHTIROYmffvoJZ86cgVarhaurK9zc3KBUKuHj44O7d+/CYDAAAJo2bYoOHTpg9erV0Gq1SElJMbmKkoiIiMiZ5eXlwdXVFQ0bNkRBQQFWrlxpsj4xMRE//vgjYmNjMW/ePERHRyMvL89B0ZKtsJBEsuHv74/4+Hhs2LABL730Enr06IFPP/0UBoMBHh4emDdvHt5991107twZSUlJHJeEiCSj1Wrxj3/8A126dEHXrl2RlZWF6dOnIzQ0FADQpUsXDBo0CADwj3/8A5cuXUKXLl2wdu1as+OVEBERETmjyMhIBAQEoFu3bhgwYIDJeLRpaWmIjY1FXFwc6tWrh/DwcHTo0AGxsbEOjJhsQSGwbxAREREREREREYnAK5KIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpKIiIiIiIiIiEgUFpLIYRYuXIhVq1ZZ3KZt27b45ZdfAAAxMTFYu3atPUIjIicybtw47Nq1y6ExhISE4L///a/FbebOnYvXXnsN6enp+Otf/2qnyIjIWqWPVezpxx9/RL9+/fDo0SOL26WkpKBfv352ioqI7M1ROajE/v378Ze//AU6nU7U9jyncw4sJJEkQkJC0KFDB2RlZZksj4yMRNu2bfHbb7+ZLP/Pf/4DtVqN9957T/RjLFq0CJMnT65WfFu3bsXgwYPRoUMHREdHV6sNIrJOSEgInn32WQQFBSE4OBhRUVHYvn07DAaDTR/3k08+waBBg6p8v7Nnz+Lpp59GUFAQgoKC0K9fP+zcudMGERbLzs7GihUr8N5776F///7Vbof5jqhqxo4di48++qjc8sOHD+OVV14RfXIkhdWrV6Nt27b47LPPTJZ/9tlnaNu2LVavXg0A0Gq1iImJwcqVK9GgQQOLbQYHByM5OdlmMZf24YcfIjw8HO3atTPGSkSW1cQcVCI7Oxvr1q3Dhx9+CJVKJeoxrDmnq4qMjAy8/fbb6Nq1q9nzUbIOC0kkmaZNm2Lfvn3G29evX0dBQYHZbYcNG4Y5c+bYKzT4+flh0qRJGDJkiN0ek4jKW79+PS5evIhjx45h/Pjx+Oc//4m5c+c6OqwK+fn54eLFi7hw4QJmzpyJ999/H7du3ZL0MUoOEOPj49GuXTt8/vnniIqKqnZ7zHdEVTNo0CDs2bMHgiCYLN+zZw/Cw8NFnxxVVUUnhy1btsTu3btNliUmJqJly5bG2z/99BOmTp2K9u3b2yS26mrRogVmzJiBHj16ODoUohqjJuagErdv38bixYvRuHFjW4RoFaVSiW7durGobSMsJJFkIiIikJiYaLydmJiIyMhIk220Wi3i4uLQs2dPvPzyy4iJicGTJ0+M6z/55BN07doVXbt2xZdffmly3+joaGNXuEePHmHChAl48cUX0blzZ0yYMAH37t2rMLZXX30Vffr0gZeXlxS7SkRW8vT0RO/evfHhhx9i165duHHjBgDLOSIrKwsTJkxAcHAwXnjhBQwfPhwGgwEbN27E1KlTTdpfsmQJlixZAgAYNWoUduzYAQAwGAyIj49Hr1698NJLL2HWrFnQaDSVxqtQKNCnT6o40OQAACAASURBVB/Ur18ft27dwvHjxxEZGYnnn38ePXr0KHeQkpiYiF69eqFLly5Yt26dybrVq1dj6tSpmDFjBp5//nns2rULly9fxrBhwxAcHIyuXbti0aJF0Gq1xvtcuHABQ4YMQadOnTBkyBBcuHChwliZ74iqpk+fPsjJyUFKSopx2aNHj3Ds2DFERkZW+vksTaPRYNasWXjxxRfRq1cvxMfHG6+6/OqrrxAVFYUPPvgAXbp0qfDk5plnnkFBQQFu3rwJALh58yYKCwvxzDPPmMT3/vvvG2+HhITg008/RXh4ODp16oR3330XhYWFAIqvruzevbvJtp988gnCw8PRsWNHzJkzBw8fPsS4ceMQFBSE0aNHm3SXO3LkCAYMGIDg4GCMGjUKt2/frvC5HDRoEHr06IF69epVuA0RmaqpOWjChAl45513MH78eJNzsZycHHTv3h1Hjx4FAOTl5aFv377G88TS53Ql+emf//wnXnrpJXTt2hWHDx/GiRMn0K9fP7zwwgtYv3698XG1Wi2WLl1qPF9cunRphc9Fo0aNMGLECJO4STosJJFkOnbsiNzcXNy+fRt6vR779u3DwIEDTbZZsWIFUlNTkZiYiIMHDyIjI8PYR/bkyZPYtGkTNm3ahIMHD+LMmTMVPpbBYMDgwYNx7NgxHDt2DG5ubli0aJFN94+IpPfss8+iSZMmxoMnSzkiISEBjRs3xpkzZ3D69GlMnz4dCoUCAwYMwIkTJ5CbmwsA0Ov1OHDgAMLCwso93ldffYVdu3Zh8+bNOHz4MPLz80XlDoPBgEOHDkGj0aBNmzZwd3dHXFwcUlJSsGHDBmzfvh2HDx8GANy6dQsLFy7EsmXLcOrUKeTk5JQrdB85cgShoaFISUlBeHg4lEol/u///g/ffvstPv/8c5w5cwb//ve/ARQfkE2YMAGjRo3C2bNnMWbMGEyYMAHZ2dnVf+KJyKhOnTro37+/yY9hX3/9NVq1aoWnn37a4uezrMWLF0Oj0eDw4cPYsmULdu/ebdIl9vLly2jevDlOnz6NiRMnVhhT6R/ndu3ahYiIiEr34+uvv8Ynn3yCI0eO4Pr16/jqq68q3PbgwYNISEhAcnKy8QrR6dOn49tvv4XBYMCWLVsAAKmpqfjb3/6GOXPm4MyZM+jevTvefvvtCk/ciKjqamIOMhgMGDRokNlzMS8vL3zwwQd4//33kZmZidjYWAQGBpa7wKDEw4cPUVhYiJMnT2Lq1KmYN28e9uzZg507d2Lbtm2Ij4/HnTt3AADr1q3DpUuXsHv3buzZswc//PAD4uPjRTzLJDUWkkhSJUnn9OnTaN26tclljoIg4IsvvsCcOXPg5eUFDw8PTJgwwdgd7uuvv8bgwYPRpk0b1K1bF++8806Fj9OwYUP069cP7u7u8PDwwMSJE/Hdd9/ZfP+ISHp+fn549OhRpTlCpVLhwYMHSEtLg1qtRnBwMBQKBZo2bYp27doZCznffvst6tSpg44dO5Z7rL1792L06NFo3rw56tWrh+nTp2P//v0VXt6dkZGB4OBgvPjii1izZg2WLVuGVq1aoUuXLmjbti2USiWefvppDBgwAOfOnQMAHDhwAD179kTnzp3h6uqKadOmQak0/brt2LEj+vTpA6VSiTp16qBDhw7o2LEjVCoVmjVrhmHDhhlz2vHjx9GiRQtERkZCpVIhLCwMrVq1wrFjxyR7DYhqu8jISCQnJxuv4klMTDSOrWbp81maXq/H/v378be//Q0eHh5o1qwZxowZgz179hi38fPzw6hRo6BSqVCnTp0K4xk4cCD27duHoqIi7N+/v9wPc+aMGjUKjRs3hpeXF3r16oVr165VuO3IkSPRqFEjNG7cGMHBwXj22WfRrl07uLm5oW/fvrh69SqA4kF0e/TogVdeeQVqtRpjx47FkydPcPHixUrjISLxaloOatiwIUJDQys8F+vatStCQ0MxevRonDhxAgsXLqzwsVQqFSZOnAi1Wo3XXnsN2dnZeOONN+Dh4YGnnnoKf/rTn3D9+nUAxcdxkydPho+PD7y9vTF58mST/SP7sU2HS6q1IiIiMHLkSPz222/lKtdZWVkoKCjA4MGDjcsEQTBebpmRkYEOHToY1zVt2rTCxykoKEBsbCxOnTplvPw6Ly8Per0eLi4uUu4SEdnY/fv30aBBg0pzxNixY7FmzRq8+eabAIrHWnvrrbcAAGFhYUhKSkJkZCSSkpLMXo0EFOeZ0rmladOm0Ol0yMzMNNu/38/PDydPniy3/NKlS1ixYgVu3ryJoqIiaLVahIaGGh+jSZMmxm3r1q1brptZ6fVA8a/+f//733HlyhUUFBRAr9cbxz7JyMhAQECAyfYBAQG4f/++2X0koqoLDg5Gw4YNcfjwYTzzzDP44YcfsGbNGgCWP5+lZWdno6ioyOTzWvazWvazX5GAgAD84Q9/wMqVK9GiRQv4+/tXeh9fX1/j/93d3ZGRkVHhto0aNTL+383NzeR2nTp1kJ+fD6B8/lEqlfD392f+IZJYTctBhYWFWL58OU6dOoUnT55AEAQ8fvzY5Fzsz3/+M7Zu3Yq3334bDRs2rPCxvLy8jPcpKW75+PgY17u5uSEvLw9A+ZwUEBBgMdeR7fCKJJJU06ZN0axZM5w4cQKvvvqqybqGDRuiTp062LdvH1JSUpCSkoLz588bf9Xy8/NDenq6cfu0tLQKH2fTpk1ITU3FF198gQsXLmDbtm0AUG6QOiKSt8uXL+P+/fvo1KlTpTnCw8MD0dHROHLkCNatW4eEhARjF9j+/fvj3LlzuHfvHg4dOoTw8HCzj+fn54e7d+8ab6elpUGlUpkcsIjxt7/9Db1798aJEydw/vx5REVFGfOPn5+fSVe2goIC5OTkmNxfoVCY3F6wYAFatWqF5ORkXLhwAe+9955Je2XzYXp6uiwHtiSqyUquqt6zZw+6du1qLK5Y+nyW1rBhQ6jVapPPa9nPatnPviWRkZFISEiosDuIPZTNP4IgMP8Q2UhNykGbNm3CzZs38cUXX+DEiRPGcYxK4tLr9YiJiUFkZCT+/e9/45dffhH9uJaUzUnp6enw8/OTpG2qGhaSSHJLly7FZ599hrp165osVyqVGDp0KD744ANkZmYCKL4S4dSpUwCA0NBQ7Nq1C7du3UJBQYGxCm9OXl4e3NzcUL9+feTk5FjcFiielaCwsBAGgwF6vR6FhYV2nUqTiEzl5ubi2LFjmD59OgYOHGjsJmYpRxw7dgy//PILBEGAp6cnXFxcjAdE3t7eeOGFF/B///d/aNasGVq3bm32ccPCwvDZZ5/hzp07yMvLw6pVq9C/f/8qz4iSl5eHBg0awM3NDZcvX0ZSUpJxXb9+/XD8+HGkpKRAq9Xi448/Nl5VZam9evXqoV69erh9+za2b99uXNejRw/8/PPP2Lt3L3Q6Hfbv349bt26hZ8+eZttiviOqnsjISJw5cwZffPGFyYmTpc9naS4uLggNDcWqVauQm5uLu3fvIiEhQVS3NHNee+01bNq0Cf3796/W/aXQv39/nDhxAmfOnEFRURE2bdoEV1dXBAUFmd2+qKgIhYWFEATBmIv0er2doyaqmWpSDnr8+DFUKhXc3NyQk5NjHM+yxPr166FQKPDBBx9g7NixmD17tiS5YMCAAVi3bh2ysrKQlZWFtWvX/r/27j++qfru//+zaZpaoFhaqivgYDpxKGP8iBfOiUw6Jm780GtzdIhOrrEf4BAvL1A+iq0CXl2BuYmWgZuy24TJbrrvcICjcE100zkvccrW4dQpOi9aBVp+pC0kTZPvH9yS9UeSnjQ/zjnJ4367eZPmnLzzOsnJOyevvN+vd9QfD6UzI6dCNd18Pl946iASRyIJSffxj388anX8pUuXavjw4fra176m8ePH6+abb9bBgwclnfmy9I1vfEPf+MY3NHXqVF122WVRH+Mb3/iGvF6vLrvsMs2ePVuTJk2KGdOPf/xjjRkzRo888oh+85vfaMyYMT1WUgKQet/97nc1btw4TZ48WRs2bNC8efNUXV0d3h6rj3j//fc1b948jRs3TrNnz9bXv/71Lv3E9OnT9cc//jHqtDZJ+spXvqKZM2dq7ty5Ki8vl8vl6rLykVFVVVVat26dxo0bp9ra2i4XWRdeeKEqKyu1ZMkSTZo0SQMHDux1KPmdd96pHTt2aPz48brnnnv0pS99Kbxt0KBB2rBhgzZt2qSJEyfqpz/9qTZs2KDi4uKIbdHfAX0zbNgwjRs3TqdOnVJ5eXn49ljvz+7uueceFRQU6Atf+ILmzJmj6dOn6ytf+Uqf4jnrrLN0+eWXx6xjkmrnn3++1qxZo5UrV+qyyy7T3r17tWHDBrlcroj733PPPRozZox27NihDRs2aMyYMT2WEQcQmZ36oJtvvlk+n0+f/exne3wXq6+v189+9jPV1NQoNzdX3/rWtyRJjzzySJ/i6GzhwoUaPXq0Zs6cqZkzZ+qSSy7RwoULo+4/ZsyYcOL7mmuu0ZgxYxKOAWfkBJkLBAAAAAAAAAMYkQQAAAAAAABDSCQBAAAAAADAEBJJAAAAAAAAMIREEgAAAAAAAAwhkQQAAAAAAABDSCQBAAAAAADAEKfZASTDsWOtCgSCcd+vpGSAmppaUhCRNXB89pYtx+dw5GjQoP5mh5MUfe2L0iXTz6nuON7Mluzjzca+yI7njB1jluwZtx1jluwZd/eYM6U/OnasVYMG9bfd6yHZ8zySiDvdMj3uWH1RRiSSAoFgn7+8WflLXzJwfPbG8dlLIn1Rulg9vmTjeDNbth2vUfH0RXZ8Du0Ys2TPuO0Ys2TPuO0Yc29Cx2TXYyPu9CLu9Eo0bqa2AQAAAAAAwBASSQAAAAAAADCERBIAAAAAAAAMIZEEAAAAAAAAQ0gkAQAAAAAAwBASSchIDkeOHI4cs8MAMgrvKwAAAABOswMAks3hyNHmPW9JkuZOHWnbJRkBK+F9BQCwq4FnFyjfldjXHq/Pr5MnTiUpIiQbrzGQXiSSkJE8rV6zQwAyDu8rAIAd5bucWvrg8wm1sWbx5CRFg1TgNQbSi6ltAAAAAAAAMCQtI5KOHTumO+64Q//85z/lcrk0fPhwrVixQsXFxbrooos0cuRIORxnclqrV6/WRRddlI6wAAAAAAAAEIe0JJJycnI0f/58TZw4UZJUU1OjtWvX6r//+78lSVu3blX//v3TEQoAAAAAZI1YP+q//vrrqqyslNfr1dChQ7VmzRqVlJRIUsxtALJbWqa2FRUVhZNIkjR27Fg1NDSk46EBAAaEVmRjVTYAADJL6Ef9uro6bd++Xeedd57Wrl2rQCCgpUuXqrKyUnV1dXK73Vq7dq0kxdwGAGkvth0IBPTEE09oypQp4dtuvPFGdXR06Morr9SiRYvkcrniarOkZECf4yktLezzfe0gW4/P6TxzaidyblhBtr5+SK/QimyeVq8K++ezKhsAABkk0o/6TzzxhOrr65Wfny+32y1JqqioUHl5uaqrq2NuA4C0J5JWrlypfv36ae7cuZKk5557TmVlZWppadHSpUtVW1ur//zP/4yrzaamlj596SktLdSRI56472cX2Xp8DkeO/H6/pL6fG1aQLa+fw5Fj+4RfJvC0enWixWd2GAAAIIU6/6jf2NioIUOGhLcVFxcrEAjo+PHjMbcVFRUZeqzQ9V26fjjMy0v8q23nWO36gydxp1e2xp3WRFJNTY3ef/99bdiwIVxcu6ysTJI0YMAAXX/99dq0aVM6QwIAAACArND5R/09e/ak9LGamlpUUjIgLT+MlpYWqr3dn3A7oVjt+oMucadXpscd6wf/tCWSHnjgAdXX1+uRRx4JT107ceKE8vPzddZZZ8nv96uurk6jRo1KV0gAsgwrSAIAgGzV/Uf9srKyLnVrm5ub5XA4VFRUFHMbAKQlkfT2229r48aNGjFihCoqKiRJw4YN0/z581VZWamcnDNTkcaNG6fFixenIyQAWYgVJAEAQDaK9KP+6NGjdfr0ae3bt09ut1tbt27VtGnTet0GAGlJJF144YV68803I27bvn17OkIAgKjFJgEAADJVtB/1a2trtXr1alVVVcnr9Wro0KFas2aNJMnhcETdBgBpL7YNAFaQihUkAQAArCbWj/rjx4+P+sN+rG0AshuJJABZKRUrSNph9bloKzQ4nU7l5QXkdDqjF9VznvnIsMNxhth1JY2+4ngBAACQaiSSAGSdVK0g2dTUokAgmNRYkynaCg0Ox5k6de3tfvn9jojHEdpHsv5xhth1JY2+4ngTE2tlEgAAAPyLw+wAACCdQsUma2tru6wgefr0aUliBUkAAAAAiIERSQCyBitIAkiXY8eO6Y477tA///lPuVwuDR8+XCtWrFBxcbFef/11VVZWdilgW1JSIkkxtwEAAFgBiSQAWYMVJAGkS05OjubPnx9eKbKmpkZr167VqlWrtHTpUlVXV8vtdmv9+vVau3atqqurFQgEom4DAACwCqa2AQAAJFlRUVE4iSRJY8eOVUNDg+rr65Wfny+32y1Jqqio0K5duyQp5jYAAACrIJEE23E4cuRw5JgdBgAAhgQCAT3xxBOaMmWKGhsbNWTIkPC24uJiBQIBHT9+POY2AAAAq2BqG2zF4cjR5j1vydPq1aCz+6niqgtssXoUACB7rVy5Uv369dPcuXO1Z8+elD9ePKvPlZYWpjCS1LBjzJI947ZjzFLkuPPyEv/ak8rnw67PNYDsRCIJtuNp9epEi09OJ6cvAMDaampq9P7772vDhg1yOBwqKytTQ0NDeHtzc7McDoeKiopibotHU1OLoR9ZSksLdeSIJ662zWbHmCV7xm3HmKXIcZeWFqq93Z9w26l6PrrH7HDkxJUQBoB0Y2obAABACjzwwAOqr69XbW2tXC6XJGn06NE6ffq09u3bJ0naunWrpk2b1us2AAAAq2BIBwAAQJK9/fbb2rhxo0aMGKGKigpJ0rBhw1RbW6vVq1erqqpKXq9XQ4cO1Zo1ayRJDocj6jYAAACrIJEEAOgiVMye+mNA31144YV68803I24bP368tm/fHvc2AAAAK2BqGwAgrLCfSz+ve1Ob97zF6ogAAAAAemBEEgCgC0+r1+wQAAAAAFgUI5IAAAAAAABgCIkkAAAAAAAAGEIiCQAAAAAAAIaQSAIAAAAAAIAhJJIAADE5HDms4AYAAABAEqu2IQVCXzgDgaDJkXRl1bgAK3M4crR5z1saUJBndigAAAAALIBEEpIq9KVTkuZOHWmZpI1V4wLswNPqlYK8ZwAAAACQSEIKeFq9ZocQkVXjAgAAAADALqiRBAAAAAAAAEPSMiLp2LFjuuOOO/TPf/5TLpdLw4cP14oVK1RcXKzXX39dlZWV8nq9Gjp0qNasWaOSkpJ0hAUAAAAAAIA4pGVEUk5OjubPn6+6ujpt375d5513ntauXatAIKClS5eqsrJSdXV1crvdWrt2bTpCAoCMZeYqa6zwBgAAAGS2tCSSioqKNHHixPDfY8eOVUNDg+rr65Wfny+32y1Jqqio0K5du9IREgBkpFBh+c173kp7QsfMxwYAAACQHmkvth0IBPTEE09oypQpamxs1JAhQ8LbiouLFQgEdPz4cRUVFaU7NCRZ5y+S8aySFrpf9/sw0gEwxszC8hS1BwAAADJb2hNJK1euVL9+/TR37lzt2bMnKW2WlAzo831LSwuTEoNVmXF8TqdThf3y9Ivf/UOeVp8K+7t0y1c/Y/j+tU/tl6Qe96l9ar/6F+TJ6XQqLy8gKfpr73Q6e2yPdJvVcX4CAAAAAKwkrYmkmpoavf/++9qwYYMcDofKysrU0NAQ3t7c3CyHwxH3aKSmppa4RryElJYW6sgRT9z3swszjs/hyJHf75ff75CnzacTLT75/X7Dr5HDkaNjJ9okdX1dQ7f7213y+/1qb/dLckVsNxRD5zYi3WZ12XJ+Ohw5aUvuUfgfAAAAABKTlhpJkvTAAw+ovr5etbW1crlckqTRo0fr9OnT2rdvnyRp69atmjZtWrpCApBlKPwPAAAAAIlJSyLp7bff1saNG3X48GFVVFRo1qxZuuWWW+RwOLR69Wrdd999+uIXv6hXXnlF//Vf/5WOkABkIQr/AwAAAEBi0jK17cILL9Sbb74Zcdv48eO1ffv2dIQBAGGpKPxvlfpbseqBRatLFao95nTm9ri/0+mMeHu8j22GbKvDxfECAAAg1dJebBuIV6pWa4u2OhyyQyoK/1uh/lasemDR6m6F7tPe7pff39Hl/pLCdc96qzNmtVpkmV5nrDuONzHprNcGAOlUU1Ojuro6HTp0SNu3b9fIkSMlSVOmTJHL5VJ+fr4kacmSJZo0aZIkUTsSQEwkkmBpDkeONu95SwMK8lLSriTNnTrS9C+8SK9UFf4HAACwmvLyct1000264YYbemxbt25dOLEUEqodWV1dLbfbrfXr12vt2rWqrq5OV8gALC5txbaBvvK0etXS5ktJu55Wb9LbhbVR+B8AAGQTt9utsrIyw/tTOxJAbxiRBCBrhAr/jxgxQhUVFZKkYcOGqba2VqtXr1ZVVVWXIdwAAACZbMmSJQoGg5owYYJuv/12DRw4MOm1I9NVzy4vL/Gvtp1jtWsdPuJOr2yNm0QSgKxB4X8AAIAztmzZorKyMvl8Pt1///1asWKF1q5dm7T2m5paVFIyIC31+0pLC9Xe7k+4nVCsdq07SNzplelxx6ofydQ2AMhifSlmH7pPKorgAwCA9AhNd3O5XJozZ47+/Oc/h2+ndiSAWBiRBABZqi/F7Av7ufTzujflafWqsH8+xeoBALChtrY2dXR0qLCwUMFgUM8884xGjRolqWvtSLfbTe1IAD2QSELWC42qiLaceaRtnUdi8CUaduZp9UrB+M5hT6tXJ1qSXwAfAAAk36pVq7R7924dPXpU8+bNU1FRkTZs2KBFixapo6NDgUBAF1xwgaqqqiRJDoeD2pEAYiKRhKwWGl0hqcfIitBoje7bQrczIgMAAABWt3z5ci1fvrzH7du2bYt6H2pHAoiFRBKynqfVG/c2RmQAAHpTU1Ojuro6HTp0SNu3b9fIkSMlSVOmTJHL5VJ+fr6kM6smTZo0SZL0+uuvq7KysssogJKSEtOOAQAAoDuKbQMAAKRAeXm5tmzZoqFDh/bYtm7dOj399NN6+umnw0mkQCCgpUuXqrKyUnV1dXK73UldQQkAACAZSCQBAPosntXbWOkN2cbtdodXRTKivr5e+fn5crvdkqSKigrt2rUrVeEBAAD0CVPbAAB90r3GWCzRao4B2WrJkiUKBoOaMGGCbr/9dg0cOFCNjY0aMmRIeJ/i4mIFAgEdP36cZbcBAIBlkEhCWnUfjZCsL5O9ra7GKAggNWLVGEtkXyCTbdmyRWVlZfL5fLr//vu1YsWKpE5hKykZYHjf0tLCpD1uutgxZsmecdsxZily3Hl5iX/tSeXzYdfnGkB2IpGEtAmNSBhQkKeWU+2SkjMyoftIh5DQaAlPq1dlg41fVAMAkEqh6W4ul0tz5szRggULwrc3NDSE92tubpbD4Yh7NFJTU4uhz9bS0kIdOeKJq22z2TFmyZ5x2zFmKXLcpaWFam/3J9x2qp6P7jE7HDlxJYQBIN2okYS08rR61dLmk6fVm9TRCdHaC62u1tLGCmsAAPO1tbXJ4znzhTEYDOqZZ57RqFGjJEmjR4/W6dOntW/fPknS1q1bNW3aNNNiBQAAiIQRSQAAACmwatUq7d69W0ePHtW8efNUVFSkDRs2aNGiRero6FAgENAFF1ygqqoqSZLD4dDq1atVVVUlr9eroUOHas2aNSYfBQAAQFckkgAAAFJg+fLlWr58eY/bt23bFvU+48eP1/bt21MZFgAAQEKY2gYAWaBz4XmKzwMAAADoK0YkAUCGCxWkP7vwLJ3wnJbUtTA9AAAAABhFIgmWxagJIHk8rV45cx1JLXIPAAAAIPsYntr229/+NuLtu3btSlowQEhhP5d+Xvem/r/fv2t2KLAY+iIA6UJ/A8AK6IsAWI3hRNLdd98d8fbKysqkBQN05mn1qqXNZ3YYsBj6IgDpQn8DwAroiwBYTa9T2z744ANJUjAYDP+78zaXy5WayACgE/oiAOlCfwPACuiLAFhVr4mkqVOnKicnR8FgUFOnTu2ybfDgwVq0aJGhB6qpqVFdXZ0OHTqk7du3a+TIM4Vep0yZIpfLpfz8fEnSkiVLNGnSpHiPA0CGS1ZflE06r9SWrscyMwYgWehvAFgBfREAq+o1kfT3v/9dkjR37lxt3ry5zw9UXl6um266STfccEOPbevWrQsnlgAgkmT1RdkiVGfM0+pV2eABaXmsAQV5XW4PrRaXjhiAZKK/AWAF9EUArMrwqm2Jdl5utzuh+wOAlHhflE08rV6daPGpsF/qa415Wr1SMGhqDECy0d8AsAL6IgBWYziR9MEHH+hHP/qR3njjDbW1tXXZ9txzzyUUxJIlSxQMBjVhwgTdfvvtGjhwYELtwX6SNf3FCtNnQjEEAj2/VCNxqeyLAKAz+hsAVkBfBMBqDCeSlixZovPOO0933nmnCgoKkhbAli1bVFZWJp/Pp/vvv18rVqzQ2rVr42qjpKTvUyZKSwv7fF87MOP4nE6nnM5cOZ1O5eUF5HQ6w69R523Sv1672qf2y9Pq08cG9++yT7T/5+UFutzf6XSqsF+efvG7f6h/QV6EGHo+ZrRYuh9LpG2Rji2k9qn9kqRbvvqZBJ9Jzs9IEu2LqNcGwKhUXfsAQDzoiwBYjeFE0ttvv60nnnhCDocjqQGUlZVJklwul+bMmaMFCxbE3UZTU0ufRn+UlhbqyBFP3PezCzOOz+HIkd/vl9/vkN/vV3v7mX83NbVIUpdtksK3HzvRphMtPhW4HF32ifb/9na/JFePdj1tPvnbXRFi6Ojx35bxRQAAIABJREFUmJFi6XwehY6l+7bQ7Z2PrfO2YyfaIrYXr2w5Px2OnLiSwYn2RdRrA2BUqq59ACAe9EUArMZwb3TppZfqwIEDSX3wtrY2eTxnvigHg0E988wzGjVqVFIfA0BmSbQvcrvd4QQ2AMSSimsfAIgXfREAqzE8Imno0KGaP3++pk6dqsGDB3fZtnjx4l7vv2rVKu3evVtHjx7VvHnzVFRUpA0bNmjRokXq6OhQIBDQBRdcoKqqqviPAkDWSLQviiXRem2JTLNNplhTO2NN8TQyDTTSvt3/3/2+0dpNh0yfHtodx5tcqexvAMAo+iIAVmM4kXTq1CldddVV8vv9+vDDD+N+oOXLl2v58uU9bt+2bVvcbQHIXon2RdEko15botMZkyHy9NLQ1M68mFM8jUwDjbRv923d7xup3XQ8T5k+PbQ7jjcxkabZpqq/AYB4ZHpfNPDsAuW7DH8tBWABht+x1dXVqYwDGS7aamqZsEob0itVfVEy6rUByCxc+wCwgkzvi/JdTi198PmE2lizeHKSogFghOFE0gcffBB123nnnZeUYJCZCvu59PO6NzWgIM/Q7cbbzUvo/rCnVPRFbW1t6ujoUGFhIfXaAIRx7QPACuiLAFiN4UTS1KlTlZOTo2DwX9MRcnLOjAZ54403kh8ZMoqn1SsFe05liXZ7ou0icyXaF1GvDYBRXPsAsAL6IgBWYziR9Pe//73L30eOHNHDDz8st9ud9KAAIJpE+yLqtQEwimsfAFZAXwTAahx9vWNpaanuvvtuPfDAA8mMBwDiki19kcORE7PWmNXqhcUTkxXjByLJlv4GgLXRFwEwW0Ll8d99912dOnUqWbEAQJ9kel/kcORo8563JElzp47ssuJZ921WEE/9s1jHBlhRpvc3AOyBvgiAmQwnkubMmROeiyudWYbyH//4h2655ZaUBAYAkWRrX+Rp9fZpm1niqV9mxfgBKXv7GwDWQl8EwGoMJ5Kuv/76Ln8XFBToU5/6lEaMGJHsmABbCU3JYSRFetAXAUgX+hsAVkBfBMBqDCeSrrvuulTGAdhSaAqPZJ1pRZmOvghAutDfALCCRPuimpoa1dXV6dChQ9q+fbtGjjxzzXrw4EEtW7ZMx48fV1FRkWpqasLJqVjbAMBwse329natW7dO5eXl+vSnP63y8nKtW7dOPp8vlfEBludp9TI1J43oiwCkC/0NACtItC8qLy/Xli1bNHTo0C63V1VVac6cOaqrq9OcOXNUWVlpaBsAGB6RtGbNGv3lL3/RfffdpyFDhqihoUHr169XS0uL7rrrrlTGCABh2d4XdV7drPt0ynhWPjOyb6pWUuvcbrR/M1UUVpDt/Q0Aa0i0L3K73T1ua2pq0oEDB7Rp0yZJ0vTp07Vy5Uo1NzcrGAxG3VZcXJzcgwNgS4YTSbt27dLTTz+tQYMGSZLOP/98XXzxxZo1axYXUwDSJpv7otBUSk+rV4X987tMp4xnpTQj+8bTXjw6t9tyqj3cfqRjI5kEs2VzfwPAOlLRFzU2Nurcc89Vbm6uJCk3N1fnnHOOGhsbFQwGo24jkQRAiiORFIyy+k602wEgFbK9L/K0enWiJfJQ9rhXSutl33jai0eoXU+br0v7sY4NMEO29zcArMGufVFJyQBJUmlpYa/75uUZ/lqa0jY6x2okbisi7vTK1rgNv9umTZumBQsW6JZbbtGQIUN06NAh/fjHP9a0adMSCgAA4kFfBCBd6G8AWEEq+qKysjJ99NFH6ujoUG5urjo6OnT48GGVlZUpGAxG3RaPpqYWlZQM0JEjnpj7lZYWqr3d3+djCUlGG6FYS0sLe43biog7vTI9bocjJ5wQ7s5wImnp0qX68Y9/rBUrVujw4cM699xz9eUvf1kLFiwwHjEsIdZy9d1rosSaWhLPvnZAfRZ7oC8CkC6J9jeslAQgGVJx7VNSUqJRo0Zpx44dmjVrlnbs2KFRo0aFp67F2gYAva7a9uqrr2rNmjVyuVxavHix9uzZo/3792v37t3y+Xw6cOBAOuJEkjgcOdq85y1t3vNWj0RQaNu2Fw5G3acv+9pBqD7Lj7f91fbHkqnoiwCkS7L6G1ZKApCIZPVFq1at0pVXXqkPP/xQ8+bN05e//GVJ0r333qvNmzfr6quv1ubNm3XfffeF7xNrGwD0mkjauHGjLr300ojbJk6cqA0bNiQ9KKRWrOXqPa1etbT5DC1pH8++dhCqz5IJx5KJ6IsApEuy+hu3291jKkhopaTp06dLOrMa0oEDB9Tc3BxzG4Dsk6y+aPny5fr973+vAwcO6MUXX9TOnTslSRdccIGefPJJ1dXV6cknn9T5558fvk+sbQDQayLpjTfe0KRJkyJuu/zyy1VfX5/0oACgO/qinhyOHEbPRcDzgkSlsr+JtVJSrG0Asg/XPgCsqtcaSS0tLWpvbw9f1HTm9/vV2tqaksAAoDP6oq5C0zEHFOSZHYqlhKbdStLiivEmRwO7snt/E60wZiR2XG3GjjFL9ozbjjFLkeNO9opeyRapbbv3RQAyV6896vnnn68XXnhBX/jCF3pse+GFFxjmCCAt6It68rR6JYsv/WsGpqciUansb9K1UpKRRSPsuNqMHWOW7Bm3HWOWIsedrFXBUvV8dI85tFIS1z4ArKrXqW0333yzqqqqtHv3bgUCAUlSIBDQ7t27de+992revHkpDxL2YJfpJEbi7L5P6G+7HGMmoi8CkC6p7G86r5QkqctqSLG2Acg+XPsAsKpeRyTNmDFDR48e1Z133qn29nYVFRXp+PHjysvL06233houCInsZpdpNkbi7L5P579bTrVb/hgzFX0RgHRJVn+zatUq7d69W0ePHtW8efNUVFSknTt36t5779WyZcu0fv16DRw4UDU1NeH7xNoGZKOBZxco3xXftDS7TsnrjmsfAFZlqFeeN2+err/+er322ms6fvy4ioqKNG7cOA0YYHwOPjKfXabZGImz+z6hvz1tPlscY6aiLwKQLsnob5YvX67ly5f3uD20GlIksbYB2Sjf5dTSB583vH9enrPHNLY1iycnO6y04doHgBUZTu8PGDAg6qoBAJAu2dYXZft0yu7HbqTuS6LthfZJ9LFgf9nW3wCwJvoiAFaT+PIFBtTU1Kiurk6HDh3S9u3bNXLkSEnSwYMHtWzZsnB2vaamRiNGjEhHSABgeaEVyLJ1OmXn42851S5Jmjt1ZJ8TPEba67zqWyKPBQAA7KXdH+gyLbIvUyS9Pr9OnjiVzLAAS0pLIqm8vFw33XSTbrjhhi63V1VVac6cOZo1a5aefvppVVZW6uc//3k6QgIAW7DLlNFU6TKtNE3tseobAADZJ8/pCE+jjDRF0gg7T6ME4tHrqm3J4Ha7eyxd29TUpAMHDoSLxE2fPl0HDhxQc3NzOkICAAAAAABAnNKSSIqksbFR5557rnJzcyVJubm5Ouecc9TY2GhWSEBCYtWyCW3r/h/Sr6amRlOmTNFFF12kt956K3z7wYMHNXv2bF199dWaPXu23nvvPfOCBAAAAACLSsvUtlQrKen7qgWZsjxoNJGOz+k887JHet6cTqecztyY+0TaN9r/8/ICve7T132tEEPo34X98vSL3/1D/QvyIj53tU/tV/+CPLWeag//X5Ju+epn4n79MokZx8dUWwAAAADoO9MSSWVlZfroo4/U0dGh3NxcdXR06PDhwz2mwBnR1NTSp4KopaWFOnLEE/f97CLS8TkcOfL7z8z37f68hbb5/Y6o+0TbN9r/29v98vs7UrKvJNNj6Lyvp80nf7urx3PncOTo2Ik2+dtd4X1C9VlinbvZcn46HDkJJYPj5Xa7e9wWmmq7adMmSWem2q5cuVLNzc0qLi5OW2zom86j+yKN9AvdFmsbRbUBAAAAY0xLJJWUlGjUqFHasWOHZs2apR07dmjUqFF8aQOQdrGm2sbTJ6UiIZbMkXPR2kvn6D1j+zrDz6WRUZLdR/yF9pXOJCxrn9ovT6tPHxvcv0t7oZGEUtfRgUZGZFpVpo9i7C7bjhcAAMAK0pJIWrVqlXbv3q2jR49q3rx5Kioq0s6dO3Xvvfdq2bJlWr9+vQYOHKiampp0hAMAKdHX0ZHRRBv519eRc1KeJUbv9b6vQ01NLZK6jjqM9BxHGvEX2je0/7ETbTrR4lOBq+djdh8dGGvUptVl+ijG7pJ9vOkeHQkAyDzt/kDCP3J4fX6dPHEqSREBqZGWRNLy5cu1fPnyHrdfcMEFevLJJ9MRAgBElcyptgAAAMhOeU6Hlj74fEJtrFk8OUnRAKlj2qptAGAVnafaSmKqLQAAAABEkRGrtiH1uhepZel62BVTbQEAAACg70gkoVcOR44273lLAwry1HKqXZ5Wr8oGU0cC9sRUWwAAAADoOxJJMMTT6pWCQXnafDrR4lNhP5/ZIQHIYFYa9RiKJVLh7VjbAAAAgExEIgkAYCmF/Vz6ed2bGlCQZ3Yo4RGZkjR36sgeK8ZF2wYAAABkKhJJAADLCY2CtAJPq7dP2wAAAIBMRCIJAAAAAGJo9wdUWlqYUBten18nT5xKUkQAYB4SSRkmnnod1PZIrdDzm+xaL53b47UDAABIvTynQ0sffD6hNtYsnpykaADAXCSSMkj3eh2xhGqQhPYlIZFcoec32SvchV5jT6tXhf3zee0AAAAAAGlFIinDxFOvg9oeqeVp9aZkhbtQu0A2inckpZVWfwMAAAAyAYkkAIAtxDOSsrCfS7VP7ddZeY50hQcAAABkBRJJAADbiG/UpU9+F4kkAAAAIJm4wgYAAAAAAIAhjEgCAAAA0AXL3QMAoiGRlKEoMGsPRgoHx1tcGAAAIFHJWO7+v2+ZlHAyCsg2iSZxS0sLSeIi5UgkZaBQQVqnM1cVV11AAsKijBQOdjhytHnPW+F9AAAA7CIZyag1iycnKRrAHhJ53+TlOdXe7ud9g5QjkZShPK1eOZ28vFZnpHBwPMWFYW/dRxIystBcvb0enf9O98jBzo/NjwUAgERMmTJFLpdL+fn5kqQlS5Zo0qRJev3111VZWSmv16uhQ4dqzZo1KikpMTlaAFZApgEALCA0+szT6lXZ4AFqOdWuAQV5ZoeVtUIjBkOvQee/W061h1+nztuk6KMLk6nzuVLYPz8tjwkAyGzr1q3TyJH/Gv0eCAS0dOlSVVdXy+12a/369Vq7dq2qq6tNjBKAVbBqGwBYhKfVqxMtPrW0+eRp9aqlzWd2SFmt+2sQ+rvz69R5WzpHD4ZiYMQiACAV6uvrlZ+fL7fbLUmqqKjQrl27TI4KgFUwIgkAAAAAstiSJUsUDAY1YcIE3X777WpsbNSQIUPC24uLixUIBHT8+HEVFRWZGCkAKyCRZAPda2/0tTaGw5ETseZK6DbqsQAAkB7UJAFgFVu2bFFZWZl8Pp/uv/9+rVixQlOnTk243ZKSM1PAjaxAlpeX+NfSZLfR1/bMPpbQfe22YqLd4g3J1rhJJFlcpFW7+lIbI9RO95orodoenet9AACA1KMmCVJl4NkFyndxmQ9jysrKJEkul0tz5szRggULdNNNN6mhoSG8T3NzsxwOR1yjkZqaWlRSMkBHjnhi7ldaWqj2dn/fgu8kmW2EVj8zO454dY67t+fdSkpLC20Vb0imx+1w5IQTwt3xCWMD3WtghGpj9KmdYM+kU6i9wn7UYwGQndI5MjPdK7zBPiLVJCkvLyeRhLjlu5x9Xj48hOXDs0NbW5s6OjpUWFioYDCoZ555RqNGjdLo0aN1+vRp7du3T263W1u3btW0adPMDheARZBIAgBktXSOzOw+ypRkUnZLVU2SaL8eRmLHIfl2jFlKf9xmT68xs41I+1vlWKKdB2ad101NTVq0aJE6OjoUCAR0wQUXqKqqSg6HQ6tXr1ZVVVWXqbYAIFkkkRStTgAAAOmQzpGZrLQGKXU1SaQz00mMJCntOCTfjjFL8cWdrGlpVpsqlK42ok1JssqxRDoPup8fsaaTJNt5552nbdu2Rdw2fvx4bd++PS1xALAXSySSpJ51AgAAADJVqmqSwP6YlgYAsDrLJJIAwGyMjgSQDtQkAQBYXTJGR3p9fp08cSpJEcFKLJNI6l4nYODAgWaHBJgiHcV+ER2jIwGkGjVJAABWx+hIxGKJRFKkOgFr1641fP9E5hDboWCj03nmZQodp9PpVF5eQE6ns8exO51OOZ25Pe7TfVv3/59pLzfq/XvbN552k7Vvb8dkhXiN7NP9dap9ar8k6ZavfkYlJQPiev3txg7vv1Rg1a7EpXOVtWRxOHJsFW8iOMdjoyYJAACwM0skkiLVCYiH0aKS3dmhYKPDkSO//0xhv6amFkmS3+9Xe7tffr+jy7GH9vX7HfL7/XI6neHt3bd1//+Z9jq63Nb9MWPtG0+7ydq3e1xmxNDbvkb2CT3Podfp2Im28Ovf1NRi+PW3m9D7L50FJY1IZHSk0ePonCzsLJkJykj7hh7DiknXePYt7JenX/zuH/K0+vSxwf2j7itfIK52E42zt3Og9qn96l+QZ2jf3kRLKFslORvtHE82qxwvAABANjE9kRStTgAApFuioyONJPY6JwsjJYKTlaCMtK+UZ9mka7z7etp8OtHiU4Er+j6Swm2l8nmNlBSO9rr721297tubrufKvxLKVvlxJNo5nmzJPl6rJbUBAACsyvREUrQ6AQCQbomOjgQAAACATGd6IilWnQAASBdGRwIAEhVrlSOmYgJIl3Z/gD4HKWV6IgkArIDRkQCyTTK+aLC0c1fRVjnKy3OG6zv2hlWOACQqz+lgxTWkFIkkk0Va2YbVbrJPsleg4hyKH6MjAWQbvmgAAKwu1khPo/jRI/lIJJnI4cjR5j1vSZLmTh0ZXrWr823IfIX9XPp53ZvytHpVNjjxQq+RzitYWzYtC5/pur+O6Xj/9XbudN4eLR6SzwAAINniGfkaaz9+9LAeEkkm87R6Dd2GzOZp9epEi0+F/XxJaw/2EEr8DSjIMzsUJCiUFB5QkKeWU+2SUp/MDT2mJC2uGN9je+j88rR6Vdg/P2I8JJ8BAEAqGB35Gmv6L0kgayKRBAAm87R6pSBf3jNB6LX0tCUnKWz4MXvZfqIldjwknwEAAGAUiSQAAABkvWTU4QAAIBvwaQkAAICsF23FtXgwBQMAkA1IJKVArKKl0bYle9WuZLVLAWBr634+dS/aHKvIbiLFdY0U7wUAAAAAZB4SSUkWq2hptBXZkr1q17/azUuo3c6FY2E9nYvshs6nzkWbO59X3YvsJlJc10jxXgDxi5S4TzTR273N3pLPRtq1wvu9848kVogHAAAgHZIxDbvdH0g4DhJJKRCraGm0bcletStZ7VIE2Nq6n0/dX69YRXYTKa5rpHgvAOM6J2jLBg/o86pvnVcBbDnV3uWHgN6Sz0ba7UtMyRaK5ZS3QwX5uabHAwAAkC5WmYZNIgkAAAvonPhPZNW3LivHdfshoLfkc6/tWoSn1as2b0B+v8PsUAAAALIOiSQAAAAAAJCR2v0BlZYWJtSG1+fXyROnkhSR/ZFIAgAAgK0lo2YEACAz5TkdCU8H++9bJkVMRsWToMqkZBSfuAAAALA1q9SMAABkpkjJqLw8p9rb/YbbiJaMsiMSSWkSz8o48bYb6d/IDEZf30Re+3hWYkrVeQwAAAAAmSwZI6Os8qMHiaQ06LyKTjKFVuCJtDoP7K2wn0u1T+3XWXmO8N+h1zrSvtG2GXmczqs4xUompeo8zmYk5TJf52XqY+0TbXuk21OVVI7VZm/txpOQNvqYyWwPAAAAyUMiKU3iWRmnL+1GWp0H9uZp9cnvcnT6O/o5lMj5Fc9KTKk6j7NRIglA2EPoNfa0elU2eEDEfWIlaCP9WBDtR4PuyedkMZJADu0jnUlIJ+sxQ+2RTAIAALAWEkkAYBISc5nP0+rViRafCvv5Yu7TW5I49GNBrB8Nuiefk8XIeRpPQtrwYwIAAMCSkn/FCQAAAAAAgIzEiCQAAACYZuDZBcp3Gb8kzZQVbwAAsCsSSQAAAOiTdn8gKYkdo6vYRFtq2Sqr2AAAkA2yKpEUbfWb3laq6qzzvt1Xlem+so2R1XriwQpPSESkczPa9pBo53ak+3Xet/PfACKza5/ePW6jq8h17xOi9R3J/sykL0qtTFrKGAAAGJM1iaTOK8+EVr053R6Q398RdVWY7veR/rWCTKRVajqvbGNktZ54sMITEhFp9adI20Pna+fzXVKvq0p13zf0N1/ggMjs2qdH6iuiHUPnfQv753fpE6J9hibrM5OV3wAAAFInaxJJUs/Vb075AvL7ew6PjnqfSNsi7Nv5795W6+lL/EBfdD//I20Pna/dz/deV5WK8TeAyOzap/foK2IcQ2jfaNuitZusOAEAAJB8rNoGAAAAAAAAQyyRSDp48KBmz56tq6++WrNnz9Z7771ndkgAshB9EQAroC8CYAX0RQCisUQiqaqqSnPmzFFdXZ3mzJmjyspKs0MCkIXoiwBYAX0RACugLwIQjek1kpqamnTgwAFt2rRJkjR9+nStXLlSzc3NKi4uNtSGkRVeHI4cDTmnUP3znSockK/++U55/QH5OwJyOh1Ri213vo+k8L6hbaHbJPVov3BAvs4e4NM5g/p1uS3afTrv25d2u/9/YP98FZzlNLSvkX2stu+5Jf1VcJbT0vEm0p7XH1C+02HaMcVzDnbfN573pVVWrjKzL0rXeRXqE6z4XknFvp3fQ5naD/alz5Aiv7dDt3dfnTQZn239+7m6tB3tMzRSnJFWextyTqG83g7l5+fG3Mfo+zKe/VItXX1RyKDC/D7FaVYbzjyn/O25pscRbxux4k5nHNnQRrTn2irH0tsquLH2Sadk9kVGjscqr0+ojXjfs6mKI16d47bacxpLb8+3VY+lL+eJVY7F6PeWaHKCQXMrfdbX1+vOO+/Uzp07w7d96Utf0po1a3TJJZeYGBmAbEJfBMAK6IsAWAF9EYBYLDG1DQAAAAAAANZneiKprKxMH330kTo6OiRJHR0dOnz4sMrKykyODEA2oS8CYAX0RQCsgL4IQCymJ5JKSko0atQo7dixQ5K0Y8cOjRo1yvDcWwBIBvoiAFZAXwTACuiLAMRieo0kSXrnnXe0bNkynTx5UgMHDlRNTY3OP/98s8MCkGXoiwBYAX0RACugLwIQjSUSSQAAAAAAALA+06e2AQAAAAAAwB5IJAEAAAAAAMAQEkkAAAAAAAAwhEQSAAAAAAAADMn6RNJ9992nadOmaebMmaqoqNBf//pXs0NKqqefflozZszQxRdfrM2bN5sdTlIcPHhQs2fP1tVXX63Zs2frvffeMzukpKqpqdGUKVN00UUX6a233jI7nKQ7duyYvvWtb+nqq6/WjBkz9L3vfU/Nzc1mh5XRMv2c6iwbz6+FCxdq5syZuvbaazVnzhy98cYbZoeUFg8//HBWnNPJFuu64NSpU7rttts0depUTZs2TXv37jUpytgOHjyoG2+8UbNmzdI111yjhx56yOyQDHv88cc1bdo0zZgxQ7NmzTI7HMNefvlljRo1yjbXkna5vs/0a1o7Hp/dryPs9tno9XpVVVWlL37xi5oxY4buueces0MyZO/evbr22ms1a9YszZw5U7t37zY7pIiifQdIynszmOWeffbZoM/nC/+7vLzc5IiS68033wy+/fbbwaVLlwYff/xxs8NJihtvvDG4bdu2YDAYDG7bti144403mhxRcr3yyivBhoaG4FVXXRV88803zQ4n6Y4dOxb805/+FP77+9//fvD//b//Z2JEmS/Tz6nOsvH8OnnyZPjfe/bsCV577bUmRpMe9fX1wW9+85tZcU4nW6zrgoceeih49913B4PBYPDgwYPByy+/PNjS0mJGmDEtWLAgHHtLS0vw85//fHD//v0mR9W7urq64Jw5c4IejycYDAaDR44cMTkiYzweT/CrX/1q8Nvf/rZtriXtcn2f6de0djw+O19H2PGzceXKlcH7778/GAgEgsGgPfrFQCAQdLvd4ef4jTfeCI4dOzbY0dFhcmQ9RfsOkIz3ZtaPSLrqqquUl5cnSRo7dqw+/PBDBQIBk6NKnpEjR+qTn/ykHI7MeKmbmpp04MABTZ8+XZI0ffp0HTh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            "text/plain": [
              "<Figure size 1440x576 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHjjLChPuU-e"
      },
      "source": [
        "Algumas coisas que podemos notar:\n",
        "\n",
        "\n",
        "1.   No desvio padrão, apesar de ambos ficarem próximos de 1, o intervalo dos resultados positivos é bem maior, chegando até 4;\n",
        "2.   Os valores de mínimo e máximo também são divergentes. Enquanto a minoria dos resultados negativos ficam em -2 e 2, nos positivos, a maioria ficam nesses valores.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Jdy7-f6uvsN"
      },
      "source": [
        "###4.2.3 C-n's x Rna polymerase inhibitor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z0ZEBPY5DK_b"
      },
      "source": [
        "Vamos realizar o mesmo procedimento com as colunas c-n's"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8psO0aot2Nu",
        "outputId": "489c9b32-a5d7-4117-c178-1264f187acb6"
      },
      "source": [
        "c_describe_0 = data.query('rna_polymerase_inhibitor == 0').loc[:, 'c_0': 'c_99'].describe().T\n",
        "c_describe_0.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>c_0</th>\n",
              "      <td>22097.0</td>\n",
              "      <td>-0.016270</td>\n",
              "      <td>0.750936</td>\n",
              "      <td>-8.500</td>\n",
              "      <td>-0.4323</td>\n",
              "      <td>0.0440</td>\n",
              "      <td>0.4792</td>\n",
              "      <td>2.989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c_1</th>\n",
              "      <td>22097.0</td>\n",
              "      <td>-0.037424</td>\n",
              "      <td>0.926861</td>\n",
              "      <td>-9.260</td>\n",
              "      <td>-0.4124</td>\n",
              "      <td>0.0642</td>\n",
              "      <td>0.5026</td>\n",
              "      <td>3.763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c_2</th>\n",
              "      <td>22097.0</td>\n",
              "      <td>-0.043544</td>\n",
              "      <td>0.889076</td>\n",
              "      <td>-9.380</td>\n",
              "      <td>-0.4278</td>\n",
              "      <td>0.0534</td>\n",
              "      <td>0.4898</td>\n",
              "      <td>2.831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c_3</th>\n",
              "      <td>22097.0</td>\n",
              "      <td>-0.073046</td>\n",
              "      <td>0.778387</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.4747</td>\n",
              "      <td>0.0162</td>\n",
              "      <td>0.4391</td>\n",
              "      <td>2.408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c_4</th>\n",
              "      <td>22097.0</td>\n",
              "      <td>-0.054017</td>\n",
              "      <td>0.945549</td>\n",
              "      <td>-9.045</td>\n",
              "      <td>-0.4289</td>\n",
              "      <td>0.0640</td>\n",
              "      <td>0.4987</td>\n",
              "      <td>4.555</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       count      mean       std     min     25%     50%     75%    max\n",
              "c_0  22097.0 -0.016270  0.750936  -8.500 -0.4323  0.0440  0.4792  2.989\n",
              "c_1  22097.0 -0.037424  0.926861  -9.260 -0.4124  0.0642  0.5026  3.763\n",
              "c_2  22097.0 -0.043544  0.889076  -9.380 -0.4278  0.0534  0.4898  2.831\n",
              "c_3  22097.0 -0.073046  0.778387 -10.000 -0.4747  0.0162  0.4391  2.408\n",
              "c_4  22097.0 -0.054017  0.945549  -9.045 -0.4289  0.0640  0.4987  4.555"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0UT6NnLqt2Fp",
        "outputId": "92b2df8c-f4fc-4a3f-dbaf-06868c3a8375"
      },
      "source": [
        "c_describe_1 = data.query('rna_polymerase_inhibitor == 1').loc[:, 'c_0': 'c_99'].describe().T\n",
        "c_describe_1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>c_0</th>\n",
              "      <td>25.0</td>\n",
              "      <td>-0.564292</td>\n",
              "      <td>1.838613</td>\n",
              "      <td>-5.705</td>\n",
              "      <td>-0.7087</td>\n",
              "      <td>0.0431</td>\n",
              "      <td>0.4219</td>\n",
              "      <td>1.5880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c_1</th>\n",
              "      <td>25.0</td>\n",
              "      <td>-1.736760</td>\n",
              "      <td>3.625777</td>\n",
              "      <td>-9.536</td>\n",
              "      <td>-0.9868</td>\n",
              "      <td>-0.0369</td>\n",
              "      <td>0.5280</td>\n",
              "      <td>0.8606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c_2</th>\n",
              "      <td>25.0</td>\n",
              "      <td>-1.319916</td>\n",
              "      <td>3.097851</td>\n",
              "      <td>-8.809</td>\n",
              "      <td>-0.6951</td>\n",
              "      <td>-0.1118</td>\n",
              "      <td>0.4758</td>\n",
              "      <td>0.8226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c_3</th>\n",
              "      <td>25.0</td>\n",
              "      <td>-0.915412</td>\n",
              "      <td>2.089956</td>\n",
              "      <td>-5.975</td>\n",
              "      <td>-1.0400</td>\n",
              "      <td>-0.2110</td>\n",
              "      <td>0.2605</td>\n",
              "      <td>1.4340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c_4</th>\n",
              "      <td>25.0</td>\n",
              "      <td>-1.217004</td>\n",
              "      <td>3.230417</td>\n",
              "      <td>-9.908</td>\n",
              "      <td>-0.6075</td>\n",
              "      <td>0.0328</td>\n",
              "      <td>0.5492</td>\n",
              "      <td>1.2560</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     count      mean       std    min     25%     50%     75%     max\n",
              "c_0   25.0 -0.564292  1.838613 -5.705 -0.7087  0.0431  0.4219  1.5880\n",
              "c_1   25.0 -1.736760  3.625777 -9.536 -0.9868 -0.0369  0.5280  0.8606\n",
              "c_2   25.0 -1.319916  3.097851 -8.809 -0.6951 -0.1118  0.4758  0.8226\n",
              "c_3   25.0 -0.915412  2.089956 -5.975 -1.0400 -0.2110  0.2605  1.4340\n",
              "c_4   25.0 -1.217004  3.230417 -9.908 -0.6075  0.0328  0.5492  1.2560"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TymQOF0rt1uV",
        "outputId": "bf598466-ae9c-40b6-9d77-bd2a9eae6916"
      },
      "source": [
        "fig, axes = plt.subplots(2, 4, figsize=(20, 8))\n",
        "fig.suptitle('Comportamento das colunas \"c-n\" em relação ao inibidor do RNA polimerase ', fontsize = 16)\n",
        "plt.subplots_adjust(hspace = 0.4)\n",
        "sns.set()\n",
        "\n",
        "sns.histplot(ax=axes[0][0], x = c_describe_0['mean'], bins = 100)\n",
        "axes[0][0].set_title('Média 0', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[0][1], x = c_describe_0['std'], bins = 100)\n",
        "axes[0][1].set_title('Desvio Padrão 0', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[0][2], x = c_describe_0['min'])\n",
        "axes[0][2].set_title('Valor Mínimo 0', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[0][3], x = c_describe_0['max'])\n",
        "axes[0][3].set_title('Valor Máximo 0', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[1][0], x = c_describe_1['mean'], bins = 100)\n",
        "axes[1][0].set_title('Média 1', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[1][1], x = c_describe_1['std'], bins = 100)\n",
        "axes[1][1].set_title('Desvio Padrão 1', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[1][2], x = c_describe_1['min'])\n",
        "axes[1][2].set_title('Valor Mínimo 1', fontsize = 12)\n",
        "\n",
        "sns.histplot(ax=axes[1][3], x = c_describe_1['max'])\n",
        "axes[1][3].set_title('Valor Máximo 1', fontsize = 12)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x576 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3w0ODd5EuRsQ"
      },
      "source": [
        "Novamente, algumas diferenças são notadas:\n",
        "\n",
        "\n",
        "1.   Primeiramente, enquanto a média dos testes negativos se manteve em 0, as dos teste positivos ficou próximo de -1,5\n",
        "2.   Em relação ao desvio padrão, os positivos ficaram 3,25 enquanto os negativos se manteve próximo de 1.\n",
        "3.   Os valores máximos não só divergem, como o maior valor dos resultados positivos não passa de 2,5, enquanto o menor valor dos negativos ficou maior que 2,5.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1UtFC3zvXMJG"
      },
      "source": [
        "# 5. Feature Engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ng5OjH7xVMxe"
      },
      "source": [
        "Modelos de Machine Learning nada mais são que modelos matemáticos e portanto só sabem trabalhar com números. Além disso, alguns modelos são extremamente sensíveis ao despadronização dos dados. Todos esses fatores influenciam na eficiência do modelo e por isso, nessa sessão iremos manipular os dados para um melhor aproveitamento do modelo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EIToMmU_XeWv"
      },
      "source": [
        "## 5.1 Features Categóricas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7KjXdE9KYq3J"
      },
      "source": [
        "Como comentado acima, dados categóricos devem ser transformados para dados numéricos. Usaremos o módulo *preprocessing* da biblioteca **Sklearn** para fazer essas manipulações. Não trataremos a coluna \"id\", já que se trata somente de um identificador do teste e não contribui para o teste.\n",
        "\n",
        "Começando pela coluna \"tratamento\", temos que se trata de um coluna categórica de baixa cardinalidade e nominal (não apresenta relação de ordem entre elas), por isso, usaremos a função *get_dummies()* para se quebrar em duas colunas binárias que representa cada categoria. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOrGzq8cc7Mc",
        "outputId": "796c14af-6b78-4b85-de95-c6a14dc49646"
      },
      "source": [
        "data = data.join(pd.get_dummies(data['tratamento'])).drop('tratamento', 1)\n",
        "\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tempo</th>\n",
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              "      <th>g_7</th>\n",
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              "      <th>g_13</th>\n",
              "      <th>g_14</th>\n",
              "      <th>g_15</th>\n",
              "      <th>g_16</th>\n",
              "      <th>g_17</th>\n",
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              "      <th>g_20</th>\n",
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              "      <th>g_24</th>\n",
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              "      <th>g_27</th>\n",
              "      <th>g_28</th>\n",
              "      <th>g_29</th>\n",
              "      <th>g_30</th>\n",
              "      <th>g_31</th>\n",
              "      <th>g_32</th>\n",
              "      <th>g_33</th>\n",
              "      <th>g_34</th>\n",
              "      <th>g_35</th>\n",
              "      <th>...</th>\n",
              "      <th>c_63</th>\n",
              "      <th>c_64</th>\n",
              "      <th>c_65</th>\n",
              "      <th>c_66</th>\n",
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              "      <th>c_68</th>\n",
              "      <th>c_69</th>\n",
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              "      <th>c_71</th>\n",
              "      <th>c_72</th>\n",
              "      <th>c_73</th>\n",
              "      <th>c_74</th>\n",
              "      <th>c_75</th>\n",
              "      <th>c_76</th>\n",
              "      <th>c_77</th>\n",
              "      <th>c_78</th>\n",
              "      <th>c_79</th>\n",
              "      <th>c_80</th>\n",
              "      <th>c_81</th>\n",
              "      <th>c_82</th>\n",
              "      <th>c_83</th>\n",
              "      <th>c_84</th>\n",
              "      <th>c_85</th>\n",
              "      <th>c_86</th>\n",
              "      <th>c_87</th>\n",
              "      <th>c_88</th>\n",
              "      <th>c_89</th>\n",
              "      <th>c_90</th>\n",
              "      <th>c_91</th>\n",
              "      <th>c_92</th>\n",
              "      <th>c_93</th>\n",
              "      <th>c_94</th>\n",
              "      <th>c_95</th>\n",
              "      <th>c_96</th>\n",
              "      <th>c_97</th>\n",
              "      <th>c_98</th>\n",
              "      <th>c_99</th>\n",
              "      <th>rna_polymerase_inhibitor</th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>0.2198</td>\n",
              "      <td>...</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>-0.2102</td>\n",
              "      <td>...</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>-1.1800</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>-1.6740</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>-0.7110</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 879 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  tempo dose  ... rna_polymerase_inhibitor  com_controle  com_droga\n",
              "0  id_000644bb2     24   D1  ...                        0             0          1\n",
              "1  id_000779bfc     72   D1  ...                        0             0          1\n",
              "2  id_000a6266a     48   D1  ...                        0             0          1\n",
              "3  id_0015fd391     48   D1  ...                        0             0          1\n",
              "4  id_001626bd3     72   D2  ...                        0             0          1\n",
              "\n",
              "[5 rows x 879 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wP_PP983vMj-"
      },
      "source": [
        "Apesar da coluna \"tempo\" poder apresentar uma relação de ordem, usaremos como uma coluna nominal também, portanto quebraremos em 3 colunas usando o *get_dummies()*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CXf8d8LuvNEU",
        "outputId": "0a4bc96e-efa0-4865-b69f-4d80e82c387d"
      },
      "source": [
        "data = data.join(pd.get_dummies(data['tempo'])).drop('tempo', 1)\n",
        "\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>dose</th>\n",
              "      <th>droga</th>\n",
              "      <th>g_0</th>\n",
              "      <th>g_1</th>\n",
              "      <th>g_2</th>\n",
              "      <th>g_3</th>\n",
              "      <th>g_4</th>\n",
              "      <th>g_5</th>\n",
              "      <th>g_6</th>\n",
              "      <th>g_7</th>\n",
              "      <th>g_8</th>\n",
              "      <th>g_9</th>\n",
              "      <th>g_10</th>\n",
              "      <th>g_11</th>\n",
              "      <th>g_12</th>\n",
              "      <th>g_13</th>\n",
              "      <th>g_14</th>\n",
              "      <th>g_15</th>\n",
              "      <th>g_16</th>\n",
              "      <th>g_17</th>\n",
              "      <th>g_18</th>\n",
              "      <th>g_19</th>\n",
              "      <th>g_20</th>\n",
              "      <th>g_21</th>\n",
              "      <th>g_22</th>\n",
              "      <th>g_23</th>\n",
              "      <th>g_24</th>\n",
              "      <th>g_25</th>\n",
              "      <th>g_26</th>\n",
              "      <th>g_27</th>\n",
              "      <th>g_28</th>\n",
              "      <th>g_29</th>\n",
              "      <th>g_30</th>\n",
              "      <th>g_31</th>\n",
              "      <th>g_32</th>\n",
              "      <th>g_33</th>\n",
              "      <th>g_34</th>\n",
              "      <th>g_35</th>\n",
              "      <th>g_36</th>\n",
              "      <th>...</th>\n",
              "      <th>c_66</th>\n",
              "      <th>c_67</th>\n",
              "      <th>c_68</th>\n",
              "      <th>c_69</th>\n",
              "      <th>c_70</th>\n",
              "      <th>c_71</th>\n",
              "      <th>c_72</th>\n",
              "      <th>c_73</th>\n",
              "      <th>c_74</th>\n",
              "      <th>c_75</th>\n",
              "      <th>c_76</th>\n",
              "      <th>c_77</th>\n",
              "      <th>c_78</th>\n",
              "      <th>c_79</th>\n",
              "      <th>c_80</th>\n",
              "      <th>c_81</th>\n",
              "      <th>c_82</th>\n",
              "      <th>c_83</th>\n",
              "      <th>c_84</th>\n",
              "      <th>c_85</th>\n",
              "      <th>c_86</th>\n",
              "      <th>c_87</th>\n",
              "      <th>c_88</th>\n",
              "      <th>c_89</th>\n",
              "      <th>c_90</th>\n",
              "      <th>c_91</th>\n",
              "      <th>c_92</th>\n",
              "      <th>c_93</th>\n",
              "      <th>c_94</th>\n",
              "      <th>c_95</th>\n",
              "      <th>c_96</th>\n",
              "      <th>c_97</th>\n",
              "      <th>c_98</th>\n",
              "      <th>c_99</th>\n",
              "      <th>rna_polymerase_inhibitor</th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "      <th>24</th>\n",
              "      <th>48</th>\n",
              "      <th>72</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>0.2198</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>-0.2102</td>\n",
              "      <td>0.1656</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>-1.1800</td>\n",
              "      <td>0.1256</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>-1.6740</td>\n",
              "      <td>-1.6720</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>-0.7110</td>\n",
              "      <td>-1.4470</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 881 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id dose      droga     g_0  ...  com_droga  24  48  72\n",
              "0  id_000644bb2   D1  b68db1d53  1.0620  ...          1   1   0   0\n",
              "1  id_000779bfc   D1  df89a8e5a  0.0743  ...          1   0   0   1\n",
              "2  id_000a6266a   D1  18bb41b2c  0.6280  ...          1   0   1   0\n",
              "3  id_0015fd391   D1  8c7f86626 -0.5138  ...          1   0   1   0\n",
              "4  id_001626bd3   D2  7cbed3131 -0.3254  ...          1   0   0   1\n",
              "\n",
              "[5 rows x 881 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3QbCYgOgDIb"
      },
      "source": [
        "Para a coluna \"dose\" podemos usar a função *LabelEncoder()* já que trata de uma coluna com categorias ordinais. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yulj1j7EhfWi",
        "outputId": "f396853f-01ae-4c61-ff01-8e76f8448d52"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "\n",
        "data['dose_num'] = le.fit_transform(data['dose'])\n",
        "data.drop('dose', 1, inplace = True)\n",
        "\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>c_99</th>\n",
              "      <th>rna_polymerase_inhibitor</th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "      <th>24</th>\n",
              "      <th>48</th>\n",
              "      <th>72</th>\n",
              "      <th>dose_num</th>\n",
              "    </tr>\n",
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              "      <td>id_000644bb2</td>\n",
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              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
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              "      <td>1.4840</td>\n",
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              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
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              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>-0.2102</td>\n",
              "      <td>0.1656</td>\n",
              "      <td>0.5300</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>-1.1800</td>\n",
              "      <td>0.1256</td>\n",
              "      <td>-0.1219</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>-1.6740</td>\n",
              "      <td>-1.6720</td>\n",
              "      <td>-1.2690</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>-0.7110</td>\n",
              "      <td>-1.4470</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>...</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 881 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id      droga     g_0     g_1  ...  24  48  72  dose_num\n",
              "0  id_000644bb2  b68db1d53  1.0620  0.5577  ...   1   0   0         0\n",
              "1  id_000779bfc  df89a8e5a  0.0743  0.4087  ...   0   0   1         0\n",
              "2  id_000a6266a  18bb41b2c  0.6280  0.5817  ...   0   1   0         0\n",
              "3  id_0015fd391  8c7f86626 -0.5138 -0.2491  ...   0   1   0         0\n",
              "4  id_001626bd3  7cbed3131 -0.3254 -0.4009  ...   0   0   1         1\n",
              "\n",
              "[5 rows x 881 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ne1neh1ujNfE"
      },
      "source": [
        "Para coluna \"droga\", usaremos o *BinaryEncoder()* da biblioteca **Category_encoders**. Por ser uma coluna de alta cardinalidade, se usarmos o *get_dummies()* irão surgir mais de 3 mil colunas, enquanto o *BinaryEncoder()* utiliza a codificação binária, podendo reduzir para 12 colunas "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEoPTe7DLg_Z"
      },
      "source": [
        "!pip install category_encoders"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t98YMEKHnn66",
        "outputId": "986aa670-1fdc-4335-ce7f-c1ae8de45281"
      },
      "source": [
        "import category_encoders as ce\n",
        "\n",
        "binarying = ce.BinaryEncoder(cols = 'droga')\n",
        "\n",
        "data = binarying.fit_transform(data)\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>droga_0</th>\n",
              "      <th>droga_1</th>\n",
              "      <th>droga_2</th>\n",
              "      <th>droga_3</th>\n",
              "      <th>droga_4</th>\n",
              "      <th>droga_5</th>\n",
              "      <th>droga_6</th>\n",
              "      <th>droga_7</th>\n",
              "      <th>droga_8</th>\n",
              "      <th>droga_9</th>\n",
              "      <th>droga_10</th>\n",
              "      <th>droga_11</th>\n",
              "      <th>droga_12</th>\n",
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              "      <th>g_1</th>\n",
              "      <th>g_2</th>\n",
              "      <th>g_3</th>\n",
              "      <th>g_4</th>\n",
              "      <th>g_5</th>\n",
              "      <th>g_6</th>\n",
              "      <th>g_7</th>\n",
              "      <th>g_8</th>\n",
              "      <th>g_9</th>\n",
              "      <th>g_10</th>\n",
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              "      <th>g_12</th>\n",
              "      <th>g_13</th>\n",
              "      <th>g_14</th>\n",
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              "      <th>g_16</th>\n",
              "      <th>g_17</th>\n",
              "      <th>g_18</th>\n",
              "      <th>g_19</th>\n",
              "      <th>g_20</th>\n",
              "      <th>g_21</th>\n",
              "      <th>g_22</th>\n",
              "      <th>g_23</th>\n",
              "      <th>g_24</th>\n",
              "      <th>g_25</th>\n",
              "      <th>...</th>\n",
              "      <th>c_67</th>\n",
              "      <th>c_68</th>\n",
              "      <th>c_69</th>\n",
              "      <th>c_70</th>\n",
              "      <th>c_71</th>\n",
              "      <th>c_72</th>\n",
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              "      <th>c_74</th>\n",
              "      <th>c_75</th>\n",
              "      <th>c_76</th>\n",
              "      <th>c_77</th>\n",
              "      <th>c_78</th>\n",
              "      <th>c_79</th>\n",
              "      <th>c_80</th>\n",
              "      <th>c_81</th>\n",
              "      <th>c_82</th>\n",
              "      <th>c_83</th>\n",
              "      <th>c_84</th>\n",
              "      <th>c_85</th>\n",
              "      <th>c_86</th>\n",
              "      <th>c_87</th>\n",
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              "      <th>c_92</th>\n",
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              "      <th>c_96</th>\n",
              "      <th>c_97</th>\n",
              "      <th>c_98</th>\n",
              "      <th>c_99</th>\n",
              "      <th>rna_polymerase_inhibitor</th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "      <th>24</th>\n",
              "      <th>48</th>\n",
              "      <th>72</th>\n",
              "      <th>dose_num</th>\n",
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              "  </thead>\n",
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              "      <td>0</td>\n",
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              "      <td>1.2570</td>\n",
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              "      <td>1.2250</td>\n",
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              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
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              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</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>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
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              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
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              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>...</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 893 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  droga_0  droga_1  droga_2  ...  24  48  72  dose_num\n",
              "0  id_000644bb2        0        0        0  ...   1   0   0         0\n",
              "1  id_000779bfc        0        0        0  ...   0   0   1         0\n",
              "2  id_000a6266a        0        0        0  ...   0   1   0         0\n",
              "3  id_0015fd391        0        0        0  ...   0   1   0         0\n",
              "4  id_001626bd3        0        0        0  ...   0   0   1         1\n",
              "\n",
              "[5 rows x 893 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLp1lWd3YBOJ"
      },
      "source": [
        "# 6. Seleção de Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aFJEfr6JvXz0"
      },
      "source": [
        "Existe um ditado muito famoso na comunidade de ciêntistas de dados chamado \"Garbage in, garbage out\", ou então, \"Se entrar lixo, sairá lixo\". Ou seja, se simplesmente colocarmos qualquer dado no modelo, não estariamos tendo o melhor resultados. Então devemos escolher as melhores variáveis para alimentar nosso modelo.\n",
        "\n",
        "Como podemos ver, temos quase 900 variáveis para trabalhar, colocar todas elas para o modelo analizar, usariamos muito processamento e muito provavelmente não traria uma melhora tão relevante assim, ou até mesmo poderia piorar o resultado. Essa sessão será focada em escolher as melhores variáveis, coletando as que mais trazem informação, sem que haja um vazamento de dados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxdAJ-tzvZHi"
      },
      "source": [
        "##6.1 Vazamento de Dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1V9ud6OLJsJ0"
      },
      "source": [
        "Vazamento de dados ocorre quando damos uma variável que da uma informação que permite o modelo \"roubar\", por exemplo, vimos que nenhum teste de controle recebe o resultado positivo, então se dessemos esse tipo de informação pro modelo poderá haver um vazamento de dados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0FVToj9LOik"
      },
      "source": [
        "features = pd.DataFrame(data.drop(columns = ['com_droga', 'com_controle']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBjyJeFnvtFd"
      },
      "source": [
        "##6.2 Alta correlação"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irwX7A88M_hq"
      },
      "source": [
        "Podemos retirar algumas variáveis quando possuem uam correlação próxima dos extremos e que não trazem nenhuma informação. Por exemplo nas colunas \"24h\", \"48h\" e \"72h\" poderiamos retirar uma delas já que podemos representar os 3 estados somente com duas colunas. Retirando a coluna \"72h\", por exemplo, o estado \"10\" representa o tempo \"24h\", o \"01\" representa o tempo de \"48h\" e o estado \"00\" representa o teste de \"72h\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umkjli6oqBOI"
      },
      "source": [
        "features = pd.DataFrame(features.drop(columns = 72))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXTnPyRtvd98"
      },
      "source": [
        "##6.3 SelectKBest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b6iHj-5rrVMZ"
      },
      "source": [
        "Para as colunas continuas, usaremos a função *SelectKbest()* da biblioteca **Sklearn** para selecionar as 500 das 872 variáveis que possuem mais informação para colocar no modelo. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CewQwxWDtTs7",
        "outputId": "5ba6dd46-7648-4bf5-efeb-0fb3d7802529"
      },
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import f_classif\n",
        "\n",
        "SKBest = SelectKBest(score_func = f_classif, k = 500)\n",
        "\n",
        "X_selected = pd.DataFrame(SKBest.fit_transform(data.loc[:, 'g_0': 'c_99'], data['rna_polymerase_inhibitor']))\n",
        "\n",
        "X_selected.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22122, 500)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7jphFRewY66"
      },
      "source": [
        "Vamos juntar agora, todas essas variaveis escolhidas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLyCLfkjwiJS"
      },
      "source": [
        "features['24h'] = features[24].astype(int)\n",
        "features['48h'] = features[48].astype(int)\n",
        "features.drop(columns = [24, 48], inplace = True)\n",
        "X_selected = pd.concat([X_selected, features.select_dtypes(int)], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1AkEvk-xpJa"
      },
      "source": [
        "Com essas técnicas aplicadas conseguimos reduzir 892 variáveis para 516! Que será um ganho de performance enorme!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6AkNOMKsYF4M"
      },
      "source": [
        "# 7. Seleção do Modelo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kMFfmLefzO3E"
      },
      "source": [
        "A escolha do modelo certo é outra etapa fundamental do processo. Existem centenas deles e saber qual é o ideal para cada base de dados é um trabalho comum na vida do ciêntista de dados. Por isso nessa sessão iremos selecionar o melhor algoritmo para nossa base."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "unjt6ZXrYzCL"
      },
      "source": [
        "##7.1 Escolha da métrica"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l2wMVDKH0Pym"
      },
      "source": [
        "Mas antes de selecionarmos o modelo, precisamos saber como vamos avaliar ele, aliás, um método de avaliação falho não escolheria o melhor modelo. Começando pelo fato que será um modelo de classifícação, nossas opções variam entre \"accuracy\", \"precission\", \"recall\" etc. Porém temos que levar em consideração também que nossos dados da variável alvo, como visto na sessão 2.4, estão totalmente desbalanceados, em uma proporção 999 para 1, ou seja, se usarmos a acurácia como método de avaliação e o modelo chutasse tudo 0, o modelo teria algo em torno de 99,9% mesmo sendo algo ruim. Outro ponto, é que no contexto de descobrimento de novas drogas, como o percentual de descobrimento de novas drogas é bem baixo, temos que garantir que as drogas apontadas como positivo sejam realmente positivas. Do mesmo jeito, não queremos que nenhuma droga que possua a característica seja passda também.\n",
        "\n",
        "Tendo tudo isso em mente, usaremos a métrica \"f1_score\" que é calculada a partir da média harmônica entre \"precision\" e \"recall\", fazendo uma balanço entre os dois."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nk6D3VXRs1jF"
      },
      "source": [
        "from sklearn.metrics import f1_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwOwMtC1Y3_N"
      },
      "source": [
        "##7.2 Importando e alimentado modelos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgga0T0jtG0v"
      },
      "source": [
        "Agora que já decidimos nossas variáveis e nosso sistema de avaliação, podemos começar a importar e alimentar os modelos. Quando se trata de modelos de classificação, os principais modelos são *LogisticRegression()*, *SuportVectorMachine()*, *GaussianNB()*, *DecisionTreeClassifier()* e *RandomFlorestClassifier()*. Como já comentado, existem centenas de modelos, porém, para esse projeto, testaremos esses citados "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lgj60s5YytaY"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "lr = LogisticRegression(max_iter = 1000)\n",
        "svm = SVC()\n",
        "gs = GaussianNB()\n",
        "tree = DecisionTreeClassifier()\n",
        "rf = RandomForestClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J3WlyrTk0GRI"
      },
      "source": [
        "Para alimentar os modelos, basta utilizar a função *fit()*, porém como temos que testar nossos dados, dividiremos nosso conjunto de dados em um dataset de treino e em um dataset de teste utilizando a função *StratifiedKFold()*, para que podemos realizar 5 testes e usar a média como o resultado. Além disso, devemos garantir duas coisas, a primeira é nossos dados sejam pegos de maneira aleatória e para garantir isso, basta alterar o parâmetro *Shuffle* para *True*. A segunda, é que queremos que todos os dados recebam os mesmos cojuntos de dados aleatórios, por isso colocaremos o parâmetro \"random_state\" para 0. Outro ponto que temos ter em nossa visão é o desbalanceamento das classes, para isso, usaremos o *SMOTE* da biblioteca **imbalanced_learn** no qual gera pontos da classe menor para diminuir esse desbalanceamento."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l-G7mIks5w_Z"
      },
      "source": [
        "from sklearn.model_selection import StratifiedKFold"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qzhkj6c4EPMc"
      },
      "source": [
        "A biblioteca **imbalanced_learn** não está no pacote do google colabs, então precisamos instalar."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luGfFLVMD72j"
      },
      "source": [
        "!pip install imbalanced-learn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4puhFOZQD6uD"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "over = SMOTE(sampling_strategy=0.3, random_state = 0)\n",
        "skf = StratifiedKFold(n_splits=5, shuffle = True, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmtYEwSlJY55"
      },
      "source": [
        "Finalmente, vamos alimentar nosso modelo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HElwtmtaMskQ",
        "outputId": "95fcfb98-e159-480c-f162-ac055d62d62f"
      },
      "source": [
        "from statistics import mean\n",
        "\n",
        "X = X_selected.drop(columns = 'rna_polymerase_inhibitor')\n",
        "y = X_selected['rna_polymerase_inhibitor']\n",
        "\n",
        "models = [lr, svm, gs, tree, rf]\n",
        "for model in models:\n",
        "  scores = []\n",
        "  for train_index, test_index in skf.split(X_selected,X_selected['rna_polymerase_inhibitor']):\n",
        "    X_train = X[X.index.isin(train_index)]\n",
        "    y_train = y[y.index.isin(train_index)]\n",
        "    X_test = X[X.index.isin(test_index)]\n",
        "    y_test = y[y.index.isin(test_index)]\n",
        "    X_train, y_train = over.fit_resample(X_train, y_train)\n",
        "    model.fit(X_train, y_train)\n",
        "    predictions = model.predict(X_test)\n",
        "    scores.append(f1_score(y_test, predictions, average = 'macro'))\n",
        "      \n",
        "  print('O modelo {} teve um score de {}'.format(model, mean(scores)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O modelo LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
            "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
            "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
            "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
            "                   warm_start=False) teve um score de 0.5845391334738561\n",
            "O modelo SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
            "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "    tol=0.001, verbose=False) teve um score de 0.4997173160823759\n",
            "O modelo GaussianNB(priors=None, var_smoothing=1e-09) teve um score de 0.4982657636904274\n",
            "O modelo DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
            "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                       random_state=None, splitter='best') teve um score de 0.5473552230112808\n",
            "O modelo RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
            "                       criterion='gini', max_depth=None, max_features='auto',\n",
            "                       max_leaf_nodes=None, max_samples=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                       n_jobs=None, oob_score=False, random_state=None,\n",
            "                       verbose=0, warm_start=False) teve um score de 0.6235720478386734\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8K4WzVfYh4Sg"
      },
      "source": [
        "Como podemos ver, nosso melhor modelo foi o *RandomForest()*, porém, 0,627 pode não ser a melhor eficiência. Veremos na próxima sessão, como melhorar."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uI7FHg-9YM8p"
      },
      "source": [
        "# 8. Seleção de Hiperparametros"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1nBSzwchL8JY"
      },
      "source": [
        "Hiperparâmetros são os valores que fazemos nos parâmetros do modelo, para que ele tenha um melhor entendimento dos dados e melhor desempenho. Nessa sessão escolheremos os hiperparâmetros do modelo de *RandomFlorest()* a dedo para obter o melhor desempenho."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IO7WijJToSph"
      },
      "source": [
        "##8.1 n_estimators"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fwiA7Gzjqnhl"
      },
      "source": [
        "O parâmetro *n_estimators* informa o número de árvores que o modelo vai criar. Caso não modificado, serão criadas 100 árvores, que foi o caso do nosso modelo anterior. \n",
        "\n",
        "Para escolher o melhor número para esse parâmetro, usaremos a função *validation_curve()* da biblioteca **yellowbrick**, onde será gerado um gráfico de desempenho de acordo com o número de árvores escolhidos. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "tPCjdtVH09sp",
        "outputId": "ff9b64d0-9e6e-4cda-add0-da2798e3be77"
      },
      "source": [
        "from yellowbrick.model_selection import validation_curve \n",
        "\n",
        "print(validation_curve(\n",
        "    rf, X = X, y = y, param_name = \"n_estimators\", param_range = [100, 150, 200, 250, 300],\n",
        "    scoring = \"f1_macro\", n_jobs = -1, cv = 5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "AxesSubplot(0.125,0.125;0.775x0.755)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5r4MsPWRisI"
      },
      "source": [
        "podemos ver que em 300 tivemos nosso melhor desempenho."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dx-zbC4goXmJ"
      },
      "source": [
        "##8.2 max_depth"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kdrBZzHHrqQN"
      },
      "source": [
        "Outro parâmetro para alterar é o *max_depth()* o qual representa a maior profundidade que as árvores do modelo pode ir. No nosso primeiro modelo ele estava como None, ou seja, as árvores poderiam crescer o quanto quisessem "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IU4imVFYQsRl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "db59f650-c875-4fab-ae67-357c7f2b6e80"
      },
      "source": [
        "rf = RandomForestClassifier(n_estimators = 300)\n",
        "\n",
        "print(validation_curve(\n",
        "    rf, X = X, y = y, param_name = \"max_depth\", param_range = np.arange(1, 11),\n",
        "    scoring = \"f1_macro\", n_jobs = -1, cv = 5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "AxesSubplot(0.125,0.125;0.775x0.755)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o--kHt9pRjUa"
      },
      "source": [
        "Para esse parâmentro temos o 7 como o melhor. A partir do 8 podemos ver uma melhora no resultado dos treinos, porém mantém o resultado nos testes e quando isso ocorre, chamamos de Overfitting, ou super adequamento."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bs0CzkIdoccd"
      },
      "source": [
        "##8.3 min_sample_splits"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cDNj4jjGN_qw"
      },
      "source": [
        "O terceiro parâametro a ser mexido é o *min_sample_split* que mostra o número mínimo de amostra que deve ter na folha para realizar a divisão. Por padrão, está ajustado como dois, mas vamos ver se esse valor realmente é o melhor."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yd8VIB6VQ61X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "dfc34c15-6d29-417d-ded4-b56f52033a19"
      },
      "source": [
        "rf = RandomForestClassifier(n_estimators = 300, max_depth = 7)\n",
        "\n",
        "print(validation_curve(\n",
        "    rf, X = X, y = y, param_name = \"min_samples_split\", param_range = np.arange(1, 11),\n",
        "    scoring = \"f1_macro\", n_jobs = -1, cv = 5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "AxesSubplot(0.125,0.125;0.775x0.755)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8IIkF0FRRj7j"
      },
      "source": [
        "Como podemos ver, temos três opções nesse momento, porém como números maiores tendem a diminuir a performance, manteremos o número 3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MveDR1-GodBD"
      },
      "source": [
        "##8.4 min_sample_leafs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16W3DBYEOjz7"
      },
      "source": [
        "Por fim, o *min_sample_leaf* ajusta o número mínimo de pontos que precisam para poder ser uma folha. Por padrão esse parâmetro está configurado para 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "seCm9_k39pbt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "19376c9e-71c1-4d15-e0b2-b671b9d2fd5f"
      },
      "source": [
        "rf = RandomForestClassifier(n_estimators = 300, max_depth = 7, min_samples_split = 3)\n",
        "\n",
        "print(validation_curve(\n",
        "    rf, X = X, y = y, param_name = \"min_samples_leaf\", param_range = np.arange(1, 11),\n",
        "    scoring = \"f1_macro\", n_jobs = -1, cv = 5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "AxesSubplot(0.125,0.125;0.775x0.755)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "73_FshtlRkfj"
      },
      "source": [
        "Podemos ver então que nosso último hiperparâmetro é o 1 e com isso nosso modelo fica pŕoximo da pontuação 0.63"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4k_FfPToZhwK"
      },
      "source": [
        "# 9. Conclusão"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5bOKcLc2Q_pY"
      },
      "source": [
        "Conseguimos chegar a um modelo final com f1_score de 0.63, o que pode ser considerado um modelo razoável dado as proporções da nossa variável alvo. Há muitas coisas que podem ser melhoradas como um melhor entendimento de cada um dos 772 genes, das 100 células e como eles se relacionam entre si. Além disso, procurar utilizar técnicas mais avançadas de dados desbalanceados ou até mesmo outras técnicas ajustar os hiperparâmetros como Gridsearch, por exemplo.\n",
        "\n",
        "Vale ressaltar também que descobrir novas substâncias que apresentam tal característica é só um dos passos feitos no descobrimento de novas drogas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87YxzDIGJjv4"
      },
      "source": [
        "# 10. Fontes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oySqeDEYKbZT"
      },
      "source": [
        "[1] https://docs.google.com/document/d/10EhrQBChlyYIcff3to7PrCQi5HcNk2r-zd2ZCKPtcz8/edit\n",
        "\n",
        "[2] https://www.boehringer-ingelheim.com.br/pesquisa-e-inovacao/pesquisa-e-inovacao/drug-discovery-process\n",
        "\n",
        "[3] https://www.saude.pr.gov.br/Pagina/Tuberculose\n",
        "\n",
        "[4] https://www1.folha.uol.com.br/fsp/mundo/ft0507200105.htm\n"
      ]
    }
  ]
}