Notebooks/Desenvolvimento_do_modelo.ipynb

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        "<a href=\"https://colab.research.google.com/github/FranciscoFoz/Projeto_Final_Bootcamp_Data_Science_Alura/blob/main/Notebooks/Desenvolvimento_do_modelo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# <font color= MediumSpringGreen\t\tsize = 6> Bootcamp Data Science Alura - Projeto Final </font>\n",
        "\n",
        "# <font color= MediumSpringGreen\t\tsize = 6>\n",
        "Previsão da admissão na UTI:\n",
        "Um modelo de Machine Learning a partir de dados dos pacientes do Hospital Sírio Libanês </font>\n",
        "\n",
        "2º Notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yp8IFAz4q_QJ"
      },
      "source": [
        "Elaborado por [Francisco Foz](https://www.linkedin.com/in/francisco-tadeu-foz/)\n",
        "\n",
        "Data: 18/02/2022"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gj6idIwGrvmN"
      },
      "source": [
        "### <font color= MediumSpringGreen> Importando bibliotecas\n",
        "\n",
        "Importarei as bibliotecas necessárias, configuradas no \"requirements.txt\" do projeto.\n",
        "\n",
        "Devido outra versão do matplotlib, será necessário restartar o ambiente após a desisntalação da versão pré instalada no colab.\n"
      ]
    },
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            "Requirement already satisfied: wget in /usr/local/lib/python3.7/dist-packages (3.2)\n"
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              "'requirements (1).txt'"
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      "source": [
        "!pip install wget\n",
        "import wget\n",
        "\n",
        "wget.download('https://raw.githubusercontent.com/FranciscoFoz/Projeto_Final_Bootcamp_Data_Science_Alura/main/requirements.txt', 'requirements.txt')"
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      "cell_type": "code",
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        "!pip install -r requirements.txt"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.ensemble import BaggingClassifier,GradientBoostingClassifier,RandomForestClassifier\n",
        "\n",
        "from sklearn.dummy import DummyClassifier\n",
        "\n",
        "from sklearn.metrics import accuracy_score,classification_report, roc_auc_score, ConfusionMatrixDisplay, RocCurveDisplay\n",
        "\n",
        "from sklearn.datasets import load_digits\n",
        "\n",
        "from numpy.ma import MaskedArray\n",
        "import sklearn.utils.fixes\n",
        "\n",
        "sklearn.utils.fixes.MaskedArray = MaskedArray\n",
        "\n",
        "from skopt import BayesSearchCV\n",
        "\n",
        "from joblib import dump, load\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VqIixNQfr2om"
      },
      "source": [
        "### <font color= MediumSpringGreen> Funções\n",
        "\n",
        "Importarei o notebook com todas as funções necessárias para o projeto, criadas neste [notebook](https://github.com/FranciscoFoz/Projeto_Final_Bootcamp_Data_Science_Alura/blob/main/Notebooks/Notebook_de_Funcoes.ipynb)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wget.download('https://raw.githubusercontent.com/FranciscoFoz/Projeto_Final_Bootcamp_Data_Science_Alura/main/Notebooks/notebook_de_funcoes.py', 'funcoes.py')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Km10ON-2VZ86",
        "outputId": "ccd6c2ef-5dcc-4d50-c1d3-c81c8958be4c"
      },
      "execution_count": null,
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            "text/plain": [
              "'funcoes.py'"
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          "metadata": {},
          "execution_count": 10
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    {
      "cell_type": "code",
      "source": [
        "import funcoes as f"
      ],
      "metadata": {
        "id": "Zw7064u7Hsul"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_22pcEN5N7E"
      },
      "source": [
        "## <font color= MediumSpringGreen> Importação dos dados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n2PHsd4z5RjO"
      },
      "outputs": [],
      "source": [
        "dados = pd.read_csv('https://raw.githubusercontent.com/FranciscoFoz/Projeto_Final_Bootcamp_Data_Science_Alura/main/Dados/dados_sem_valores_unicos.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GuNiRXVfr3SN"
      },
      "source": [
        "## <font color= MediumSpringGreen> 1. Treinamento e desenvolvimento do modelo\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0feOu29R6rCS"
      },
      "source": [
        "Escolhi 3 algoritmos para realizar o treinamento e desenvolvimento do modelo:\n",
        "\n",
        "*   [Bagging Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html)\n",
        "\n",
        "*   [Gradient Boosting Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html)\n",
        "\n",
        "*   [Ramdom Florest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier)\n",
        "\n",
        "\n",
        "Baseado no artigo de [Subudhi et al (2021)](https://doi.org/10.1038/s41746-021-00456-x) \"Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19\".\n",
        "\n",
        "Quis utilizar o artigo como forma de melhor referencial técnico para escolher os melhores modelos para iniciar os testes.\n",
        "\n",
        "Escolhi os que foram indicados com as melhores métricas de F1 score.\n",
        "\n",
        "A partir deles, realizarei os treinos e melhorarei os parâmetros."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkiwNqjOHgbI"
      },
      "source": [
        "Para cada algoritmo, criarei rodarei ele e verificarei o AUC com a tabela de classificação de \"precision\", \"recall\" e \"f1-score\". \n",
        "Para cada um também rodarei o modelo 50 vezes e mostrarei  valor do AUC médio e seu intervalo de mínimo e máximo.\n",
        "\n",
        "O algoritmo que tive o mairo AUC médio será o escolhido para promover os hiperparâmetros."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dentre os testes dos modelos utilizarei a métrica de AUC, que é a área sob a curva ROC.\n",
        "\n",
        "> A curva ROC é uma medida para se testar o quanto o modelo está com um índice de especificidade e sensibilidade. Quanto ele está conseguindo distinguir corretamente entre duas classificações.\n",
        "\n",
        "> Especificidade é a medida da Taxa de falso positivo, que é VN / VN + FP. É a capacidade do modelo reconhecer resultados negativos.\n",
        "\n",
        "> Sensibilidade é o a medida da Taxa de verdadeiro positivo, que é: VP / VP + FN. É a capacidade do modelo reconhecer resultados positivos.\n",
        "\n",
        "A analisarei também a medida do f1-score (que é a média harmônica entre a precisão e a sensibilidade).\n",
        "\n",
        "> A precisão é a quantidade de valores positivos verdadeiros do modelo, que é: VP / VP + FP.\n",
        "> É a capacidade do modelo de acertar os resultados positivos.\n",
        "\n",
        "Utilizarei também a matriz de confusão para entender dentre os valores preditos e reais, o quanto está fluindo o modelo.\n",
        "\n",
        "A matriz de confusão é um quadrante dividido pelos valores reais (1º e 3º quadrante) pelos valores preditos (2º e 4º quadrante). Com ela é possível visualizar os valor VN, FP, FN e VP.\n",
        "\n",
        "No caso deste modelo importará (além dos valores VN e VP, 1º e 4º quadrante) os baixos valores do FP (2º quadrante) para que não tenhamos uma superlotação dos leitos de UTI.\n",
        "\n"
      ],
      "metadata": {
        "id": "HDRGeCviZSdS"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IE2pgLfesbT3"
      },
      "source": [
        "### <font color= MediumSpringGreen> 1.1 Treinamento e testes iniciais\n",
        "\n",
        "Verificarei os índices de AUC para cada modelo e entenderei qual será o melhor para otimizar os hiperparâmetros e desenvolver o modelo.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8bWIlUxjBdjF"
      },
      "source": [
        "#### <font color= MediumSpringGreen> 1.1.1  BaggingClassifier\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xJbEIhI24OHl"
      },
      "outputs": [],
      "source": [
        "modelo_bagging = BaggingClassifier()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rodarei o modelo nos dados e printarei os resultados."
      ],
      "metadata": {
        "id": "VvzNy9wuWJSZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bagging = f.roda_modelo(modelo_bagging,dados,'BaggingClassifier')\n",
        "bagging"
      ],
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              "                        AUC  F1-score_0  F1-score_1  Acurácia\n",
              "BaggingClassifier  0.864224    0.825397    0.744186  0.792453"
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          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotarei uma matriz de confusão dos resultados do Bagging Classifier."
      ],
      "metadata": {
        "id": "sz4pm-CfWPQp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "1uqbedFssnaV",
        "outputId": "94c09d52-1713-4828-e5eb-5958ad9bf425"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ],
            "image/png": 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A2Kdt/L2sTgF+A+yVpP/L8d+4c3spaLJgVwPvSvLg/olp+oKaP8Xtf6p9PjjJndqZ9Y37Uvv8gd4flvb/g9rBL05xu8vj1zSZh6e17/XY9temqSZeSpLNk9x/wHrWpal+uJU72gctbJ/n961jN5rLuJfSNsz/X5rs1r59yzyEplrxKuB/JtupDo1lDM7taay8sH1+eHr6zmovAvg8g7N+Y22+3ts28B5bZkPgHf0zt9XA36IJbpe6ND7JA2guoOhEB9v+Bk1V6A7Ad9sM0lLS9GX2Wpo2SxNZ2D7P71v+HtxRhdc7/u7ttH4b01QH3tAz794Z3NBn7HMy7sU8y6OaDm2/BsxL0w/WnU5Sk9yz7T5kpWM/U1ppVdMr71R7QD6d5kqwZyY5haZR5hY0ba5+Q3NvqX6/oPlC2bfNRozV23+qqpb3Ngnvpgl2zgIeluRhA+b5eJt5G6iqKsnLaX6Yv5Okt5+pR9E0DH983zJXJNmH5of6l0l+THMVVtFkbR5K06bhTp0ADtj+D9uG//sDv0pyNE115RY0Z/G/pKkWo6q+3gZ8zwYuaOctmtts3B34Vpv56UTb4PgTND/gZyf5H5rvhsfQvOf97/td2/nOo7nH3l9oAt8n01TPfbIncD+CpvH5x5PsTdO1wA7tvEfRtL3q92qa4/DDSR5L00h6rJ+p22hucjvZicGKsH1fI+BNaLJID6L5wf23sQlVdVnbQPm5NI3yf0gTeDyGpk+0c2iOvV5HtPM/Hjg/yTE0DYyfRfNZvDd3vlrybTR9dO3XBpen0FR9PpvmBOTpA5ZZUVbYtqvqtiT/TNMU4WnAH9vP269ost3bt9vanOZ+dhMZ6w/uTW2QfzbNyd2Tafqc6j/RewBwVJLT2+1d0m7naTSvf28A9j/A4rZaciFNtnlP2tsjsXR/aivKv9F8Rt4NvKi9WOZymn7Xdmq3/TyamyRrljCY0u3ahplPpbn0+Yk0bUAupunL6L0MuIKtqq5K8iyadgkLuONKrf9i+e85NZadeSB3XJXX73Am6fyuqk5u2129jyYghKa9y3yaKqk7VcFV1Y+T7AK8pZ1nT5osyyU0jVwHVVeNt/13JPkFzev4ZJrX5m80wUH/lWjPo7lS6GU0bTeg+aI/GPjsVLc5De+iCYpfQdOh4mXAN2naRfW/7wvb+efTVLttRtOo+Dc0P7jfHJuxqi5p34ODaILIx9Fkwl5D80N0p2Cqqv7Ytt/bn+Y4nE+TOTseeF9VnT7tvZ2asa4RxtxM83n4IvDh6unQsvVymgs3nkPTjcLfgWNoGjrf6bhpA/5n0NxD80U03WlcStP/1iE0wcm1fctcnqYz2vfTvDYPoXndX0NTlX+nZVaUFb3tNiB+ehswL6A5WXkUTcByCc3xcURVTXhFbFVd3zYoP4jmWNmT5n14D81FAf3H2BntvI+g+Q7YmOa9OpPmROD/9cz7Nppj9oHtPt9Ic8/Gt9L0GzboitNpaa+WfQTN5/D5NMH1WjQB1e9o2nr974rebtcShp496lIGd80hSRqWJI+h6VX8oKq6U3XoOMu8jyYwe3xV/aDL8s2mbWvlsNpam9d62zyr021c84dDz6yqeZ1uZByjGyZK0iyXZOsB4zbljrZyd2obNs4y96fJgF7JgL7VVpRhblsruxDmdPoYJqv5JGl4Pto24D6FpqppG5oq6U2AQ6tqUEeMZyT5PU03DNfTtK95Es3J8avanrK7MsxtS7OWwZQkDc9RNBclPIWmf6MbaS56+CLjX8F5KE37pOfR9PR9Nc2VcR+pqhO7LOyQt62VnG2mJEmSltNqa92lNrjbszvdxlW//8zQ2kyZmZIkSR3LSGemDKYkSVLnRjmYGt09kyRJmgFmpiRJUqdC0znCqDIzJUmSNA0GU5IkqWMZ+o2Ok7w9yelJrk3y9yTHJrlf3zxJckCSS5LckOTEJPedbN0GU5IkaVUwn+a+l3vQ3Ej7VuBHSTbpmWc/4M0098rcneZ+qv+bZP2JVmybKUmS1K1ZcKPjqnpc73CSFwHXAA8Djk0SYF+ae2J+p53nJTQB1fNpOq0dyMyUJElaFa1PEwdd1Q7fHdiS5ibjAFTVDcBPabJZ4zIzJUmSOjcDmanNkpzRM3xYVR02wfyfAM4BftEOb9k+X9433+XAXSfasMGUJEkaBYumejuZJB8FHg48vKqWTHfDBlOSJKljYba0LEryMeC5wN5V9ceeSZe1z1sAf+4Zv0XPtIFmx55JkiR1LMkngOcBj6yqX/dNvogmaHpMz/xrAXsCp0y0XjNTkiSpc8O+mi/JZ4AXAU8Hrkoy1kZqcVUtrqpK8nHgP5L8GvgtsD+wGPj6ROs2mJIkSauC17TPP+4bfyBwQPv/h4C1gc8AGwOnAo+tqusmWrHBlCRJ6lTaHtCHqaomvTlgVRVNYHXAsqzbNlOSJEnTYGZKkiR1LiOcvxndPZMkSZoBZqYkSVLnht1mqkuju2eSJEkzwMyUJEnqVkIy6cV0Ky2DqRGQuWtW5q477GJIs9bO99102EWQZq2L/3w5V11x7ehGOjPAYGoEZO66rLnl44ZdDGnWOurE5w+7CNKs9cz5b5qR7YxymymDKUmS1Klg1wiSJEkah5kpSZLUseHfTqZLo7tnkiRJM8DMlCRJ6pyZKUmSJA1kZkqSJHUsXs0nSZKkwcxMSZKk7tlmSpIkSYOYmZIkSd2KV/NJkiRpHGamJElSp0JIMuxidMbMlCRJ0jSYmZIkSZ2znylJkiQNZGZKkiR1zqv5JEmSNJCZKUmS1LGAV/NJkiRpEDNTkiSpW2Gk0zcGU5IkqXtW80mSJGkQM1OSJKl7ZqYkSZI0iJkpSZLUvRFO34zwrkmSJHXPzJQkSepWoGwzJUmSpEHMTEmSpO6NbmLKzJQkSdJ0mJmSJEndmzO6qSkzU5IkSdNgZkqSJHUs9oAuSZKkwcxMSZKkbgWv5pMkSdJgZqYkSVL3vJpPkiRJg5iZkiRJ3fNqPkmSJA1iZkqSJHVvdBNTZqYkSVLHQtMAvcvHZEVI9kpyTJKLk1SSBX3T10vyqSR/TXJDkt8keeNUds/MlCRJWhWsB5wPHNE++n0UeDTwIuAiYC/g80kWVdVXJ1qxwZQkSerekKv5quo44DiAJIcPmGUP4KtVdUI7vDDJy4GHABMGU1bzSZIkwc+BpyS5G0CSPYBdgeMnW9DMlCRJ6lx13zXCZknO6Bk+rKoOW4blXw8cCvw5ya3tuNdV1fcmW9BgSpIkjYJFVTVvGsu/jqaq76nAn2jaTH0kycKqmjA7ZTAlSZK6laldcTcsSdYGPgD8c1Ud244+N8muwFuYpKrPNlOSJGlVt3r7WNI3fglTiJXMTEmSpO4NOTGVZD3gXu3gHGDbNvN0ZVX9OclJwEFJFtNU8z0CeDGw32TrNjMlSZJWBfOAs9vH2sCB7f/vbqc/Fzgd+BpwIfA24B3ApydbsZkpSZLUvSHf6LiqTmSC/FhVXQa8dHnWbWZKkiRpGsxMSZKk7s3iq/mmy8yUJEnSNJiZkiRJ3QpDv5qvS2amJEmSpsHMlCRJ6t6Qr+brkpkpSZKkaTAzJUmSOhYzU5IkSRrMzJQkSepWGOn0jcGUJEnqntV8kiRJGsTMlCRJ6t7oJqbMTEmSJE2HmSlJktSpAsobHUuSJGkQM1OSJKlbwav5JEmSNJiZKUmS1L3RTUyZmZIkSZoOM1OSJKl7Xs0nSZKkQcxMSZKkjsWr+SRJkjSYmSlJktSt4NV8kiRJGszMlCRJ6p5X80mSJGkQM1OSJKl7ZqYkSZI0iJkpSZLUrUCNbmLKzJQkSdJ0mJmSJEndG+E2UwZTkiSpe95ORpIkSYOYmZIkSd1KRrqaz8yUJEnSNJiZkiRJ3Rvh9M0I75okSVL3zExJkqTujfDVfAZT0jJ6y2v35OmP34kd7rEpN928hNPO/ivvPOhHXPjbv90+z9MevxMvf8E8dr3fVmy+6bo89tlf5me/XDi8QktDtGTJbXz2kPP4/vcXsujvN7DZ5mvzpCduz6tfc39WW80KEq38PIqlZbTXP23PoUecxt7P+CJPeO7hLLn1Nr7/9Rez8YZr3z7POuuszi/P/Atvfc/xQyypNDt8+Uu/4lvf/B1vfduDOPqYJ/PWtz6Ib37zt3zxCxcMu2iaKaG5mq/LxxB1lplKUsBHq+rN7fBbgPWq6oAkawBHA2sCv6uqV3dVjnbb+wKHVdU/utzOJGVYXFXr9QwvAOYBlwL/3I6+P3Be+/+XgE2AxVX1kRksqibx1Bd9danhl+17FJdf8HYeuvvdOO5HvwXgG0edC8CmG68z4+WTZptzzvk7j5h/V+bP3waAu951PebvvQ3nnXfFkEsmrRhdZqZuAp6ZZLP+CVV1c1U9saoe1XUg1doXGPirlmTuDGx/XFX1vqratap2BW4Y+7+qPjnMcmnq1l9vDebOncPV19w47KJIs9Juu23O6addzkV/vAaAP/zhGk479XL23HPrIZdMM6mSTh/D1GWbqVuBw4A3Av/ZOyHJU4D9gTWAK4AXVNXlSTahycjcA/gH8MqqOrdv2bnAQcB8mszWZ6rq0CTzgQOARcD9gDOBFwKvA7YGTkiyqKr2TrIYOBR4NPDaJI8EngKsDZwCvKqqqm+7hwPX0mSTtgT2q6ojk6wHfBfYGFgd2L+qvrvcr5pWOh854Amcc/6l/PLMvwy7KNKs9LKX78w//nErz3j695k7N9x6a/GKV9yX5zx3x2EXTVohum6A/hng3CQf6hv/c+CfqqqS/AuwH/Bm4EDg7Kp6ehvgHAHs2rfsy4Frqmr3JGsCJyf5YTttN+C+wCXAycDDquqTSd4E7F1Vi9r51gVO7amCvLCq3t3+/1XgycCxA/ZnK+DhwH2AY4AjgRuBZ1TVtW0W7pdJjukPxjSaPviOx7HH7tvyyGd9idtu8y2XBjn++D9x7DEX8YEP7sG97rkRv/7NVXzooDPZepv1eOYz7zns4mmmjHAr7U6DqTbAOAJ4PXBDz6RtgG8l2YomO3VRO/7hwLPaZX+SZNMkG1TVtT3LPhbYJck+7fCGwA7AzcBpVfVXgCTnANvTBG79lgDf6RneO8l+NFWBmwAXMDiYOrqqbgMuTLJFOy7A+5PsBdwG3BXYArhs3BfmDsv965vklcArAZhru5xh+NA7H88+T70fj3/O4Sz881XDLo40a33s4HN4yYL78IQnbA/ADjtuxKWXXM+XvnCBwZRGwkx0jfBx4Czgyz3jPkXTOP2Ynuq5qQrwuqr6wVIjm/Xc1DNqCePv341VtaRdbi3gEGBeVf0lyQHAWuMs17v+sQraFwCbAw+qqluSLBxn+RuSrFFVN7fDm9BUSS6XqjqMphqVOWtsYkpkhn3kgCfwrCffl8c/93B++4flfhulVcKNN97KnL6rrebOjdncVcnY1XwjqvOkW1VdCfw3TfXcmA2Bi9v/X9Iz/mc0wclYcLSoLysF8APgX5Os3s63Y5J1JynGdcD640wbC3wWte2f9hlnvvFsCPytDaT2BrYbZ76TaNpwkWRt4NnACcu4Lc0CH3vPk3jRP+/Kgtd/h6uvuZEtNl+PLTZfj3XXWeP2eTbecG122XlLdr73XQC45/absMvOW7LF5uuNt1ppZD3iEXflS1+8kJ/+9GIuvngxP/7xX/jqEb/mkY+627CLJq0QM9Vp58HAv/UMHwB8O8lVwE+Au/eM/1KSc2kaoPcGWmO+QFN9d1aSAH8Hnj7J9g8Djk9ySVXt3Tuhqq5O8nngfJqqudOnvFeNrwHHJjkPOAP49TjzvQE4NMnraWL0I6rqp8u4Lc0Cr37JgwE4/psLlhr/3o+dwPs+diIAT3rMvfn8R59x+7TPfuhpd5pHWlW87T/m8ZlPn8v733s6V155E5ttthbPfNY9edWr7z/somnGZKR7QI/tpFd+c9bYpNbc8nHDLoY0a/3fec8fdhGkWeuZ89/E+Wf/rtNIZ80td6itX/jRLjfBwoOfemZVzet0I+MY4bb1kiRp1hhyD+hJ9kpyTJKLk1TbeXb/PDsmOSrJ1Un+keSsJDtNumvL94pIkiStVNajadLzBpbuYQCAJHen6VbpIuCRNH1W7g8snmzF3uhYkiR1b8hNpqrqOOA4uL0j7n7vA3441gdl649TWbeZKUmS1K1AzUmnD2CzJGf0PF455eIlc2juhHJhkuOT/D3J6UmeM5XlzUxJkqRRsGgaDdDvQlMN+B/AO4C30VT1fS3J4qr6/kQLG0xJkqTuze5OO8dq6r5bVWOXHZ6TZB5N104TBlNW80mSpFXdIuBW4MK+8b8Ctp1sYTNTkiSpe7O4086qujnJ6cC9+ybtCPxpsuUNpiRJ0shrbxl3r3ZwDrBtkl2BK6vqz8CHgP9O8jOau7PsDTyXye+yYjWfJEnqWGgiji4fk5sHnN0+1gYObP9/N0BVHQ28EngLcB7wOuDFkzU+BzNTkiRpFVBVJzJJb1dVdThw+LKu22BKkiR1bLRvdGw1nyRJ0jSYmZIkSd2b3f1MTYuZKUmSpGkwMyVJkroVzExJkiRpMDNTkiSpc+XVfJIkSRrEzJQkSereCKdvRnjXJEmSumdmSpIkdSvYA7okSZIGMzMlSZI6lpHuZ8pgSpIkdW+Egymr+SRJkqbBzJQkSepW2seIMjMlSZI0DWamJElSpwoo20xJkiRpEDNTkiSpe3baKUmSpEHMTEmSpG4F+5mSJEnSYGamJElS90Y3MWVmSpIkaTrMTEmSpE4FmDPC6ZsR3jVJkqTumZmSJEmdG+FupsxMSZIkTYeZKUmS1K2YmZIkSdI4zExJkqSOhYxwasrMlCRJ0jSYmZIkSZ0Ko91mymBKkiR1bpSDKav5JEmSpsHMlCRJ6lYgI5y+GeFdkyRJ6p6ZKUmS1DnbTEmSJGkgM1OSJKlTAeaYmZIkSdIgZqYkSVLnbDMlSZKkgcxMSZKkzpmZkiRJ0kBmpiRJUrcCGeHUlJkpSZKkaTAzJUmSOue9+SRJkjSQwZQkSepUaK7m6/IxaRmSvZIck+TiJJVkwQTzHtrO85ap7J/BlCRJWhWsB5wPvAG4YbyZkuwDPBi4ZKorts2UJEnq1hSzR12qquOA4wCSHD5oniTbAZ8AHg38v6mu28yUJEla5SVZDfgG8N6q+tWyLLtMmakkGwN3q6pzl2U5SZK0apuBzNRmSc7oGT6sqg5bhuUPBBZV1WeXdcOTBlNJTgSe2s57JvC3JCdX1ZuWdWOSJEkdWVRV85ZnwSTzgQXArsuz/FSq+TasqmuBZwJHVNVDaOoSJUmSpmROun1M03xgK+DSJLcmuRXYDvhgkr9OtvBUqvlWS7IV8GzgP6dTUkmStOoZ6xphFjsEOLJv3A9o2lB9frKFpxJMvbtd4c+r6vQk9wB+t6yllCRJGpYk6wH3agfnANsm2RW4sqr+DPytb/5bgMuq6jeTrXvSYKqqvg18u2f4j8Czplx6SZK0ypsFmal5wAk9wwe2j6/QtJdabuMGU0n2q6oPJfkUUP3Tq+r109mwJEnSTKmqE2lqHKc6//ZTnXeizNRYHwtnTDCPJEnSxAJZAa3EZ6txg6mqOrZ9/srMFUeSJGnlMpV+pjYH3grsDKw1Nr6qHtlhuSRJ0giZBW2mOjOVfqa+RlPld3eahloLgdM7LJMkSdJKYyrB1KZV9UXglqo6qapeBpiVkiRJU5Z0+ximqfQzdUv7fGmSJwGXAJt0VyRJkqSVx1SCqfcm2RB4M/ApYAPgjZ2WSpIkjYyVoAf0aZlKp53fa/+9Bti72+JIkiStXCZtM5VkxyQ/TnJ+O7xLkv27L5okSRoJHd/keNhdWE2lAfrngbfTtp2qqnOB53ZZKEmSpJXFVNpMrVNVp2Xpys5bOyqPJEkaQaPcZmoqmalFSe5Je3++JPsAl3ZaKkmSpJXEVDJTrwUOA+6T5GLgIuAFnZZKkiSNlEwlfbOSmjCYSjIXeE1VPTrJusCcqrpuZoomSZI0+00YTFXVkiQPb/+/fmaKJEmSRskq388UcHaSY4BvA7cHVFV1VGelkiRJWklMJZhaC7iCpe/HV4DBlCRJmlwgI5yamkoP6C+diYJIkqTRNcKx1JS6RpAkSdI4plLNJ0mSNC1mpiRJkjTQVG50/IYkG6TxxSRnJXnsTBROkiSNhqTbxzBNJTP1sqq6FngssDHwIuCgTkslSZK0kphKm6mxeO+JwFer6oKM8vWNK6EH7rI1Z5xx4LCLIc1aO8z7wbCLIM1af/ntTZ1vI8CcEY4cppKZOjPJD2mCqR8kWR+4rdtiSZIkrRymkpl6ObAr8Meq+keSTQH7npIkSVMTM1MF7Ay8vh1el6ZXdEmSpFXeVDJTh9BU6z0SeDdwHfAdYPcOyyVJkkZE02aqhl2MzkwlmHpIVT0wydkAVXVVkjU6LpckSdJKYSrB1C1J5tJU95Fkc2yALkmSlsGq3mbqk8D/AHdJ8j7g58D7Oy2VJEnSSmLCzFSSOcBFwH7Ao2iqPZ9eVb+agbJJkqQRMcr3r5swmKqq25J8pqp2A349Q2WSJElaaUylzdSPkzwLOKqqRrcpviRJ6sSoX803lazbq4BvAzcnua59XNtxuSRJklYKk2amqmr9mSiIJEkaXaN8Nd9UqvlI8lRgr3bwxKr6XndFkiRJWnlMGkwlOYimt/OvtaPekORhVfX2TksmSZJGQliFr+ZrPRHYtapuA0jyFeBswGBKkiRNzhsdA7BRz/8bdlAOSZKkldJUMlMfAM5OcgJNpm4v4G2dlkqSJI2UjHDXCFO5mu8bSU6kaTcF8NaquqzTUkmSJK0kxg2mkjywb9Rf2+etk2xdVWd1VyxJkjQqmk47h12K7kyUmTp4gmkFPHIFl0WSJGmlM24wVVV7z2RBJEnS6FrVu0Ygyf2AnYG1xsZV1RFdFUqSJGllMZVOO98FzKcJpo4DngD8HDCYkiRJkwq1yt/oeB/gUcBlVfVS4AHY15QkSRIwtWq+G6rqtiS3JtkA+Btwt47LJUmSRsiqejXfmDOSbAR8HjgTWAz8ostCSZIkrSzGreZL8pn2hsavqaqrq+pzwGOAl7TVfZIkSVMyp+PHZJLsleSYJBcnqSQLeqatnuSDSc5Ncn2SS5N8Pcm2U9238fwW+EiShUk+lGS3qlpYVedOZcWSJEmzyHrA+cAbgBv6pq0DPBB4X/v8NJomTccnmbQWb6J+pj4BfCLJdsBzgS8lWRv4BvCNqvrtcuyIJElaxSTDbzNVVcfR9EpAksP7pl1DU/t2uySvAi4AdgLOm2jdk2bGqupPVfXBqtoNeB7wdOBXUy++JEnSSmeD9vmqyWacSj9Tq9H0LfVcmi4STgQOWP6ySZKkVc0M9DO1WZIzeoYPq6rDlmdFSdagua3esVX118nmn+hGx4+hyUQ9ETgN+Cbwyqq6fnkKJkmS1KFFVTVvuitpk0j/BWwEPHUqy0yUmXo78HXgzVU1aYpLkiRpkDD8NlNT0QZS3wDuD8yvqiumstxEDdAfuYLKJkmSNKslWZ2mFu5+NIHUZVNddko3OpYkSZqOqfQF1aUk6wH3agfnANsm2RW4ErgE+DawO/AUoJJs2c57TVX1d6WwlGHvmyRJWgXMSXX6mIJ5wNntY23gwPb/dwPb0PQttTXN3V4u7Xk8Z7IVm5mSJEkjr6pOpGm+NZ7lbtVlMCVJkjq1sjRAX15W80mSJE2DmSlJktStWXA7mS6ZmZIkSZoGM1OSJKlTYbSzN6O8b5IkSZ0zMyVJkjo3Azc6HhozU5IkSdNgZkqSJHXOq/kkSZI0kJkpSZLUKa/mkyRJ0rjMTEmSpM7ZZkqSJEkDmZmSJEmdi/1MSZIkaRAzU5IkqVOJbaYkSZI0DjNTkiSpc6OcvRnlfZMkSeqcmSlJktSpUMwZ4av5DKYkSVLnbIAuSZKkgcxMSZKkzpmZkiRJ0kBmpiRJUqcCzB12ITpkZkqSJGkazExJkqTOjXLXCGamJEmSpsHMlCRJ6pQ3OpYkSdK4zExJkqTOmZmSJEnSQGamJElSpwLMNTMlSZKkQcxMSZKkztlmSpIkSQOZmZIkSZ0K9oAuSZKkcZiZkiRJ3bIHdEmSJI3HzJQkSepUgLnDLkSHDKYkSVLnrOaTJEnSQGamJElS5+waQZIkSQOZmZIkSZ3yRseSJEkal5kpSZLUOa/mkyRJ0kAGU5IkqVNpbyfT5WPyMmSvJMckuThJJVnQNz1JDkhySZIbkpyY5L5T2T+DKUmStCpYDzgfeANww4Dp+wFvBl4H7A78DfjfJOtPtmLbTEmSpM4Nu81UVR0HHAeQ5PDeaUkC7AscVFXface9hCagej5w6ETrNjMlSZJWdXcHtgR+ODaiqm4AfgrsMdnCZqYkSVKnmn6mOu8BfbMkZ/QMH1ZVh01x2S3b58v7xl8O3HWyhQ2mJEnSKFhUVfOGsWGDKUmS1LlZ3q7osvZ5C+DPPeO36Jk2LoMpaQV470dP4H0fP3GpcVtsvh4Lz/z34RRIGrLdd9uYl79oe+57nw3Y8i5r8dYDzuOo711y+/RNN1mD/V63Iw/7p03ZYP3VOf2sq3j3h3/Fn/7yjyGWWquwi2iCpscApwMkWQvYE5j0i9xgSlpBdrznZvzgWwtuH547d5afh0kdWmedufz294s5+vuX8KED73+n6Z/9yG7cVsVr3nIO1y2+hZe9YHu+csg8nvDPJ3PDjUuGUGJ1KQz/ar4k6wH3agfnANsm2RW4sqr+nOTjwH8k+TXwW2B/YDHw9cnWvVJ+2ydZkuScnsfbJpl/fpJJW+N3KcnhSfbpG7c4yf179uPKJBe1//8oyfZJzh9WmbVsVps7hy3vsv7tj803XXfYRZKG5qSTF/HRQ37H8T++nNtuW7rh8fbbrsNuu2zEAQf9inMvuIaL/vQP3vmBC1lrzTk8+XFbjrNGadrmAWe3j7WBA9v/391O/xDwMeAzwBnAVsBjq+q6yVa8smambqiqXZdh/vk00eUp/ROSrFZVt66gci2zqjoP2LUty+HA96rqyHZ4+2GVS8vuoj9fxd3nfYQ115zL7rtuw7v3exR3326TYRdLmnXWWL05j7/p5jsyUFVw8823MW/Xjfn2dy8eVtHUoWFnpqrqRJok2XjTCzigfSyTlTIzNZ4kC5McmOSsJOcluU8bkLwaeGOb8dmzzRJ9LsmpwIeSPDjJL5KcneSUJPcesO75bdfyRyb5dZKvtZ18keSdSU5Pcn6Sw8bGa9Wx+27bcNjBT+eYr76QQw56Kpf/fTF7P/OLXHGV7T+kfn9ceD0XX3oDb37tjmy4weqsvlp45UvuzlZbrs3mm6057OJJy2xlzUytneScnuEPVNW32v8XVdUDk7wGeEtV/UuSzwGLq+ojAEleDmwD7FFVS5JsAOxZVbcmeTTwfuBZA7a7G3Bf4BLgZOBhwM+BT1fVu9t1fxV4MnDsCt5nzWKP23uHpYYf/MBt2Pnhn+C/jjyHN7xiqDXM0qxz65Litf9+Dh94x3054yeP5NZbb+OU067kxJP/Pn7aQCu1pGain6mhWVmDqYmq+Y5qn88EnjnBOr5dVWM55g2BryTZAShg9XGWOa2q/grQBnPb0wRTeyfZD1gH2AS4gDsHU4OOouU+spK8EnglwLbbbr28q1FH1lt3TXbacXP+cNEVwy6KNCtd8OtreeoLfsF6667GGquHK6++hSMPfwjnXXjtsIumjgy7mq9LI1XN17qpfV7CxMHi9T3/vwc4oaruBzwFWGuSdd++/vbSyUOAfarq/sDnx1n+CmDjsYEkmwCLJijfhKrqsKqaV1XzNt9848kX0Iy68cZb+O3vF7HlXSa9P6a0Slt8/a1cefUtbHe3dbjfThvy45P+NuwiSctsZc1MLavrgA0mmL4hMNbiccEyrnsscFrUXna5D3DkgPlOBPZN8pWqurndzgnLuC3NUm977w940qPvzd223pC/XXE9B33iJK6/4RZesM+uwy6aNBTrrD2X7e62DgBz5oStt1ybnXZcn6uvuYVLL7+Rxz9qC666+hYuuewGdrzXeuz/5p340Ul/4+enms0dRbOha4QurazBVH+bqeOraqLuEY4FjkzyNOB1A6Z/iKaab3/g+8tSkKq6OsnngfNpOvw6fZz5vpfkQcCZSZYAf6BpGK8RcPGl1/LifzuSK676B5ttsg4PfuA2nHT0v7DdNhsNu2jSUNxv5w342qEPvn34Da++F2949b046tiLeeuB53OXzdbkP954bzbddE3+vugmjv7+JXzmC38YYoml5ZfmSkCtzObNu1+dccZRk88oraJ2mPeDYRdBmrX+8qsPc+P1f+40b7TDLjvUx7//yS43wZO3feKZw7o33yi2mZIkSZoxK2s1nyRJWkkEmDvCbabMTEmSJE2DmSlJktStwJwR7rTTzJQkSdI0mJmSJEmdG+XszSjvmyRJUufMTEmSpE6Neg/oZqYkSZKmwcyUJEnqnP1MSZIkaSAzU5IkqVNNmyn7mZIkSdIAZqYkSVLnvJpPkiRJA5mZkiRJnUpGOzNlMCVJkjo3ylVho7xvkiRJnTMzJUmSOpcRruYzMyVJkjQNZqYkSVLnRjgxZWZKkiRpOsxMSZKkTgXbTEmSJGkcZqYkSVLnRjl7M8r7JkmS1DkzU5IkqXNJDbsInTEzJUmSNA1mpiRJUudG+GI+M1OSJEnTYWZKkiR1yn6mJEmSNC4zU5IkqXMjnJgyMyVJkjQdZqYkSVK3AnNGODVlZkqSJGkazExJkqTOjXBiysyUJEnSdJiZkiRJnRr1fqYMpiRJUudGOJaymk+SJGk6zExJkqTOmZmSJEnSQAZTkiSpc3PS7WMySeYmeU+Si5Lc2D6/N8m0a+ms5pMkSauCtwKvBV4CnAfsAnwFuAl4z3RWbDAlSZI6FWZFm6k9gGOr6th2eGGSY4CHTHfFVvNJkqRVwc+BvZPcByDJzsAjgeOmu2IzU5IkqXNJdb2JzZKc0TN8WFUd1jP8QWB94MIkS2hioPdV1SHT3bDBlCRJGgWLqmreBNOfA7wYeD5wAbAr8IkkF1XVF6ezYYMpSZLUuVnQZurDwEeq6pvt8HlJtgPeDkwrmLLNlCRJWhWsAyzpG7eEFRALmZmSJEndyqy40fGxwNuSXERTzbcb8CbgiOmu2GBKkiStCl5H05/UIcBdgEuBzwPvnu6KDaYkSVKnwvDbFVXVdcC+7WOFGva+SZIkrdTMTEmSpM7NgjZTnTEzJUmSNA1mpiRJUudGODFlZkqSJGk6zExJkqTOjXKbKYMpSZLUuRGOpazmkyRJmg4zU5IkqVMB5oxwasrMlCRJ0jSYmZIkSZ0b4cSUmSlJkqTpMDMlSZI6ViQ17EJ0xsyUJEnSNJiZkiRJnbPNlCRJkgYyMyVJkjqVjPbtZMxMSZIkTYOZKUmS1LkRTkyZmZIkSZoOM1OSJKlzo5y9GeV9kyRJ6pyZKUmS1Dmv5pMkSdJAZqYkSVLHwihfz2dmSpIkaRrMTEmSpE41eanRzUwZTEmSpM4lo1sZNrp7JkmSNAPMTEmSpBkwutV8ZqYkSZKmwcyUJEnqWEa6AbqZKUmSpGkwMyVJkmaAmSlJkiQNYGZKkiR1zn6mJEmSNJCZKUmSNANsMyVJkqQBzExJkqROxX6mJEmSNB4zUyPgzDMvWJTc+0/DLodutxmwaNiFkGYxPyOzy3YzsZFRzkwZTI2Aqtp82GXQHZKcUVXzhl0OabbyM6JRYzAlSZJmwOi2LBrdPZMkSZoBZqakFe+wYRdAmuX8jKyCktFtM2VmSlrBqsofCmkCfkY0asxMSZKkjoVR7gHdYEqSJHVulLtGsJpPK5UkleTgnuG3JDmg/X+NJMcl+XGSz81AWfZNsk7X25mkDIv7hhck+XSS/0xyTvtY0vP/65MckOQtwyqzZo++Y+OcJG+bZP75SfaYqfKNU4bDk+zTN25xkvv37MeVSS5q//9Rku2TnD+sMmv0mZnSyuYm4JlJPlBVS3X6V1U3A0+cwbLsC/wX8I/+CUnmVtWSGSzLUqrqfcD72rIsrqpde8p2wJCKpdnnht5jYwrmA4uBU/onJFmtqm5dQeVaZlV1HrBrW5bDge9V1ZHt8PbDKpd6DT9/k2Qr4CCa34r1gT8C/1pVJ01nvcPfM2nZ3EpzJdAb+yckeUqSU5Oc3Z6NbtGO3yTJ0UnOTfLLJLsMWHZukg8nOb2d71Xt+PlJTkxyZJJfJ/laGq8HtgZOSHJCO+/iJAcn+T/goUne2a7v/CSHZcClLO1Z9ieTnJLkj2Nn3EnWazNsZyU5L8nTVuBrKE0oycIkB/Ycf/dpA5JXA29sMz57tsfv55KcCnwoyYOT/KL9DJ6S5N4D1j3wM9VOm/QzIy2vJBsBJ9M03noSsBPwOuBv0123wZRWRp8BXpBkw77xPwf+qap2A74J7NeOPxA4u6p2Af4DOGLAOl8OXFNVuwO7A69Icvd22m40WaidgXsAD6uqTwKXAHtX1d7tfOsCp1bVA6rq58Cnq2r3qrofsDbw5HH2Zyvg4e30g9pxNwLPqKoHAnsDB/vDog6s3VfN95yeaYva4++zwFuqaiHwOeBjVbVrVf2snW8bYI+qehPwa2DP9jP4TuD942z3Tp+pdvxUPzNaCaXjvynYD7i0ql5cVadV1UVV9eOq+tV0981qPq10quraJEcArwdu6Jm0DfCtNo27BnBRO/7hwLPaZX+SZNMkG1TVtT3LPhbYpactxobADsDNwGlV9VeAJOcA29MEbv2WAN/pGd47yX7AOsAmwAXAsQOWO7qqbgMuHMum0Zw5vT/JXsBtwF2BLYDLxn1h7lBTmEeCiav5jmqfzwSeOcE6vt1Tpb0h8JUkO9Ach6uPs8x4n6mpfGYGHd8e85qKpwPHJ/kWzUnqJcAXgM9U1bSOITNTWll9nCabtG7PuE/RnNneH3gVsNYyrC/A69oz7l2r6u5V9cN22k098y1h/JOQG8d+VJKsBRwC7NOW5/MTlKd3/WOnVy8ANgce1P7YXT7O8jckWaNneBO8gaxWjLHjcqJjHuD6nv/fA5zQZpaewtSO+SXAasvwmbkC2HhsIInH/EogSecPYLMkZ/Q8XtlXjHsAr6FpJ/U44BM0tQGvne7+GUxppVRVVwL/TRNQjdkQuLj9/yU9439GE5yQZD5N9UVvVgrgB8C/Jlm9nW/HJOsysetoGjAOMvYjsCjJesA+48w3ng2Bv1XVLUn2Zvy7up8EvLAt89rAs4ETlnFb0lRNdMzD0p/BBcu47ql+Zk4EntNzErEAj3k1FlXVvJ5Hf+ewc4CzqurtVXV2VX0Z+CQGU1rFHQxs1jN8APDtJGey9JnqAcCDkpxLcxbSG2iN+QJwIXBWmkuoD2XyavDDaFLGd/oir6qrac6sz6cJ1E6ffHeW8jVgXpLzgBfTtEUZ5A00VzeeA/ySpsrlp8u4La26+ttMHTTJ/McCzxhrgD5g+oeADyQ5m2VsRjLVz0xVfY/mBOnM9rh/GPDWZdmWhiUdPyZ1Kc33fK9fAdsu/z41Ms1qQkmSpAk9aN5OdfKpX+50G2uv9tAzq2reeNOTfB24W1Xt2TPuPcCzqmrn6WzbBuiSJKlzGX5l2MeAU5L8J/AtmqtKX09zlfe0DH3PJEmSulZVp9Nc0fdsmurk9wHvoLnwYVrMTEmSpBkw/K7yqur7wPdX9HrNTEmSJE2DmSlJktSx2/uCGklmpiRNS5ITkjyub9y+ST47wTInJhn3qpvZrLfsSY5LslH7eM2wyyZpOAymJE3XN4Dn9o17bjt+hUgyd0Wta5z1L1eWvqqe2PaPtBFNz8qSxjX0fqY6YzAlabqOBJ401iN1ku2BrYGfJflse1uHC5IcOGjhJM9Lcl6S85N8sGf84iQHJ/k/4KFJXpjktLbDyEOTzG0fh7fLnpfkjQPWf3iSz7Xl+G2SJ7fjFyQ5JslPgB8nWTfJl9ptnJ3kae18ayf5ZpJfJfkfmhvwjq17YZLNaDqDvWdbtg+n8eGecj2nnX+rJD9t5zt/nI4vJa1kbDMlaVqq6sokpwFPAL5Lk5X676qqJP/ZTp9LE7DsUlXnji2bZGvgg8CDgKuAHyZ5elUdTXPfxVOr6s1JdqLp5fph7S12DqG5RdAFwF3be8GRZKNxirk98GDgnsAJSe7Vjn8gsEtbxvcDP6mql7XrOS3Jj2ju8/iPqtopyS7AWQPW/zbgfmM3DU7yLGBX4AE0vfSfnuSnwPOBH1TV+9rXZJ2pvcrSym8W9DPVmdHdM0kzqbeqr7eK79lJzgLOBu4L9PcyvDtwYlX9vapupbmNzl7ttCXAd9r/H0UTcJ3e3kLkUTQ3Lf0jcI8kn0ryeKD/notj/ruqbquq37XL3Kcd/7/tfR4BHgu8rV3/iTT3itu2Lc9/AbSB4LlM7uHAN6pqSVVdTnMPxd1pbpHy0iQHAPevquumsC5Js5yZKUkrwneBjyV5ILBOVZ2Z5O7AW4Ddq+qqJIdzx81sp+LGqlrS/h/gK1X19v6ZkjyA5g7wr6bpjO9lA9bVf9+sseHre1dFc1uJ3/StfxmKPLGq+mmSvYAnAYcn+WhVHbHCNiDNal7NJ0njqqrFwAnAl7gjK7UBTbByTZItaKoB+50GPCLJZm211/Nosjj9fgzsk+QuAEk2SbJd215pTlV9B9ifptpukH9OMifJPWkyWr8ZMM8PgNeljZ6S7NaOH6ueI8n9gF0GLHsdsH7P8M+A57RtujanyW6dlmQ74PKq+jzNzbXHK6+klYiZKUkryjeA/6Gt7quq/0tyNvBr4C/Ayf0LVNWlSd5GE4gF+H5VfXfAfBcm2Z+mTdUc4BbgtcANwJfbcQB3yly1/kwTuG0AvLqqbhyQcXoP8HHg3HZ9FwFPBj7bbuNXNHeYP3NA+a5IcnKS84H/B+wHPBT4P5os2H5VdVmSlwD/nuQWYDHw4nHKK42YkBHOTKWqP/stSaOjrV78XlUdOeyySKuqefPuW6eevsJ6SxlotTkPOLOqhtJ/ndV8kiRJ02A1n6SRVlULhl0GSTDK+ZvR3TNJkqQZYGZKkiR1bpQboJuZkiRJmgYzU5IkqWPDvxlxl8xMSZIkTYOZKUmS1LkVeWum2cbMlCRJ0jSYmZIkSTNgdPM3o7tnkiRJM8DMlCRJ6pz9TEmSJGmgVNWwyyBJkkZYkuOBzTrezKKqenzH2xjIYEqSJGkarOaTJEmaBoMpSZKkaTCYkiRJmgaDKUmSpGkwmJIkSZqG/w8r6P1KepWljgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "f.grafico_matriz_confusao(modelo_bagging,'Matriz de confusão - Bagging Classifier',dados)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rodarei o modelo 50 vezes e verificarei qual é o seu AUC médio e em qual intervalo se encontra."
      ],
      "metadata": {
        "id": "8nuThJEOXNY_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lsG3No33vxzZ",
        "outputId": "1cb69c04-b5ef-4dc5-d032-3bd2cf7d214b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC médio 0.7514798850574713\n",
            "Intervalo 0.6289125908452037 - 0.874047179269739\n"
          ]
        }
      ],
      "source": [
        "f.roda_n_modelos(modelo_bagging,dados,50)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Este modelo não apresentou um resultado interessante, principalmente em relação ao menor índice de recall. \n",
        "\n",
        "O que demonstra que ele não está prevendo corretamente a entrada na UTI dos pacientes. "
      ],
      "metadata": {
        "id": "ejanuoJ9Qn6k"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bId0eqZ8Blj0"
      },
      "source": [
        "#### <font color= MediumSpringGreen> 1.1.2  GradientBoostingClassifier\n",
        "\n",
        "\n",
        "Farei as mesmas etapas para os os próximos dois algoritmos.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6wwROjoR3sGf"
      },
      "outputs": [],
      "source": [
        "modelo_gradient_boosting = GradientBoostingClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "WomAox3o5jkD",
        "outputId": "363620a8-bc24-4c19-9bbd-b5ef97338453"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 AUC  F1-score_0  F1-score_1  Acurácia\n",
              "GradientBoostingClassifier  0.867816    0.847458    0.808511  0.830189"
            ],
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              "          const element = document.querySelector('#df-19711a2c-bd2b-4da7-a692-b16de938206e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "gradient_boosting = f.roda_modelo(modelo_gradient_boosting,dados,'GradientBoostingClassifier')\n",
        "gradient_boosting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "G-4dNB78s-_D",
        "outputId": "f0448c3f-deff-414f-e97a-d1d487e99fc2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ],
            "image/png": 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VO5MY+5XaP4DemI0HzBvb5nFVNWEWY0jXA+snWW2IQOwGYL0kK/dnf5KM3fZiUMZraRkLbCf6PliXvn8Q7WuaD8xP8iCaxuZ70mSpNqcZRgCaDOwWwL41ftiB7WmCsF5j78Gg9weaBtG9602o7sWAmW1QcwpN9uRp9AVhS7KrQTPb93M+zY+Abavqir7lg3ru9l7Li5Wn5/oYZsDYsf3sWlVHDrH+dHNK+9xbNbek18sSX19tdf+BwIFpxrjbkaam4MXAo5I8qnoazS8DY98FE323TTRf09DyWYmqoVTTrftwmt59N9O0p5nMWI/JI2t8j68nAKsN2GasKmSp/hpO8nc0VTy30QwTsKSDTo5VVzxlwL5n07YR6fOr9vnJA5YtqV/RtJt69hDrnkXzWdxpwLKdaM7tUD0y76Xr2ucH9S9IM9r9Ov3ze1XVpW17nGfRNPzesa2ChHuuqUFdtce9N9zT823HNsDot3P73OX5+FL7vE+S1ZfyvjegCWpPGhCArcng3lUTXss01/Gwn72leX1PZhHc/Tlbmsaq5Hr/b41dL/Mm2Kb/ermQJuv4uDZLO9X6i2nb8323ql5CU3X5UJpOO2MW0XFmkJ7PSP+C9sfyDh0f/14KzVvX5WP5s3yWWkviQzTZrWcNagzc55L2eV7vzPbX30ETbDPWCHuzCZYvsTQjFB9NMwjmHm2j+CV1Es0X7k5Jdu1b9jbGtweDJut2PfCRJOMaG6cZy2vekMf/XPt8wKBxfvrmfbl9/njvP/32739vJ//fkMe9Ny6g+XW9a/tejx1/NZrq7MUk2TDJYwbsZw2aqqw7uWfIhEva53l9+9iGAY3K2w4LP6HJpu3dt80Taao/r6Nn4MQOfJVmHKGHA0e1nQUGWfde7PtqmiDg8b0dJ9q2mJ9h8M1+D2mfP5hkvZ5tVqWpmhvWD2g6pLw1yXMHrdCOM3VfA88uvhNmc0/W9ISeRSfSfM53TLJH3zZ70AScF9G2n2qr/L9GM5zFR/vWfyjNMCB30NQikGSVJE8aUJ6VaTpxwOIj4F8DbNh+drpyIs37uHOS/vsAvQnbgy1XrI6c4arqzzRtToZxGs0H/EVt485f0qS2n0PzRXf5gG1OpvkS2rvNfoy1tfrcfWi4+q80QdKZwJMGfQnS3EPu+ol2UFWV5PU0/9CPSNI7TtjTaBrMP7tvm2vaL+7vAb9KchzNPQKLJku0PU27mlWnegFV9eO2Q8SHgN8m+T5NtepGNL9gf0VTfUdVfb0NFF8CnN+uO9Y+aAvgW/09v5amqrojza08/gU4K8n3aL4bnkHznve/7w9o1zuXpvH5pTQB8/Noqnk+2xPwH0bTKP/ANPdh/B1NA/Dn0Yxv9NIBRdqL5jr8ZJJn0jRYHhsn7C6a+5RO9YPiXms7bryoLfuuwMVJfkYzJtrfaAYLfRRNxuF27qkmG2bfdyX5LE27onOT/ICmN97ONP/Uj+eebMzYNicm+RxN4/Dz2nZ3Y+OEXceQtyZq3+cX0QwB8r/tZ/zs9jU9iGbcq4fQVMndl1vrHEfzXn23bTd1C/CnqvqfIbffuq/jwP1pxpV7OM3wMXc3i2g/56+h+Zx/qz2fF7Tr7gbcBLy6ry3p+2mCs7elGWPseO4ZJ2wt4G3VDL4JTfb/l0l+T9Mu9U80n/9n0HRgOLKqftv32rcDjknyc5pM/q+ruRH0UtFeQ2+g+Q47su0Y8wfgsW25/o/mO3vY9rPLzPLag7FLBmG6W/vP5wU0Y4g9l+ZX4WU01TMfY0CPwKq6LsnuND3G9uSenm9f5d7fNmXsl/i2TDz43SFMMdho+8/ryTSDLY79YjyFJivzLAZUFVbVcUkeSzOC8rNovqxvpwlEfsrgarWJjv8vSU6mOY/Pozk3V9MEFf09+15O0+vqdTS9U6EZ/+gA4L+GPeZ98BGaf7xvpPk1fSXwTZr2S/3v+yXt+vNoAoYNaBq0X0jzD+6bYytWc/PbJ9Nk9HakOacX0AxMeSwDgrCqurhtn/ghmutwHk2m7hiaYRpO699maWt7ne6W5GnAa2gCrh1oGuxfRxOcfxA4rJb8Bt7/AvyVpr3cm2k+Jz+heb2DBimGJgt0EfDWdptraH4s/DPNvRaHfV3nJHkczeDLz6NpbH4XTSB3Fs37el+HXfgSTYeIl9EETCvRXNvDBmGPax9jbqW55j4D7N8Op3G3tif4djTn7+k0vXYX0DS/+GhVXdi3/rVt27sP0Ax18m6aQPFUmgF6f9yz+tgwMTvTvP+70QR2fwD+kXuy2GM+RpMhfT7NrWtm0/SeXWpBWPsaTkjylPZ4u7SzT2nLOTawc5ftSLWUZIIOPJIkaTmT5ESa0fbXWcLOTJ1aadUNa80H7t7pMW74wxfOqKq5nR5kKTM3KEnSciTJ6oM6FqS5V+gOwI+nUwCmiVkdKUnS8mUzmnaZP6Fp67oSzSCuO9I009hndEWbSIh5n3EMwiRJWr5cRdPL8yk07cBWoWnH+RWadpODbsumacggTJKk5UhVXcfg+8lOa/aOHM8zIkmSNAJmwiRJUsdiJmwAgzBJktQ5g7DxPCOSJEkjYCZMkiR1KjSDVGhxZsIkSZJGwEyYJEnqmA3zB/GMSJIkjYCZMEmS1K3YO3IQz4gkSdIImAmTJEmdMxM2nmdEkiRpBMyESZKkjgXzPuN5RiRJkkbATJgkSeqcbcLG84xIkiSNgJkwSZLUqThi/kCeEUmSpBEwEyZJkjoX8z7jeEYkSZJGwEyYJEnqnG3CxvOMSJIkjYCZMEmS1K2EJKMuxbRjEDYDZPYqldlrjLoY0rS17WM3HXURpGnrkksuY8GC64yQRsAgbAbI7DVYZeNnjboY0rR1+un7jroI0rQ1d+6LlslxbBM2nkGYJEnqVHCIikE8I5IkSSNgJkySJHXM2xYN4hmRJEkaATNhkiSpc2bCxvOMSJIkjYCZMEmS1LHYO3IAz4gkSdIIGIRJkqTuZVa3j6kOn3wgyWlJbkzy1yRHJXl0z/KVk3wiyTlJbk5yRZKvJ9lsiv3OS1IDHltNVSaDMEmStCKYB3we2AF4KnAncGyS9drlqwPbAvu1z7sCDwKOSTJM861HAZv0PH431Qa2CZMkSd3K6HtHVtVi9/dL8irgBuBJwFFVdQPwjL513gycDzwCOHeKQ1xdVQuWpExmwiRJ0opoLZo46LpJ1lm7fZ5snTGnt1WYxyXZeZgCmAmTJEmdCiHJqIvR7zPA2cDJgxYmmQMcQJMl+8sk+7kC+EfgNGAO8CrguCRPqapfTFYAgzBJkjQTbJDk9J7pg6vq4EErJvkUsCOwY1UtGrB8JeCrwLrACyY7aFVdCFzYM+vkJJsD7wUMwiRJ0mgtg3HCFlTV3CnLkXwaeBmwc1VdPGD5SsA3gMcA86rqmntRllPaY0zKIEySJK0QknwGeClNAHbBgOUrA98EHk0TgF15Lw+1NU015aQMwiRJUudG3TsyyUE07bV2A65LsnG7aGFVLWwzYN8BtgOeD1TPOjdU1S3tfg4DqKpXt9N7A5fQ9KKcA7yyPcbuU5XJIEySJK0I3tI+H9c3f19gPvBAmrHBAM7oW+e1wCHt3/2Dt84BPtlufwtNMLZLVR09VYEMwiRJUscCI+4dWVWTFqCqLgGmLGRVzeub3h/Y/96UyXHCJEmSRsBMmCRJ6lYw7TOAQZgkSere9BusdeSMSyVJkkbATJgkSeqembBxzIRJkiSNgJkwSZLUPdM+43hKJEmSRsBMmCRJ6lagbBM2jpkwSZKkETATJkmSumcibBwzYZIkSSNgJkySJHVvlqmwfmbCJEmSRsBMmCRJ6lgcMX8AM2GSJEkjYCZMkiR1K9g7cgAzYZIkSSNgJkySJHXP3pHjmAmTJEkaATNhkiSpe/aOHMdMmCRJ0giYCZMkSd0zETaOQZgkSepWsGH+AFZHSpIkjYCZMEmS1D0TYeOYCZMkSRoBM2GSJKlz5RAV45gJkyRJGgEzYZIkqVuJvSMHMBMmSZI0AmbCJElS90yEjWMmTJIkaQTMhEmSpO7ZO3IcM2GSJEkjYCZMkiR1z96R45gJkyRJGgEzYZIkqVvB3pEDmAmTJEkaATNhkiSpe/aOHMdMmCRJ0giYCZMkSR2LmbABzIRJkiSNgJkwSZLUrWDaZwCDMEmS1D2rI8cxLpUkSRoBM2GSJKl7JsLGMRMmSZI0AgZhkiSpUwXUrHT6mEqSDyQ5LcmNSf6a5Kgkj+5bJ0nmJ7k8yS1JTkjyqCH2vXuS3yS5rX1+4TDnxSBMkiStCOYBnwd2AJ4K3Akcm2S9nnXeB+wDvB3YDrga+EmStSbaaZLtgW8BXwO2bp+/k+SJUxXINmGSJKlbYeS9I6vqWb3TSV4F3AA8CTgqSYC9gX+vqiPadV5DE4i9AvjCBLveGzi+qvZrp/dLsnM7/+WTlclMmCRJWhGtRRMHXddObwFsDPx4bIWqugX4OU32bCLb927T+tEU2wBmwiRJ0rLQfSJsgySn90wfXFUHT7L+Z4CzgZPb6Y3b56v61rsKeMAk+9l4gm02HrDuYgzCJEnSTLCgquYOs2KSTwE7AjtW1aJuizUxgzBJktS9IXowLgtJPg28DNi5qi7uWXRl+7wR8Oee+Rv1LBvkynadXlNtA9gmTJIkrSCSfIamsfxTq+qCvsV/pAmcntGz/qrAk4GTJtntyb3btJ4xxTaAmTBJktS5jLx3ZJKDgFcBuwHXJRlrs7WwqhZWVSU5EPjnJBcAFwEfAhYCX+/Zz3HAqVX1gXbWZ4CfJ3k/8H3ghcDONNWdkzIIkyRJK4K3tM/H9c3fF5jf/r0/sBpwEHA/4BTgmVV1U8/6DwUuHZuoqpOSvAz4GPCvwB+Al1bVKVMVyCBMkiR1K4z83pFVNWUJqqpoArL5k6yz+YB5hwOHL2mZbBMmSZI0AmbCJElS96ZJ78jpxEyYJEnSCJgJkyRJ3TMTNo6ZMEmSpBEwEyZJkroVmLpv4orHTJgkSdIImAmTJEnds03YOAZhkiSpeyO+bdF0ZHWkJEnSCJgJkyRJ3UqsjhzATJgkSdIImAmTJEndM+0zjqdEkiRpBMyESZKk7tk7chyDMGkJveetT2a3Zz+CLR+yPrfdvohTz/oLH/73Y/nNRVffvc7BB+zGq168zWLbnXrmpTxlty8t6+JK084n//PnfHj/43jza57AgR/dZdTFkUbGIExaQjv9/eZ84bBTOePXl5PAh/d5Kv/79Vez7dMO4robbrl7veN+8Qdev/d3756+/fZFoyiuNK2ccual/L9vnMFjHrHRqIuiZSnYO3KAzoKwJAV8qqr2aaffA6xZVfOTzAG+D6wC/K6q9uqqHO2x9wYOrqq/dXmcKcqwsKrW7JneE5gLXAG8uJ39GODc9u8vA+sBC6vqP5ZhUTWFF7zqfxabft3e3+Wq8z/A9ts9iKOPveju+bfdfidX/XXhsi6eNG3dcOOtvPYdR/CFT+7GfgeeMOriSCPXZcP824AXJdmgf0FV3V5Vz62qp3UdgLX2BlYftCDJ7GVw/AlV1X5VtXVVbQ3cMvZ3VX12lOXS8NZacw6zZ8/i+htuXWz+DnM3409nvpdzTng7B33iBWy4/hojKqE0Pbz1/Ufywuc+kqfssMWoi6IRqKTTx/KoyyDsTuBg4F39C5I8P8kpSc5KcmySjdr56yX5fpJzkvwqyWMHbDs7ySeTnNau9+Z2/rwkJyQ5PMkFSb6WxjuATYHjkxzfrrswyQFJfg1sn+TD7f7OS3JwMv7dTHJIks8mOSnJxUn2aOevmeS4JGcmOTfJrkvxHGo58B/zn8PZ513Br8649O55Pznh97zh3d/juS8/lPd/7EfMfdwD+L9vvoY5c0Ya80sj8+Wvn87Fl1zL/Pc+bdRFkaaNrtuEHQSck2T/vvm/BP6+qirJG4D3AfsA+wJnVdVuSZ4KHAZs3bft64Ebqmq7JKsAJyb5cbtsG+BRwOXAicCTquqzSd4N7FxVC9r11gBO6akq/U1V/Wv79/8AzwOOGvB6NgF2BLYCjgQOB24FXlhVN7ZZv18lObKqagnPlZZDn/iXZ7HDdpvx1N2/zF133fOWf+eo8+7++/wLr+asc6/gwpPexXOe+nf84JjfjqKo0shc9IcFfGT/4zjuiNez8sr+EFlhOSjWOJ0GYW1gchjwDuCWnkUPBL6VZBNgDvDHdv6OwO7ttj9Nsn6Stavqxp5tnwk8diwTBawDbAncDpxaVX8BSHI2sDlNwNdvEXBEz/TOSd5HU2W5HnA+g4Ow71fVXcBvxrJ3NM0N/y3JTsBdwAOAjYArJzwx97jXgVqSNwFvAmD2wJpWdWz/Dz+bPV7waJ790kO45M/XTbruFVfdxGVX3MjDtlh/GZVOmj5OOfNSFlz7N7Z9+kF3z1u06C5+ecqf+NJXT+eaCz7IKqvYT0wrnmVx1R8InAl8pWfe52ga7R+ZZB4wfwn2F+DtVfWjxWY2+7mtZ9YiJn59t1bVona7VYHPA3Or6tIk84FVJ9iud/9jVZb/AGwIPL6q7khyyQTb35JkTlXd3k6vBywYsN5QqupgmupeZs1Zz6zbMvYf85/D7s97FM9+2SFc9Iep38b177c6m268FldcfdMyKJ00vTz/mVtx+k/esti8N+3zfR62xfq8721Ptpp+RWDvyIE6Tw5W1bXAt2mqEcesA1zW/v2anvm/oAlqxoKqBX1ZMIAfAf+YZOV2vb9LMlWL55uAtSZYNhYwLUiyJrDHBOtNZB3g6jYA2xl48ATr/Qx4ZVvm1YCXAMcv4bE0DXz6o7vwqhdvzZ7vOILrb7iVjTZck402XJM1Vp8DwBqrz+HjH3wmT9z2gWz2wHV58t9vzhFffgV/veZmjrQqUiugdddZjUc9fKPFHmusPof7rdvMH9AMV1ohLKv87wHA23qm5wPfSXId8FNgi575X05yDvA3Fg/QxnyJpprxzLYB/V+B3aY4/sHAMUkur6qdexdU1fVJvgicR1OFeNrQr6rxNeCoJOcCpwMXTLDeO4EvtB0FAhxWVT9fwmNpGtjrNU8A4Jhv7rnY/I99+nj2+/QJLFp0F4/aaiNesfvjWHftVbny6oX87OQ/8sq3fJuFN98+YI+SNNPFEfMHiO3Hl3+z5qxXq2z8rFEXQ5q2bvnzvqMugjRtzZ37Ik4//bxOI6RVNt6yNn3lp7o8BJcc8IIzqmpupwdZymwJKUmSumebsHHsMCpJkjQCZsIkSVL3TISNYxAmSZK6FSirI8exOlKSJGkEzIRJkqTumQkbx0yYJEnSCJgJkyRJ3XOw1nHMhEmSJI2AmTBJktStYNpnAE+JJEnSCJgJkyRJHfMG3oOYCZMkSRoBM2GSJKl7jhM2jpkwSZKkETATJkmSuhXMhA1gJkySJGkEzIRJkqTOlb0jxzETJkmSNAJmwiRJUvdM+4zjKZEkSRoBgzBJktSt0IyY3+VjqiIkOyU5MsllSSrJnn3La4LHQZPsc/MJtnn2MKfF6khJkrQiWBM4DzisffTbpG96LnAU8O0h9v1s4Nc909cOUyCDMEmS1LGMfJywqjoaOBogySEDll/ZO51kV+CiqvrZELu/pn/7YVgdKUmSujcr3T6WoiRrAi8DvjjkJt9NcnWSE5PsMexxDMIkSdJMsEGS03seb7oP+3oFMAc4dIr1FgLvAV4CPBc4DvhWklcOcxCrIyVJUrfSPrq1oKrmLqV9vRH4QVX9dbKVqmoBcEDPrNOTbAC8D/jqVAcxEyZJktRKsjVNo/xhqyL7nQJsOcyKZsIkSVKnCqjl5wbebwL+CBx7L7ffGrhimBUNwiRJ0ozXNrZ/WDs5C9iszXpdW1V/btdZHfgHYP+qqgH7+DjwhKp6Wjv9GuAO4CzgLuD5wFuBfxqmTAZhkiSpe6O/gfdc4Pie6X3bx6HAnu28lwJrAF+ZYB+bAA/tm/ch4MHAIuAi4HVVNWV7MDAIkyRJK4CqOoEpugdU1VeYOACjqvbsmz6UqXtQTsggTJIkdSuMfLDW6cjekZIkSSNgJkySJHXPRNg4ZsIkSZJGwEyYJEnqVIBZpn3G8ZRIkiSNgJkwSZLUudEPEzb9mAmTJEkaATNhkiSpWzETNoiZMEmSpBEwEyZJkjoWYipsHDNhkiRJI2AmTJIkdSrYJmwQgzBJktQ5g7DxrI6UJEkaATNhkiSpW4GY9hnHUyJJkjQCZsIkSVLnbBM2npkwSZKkETATJkmSOhVglpmwccyESZIkjYCZMEmS1DnbhI1nJkySJGkEzIRJkqTOmQkbz0yYJEnSCJgJkyRJ3QrEVNg4ZsIkSZJGwEyYJEnqnPeOHM9TIkmSNAJmwiRJUqeCvSMHMRMmSZI0AmbCJElSt2ImbJAlyoQluV+Sx3ZVGEmSpBXFlJmwJCcAL2jXPQO4OsmJVfXujssmSZJmCDNh4w2TCVunqm4EXgQcVlVPBJ7ebbEkSZJmtmHahK2UZBPgJcAHOy6PJEmagWaZCRtnmCDsX4EfAb+sqtOSPAT4XbfFkiRJM4VDVAw2ZRBWVd8BvtMzfTGwe5eFkiRJmukmDMKSvK+q9k/yOaD6l1fVOzotmSRJmjHMhI03WSbst+3z6cuiIJIkSSuSCYOwqjqqfT502RVHkiTNOIHYMn+cYcYJ2xD4J+CRwKpj86vqqR2WS5IkaUYbZpywr9FUTW4B7AtcApzWYZkkSdIMk3T7WB4NE4StX1X/D7ijqn5WVa8DzIJJkiTdB8OME3ZH+3xFkl2Ay4H1uiuSJEmaaZbXbFWXhgnCPpZkHWAf4HPA2sC7Oi2VJEnSDDfMYK0/bP+8Adi52+JIkqSZxhHzB5uyTViSv0tyXJLz2unHJvlQ90WTJElaOpLslOTIJJclqSR79i0/pJ3f+/jVEPt9SpIzktya5OIkew1bpmEa5n8R+ABt27CqOgd42bAHkCRJK7g0N/Du8jGENYHzgHcCt0ywzrHAJj2P5076spItgKOBk4BtgI8Dn0sy1O0dh2kTtnpVnZrF84h3DrNzSZKk6aCqjqYJmEhyyASr3VZVVy7BbvcCLq+qt7fTv03yROA9wBFTbTxMJmxBkofS3j8yyR7AFUtQQEmStIJbTsYJ2zHJ1UkuSvLFJPefYv3tgR/3zfsRMDfJylMdbJhM2FuBg4GtklwG/BH4hyG2kyRJWl4cA3yXJs7ZHPgY8NMkj6+q2ybYZmOaKsxeV9HEVxswRdJq0iAsyWzgLVX19CRrALOq6qapXoUkSVKvDFP3dt9skOT0numDq+rgYTeuqm/2TJ6b5AzgT8AuNMHZUjdpEFZVi5Ls2P59cxcFkCRJWgoWVNXcpbWzqro8yV+ALSdZ7Upgo755G9G0nV8w1TGGqY48K8mRwHeAuwOxquokKpQkSTPL8jhOWJINgAcweZXiycAL++Y9Azi9qu4YsP5ihgnCVgWuYfH7RRYdpeYkSZKWtiRrAg9rJ2cBmyXZGri2fcyn6dF4BU2bsI8DVwPf69nHYQBV9ep21n8Db0tyIPAF4EnAnsDLhynTMCPmv3aYHUmSJA0UyOhTYXOB43um920fhwL/CDwGeDWwLk0gdjzwkr628Jv17rCq/pjkucCn231cDryjqqYcngKGy4RJkiTdJ6OOwarqBJqa0Yk8a4h9zBsw72fAtvemTN33VZAkSdI4ZsIkSVLnRp0Jm46GuYH3O5Osncb/S3Jmkmcui8JJkiTNVMNUR76uqm4EngncD3gV8O+dlkqSJM0oy8lti5apYYKwsZf2XOB/qup8Jm/YJkmSpCkM0ybsjCQ/BrYAPpBkLeCuboulJbHNYzfipFPeNepiSNPWFm8/f9RFkKaty/98a+fHCDDL9M04wwRhrwe2Bi6uqr8lWR9w7DBJkqT7YJjqyAIeCbyjnV6DZhR9SZKkqaXJhHX5WB4NE4R9Htiee4bgvwk4qLMSSZIkrQCGqY58YlVtm+QsgKq6LsmcjsslSZJmiKZNWI26GNPOMJmwO5LMpqmWJMmG2DBfkiTpPhkmE/ZZmjuI3z/JfsAewIc6LZUkSZpRltd2W12aNAhLMgv4I/A+4Gk0GcXdquq3y6BskiRJM9akQVhV3ZXkoKraBrhgGZVJkiTNMMO0f1rRDHNOjkuye7K83hRAkiRp+hmmTdibgXcDi5KMDatbVbV2d8WSJEkzhb0jB5syCKuqtZZFQSRJklYkw2TCSPICYKd28oSq+mF3RZIkSTONvSPHm7JNWJJ/B94J/KZ9vDPJx7sumCRJ0kw2TCbsucDWVXUXQJJDgbOAD3RZMEmSNDMEe0cOMlR1JLAucG379zrdFEWSJM1Iy/FNtrs0TBD2ceCsJMfTBLM7Ae/vtFSSJEkz3DC9I7+R5ARgu3bWP1XVlZ2WSpIkzShxiIpxJgzCkmzbN+sv7fOmSTatqjO7K5YkSdLMNlkm7IBJlhXw1KVcFkmSNAM1g7WOuhTTz4RBWFXtvCwLIkmStCIZdrDWRwOPBFYdm1dVh3VVKEmSNLM4RMV4UwZhST4CzKMJwo4GngP8EjAIkyRJupeGyYTtATwOOKuqXptkI+Cr3RZLkiTNFKG8gfcAw2QHb2lHy78zydrA1cCDui2WJEnSzDZMJuz0JOsCXwTOABYCJ3dZKEmSNLPYO3K8ycYJOwj4elW9pZ3130mOAdauqnOWSekkSZJmqMkyYRcB/5FkE+DbwDeq6qxlUyxJkjST2DtyvAnPSVV9pqq2B54CXAN8OckFST6S5O+WWQklSZJmoCkD06r6U1V9oqq2AV4O7Ab8tuuCSZKkmSFp2oR1+VgeTRmEJVkpyfOTfA34P+BC4EWdl0ySJGkGm6xh/jNoMl/PBU4Fvgm8qapuXkZlkyRJM4TjhI03WcP8DwBfB/apquuWUXkkSZJWCJPdwPupy7IgkiRpZgrLb7utLtljVJIkaQSGGTFfkiTpPjHrM55BmCRJ6pwN88czMJUkSRoBM2GSJKlTNswfzEyYJEnSCJgJkyRJ3VqOby3UJTNhkiRJI2AQJkmSOhWagKPLx5RlSHZKcmSSy5JUkj17lq2c5BNJzklyc5Irknw9yWZT7HNeu6/+x1bDnBeDMEmStCJYEzgPeCdwS9+y1YFtgf3a512BBwHHJBmm6dajgE16Hr8bpkC2CZMkSZ0b9ThhVXU0cDRAkkP6lt0APKN3XpI3A+cDjwDOnWL3V1fVgiUtk5kwSZKk8dZun68bYt3T2yrM45LsPOwBzIRJkqTOLU+9I5PMAQ4Ajqqqv0yy6hXAPwKnAXOAVwHHJXlKVf1iquMYhEmSpJlggySn90wfXFUHL+lO2jZgXwXWBV4w2bpVdSFwYc+sk5NsDrwXMAiTJEmjNdY7smMLqmrufdlBG4B9A3gMMK+qrrkXuzkFeNkwKxqESZKkFV6SlYFvAo+mCcCuvJe72pqmmnJKBmGSJKlzo24TlmRN4GHt5CxgsyRbA9cClwPfAbYDng9Uko3bdW+oqlvafRwGUFWvbqf3Bi6h6UU5B3glsBuw+zBlMgiTJEkrgrnA8T3T+7aPQ4H5NGODAZzRt91rgUPav/sHb50DfBJ4IM3YY+cDu7TDYUzJIEySJHUuox8n7ASa5mkTmTJXV1Xz+qb3B/a/t2VynDBJkqQRMBMmSZI6lYy+Tdh0ZCZMkiRpBMyESZKkzpn1Gc9zIkmSNAJmwiRJUqdCMWvEvSOnI4MwSZLUORvmj2d1pCRJ0giYCZMkSZ0zEzaemTBJkqQRMBMmSZI6FWD2qAsxDZkJkyRJGgEzYZIkqXMOUTGemTBJkqQRMBMmSZI65Q28BzMTJkmSNAJmwiRJUufMhI1nJkySJGkEzIRJkqROBZhtJmwcM2GSJEkjYCZMkiR1zjZh45kJkyRJGgEzYZIkqVPBEfMHMRMmSZI0AmbCJElStxwxfyAzYZIkSSNgJkySJHUqwOxRF2IaMgiTJEmdszpyPKsjJUmSRsBMmCRJ6pxDVIxnJkySJGkEzIRJkqROeQPvwcyESZIkjYCZMEmS1Dl7R45nJkySJGkEzIRJkqROxdsWDWQmTJIkaQTMhEmSpM6ZCRvPTJgkSdIImAmTJEmdasYJc8T8fmbCJEmSRsBMmCRJ6pxZn/EMwqSl4OMHnswnPvOrxebdf4PVuei0N4+oRNJoPeGhq/PGp63Pox+0GhuvuzLv+eplHHHK9Xcv32Ct2fzTrhvx5K3WZO3VZnPq729m/uFXcslfbx9doaVlzCBMWkq2fMj9+OE3X3z39Gy7AmkFtvoqs7jwitv47qk3cMCrHjBu+RfeuBl3Fbz5i5dy0y2LeP3O6/PVtz2YZ+z3e2653bZDM02wd+Qgy2V2MMmiJGf3PN4/xfrzkuywrMo3QRkOSbJH37yFSR7T8zquTfLH9u9jk2ye5LxRlVlLZqWVZrHRhmvc/dhg/dVHXSRpZE74zUL+46ir+b+zb+SuWjyo2mLDOWy7xep8+NuX8+s/3cLFV9/Oh759BausPIsXPH6dEZVYWvaW10zYLVW19RKsPw9YCJzUvyDJSlV151Iq1xKrqnOBrduyHAL8sKoOb6c3H1W5tOQu+fMNbPXEg5kzZzZzt96YD7/3SWy+2bqjLpY07cxZqUmJ3HbHPcFZFdx+ZzH3oavzrZOvH1HJ1CUzYeMtl5mwiSS5JMm+Sc5Mcm6SrdpAZi/gXW2G6cltVuq/k5wC7J/kCUlOTnJWkpOSPHzAvuclOSHJ4UkuSPK1JGmXfTjJaUnOS3Lw2HytOOZuvTGf/+QzOfyQF/LZjz+dq/76N565+7e49rpbRl00adr5w1W3cdm1t/Oe52/EOqvPZuXZ4c1P34BN77cy91975VEXT1pmltdM2GpJzu6Z/nhVfav9e0FVbZvkLcB7quoNSf4bWFhV/wGQ5PXAA4EdqmpRkrWBJ1fVnUmeDvwbsPuA424DPAq4HDgReBLwS+A/q+pf233/D/A84Kil/Jo1jT1j3haLTc/dZhO2fsqX+foRv+Ftb3j8iEolTU933gV7felSPvGKTTn7E1tx56LixAsXcvz5N+FP2JkpKccJG2B5DcImq478bvt8BvCiSfbxnapa1P69DnBoki2BAib6KXZqVf0FoA0CN6cJwnZO8j5gdWA94HzGB2GDrr57fUUmeRPwJoAHbbbxvd2NOrLmGnPYasv1ufiS60ddFGlaOu/SW9nlExez1qqzWHmlcO3CRXxvny0498+3jrpo6sioqyOT7AS8B3g8sCnw2qo6pGd5gI/Q/G+9H3AK8NaqOn+K/e4OfBR4KPAH4INV9b1hyjSjqiNbt7XPi5g8yLy55++PAsdX1aOB5wOrTrHvu/efZFXg88AeVfUY4IsTbH8NzZsKQJL1gAWTlG9SVXVwVc2tqrkbbrjuvd2NOnLrbXfyuz9cy0b3X2PURZGmtZtuvYtrFy5i8w3n8JjNVuMn59446iJp5loTOA94JzCorcj7gH2AtwPbAVcDP0my1kQ7TLI98C3gazTtu78GfCfJE4cp0PKaCVtSNwFrT7J8HeCy9u89l3DfYwHXgiRrAnsAhw9Y7wRg7ySHVtXt7XGOX8JjaZr60H4/59lPewgPfMBaLFjwNz75n6fwt1vu5OUveuSoiyaNxOpzZvHgDecAMCth0/utzCMesCo3/G0Rl193B8/dem2uvflOLrv2DrbadFU+vPvG/Picm/jFBTdPsWctj6bDEBVVdTRwNNzdEe5ubRZsb+Dfq+qIdt5raAKxVwBfmGC3e9MkcfZrp/dLsnM7/+VTlWl5DcL624QdU1WTDVNxFHB4kl1pItx++9NUR34I+N8lKUhVXZ/kizTR9ZXAaROs98MkjwfOSLKIJmW515IcS9PX5VfexBveeTTXXHcLG6y3GnO32YSffPdlbPbAyWJ/aeZ6zGar8s133tNW8t273J9373J/Dj/lOt771cu5/zor8cEXbcwGa83mrzfeyXdPvYHPHfPXEZZYK7gtgI2BH4/NqKpbkvwc2IGJg7Dtgc/1zfsR8LZhDpoqG8ot7x4/9xF10imHjroY0rS11d6XTb2StIK6/Fvv5barf99pnmrLx25ZB/7vZ7s8BM/b7Ll/YvFmPgdX1cGD1k2yEHjbWJuwdizRE4EHV9Wfe9b7MvCAqnrWBPu5HXhDVR3WM+/VwBerapWpyry8ZsIkSZJ6LaiquaMuxJIwCJMkSZ0KMHt6Dz9yZfu8EfDnnvkb9SybaLuN+uZNtc3dZmLvSEmSpCXxR5rA6RljM9rRD57MgLvt9Di5d5vWM6bY5m5mwiRJUrcCs0Y8WGs7gsHD2slZwGZJtgaurao/JzkQ+OckFwAXAR+iueXh13v2cRzNmKEfaGd9Bvh5ew/r7wMvBHYGdhymTGbCJEnSimAucFb7WA3Yt/37X9vl+wOfBg4CTgc2AZ5ZVTf17OOh7XwAquok4GU0w06dA7waeGlVnTJMgcyESZKkzo0661NVJ9A0T5toeQHz28dE62w+YN7hDB4fdEqjPieSJEkrJDNhkiSpU9NhxPzpyEyYJEnSCJgJkyRJnZvm44SNhJkwSZKkETATJkmSOtW0CfNe1f3MhEmSJI2AmTBJktQ5e0eOZyZMkiRpBMyESZKkTiVmwgYxCJMkSZ2z6m08z4kkSdIImAmTJEmdi9WR45gJkyRJGgEzYZIkqXMmwsYzEyZJkjQCZsIkSVKngm3CBjETJkmSNAJmwiRJUufM+oznOZEkSRoBM2GSJKlzSY26CNOOmTBJkqQRMBMmSZI6Z+fI8cyESZIkjYCZMEmS1CnHCRvMTJgkSdIImAmTJEmdMxE2npkwSZKkETATJkmSuhWYZSpsHDNhkiRJI2AmTJIkdc5E2HhmwiRJkkbATJgkSeqU44QNZhAmSZI6Zww2ntWRkiRJI2AmTJIkdc5M2HhmwiRJkkbATJgkSeqcg7WOZyZMkiRpBMyESZKkTgXbhA1iJkySJGkEzIRJkqTOJTXqIkw7ZsIkSZJGwEyYJEnqnG3CxjMTJkmSNAJmwiRJUrfiDbwHMRMmSZI0AgZhkiSpU6EJOLp8TFmG5JIkNeDxv5NsM2j9ve7FKRjI6khJkrQi2A6Y3TO9CXAG8O0ptnsj8MOe6RuWVoEMwiRJUudG3Sasqv7aO53k9cCNTB2EXV9VV3ZRJqsjJUnSCiVJgNcDX62qW6ZY/TNJFiQ5LcleSZZa7GQmTJIkdW4ZJMI2SHJ6z/TBVXXwBOs+A9gC+OIU+/wwcDywEHgacACwAfCx+1hWwCBMkiTNDAuqau6Q674ROK2qfj3ZSlX10Z7Js5PMBj6IQZgkSVpejLpN2Jgk9wd2Bd56LzY/BVg7yUZVddV9LYtBmCRJ6tw0icEA9gRuA75xL7bdGrgVuH5pFMQgTJIkrRDaBvlvAL5ZVQv7lr0NeFtVbdVOPx/YGDgZuAXYGfhXmrZmty2N8hiESZKkTgWYNT1SYfOALYFXDli2AfDwnuk7gLcAn6IZTeJimob6By2twhiESZKkFUJVHc8ENaNVNR+Y3zN9DHBMl+UxCJMkSZ2bHomw6cXBWiVJkkbATJgkSepYkdSoCzHtmAmTJEkaATNhkiSpc7YJG89MmCRJ0giYCZMkSZ1Kps9ti6YTM2GSJEkjYCZMkiR1zkTYeGbCJEmSRsBMmCRJ6pxZn/E8J5IkSSNgJkySJHXO3pHjmQmTJEkaATNhkiSpY8H+keOZCZMkSRoBM2GSJKlTTR7MTFg/gzBJktS5xMq3fp4RSZKkETATJkmSlgGrI/uZCZMkSRoBM2GSJKljsWH+AGbCJEmSRsBMmCRJWgbMhPUzEyZJkjQCZsIkSVLnHCdsPM+IJEnSCJgJkyRJy4BtwvqZCZMkSRoBM2GSJKlTcZywgcyESZIkjYCZsBngzDMuWLDqSk/806jLobttACwYdSGkaczPyPTy4GVxEDNh4xmEzQBVteGoy6B7JDm9quaOuhzSdOVnRGoYhEmSpGXAFlD9PCOSJEkjYCZMWvoOHnUBpGnOz8gKKLFNWD8zYdJSVlX+g5Em4WdEapgJkyRJHQuOmD+eQZiWK0kK+FRV7dNOvwdYs6rmJ5kDfB9YBfhdVe3VcVn2Bg6uqr91eZwpyrCwqtbsmd4TmAtcAby4nf0Y4Nz27y8D6wELq+o/lmFRNQ0lWcQ91wbAN6vq3ydZfx5we1Wd1HHRJpTkEOCHVXV4z7yFwPbA/7SzNgNuaB8LgDe02zx62ZZWvRyiYjyDMC1vbgNelOTjVbXYOENVdTvw3GVYlr2BrwLjgrAks6tq0TIsy2Kqaj9gv7YsC6tq656yzR9RsTT93NJ7bQxhHrAQGBeEJVmpqu5cSuVaYlV1LrB1W5ZD6AnUkmw+qnJJk7FNmJY3d9I06n1X/4Ikz09ySpKzkhybZKN2/npJvp/knCS/SvLYAdvOTvLJJKe16725nT8vyQlJDk9yQZKvpfEOYFPg+CTHt+suTHJAkl8D2yf5cLu/85IcnAGtUpMckuSzSU5KcnGSPdr5ayY5LsmZSc5NsutSPIfSpJJckmTfnutvqzaQ2Qt4V5Kzkzy5vX7/O8kpwP5JnpDk5PYzeFKShw/Y98DPVLtsys+MlmezOn4sf5bPUmtFdxDwD0nW6Zv/S+Dvq2ob4JvA+9r5+wJnVdVjgX8GDhuwz9cDN1TVdsB2wBuTbNEu24Ym6/VI4CHAk6rqs8DlwM5VtXO73hrAKVX1uKr6JfCfVbVdWwWyGvC8CV7PJsCO7fKxqqBbgRdW1bbAzsAB/kNSB1ZrA6qxx0t7li1or7//At5TVZcA/w18uqq2rqpftOs9ENihqt4NXAA8uf0Mfhj4twmOO+4z1c4f9jMjzQhWR2q5U1U3JjkMeAdwS8+iBwLfSrIJMAf4Yzt/R2D3dtufJlk/ydpVdWPPts8EHjuWiQLWAbYEbgdOraq/ACQ5G9icJuDrtwg4omd65yTvA1anaYd1PnDUgO2+X1V3Ab8Zy97RtGD9tyQ7AXcBDwA2Aq6c8MTco4ZYR4LJqyO/2z6fAbxokn18p6fqfR3g0CRb0lyHK0+wzUSfqWE+M4Oub6/55YBtwsYzE6bl1YE02as1euZ9juaX9GOANwOrLsH+Ary9/YW/dVVtUVU/bpfd1rPeIib+8XLr2D+jJKsCnwf2aMvzxUnK07v/sW+pfwA2BB7f/pO8aoLtb2k7JIxZD+/Jp6Vj7Lqc7JoHuLnn748Cx7eZrOcz3DW/CFhpCT4z1wD3G5tI4jWv5ZZBmJZLVXUt8G2aQGzMOsBl7d+v6Zn/C5qgZqx314K+LBjAj4B/TLJyu97fJVmDyd0ErDXBsrF/HguSrAnsMcF6E1kHuLqq7kiyMxPfYPdnwCvbMq8GvAQ4fgmPJQ1rsmseFv8M7rmE+x72M3MC8NKeHx974jU/7SXp/LE8MgjT8uwAYIOe6fnAd5KcweK/jOcDj09yDk2bq94AbcyXgN8AZyY5D/gCU1fXHwwcM9Ywv1dVXU/zS/48mgDvtKlfzmK+BsxNci7wapq2NoO8k6a36NnAr2iqhn6+hMfSiqu/TdiEw1O0jgJeONYwf8Dy/YGPJzmLJWzuMuxnpqp+SPPD6oz2un8S8E9LcixpukiVVemSJKk7j5/7iDrxlK90eozVVtr+jKqa2+lBljIzYZIkacZLMj9J9T0m7eyU5DFJfpbkliSXtcOoLLW6T3tHSpKkzmV65H0upBl0eMyEg2onWRv4CfBzmqGLtgK+QtMZ5YClURiDMEmStKK4s6qGGeoHmg5dqwOvqapbgPOSbAW8O8mnaim055oWYakkSZrp0vFjKA9JcnmSPyb5ZpKHTLLu9sAv2gBszI9o7pay+bAHnIxBmCRJWhGcQjOkybOBNwIbAyclWX+C9TemGaOx11U9y+4zgzBJ90mS45M8q2/e3kn+a5JtTkiyXPViGtNb9iRHJ1m3fbxl1GWTpq9lMk7YBklO73m8qbcEVfV/VfXtqjqnqo6luS3WLAYPW7RMGIRJuq++Abysb97L2vlLRZLZS2tfE+z/XrWPrarntuNbrQsYhEmjtaCq5vY8Dp5s5apaSHNrrC0nWOVKmtvF9dqoZ9l9ZhAm6b46HNhlbATzJJvTtJn4RZL/an+Rnp9k30EbJ3l5knOTnJfkEz3zFyY5IMmvge2TvDLJqe1AoV9IMrt9HNJue26Sdw3Y/yFJ/rstx0VJntfO3zPJkUl+ChyXZI0kX26PcVaSXdv1Vmvbjvw2yfdobiw9tu9LkmxAMwjwQ9uyfTKNT/aU66Xt+psk+Xm73nkTDHgqzVDTok3YPaVpbpW1FXDFBKucDDy5XW/MM4DLgUuW+IAD2DtS0n1SVdcmORV4DvADmizYt6uqknywXT6bJtB5bFWdM7Ztkk2BTwCPB64Dfpxkt6r6Ps19QU+pqn2SPIJmVPQntbdy+jxNz6XzgQe09yokyboTFHNz4AnAQ4Hjkzysnb8t8Ni2jP8G/LSqXtfu59Qkx9Lch/RvVfWIJI8Fzhyw//cDjx67GXaS3YGtgcfR3NXhtCQ/B14B/Kiq9mvPyerDnWVJ91WS/6C568OfgfsD/0LzPXNou/zjwBOq6mntJl8HPgIckuRjwN/RfNb3XRo9I8FMmKSlo7dKsrcq8iVJzgTOAh4FPLJvu+2AE6rqr1V1J83tmnZqly0Cjmj/fhpNoHZamlvVPA14CHAxTW+nzyV5NtB/T9Ax366qu6rqd+02W7Xzf9LehxTgmcD72/2fQHMvw83a8nwVoA0gz2FqOwLfqKpFVXUVzT0+t6O5Fc9rk8wHHlNVNw2xL2lGCLM6fQzhgTTfTRcC36W5kfzfV9Wf2uWb0PxQA6CqbqDJfG0KnA4cRDM+2KeW1jkxEyZpafgB8Okk2wKrV9UZSbYA3gNsV1XXJTmEe27SPIxbq2psIMUAh1bVB/pXSvI44FnAXjQ3MH/dgH31/2odm765d1fA7lV1Yd/+l6DIk6uqnyfZCdiF5tf1p6rqsKV2AEkTqqr+tqv9y/ccMO9c7vlhuNSZCZN0n7UNXI8Hvsw9WbC1aYKcG5JsRFNd2e9U4ClJNmir515OkzXqdxywR5L7AyRZL8mD2/ZYs6rqCOBDNNWLg7w4yawkD6XJoF04YJ0fAW9PG3Ul2aadP1aNSJJHA48dsO1NwFo9078AXtq2WduQ5kv81CQPBq6qqi/S3DR+ovJKM9D0ahM2HZgJk7S0fAP4Hm21ZFX9OslZwAXApcCJ/RtU1RVJ3k8TwAX436r6wYD1fpPkQzRtxmYBdwBvBW4BvtLOAxiXKWv9mSbgWxvYq6puHZDh+ihwIHBOu78/0nRh/6/2GL8FfgucMaB81yQ5Mcl5wP8B76MZ6PHXNFm391XVlUleA7w3yR3AQuDVE5RX0gogS6ltmSRNS2016A+r6vBRl0VaUT1+7qPqlFO/3ukxVp699RlVtVyNP2gmTJIkdSos3faVM4VBmKQZbVBjW0maDgzCJEnSMmBfwH6eEUmSpBEwEyZJkjqX5XQYiS6ZCZMkSRoBM2GSJKljy++Aql0yEyZJkjQCZsIkSVLnHCdsPDNhkiRJI2AmTJIkLQPmffp5RiRJkkbATJgkSeqc44SNZyZMkiRpBFJVoy6DJEmawZIcA2zQ8WEWVNWzOz7GUmUQJkmSNAJWR0qSJI2AQZgkSdIIGIRJkiSNgEGYJEnSCBiESZIkjcD/B6OzAofVU2dOAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "f.grafico_matriz_confusao(modelo_gradient_boosting,'Matriz de confusão - Gradient Boosting',dados)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sx0fXUFC6FfS",
        "outputId": "72305ba7-5957-4aac-db45-e5d520d4e4ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC médio 0.7935344827586208\n",
            "Intervalo 0.6784164414715169 - 0.9086525240457247\n"
          ]
        }
      ],
      "source": [
        "f.roda_n_modelos(modelo_gradient_boosting,dados,50)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No geral este modelo já apresentou resultados melhores do que o anterior. "
      ],
      "metadata": {
        "id": "QGBl4gjMS3jw"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y78ebLq1BqH1"
      },
      "source": [
        "#### <font color= MediumSpringGreen> 1.1.3  RandomForestClassifier\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seguirei com as etapas já aplicadas nos modelos anteriores."
      ],
      "metadata": {
        "id": "qwU94BwsXixu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FmOLI_yf3r_P"
      },
      "outputs": [],
      "source": [
        "modelo_random_forest = RandomForestClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "APNp48-x5jbD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "bc0ce988-0db3-49db-8234-995597f8a24e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             AUC  F1-score_0  F1-score_1  Acurácia\n",
              "RandomForestClassifier  0.889368    0.852459         0.8  0.830189"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AUC</th>\n",
              "      <th>F1-score_0</th>\n",
              "      <th>F1-score_1</th>\n",
              "      <th>Acurácia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.889368</td>\n",
              "      <td>0.852459</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.830189</td>\n",
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              "      border: none;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0db2de40-0f16-439e-a2e8-85a934af28ca button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0db2de40-0f16-439e-a2e8-85a934af28ca');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "random_forest = f.roda_modelo(modelo_random_forest,dados,'RandomForestClassifier')\n",
        "random_forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "3G9KnBA3qeGi",
        "outputId": "74ba0d09-a41e-4a0a-edb5-98d3a74f3ef4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "f.grafico_matriz_confusao(modelo_random_forest,'Matriz de confusão - Random Forest',dados)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lshTWeaW6Fmq",
        "outputId": "17ae438f-f7ae-46d3-c4e7-02d74f002577"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC médio 0.799353448275862\n",
            "Intervalo 0.679210924769189 - 0.919495971782535\n"
          ]
        }
      ],
      "source": [
        "f.roda_n_modelos(modelo_random_forest,dados,50)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### <font color= MediumSpringGreen> 1.1.4  DummyClassifier\n"
      ],
      "metadata": {
        "id": "_Uff79cGXjlG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como forma de comparar os resultados com o um modelo mais simples, farei os testes também com o Dummy Classifier."
      ],
      "metadata": {
        "id": "V4TR7U22XnR-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "O Random Forest também teve resultados bons. \n",
        "\n",
        "Para a segunda parte, irei otimizar os parâmetros para que tenham resultados melhores.\n",
        "Tanto para o Random forest, quanto para o Gradient Boosting eu irei otimizar com os melhores parâmetros."
      ],
      "metadata": {
        "id": "ADYT5gCYVIQK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelo_dummy = DummyClassifier()"
      ],
      "metadata": {
        "id": "gj8TijBUX1s0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dummy = f.roda_modelo(modelo_dummy,dados,'DummyClassifier')\n",
        "dummy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "S7xix2zmX4sq",
        "outputId": "4b04ebee-abf5-41d1-c44f-1b741f285360"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 AUC  F1-score_0  F1-score_1  Acurácia\n",
              "DummyClassifier  0.5    0.707317         0.0   0.54717"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12219dd5-19bb-4ccf-a2f0-a77426a262c2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AUC</th>\n",
              "      <th>F1-score_0</th>\n",
              "      <th>F1-score_1</th>\n",
              "      <th>Acurácia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.707317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.54717</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12219dd5-19bb-4ccf-a2f0-a77426a262c2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-12219dd5-19bb-4ccf-a2f0-a77426a262c2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-12219dd5-19bb-4ccf-a2f0-a77426a262c2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f.grafico_matriz_confusao(modelo_dummy,'Matriz de confusão - Dummy',dados)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "HkD84VM_X4nd",
        "outputId": "35f74a8c-1248-4e94-bbe1-9b979bb8355d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ],
            "image/png": 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aqUIGeMqCgZ4kaUZJsibNBJh1adbi/Cvwvb42SpqmDPQkSTPNo2muf30rzcSmNy/EouXSAwzyGD0DPUnSjFJVx3Pfpd4kLYCBniRJGmIZ6Ize4J6ZJEnSkDOjJ0mShpoZPUmSJM04ZvQkSdLQCs1KeoPKjJ4kSdKAMqMnSZKGmLNuJUmSNAOZ0ZMkScMrzrqVJEnSDGRGT5IkDTUzepIkSZpxzOhJkqQhFgY572WgJ0mShppdt5IkSZpxzOhJkqShFRdMliRJ0kxkRk+SJA21DHDea3DPTJIkaciZ0ZMkSUPNMXqSJEmacczoSZKk4ZWQpN+t6BkzepIkSQPKjJ56JrOXqsxett/NkKat9Z/0yH43QZrWTjvt7/OqaqVeH2eQx+gZ6KlnMntZllp1i343Q5q2Tj31w/1ugjStJY+9uN9tmOkM9CRJ0tAKrqMnSZKkGciMniRJGmJe61aSJEkzkBk9SZI01MzoSZIkacYxoydJkoZYnHUrSZKkmceMniRJGm4DPEbPQE+SJA2vOBlDkiRJM5AZPUmSNLRCSNLvZvSMGT1JkqQBZUZPkiQNNZdXkSRJ0oxjRk+SJA01Z91KkiRpxjHQkyRJQyyQHt4mOnrygSSnJLkxyTVJfpzkCaP2OSxJjbqd2M3ZGehJkiT1z+bAgcAmwH8D84FfJ1lx1H6/Bh7Rcdu6m8odoydJkoZX6Gvaq6q26Hye5HXADcAzgR93bLqjqq5a2PrN6EmSJE0fD6GJz64bVb5pkrlJzk1ySJKVu6nMjJ4kSRpu0+vKGF8AzgD+3FH2c+Bo4EJgbeBjwHFJNqiqOxZUmYGeJElS78xJcmrH84Or6uCxdkzyOWBTYNOqunukvKq+07HbmUlOAy4GXkATAI7LQE+SJA233mb05lXVhhM3IfsDrwaeXVUXLGjfqroiyWXAuhPVa6AnSZLUR0m+ALyKJsj7Zxf7zwFWA66caF8DPUmSNNz6ODU1yZeB1wHbANclWbXddHNV3ZxkOWBf4Ac0gd3awCeBucD/TVS/s24lSZL65y00M21/QxPIjdx2b7ffDTwROAY4F/gmcA7wjKq6aaLKzehJkqThFag+zrqtqgUevKpuA7ZY0D4LYkZPkiRpQJnRkyRJw21aLaM3uczoSZIkDSgzepIkabjNGtyUnoGeJEkaYplul0CbVHbdSpIkDSgzepIkaXgFJ2NIkiRp5jGjJ0mShtsAT8YwoydJkjSgzOhJkqTh5qxbSZIkzTRm9CRJ0nAb3ISeGT1JkqRBZUZPkiQNr+CsW0mSJM08ZvQkSdJwG9yEnhk9SZKkQWVGT5IkDbVyHT1JkiTNNGb0JEnS8EqcdStJkqSZx4yeJEkaboOb0DOjJ0mSNKjM6EmSpOE2wLNuDfQkSdJwczKGJEmSZhozepIkaXgFJ2NIkiRp5jGjJ0mShtsAT8YwoydJkjSgzOhJkqQhFjN6kiRJmnnM6EmSpOEVBjrtNcCnJkmSNNzM6EmSpOHmGD1JkiTNNGb0JEnScBvchJ4ZPUmSpEFlRk+SJA2tAmrW4Kb0zOhJkiQNKDN6kiRpeAVn3UqSJGnmMaMnSZKG2+Am9MzoSZIkDSozepIkabgN8KxbAz1JkjTE4mQMSZIkzTxm9CRJ0vAKTsaQJEnSzGNGT5IkDbcBnoxhRk+SJGlAmdGTJEnDzYyeJEmSZhozepIkaXgFanATemb0JEmSBpUZPUmSNNwcoydJkqSZxoyeJEkabl7rVpIkSTONGT1JkjS8EsfoSZIkaeYxoydJkobbAKe9BvjUJEmShpsZPWmA7P7WZ7HNlo9n3Uc/nDvuvJuTT7+MvT/1a84+d+69+6w8Z1k+9oHn8dzN1mH5hy7NH066mHfvfSznX3RtH1su9ddBh5/M/gf9kavm3sx/rrsSn95nKzZ92lr9bpamirNup1aSjZLMS3J4krWT/LHfbZJmgs2evjYHHX4yz37poWz16sO4e/49/PTI1/Ow5Ze5d5/vHrItj3nUw3nlTt/h6Vt9lUsuv4Fjj3wDD15miT62XOqf7/3oLHbf92fs8dZnceKxu/K0DddgmzccwSWXX9/vpkmLbcoCvSSHJakke40q37wtn9NR/CLgQ8DFwPHAgVPVzgVpg85KsmG/27Kwkhyf5EtjlG+f5Ob28UXt+Y13O75jv92n+BTUhRe/7lt863tncPa5c/n7OXPZcbejWenhy/KMjdYA4DGPejhP22AN3vmhn3DqXy/nXxf8m3d88CcsvfSDeOVLntjn1kv9ccDX/sTr/ucp7PiaDXncuiux/0dewKorL8ch3zql303TVAjNrNte3fpsqjN6twPvTbLSgnaqqr2r6qCq2quq1q6q/zdF7ZsUSZbsdxsW0UbAI9rblm3Zxh1lL+tTu7SIHrLcksyePYvrb7gdgKWWnA3A7XfMv3efquLOO+9mk43W7EsbpX668875nH7mlTxns8fcr/y5m63Diadd2qdWaapV0rNbv011oPdb4CJgr/F2SDI7yaFJLkxyW5J/JdkjyayOfWYl2SvJpUnuSHJmkpdMdPAkOyQ5O8ntSc5N8q5R9VaSXZJ8L8ktSS5Isl1HFRe296eMynAdluQnSd6X5DLgsrZ8uySnJLkpydy23tUmaOPxSQ5M8om2+3pukv1GtXOh6+1GVV1TVVdV1VXAv9vie8uqykFcM8x++27FGWddee8vrHPOn8cll13PR/Z4Lg9bfhmWWGI273nzpqz+yOVZdeWH9Lm10tSbd+2t3H33Payy0rL3K195znJcfc3NfWqVNHmmOtC7B3g/sGuSdcbZZxZwOfBK4PE0XbgfBHbo2OedwHuB9wFPBP4PODrJU8Y7cJKdgU8Ae7f1vqd9/VtG7bo3cAzwZOAo4OtJRlIdG7f3W/LADNd/AU9qtz2nLVsS2Ket64XAHODb47Wxw2uB+cAmwNuA3YBXdWxf1Ho1RP53ry3YZKM12XbXo7jnngJg/vx7ePWbvsOj1noYV5z5fq4950Ns9oy1+flx5967jyQNnVk9vPXZlM+6rapj28kVHwdePcb2u2iCrREXJVkf2BY4tC3bHdivqo5sn++dZLO2vDMD12kvYI+q+n77/MIkn6IJ9DrHrn2rqo4AaMcTvhPYDDgCuKbd599t1qvT7cCOVXVHx7l8vWP7BUneDPwjyepVddk47QQ4u6pG3oNz2yD1ObTB3GLU23NJdgF2AWD2g/vZlKH26b235H9e/AS2fNVhXHTJdffbdvqZV/L0rb7KQx+yFEsuMZt5197KCcfszGl/u6JPrZX6Z86KD2b27Flcfc0t9yufO+9mVllpuT61Spo8/Yo13we8IskGY21MsmuSU5Nc004UeBewZrvtocAjgdEzcf8A/Oc49a0ErAEclOTmkRvwKWB0ZvFvIw+qaj5NcLdyF+d0VmeQ1x53/STHJLk4yU3Aqe2miQZD/W3U8ys627AY9fZcVR1cVRtW1YaZtVS/mzOU9tt3K17x4iew1asP49zz542734033cG8a29lnbVXZP0nPZKf/PKfU9hKaXpYcskH8dQnPoLjfn/+/cp/8/sLePoGa/SpVZpSAz4Zoy/r6FXVyUl+AHwa+GjntiSvAj5Pk537E3Aj8Fbgpd1UPU75SEC7a1vngtw1Rp3dBMT3+3MwybLAL4BfA68D5tJ0sf6eput1kdqwGPXeCCw/RvkKwA0TtEczxP4ffQGvedmTeOXO3+H6G26/NyNx8y13csutdwLwshf8J/OuvZVLLr+BJzx2Zfbbdyt+/It/8ptRv+ikYfGOnTbhje86mg2fshrP2HBNDjniFK68+iZ22m6jfjdNWmz9XDD5g8DZ3De7c8SmwElVdW93aud4vqq6MckVwDOB34x63dljHaiqrm5fs05VHb4Ybb6zvZ/dxb6PownAPlhVFwIkmYxZq4ta7znA1klSVZ0B8frtNg2AXd/QDCP9+Xe2v1/5x/b/LR/f/3gAVl35IfzvXluy8pxluWruzfy/H/yVTx7wuyluqTR9vOLFT+Da62/lU188gavm3sR6/7EyP/zma1lr9RX63TRNiQz0gsl9C/Sq6rwkB9OMget0LrB9kq2A82jG8f0X0DnQ6DPAR5L8CziNZlzes2iClvHsA3wxyfXAscAS7f6rVdUnu2z2XOA2YIskFwG3V9V42bBLgDuAtyX5Ms0EkI+Os+/CWNR6v0IzseOLSQ6hGVO4Nc3YxxdPQrs0DSyz5j4T7nPgN07iwG+cNAWtkWaON71+Y970+o0n3lGaZEk+QDO587E0v99PBD5QVWd17BOaOGYX4GHAScBbq+rvE9Xf7/kgH6GZXdrpIOC7wJHAKcDawGdH7XMATbD3aeAsmm7dl1fVX8c7UFV9DdiRprvzrzRdnbtw35IpE2rH7L0D2Ilm3NwxC9j3GuANwDY0mcZ9gHd3e6zJrreqLqCZVLIu8EvgZJog+hVV9bPFbZckSTNWf8fobU5zYYhNgP+miYt+nWTFjn32oFkt5O00a97OBX6VZMJ1sXL/Xjxp8sxacsVaatUt+t0Madq67ZIP97sJ0rSWPPa0qurp1aiWWnXdeuTr9+9Z/Rd95kULdQ5JlqMZO79NVf24zeZdAXypqj7e7rMMTbC3e1UdtKD6+p3RkyRJ6q/08LbwHkITn40MWXsUsCpNbxwAVXUbcAJNFnCB+jkZQ5IkadDNSXJqx/ODq+rgBez/BeAM4M/t81Xb+6tH7Xc1MOFVsQz0JEnS8ApUb9e7m9dt122Sz9GsIrJpVd09GQe361aSJKnPkuxPsxLGf7cTKEeMXIlrlVEvWaVj27gM9CRJ0nDr85UxknyB+4K80ZcpupAmoHtex/5L0ywrN9FFIOy6lSRJ6pd2TdzX0Sybdl2SkTF5N1fVzVVVST4PfDDJP2nWG94TuJlmKboFMtCTJEnDrb9XxnhLe/+bUeUfBvZtH38aWAb4MvctmPz8qrpposoN9CRJkvqkqiaMMttLl+7LfYFf1wz0JEnS8AoDPWNhgE9NkiRpuJnRkyRJQyz9HqPXU2b0JEmSBpQZPUmSNNx6e2WMvjLQkyRJwysMdKBn160kSdKAMqMnSZKGWjkZQ5IkSTONGT1JkjTcBjjtNcCnJkmSNNzM6EmSpOEVXDBZkiRJM48ZPUmSNMTiOnqSJEmaeczoSZKk4WZGT5IkSTONGT1JkjS80t4GlBk9SZKkAWVGT5IkDa0CyjF6kiRJmmnM6EmSpOHmlTEkSZI005jRkyRJwyu4jp4kSZJmHjN6kiRpuA1uQs9AT5IkDa8Aswa4f3OAT02SJGm4mdGTJElDbYBXVzGjJ0mSNKjM6EmSpOEVM3qSJEmagczoSZKkIRYywCk9M3qSJEkDyoyeJEkaWsExepIkSZqBzOhJkqShZkZPkiRJM44ZPUmSNLwCGeC01wCfmiRJ0nAzoydJkoaaY/QkSZI04yxyRi/JElV112Q2RpIkaSoFmDXsGb0k70jy8o7nhwK3JTknyWN71jpJkiQtsm67bt8BXAOQZDPglcBrgDOAz/akZZIkSVMg6d2t37rtul0NuLB9/CLge1X13SRnAr/vScskSZK0WLrN6N0IrNw+fh7wm/bxXcDSk90oSZKkqWJGD34JHJLkL8BjgJ+15etxX6ZPkiRpZglkOkRkPdJtRu+twB+BlYD/qapr2/L1gW/3omGSJElaPF1l9KrqRuDtY5TvM+ktkiRJmkKDfAm0cQO9JCuOZO6SrLigSjoyfJIkSZomFpTRuybJI6pqLjAPqDH2SVs+uxeNkyRJ6qUwPSZN9MqCAr3/BkYydc+egrZIkiRpEo0b6FXV78Z6LEmSNDCmyTIovbLQ17pNsiqwZGdZVV0yaS2SJEnSpOgq0EuyPHAAzaXPlhxjF8foSZKkGWmQM3rdTijeD3gysA1wO811bt8LXAa8qictkyRJ0mLptut2K2Dbqvp9kruB06rqqCRXAm8Cvt+zFkqSJPXQLDN6rABc3D6+AXh4+/jPwCaT3CZJkiRNgm4DvfOBR7eP/wG8Os2F4V7GfUuwSJIkzSgj6+j16tZv3QZ6hwFPah9/iqa79k7gM8D/Tn6zJEmStLi6vdbt/h2Pj0vyOGBD4F9VdWavGidJktRr0yHz1isLvY4e3LtunmvnSZIkTWPddt2S5C1J/p7k1iSPbsven+SVvWueJElSDwUyKz279VtXgV6S3YA9gYNpxi2OuBx42+Q3S5IkSYur24zersDOVfUFYH5H+V+A9Sa9VZIkSVPEWbewFnDWGOV3ActMXnMkSZI0WbqdjHEBsD73LZo8Ymvg7EltkSRJ0hSaDpm3Xuk20NsP+FKSB9OM0XtGktcBewA79qpxkiRJvTSyYPKg6nYdvW8keRDwCeDBwLeAK4B3VNVRPWyfJEmSFtGEgV4b4O0C/LCqDkkyB5hVVXN73jpJkqReCkyDVVB6ZsLJGFU1n+ZSZ0u0z+cZ5EmSJE1/3Y7ROxHYgAdOxpAkSZrRBnmMXrfLqxwC7JdktyTPSrJ+562XDZQkSRpkSTZL8qMklyepJNuP2n5YW955O7GburvN6B3Z3n9ujG0FzO6yHkmSpGklXV8QtmeWo1mv+PD2NpZfA6/reH5nNxV3G+g9qsv9JEmStBCq6ljgWGiyd+PsdkdVXbWwdXe7vIpj8yRJ0sCZQevobZpkLnA98DvgQ91Mju02oydJkqSFNyfJqR3PD66qgxeyjp8DRwMXAmsDHwOOS7JBVd2xoBca6EmSpOEVSG9TevOqasPFqaCqvtPx9Mwkp9GshPICmgBwXP0ffihJkqSuVdUVwGXAuhPta0ZPkiQNtRkyRu9e7VXKVgOunGjfrgK9pJl4XFX3tM9XBV4I/KOq/rjoTZUkSRpuSZYDHtM+nQWsmeQpwLXtbV/gBzSB3drAJ4G5wP9NVHe3Xbc/Bd7e0ZhTaS6LdnyS13dZhyRJ0rST9O7WpQ2B09vbMsCH28cfAe4GnggcA5wLfBM4B3hGVd00UcXddt1uCOzRPn4ZcCPN2nqvBXZn/MX9JEmStABVdTzNSi/j2WJR6+42o7cczbotAM8H/q+q7gKOA9ZZ1INLkiT12zTI6PVMt4HeJcAzkyxLE1X+qi1fEbi1Fw2TJEnS4um26/ZzwLeAm2nWbTmhLd8MOLMH7dIAWHLph7Lafzyn382QJGlcAWZNg8xbr3R7CbSD2sX51gB+NTL7Fjgf2KtXjZMkSdKi63odvao6lWa2bWfZTye9RZIkSVMlg53R6/rKGEnekuTvSW5N8ui27H1JXtm75kmSJGlRdRXoJdkN2BM4mPtP/70CeNvkN0uSJKn3mjF61bNbv3Wb0dsV2LmqvgDM7yj/C7DepLdKkiRpisxK72791m2gtxZw1hjld9Gs4CxJkqRpptvJGBcA69MsrdJpa+DsSW2RJEnSFOp6wsIM1G2gtx/wpSQPpunOfkaS19FcFm3HXjVOkiRJi67bdfS+keRBwCeAB9MsnnwF8I6qOqqH7ZMkSeqZkckYg2rCQK8N8HYBflhVhySZA8yqqrk9b50kSZIW2YSBXlXNT/IZ4Kft83k9b5UkSdIUmQ6zY3ul2/GHJwIb9LIhkiRJmlzdTsY4BNgvyZrAacAtnRur6i+T3TBJkqReC866BTiyvf/cGNsKmD05zZEkSdJk6TbQe1RPWyFJktQP0+QKFr3S7fIqoxdKliRJ0jTXbUaPJE8Cdgf+k6a79mzgM1U11qXRJEmSZoQM8Dp6XY0/TPJi4C/AGsDPgJ8DawKnJ3lR75onSZKkRdVtRu9jwMerap/OwiQfabf9eLIbJkmS1GvNlTH63Yre6XZG8X/QXPZstG8Bj5285kiSJGmydJvRm0uzYPJ5o8o3AK6e1BZJkiRNIdfRaxZMPijJY4A/tWXPpJmc8ZleNEySJEmLZ2HG6N0MvAf4aFt2BbAPcEAP2iVJktRzoZg1wLNuu11Hr4D9gf2TPKQtu6mXDZMkSdLi6XodvREGeJIkaZAM8qzbcQO9JGfSLIw8oap60qS1SJIkaQoN62SM709ZKyRJkjTpxg30qurDU9kQSZKkqZYMdtftIGcrJUmShlrXkzGS7ABsS3ON2yU7t1XVoye5XZIkSVNikJdX6Sqjl+S9wGeB04C1gR8CZwErAl/vUdskSZK0GLrN6O0M7FJV30/yNuBLVXVBkr2AtXrXPEmSpN4JjtEDWB04uX18G/DQ9vG3gZdPdqMkSZK0+LoN9K4C5rSPLwae0T5+DF2utSdJkjQdzerhrd+6bcNxwIvbx4cCn0vyW+Ao4OheNEySJEmLZ4Fj9JI8t6p+DexCGxRW1VeTXAc8E/gBcFDPWylJktQjgzzrdqLJGL9MchFNFu8bwBUAVXUUTTZPkiRJ09REXbfr0XTNvh24OMlPk7w0yezeN02SJKm3Rmbd9urWbwsM9KrqH1W1O82s21fRTLz4LnB5kv9N8tgpaKMkSZIWQVeTMapqflUdXVUvpFk37wDgZcDZSU7oZQMlSZJ6pofZvGmf0RtLVV0BHEgT7F1PMylDkiRJ00zX17qFZhYusCOwDXA7zYLJX5v8ZkmSJPVemB7r3fXKhIFekjWBHYDtabptf0ez3Mr3q+r2nrZOkiRJi2yidfR+DWwOzAW+CRxaVedNQbskSZKmxDCvo3cLzaSLn1bV3VPQHkmSJE2SBQZ6VfWSqWqIJElSP0yH2bG9slCTMSRJkgbJoE/GGORzkyRJGmpm9CRJ0lAb5K5bM3qSJEkDyoyeJEkaahng5VXM6EmSJA0oM3qSJGloJY7RkyRJ0gxkRk+SJA21Qc56DfK5SZIkDTUzepIkaWiFYpazbiVJkjTTmNGTJElDzVm3kiRJmnHM6EmSpKFmRk+SJEkzjhk9SZI0tALM7ncjesiMniRJ0oAyoydJkoaa6+hJkiRpxjGjJ0mShlYy2LNuDfQkSdJQG+RAz65bSZKkAWVGT5IkDa0As83oSZIkqReSbJbkR0kuT1JJth+1PUn2TXJFktuSHJ9kvW7qNtCTJElDbVZ6d+vScsBZwDuB28bYvgfwHuDtwEbAXOBXSR4y4bl13QRJkiRNuqo6tqo+WFXfB+7p3JYkwG7Ap6rqB1V1FvAG4CHAayaq20BPkiQNrdAsmNyr2yR4FLAq8MuRgqq6DTgB2GSiFxvoSZIkTV+rtvdXjyq/umPbuJx1K0mShlfvF0yek+TUjucHV9XBPT1iBwM9SZKk3plXVRsuxuuvau9XAS7pKF+lY9u47LqVJElDK8DsHt4mwYU0Ad3z7m1zsjTwLOBPE73YjJ4kSVIfJVkOeEz7dBawZpKnANdW1SVJPg98MMk/gXOBPYGbgSMnqttAT5IkDbVpcK3bDYHfdjz/cHv7JrA98GlgGeDLwMOAk4DnV9VNE1VsoCdJktRHVXU8TS/yeNsL2Le9LRQDPUmSNNQmab27acnJGJIkSQPKjJ4kSRpaAWb3f4xez5jRkyRJGlBm9CRJ0lCbBrNue8aMniRJ0oAyoydJkoZWen+t274yoydJkjSgzOhJkqShNsgZPQM9SZI0tJrlVVwwWZIkSTOMGT1JkjTUBjnrNcjnJg2dXbddg6O//FTOOGYTTv7+0zn4o+ux7toPHnf/j+62Luf9ejPe+IrVp7CV0vRz0OEn87hn7s8K636UTbb+Kn846eJ+N0maFAZ60gB52pOX5//96Ape8c4z2O69f2P+3cXhn34Syz/kgcn7LZ81hyc/7iFcNe+OPrRUmj6+96Oz2H3fn7HHW5/FicfuytM2XINt3nAEl1x+fb+bpikQmskYvbr129AEekkOS1Jj3E5ciDrWbl+zYS/b2gtJjk/ypTHKt09yc/v4onHeo5Hb8R377T7Fp6Au7PD+s/jBL67mXxfdyrkX3srun/onKy6/BBus99D77ffIlZdiz7euw7s+8Q/mzx/cQchSNw742p943f88hR1fsyGPW3cl9v/IC1h15eU45Fun9Ltp0mIbtjF6vwZeN6rszsk+SJIlq2rS650CGwGz28dPBn4ObAxc2pbNxHMaass++EHMnh1uuHn+vWWzZ8HnP/Q4Dvx/l3D+Jbf1sXVS/91553xOP/NKdtvlmfcrf+5m63DiaZeO8yoNmumQeeuVocnote6oqqtG3a4d2dhmrXZJ8r0ktyS5IMl2Ha+/sL0/ZVSG67AkP0nyviSXAZe15dslOSXJTUnmtvWutqAGtpm3A5N8Ism89nX7JZnVsc9C19uNqrpm5H0B/t0WXzPWe6WZYa+3rsPZ593M6WffeG/ZO9+wNtfdMJ8jf3xlH1smTQ/zrr2Vu+++h1VWWvZ+5SvPWY6rr7m5T62SJs+wBXrd2Bs4hiajdRTw9SRrtts2bu+3BB4BvKzjdf8FPKnd9py2bElgn7auFwJzgG930YbXAvOBTYC3AbsBr+rYvqj1aoh8cNdHs8F6D+WtHz6be+5pyp725OV52Rar8IHPntvfxknSNJEUs3t467dh67rdcmQ8WocvV9X7Op5/q6qOAEiyF/BOYDPgCOCadp9/t1mvTrcDO1bVvSPbq+rrHdsvSPJm4B9JVq+qyxbQzrOrau/28blJdqYJHr+9mPX2XJJdgF0AHrT0iv1sylD70JsfzQs2X4ntdv8bl155+73lT3vyCqy84pL86btPv7fsQbPDHjs9ih1ethqbbntSP5or9c2cFR/M7NmzuPqaW+5XPnfezayy0nJ9apU0eYYt0DuBNgjpcP2o538beVBV85NcA6zcRd1ndQZ5AEnWp8m8PQVYkWZyD8CatN274/jbqOdXdLZhMertuao6GDgYYOmHrt3/P2WG0J5vWacN8v7KBZfefwzeET+6gp+dcM39yr7xqSfyk+PmctSxo/92kQbfkks+iKc+8REc9/vzefkL17u3/De/v4Bttnp8H1umqTTIY/SGLdC7tarOm2Cfu0Y9L7rr4r7fn4NJlgV+wX0TQObSdLH+nqbrdZHasBj13ggsP0b5CsANE7RHM8S+b38M2zx3ZXbd52xuuGk+cx62BAC33nY3t95+D9defxfXXn//r9f8+cU1193FhZc5MUPD6R07bcIb33U0Gz5lNZ6x4ZoccsQpXHn1Tey03Ub9bpq02IYt0FtcI7NOZy9wr8bjaAKwD1bVhQBJXrbgl3RlUes9B9g6SaqqM9O2frtNA2C7lzwSgCP2e9L9yg84/GIOONwFYKWxvOLFT+Da62/lU188gavm3sR6/7EyP/zma1lr9RX63TRNgZF19AbVsAV6SyVZdVTZ3VV1zZh7P9Bc4DZgiyQXAbdX1XjZsEuAO4C3Jfky8Hjgo4vQ5smq9ys0Ezu+mOQQmjGFWwPbAi+ehHZpGnjMc09Y6Ndsvt3JPWiJNLO86fUb86bXbzzxjtIMM2yzbp8LXDnqdnq3L66q+cA7gJ1oxs0ds4B9rwHeAGwDnE0zpu7di9juxa63qi6gmVSyLvBL4GTg1cArqupni9suSZJmqkG+MsbQZPSqantg+wn2ecBHUlVrj3r+NeBrY9Q9Vn1H0SzR0mmBH3tVbT5G2fajni90ve3rTgG2mGi/dt9Tx6tz9HsiSZKmp6EJ9CRJkkYLMHsaZN56Zdi6biVJkoaGGT1JkjS8ArOmwRUsesWMniRJ0oAyoydJkobaIGe9DPQkSdLQGvQFkwc5iJUkSRpqZvQkSdJQc3kVSZIkzThm9CRJ0tBqxui5vIokSZJmGDN6kiRpqDnrVpIkSTOOGT1JkjS0EjN6kiRJmoHM6EmSpKE2yFmvQT43SZKkoWZGT5IkDbU4Rk+SJEkzjRk9SZI01AY4oWdGT5IkaVCZ0ZMkSUMrOEZPkiRJM5AZPUmSNNQGOes1yOcmSZI01MzoSZKkoZZUv5vQMwZ6kiRpqA3wXAy7biVJkgaVGT1JkjS0XF5FkiRJM5IZPUmSNNQGOKFnRk+SJGlQmdGTJEnDKzBrgFN6ZvQkSZIGlBk9SZI01AY4oWdGT5IkaVCZ0ZMkSUPLdfQkSZI0I5nRkyRJQ22AE3pm9CRJkgaVGT1JkjTUzOhJkiRpxjGjJ0mShppXxpAkSdKMY0ZPkiQNreAYPUmSJM1AZvQkSdJQS6rfTegZAz1JkjTU7LqVJEnSjGOgJ0mShlcgPbxNePhk3yQ16nbVZJ2eXbeSJEn9dQ6wecfzuyerYgM9SZI0tMK06N6cX1WTlsXrNA3OTZIkaag9OskVSS5M8p0kj56sis3oSZKkodbNWLrFMCfJqR3PD66qgzuenwRsD/wTWBnYE/hTkvWq6t+Le3ADPUmSpN6ZV1Ubjrexqn7W+TzJicAFwBuAzy3uwQ30JEnSUJtO6+hV1c1J/g6sOxn1OUZPkiRpmkiyNPA44MrJqM+MniRJGmo9HqM3wbGzH/Bj4BKaMXp7AcsC35yM+g30JEmS+md14NvAHOAa4ETg6VV18WRUbqAnSZKGWj/H6FXVq3tZv2P0JEmSBpQZPUmSNLQCzJpO024nmRk9SZKkAWVGT5IkDbUBTuiZ0ZMkSRpUZvQkSdIQK5LqdyN6xoyeJEnSgDKjJ0mShppj9CRJkjTjmNGTJElDK+nvtW57zUBPkiQNtQGO8+y6lSRJGlRm9CRJ0lAb5KzXIJ+bJEnSUDOjJ0mShtogT8YwoydJkjSgzOhJkqQhFgZ53q0ZPUmSpAFlRk+SJA2tJp9nRk+SJEkzjBk9SZI01JLBzXsN7plJkiQNOTN6kiRpyDlGT5IkSTOMGT1JkjTE4qxbSZIkzTxm9CRJ0pAzoydJkqQZxoyeJEkaaq6jJ0mSpBnHjJ4kSRpygztGz0BPkiQNrbi8iiRJkmYiM3qSJGmoDXJGz0BPPXPHTRfPO/83O1/c73bofuYA8/rdCDUyuL9bZip/PqaftfrdgJnOQE89U1Ur9bsNur8kp1bVhv1uhzQd+fMxzAZ3JNvgnpkkSdKQM6MnSZKGWgZ4HIUZPWm4HNzvBkjTmD8fGjhm9KQhUlX+IpPG4c/HsAqDvGCyGT1JkqQBZaAnLYQkGyWZl+TwJGsn+WO/2yRJWjzp4b9+M9DT0EtyWJJKsteo8s3b8jkdxS8CPgRcDBwPHDh1LR1fG3RWkhm3NESS45N8aYzy7ZPc3D6+qD2/8W7Hd+y3+xSfgibQ8TM2+nbiQtThdxy/41p4jtGTGrcD703y1aq6Zrydqmrvjqd7jbffdJVkyaq6s9/tWAQbAbPbx08Gfg5sDFzals3Ecxo2vwZeN6ps0j83v+NaNIOb9xrcM5MWzm+Bi1hA8JZkdpJDk1yY5LYk/0qyR5JZHfvMSrJXkkuT3JHkzCQvmejgSXZIcnaS25Ocm+Rdo+qtJLsk+V6SW5JckGS7jioubO9PGfXX/2FJfpLkfUkuAy5ry7dLckqSm5LMbetdbYI2Hp/kwCSfaLuv5ybZb1Q7F7reblTVNVV1VVVdBfy7Lb63rKquXdxjqOfu6Pi8HvC5+R33O67eMNCTGvcA7wd2TbLOOPvMAi4HXgk8nqYL94PADh37vBN4L/A+4InA/wFHJ3nKeAdOsjPwCWDvtt73tK9/y6hd9waOoflr/yjg60nWbLdt3N5vCTwCeFnH6/4LeFK77Tlt2ZLAPm1dL6S59NO3x2tjh9cC84FNgLcBuwGv6ti+qPVK4HdcfTLIY/TsupVaVXVsmskVHwdePcb2u2h+EY24KMn6wLbAoW3Z7sB+VXVk+3zvJJu15Z3ZiU57AXtU1ffb5xcm+RRNoNc5rudbVXUEQJrxhO8ENgOOAEa6m//dZgQ63Q7sWFV3dJzL1zu2X5DkzcA/kqxeVZeN006Aszu6r89tg9Tn0P6iW4x6Nfi2HBmP1uHLVfW+jud+x6VJZqAn3d/7gD8n+cxYG5PsCuxEc6HtZYAlaCZmkOShwCOB0TNx/wBsPU59KwFrAAcl+UrHpgfxwIWd/jbyoKrmJ7kGWLmLczqr8xdge9z1abISTwFW7DjWmrRdX+P426jnV3S2YTHq1eA7AdhlVNn1o577HdeUS+KVMaRhUVUnAz8APj16W5JXAZ8HDgO2oPmP/kCarpwJqx6nfORncNe2vpHbE4D1Ru171xh1dvMzfEvnkyTLAr8AbqUZHL8RTZcXTHwu47ZhMeq9EVh+jPIVgBsmaI9mjlur6rxRt3mj9vE7Lk0yM3rSA30QOJv7/gMfsSlwUlXd253aOZ6vqm5McgXwTOA3o1539lgHqqqr29esU1WHL0abR2bkzV7gXo3H0Ywr+mBVXQiQ5GULfklXFrXec4Ctk6SqOgPi9dttEvgdV08NbkbPQE8aparOS3IwzfigTucC2yfZCjiPZhzffwHXdezzGeAjSf4FnEYzLu9ZNP+hj2cf4ItJrgeOpekOXh9Yrao+2WWz5wK3AVskuQi4varGyxRcAtwBvC3Jl2kmgHy0y+MsyKLW+xWaQe9fTHIIzXirrWnGPr54Etql6WGpJKuOKrt7QcsZjeJ3XFoEdt1KY/sIzcy7TgcB3wWOBE4B1gY+O2qfA2iCvU8DZwEvBV5eVX8d70BV9TVgR5quoL8Cv6cZy3TheK8Zo475wDtoxg9eQTNzcbx9rwHeAGxDk2ncB3h3t8ea7Hqr6gKaAffrAr8ETqYJol9RVT9b3HZp2ngucOWo2+ndvtjvuHopzOrZrd9y/yyyJEnS8Nhgw8fXn05anJEzC7b0gzY+rar6dkWX/oeakiRJ6gnH6EmSpCHm8iqSJEmagczoSZKkIWdGT5IkSTOMGT1JkjTUpsMyKL0yuGcmSa0k+yY5q9/t6Lckc5JUks3b52u3z/u29IOk3jLQkzRtJflRkt+Ms+3xbZDy/Klu1wC5FHgEcAZAks3b93ROX1slTbn08NZfBnqSprNDgWcnWXuMbW8ELgZ+PRUNSbLEVBynG5PVlqq6u6quaq86IWkAGehJms5+ClwN7NBZ2AY6rwO+3jzNoUkuTHJbkn8l2SPJuP+/JZmVZK8klya5I8mZSV7SsX2kS3PbJMcluQ14U7tthyRnJ7k9yblJ3tV5rCRvastvTzI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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### <font color= MediumSpringGreen> 1.1.5  Comparação dos modelos"
      ],
      "metadata": {
        "id": "YpFzfvrDYqF_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tabela_metricas = pd.concat([bagging,gradient_boosting,random_forest,dummy]).sort_values(by=['AUC','F1-score_0'],ascending=False)\n",
        "tabela_metricas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "JTG_jq-cD72m",
        "outputId": "99b60b49-b7eb-446c-b273-f5a25f79d5ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 AUC  F1-score_0  F1-score_1  Acurácia\n",
              "RandomForestClassifier      0.889368    0.852459    0.800000  0.830189\n",
              "GradientBoostingClassifier  0.867816    0.847458    0.808511  0.830189\n",
              "BaggingClassifier           0.864224    0.825397    0.744186  0.792453\n",
              "DummyClassifier             0.500000    0.707317    0.000000  0.547170"
            ],
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>AUC</th>\n",
              "      <th>F1-score_0</th>\n",
              "      <th>F1-score_1</th>\n",
              "      <th>Acurácia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.889368</td>\n",
              "      <td>0.852459</td>\n",
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              "      <td>0.830189</td>\n",
              "    </tr>\n",
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              "      <th>GradientBoostingClassifier</th>\n",
              "      <td>0.867816</td>\n",
              "      <td>0.847458</td>\n",
              "      <td>0.808511</td>\n",
              "      <td>0.830189</td>\n",
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              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>0.864224</td>\n",
              "      <td>0.825397</td>\n",
              "      <td>0.744186</td>\n",
              "      <td>0.792453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.707317</td>\n",
              "      <td>0.000000</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bRwwtZ3sskNX"
      },
      "source": [
        "### <font color= MediumSpringGreen> 1.2 Otimização de hiperparâmetros\n",
        "\n",
        "Irei realizar a otimização dos hiperparametros dos dois modelos que tiveram maior AUC médio, para verficar qual teve o melhor AUC e recall de acordo com a previsão de entrada na UTI.\n",
        "\n",
        "\n",
        "\n",
        "Utilizarei a otimização Bayesiana para encontrar os melhores hiperparâmetros."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rN3MzltIt8N7"
      },
      "source": [
        "#### <font color= MediumSpringGreen> 1.2.1 Gradient Boosting Classifier\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MO7_UcGavVVL"
      },
      "source": [
        "Listarei a faixa de cada hiperparâmetro a ser verificado pelo algoritmo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fo4-ZzkUuEl5"
      },
      "outputs": [],
      "source": [
        "params_gradient_boosting_classifier = {\n",
        "    'n_estimators': (10,1000),\n",
        "    'learning_rate': (0.0001,1.0),\n",
        "    'max_depth': (2,10),\n",
        "    'subsample': (0.1, 1.0)\n",
        "    }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XJwYvqo7vX_i"
      },
      "source": [
        "Passarei essa função que irá me devolver quais são os melhores hiperparâmetros."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f.otimizar_param_bayesiano(modelo_gradient_boosting,params_gradient_boosting_classifier,dados)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QPOLL9bDVgQh",
        "outputId": "616e3a90-955f-41f3-eee5-ceb12181b034"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('learning_rate', 0.18897551641625762),\n",
              "             ('max_depth', 5),\n",
              "             ('n_estimators', 854),\n",
              "             ('subsample', 0.5407582324959316)])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V8hoUhh6vjAb"
      },
      "source": [
        "Treinarei o modelo com os melhores hiperparâmetros."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vua5UaJLvTPp"
      },
      "outputs": [],
      "source": [
        "modelo_gbc_otimizado = GradientBoostingClassifier(n_estimators=854,learning_rate=0.18897551641625762,max_depth=5,subsample=0.5407582324959316)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7w2ju4JFvmO5"
      },
      "source": [
        "Farei os testes novamente.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "stRjN8Rbvm9R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "2b9e4a02-7d76-4fc7-83e7-d4fc0776c164"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 AUC  F1-score_0  F1-score_1  Acurácia\n",
              "GradientBoosting_otimizado  0.841954    0.847458    0.808511  0.830189"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a6660de2-a6d3-4b30-b520-a98c3b91c910\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>AUC</th>\n",
              "      <th>F1-score_0</th>\n",
              "      <th>F1-score_1</th>\n",
              "      <th>Acurácia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>GradientBoosting_otimizado</th>\n",
              "      <td>0.841954</td>\n",
              "      <td>0.847458</td>\n",
              "      <td>0.808511</td>\n",
              "      <td>0.830189</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a6660de2-a6d3-4b30-b520-a98c3b91c910')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a6660de2-a6d3-4b30-b520-a98c3b91c910 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a6660de2-a6d3-4b30-b520-a98c3b91c910');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "GradientBoosting_otimizado = f.roda_modelo(modelo_gbc_otimizado,dados,'GradientBoosting_otimizado')\n",
        "GradientBoosting_otimizado"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CpApqWZc0UCp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "8ac38138-b486-46d0-aec0-20626e486b1c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "f.grafico_matriz_confusao(modelo_gbc_otimizado,'Matriz de confusão'+'\\n'+'Gradient Boosting otimizado',dados)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6p9bLpn0UMg",
        "outputId": "e14c0447-744f-4197-a7c6-b24c9b476eec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC médio 0.7941666666666666\n",
            "Intervalo 0.6873693298146948 - 0.9009640035186384\n"
          ]
        }
      ],
      "source": [
        "f.roda_n_modelos(modelo_gbc_otimizado,dados,50)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8iojXfjNt1Zz"
      },
      "source": [
        "#### <font color= MediumSpringGreen> 1.2.2 Ramdom Forest\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R4OjWozouXxL"
      },
      "source": [
        "Listarei a faixa de cada hiperparâmetro a ser verificado pelo algoritmo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6QN37ocS6M-W"
      },
      "outputs": [],
      "source": [
        "params_random_forest = {\n",
        "    'n_estimators': (10,1000),\n",
        "    'min_samples_split': (2,10),\n",
        "    'max_depth': (2,10)\n",
        "    }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZQT8s6OukVW"
      },
      "source": [
        "Passarei essa função que irá me devolver quais são os melhores hiperparâmetros."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cIamnHov6MlY",
        "outputId": "5aaa8d33-0ea4-4508-d0d0-5c709d115e95"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('max_depth', 10),\n",
              "             ('min_samples_split', 10),\n",
              "             ('n_estimators', 156)])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "f.otimizar_param_bayesiano(modelo_random_forest,params_random_forest,dados)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9hbbNE9u4TJ"
      },
      "source": [
        "Treinarei o modelo com os melhores hiperparâmetros."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelo_rf_otimizado = RandomForestClassifier(n_estimators=156,\n",
        "                                             min_samples_split=10,\n",
        "                                             max_depth=10)"
      ],
      "metadata": {
        "id": "tyMpVpDszluk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PJL6bjXFvB15"
      },
      "source": [
        "Farei os testes novamente.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "7-7cJR1aAR8C",
        "outputId": "21679fd5-f85b-4bf9-b0d1-a3064a626006"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             AUC  F1-score_0  F1-score_1  Acurácia\n",
              "RandomForest_otimizado  0.909483    0.847458    0.808511  0.830189"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4765af2e-d7c1-4ba5-acd7-87feb28b6b98\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>AUC</th>\n",
              "      <th>F1-score_0</th>\n",
              "      <th>F1-score_1</th>\n",
              "      <th>Acurácia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RandomForest_otimizado</th>\n",
              "      <td>0.909483</td>\n",
              "      <td>0.847458</td>\n",
              "      <td>0.808511</td>\n",
              "      <td>0.830189</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4765af2e-d7c1-4ba5-acd7-87feb28b6b98')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4765af2e-d7c1-4ba5-acd7-87feb28b6b98 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4765af2e-d7c1-4ba5-acd7-87feb28b6b98');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "RandomForest_otimizado = f.roda_modelo(modelo_rf_otimizado,dados,'RandomForest_otimizado')\n",
        "RandomForest_otimizado"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "Rg3ga01JAWHR",
        "outputId": "13a41bb8-77ce-43a1-c62f-5a4f2e2b3c29"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "f.grafico_matriz_confusao(modelo_rf_otimizado,'Matriz de confusão'+'\\n'+'Random Forest otimizado',dados)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V8dr4bieAUha",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6073a0a1-76b1-477d-e46a-2b593d2d6aca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC médio 0.7902586206896552\n",
            "Intervalo 0.6692703611857989 - 0.9112468801935116\n"
          ]
        }
      ],
      "source": [
        "f.roda_n_modelos(modelo_rf_otimizado,dados,50)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### <font color= MediumSpringGreen> 1.2.3  Comparação dos modelos"
      ],
      "metadata": {
        "id": "45uA3uFRYv32"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tabela_metricas = pd.concat([GradientBoosting_otimizado,RandomForest_otimizado]).sort_values(by=['AUC','F1-score_0'],ascending=False)\n",
        "tabela_metricas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "ZnwKhmjNR7Eo",
        "outputId": "172fa621-d084-4227-a2f5-54655abcd65e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 AUC  F1-score_0  F1-score_1  Acurácia\n",
              "RandomForest_otimizado      0.909483    0.847458    0.808511  0.830189\n",
              "GradientBoosting_otimizado  0.841954    0.847458    0.808511  0.830189"
            ],
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              "      <th>AUC</th>\n",
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              "      <td>0.909483</td>\n",
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              "      <td>0.841954</td>\n",
              "      <td>0.847458</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a15baef6-5ef7-4522-a551-89a51276d43e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a15baef6-5ef7-4522-a551-89a51276d43e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color= MediumSpringGreen> 2. Salvando o modelo\n"
      ],
      "metadata": {
        "id": "T-KW5dSpaXqU"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zn7QaUWuDoHB"
      },
      "source": [
        "Com o modelo treinado e testado irei salvar ele para que não haja a necessidade de repasasar novamente pelo treinamento. \n",
        "\n",
        "Escolhi o modelo otimizado do Gradient Boosting devido seu melhor desempenho médio da métrica AUC, levando a uma maior probabilidade de resultados corretos com o modelo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pT5jCSz8HgMq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d475a2ce-f864-4f01-feea-0058e5a4b9c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['UTI_GradientBosting_preditor.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "dump(modelo_gbc_otimizado, 'UTI_GradientBosting_preditor.joblib') "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "UTI_GradientBosting_preditor = load('UTI_GradientBosting_preditor.joblib') \n"
      ],
      "metadata": {
        "id": "ANwgzkbC9MMe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f.roda_modelo(UTI_GradientBosting_preditor,dados)"
      ],
      "metadata": {
        "id": "_McFuzCd_HP6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "538d9688-ab76-42e0-978f-aa145094dd62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC 0.8419540229885056\n",
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.86      0.85        29\n",
            "           1       0.83      0.79      0.81        24\n",
            "\n",
            "    accuracy                           0.83        53\n",
            "   macro avg       0.83      0.83      0.83        53\n",
            "weighted avg       0.83      0.83      0.83        53\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color= MediumSpringGreen> 3. Considerações finais\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "UH4oDaZisCT0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Devido as características do conjunto de dados iniciais, foi realizada diversas etapas na **limpeza e modelagem dos dados**:\n",
        "\n",
        "1. Excluiu os pacientes que já entraram nas duas primeiras horas na UTI, devido a incerteza se foi coletado os dados antes ou depois da entrada.\n",
        "2. Foi preenchido os dados faltantes do conjunto a partir do dado anterior e posterior a ele.\n",
        "3. Verifiquei se o paciente iria para a UTI ou não até a sua última janela e coloquei a informação na linha da primeira janela (0-2). Exclui as linhas com as demais janelas, para que pudesse prever com os resultados a partir das primeiras horas.\n",
        "4. Trasnformei a variável \"AGE_PERCENTIL\" em dados categóricos, afim de se tornar uma variável para o modelo.\n",
        "5. Exclui as variáveis com alta correlação entre elas, para que o modelo não sofra sobreajuste (overfitting).\n",
        "6. Mantive o gênero dos pacientes, pois de acordo com as literaturas citadas [[1](https://doi.org/10.1038/s41467-020-19741-6),[2](https://doi.org/10.1371/journal.pone.0237297)] há uma diferença entre os gêneros nas complicações da doença.\n",
        "7. Exclui as colunas que possuaim valores únicos (apenas 1, no qual foram as medidas \"DIFF\" das variáveis) em todas as linhas, para que pudesse ter um conjunto de dados mais limpo possível para o modelo.\n",
        "\n",
        "Na **análise exploratória** foi possível entender melhor as variáveis e o quadro geral:\n",
        "\n",
        "1. O total de pacientes do conjunto foi de 353.\n",
        "2. Desses 190 (54%) não tiveram entrada e 163 (46%) tiveram.\n",
        "3. Pacientes acima de 65 anos de idade tem uma maior frequência de entrada na UTI.\n",
        "4. Ao visualizar todas as faixas etárias, foi possível confirmar que a partir da faixa de 61-70 anos frequência se iguala e a partir dos 70 anos a entrada na UTI é maior.\n",
        "5. Também pude confirmar a questão da entrada na UTI através do gênero, no qual os homens tem maior incidência de complicações na doença.\n",
        "6. Analisei em conjunto todas as doenças anteriores dos pacientes, para pudesse entender ao todo como cada pessoa com estas características se comportaria na entrada ou não na UTI. A maior frequência está em pacientes que não tiveram nenhuma outra doença mas se concentrou em outras doenças. Talvez se esta variável estivesse mais detalhada em outras classificações, poderíamos ter um modelo mais assertivo do ponto de vista das doenças anteriores.\n",
        "7. Dentre os indicadores sanguíneos, observei os que tiveram maior diferença (positiva e negativa), entre as pessoas que entraram ou não na UTI foram: \n",
        "  * Negativa:\n",
        "    * Sódio\n",
        "    * Hematócritos\n",
        "    * Linfóciotos\n",
        "    * BIC Venoso\n",
        "    * PH Venoso\n",
        "  * Positiva:\n",
        "    * Lactato\n",
        "    * PCR\n",
        "    * SAT02 Venoso\n",
        "    * Cálcio\n",
        "    * Potássio\n",
        "8. Já nos sinais vitais os dois indicadores que tiveram maior diferença foram o de temperatura (-35%) e D-Dímero (22%) dos pacientes que não entraram na UTI.\n",
        "\n",
        "Durante o **desenvolvimento do modelo**, escolhi algumas etapas para definir o melhor:\n",
        "\n",
        "1. Baseado no [artigo científico](https://doi.org/10.1038/s41746-021-00456-x) citado, escolhi 3 modelos que tiveram maior índice de F1 para realizar os testes do conjunto de dados:\n",
        "  * Bagging Classifier\n",
        "  * Gradient Boosting Classifier\n",
        "  * Ramdom Florest\n",
        "2. Realizei os testes com cada um deles, visualizando as métricas de AUC, a tabela de precision, recall e f1-score, a matriz de confusão do teste e o AUC médio do intervalo de AUC coletado, ao rodar ele 50 vezes.\n",
        "3. A partir desse etapa, escolhi os dois modelos que tiveram melhor desempenho de AUC médio e Reccal no caso de entrada na UTI (pois é uma medida muito importante para que o modelo não errasse em pessoas que deveriam entrar na UTI e o modelo indicasse que ela não precisaria).\n",
        "4. Otimizei os hiperparâmetros do modelo com uma abordagem Bayesiana, procurando os melhores parâmetros. \n",
        "5. Realizei os testes das métricas novamente.\n",
        "6. Os resultados mostraram o Gradient Boosting otimizado com um melhor resultado de AUC médio (0.794) dentre o intervalo, além dele ter tido um reccal em pessoas que deveriam entrar na UTI de 0.79.\n",
        "\n",
        "\n",
        "Foi um modelo definido com uma métrica média não muito alta, mas ele poderá ser uma importante ferramenta para os profissionais da saúde poderem já se alertarem aos primeiros sinais dos pacientes ao entrarem no hospital.\n",
        "\n",
        "Como próximos passos seria interessante que ele estivesse fazendo parte de um sistema integrado as ferramentas dos profissionais da saúde, mostrando a previsão do modelo para que o profissional pudesse tomar a decisão."
      ],
      "metadata": {
        "id": "yLZRMBuT6uC5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color= MediumSpringGreen> Referências\n",
        "\n",
        "IACCARINO, G. et al. Gender differences in predictors of intensive care units admission among COVID-19 patients: The results of the SARS-RAS study of the Italian Society of Hypertension. **PloS one**, v. 15(10), oct. 2020. DOI https://doi.org/10.1371/journal.pone.0237297 . Disponível em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0237297 . Acesso em: 25 jan. 2022.\n",
        "\n",
        "PECKHAM, H. et al. Male sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission. **Nature Communications**, v. 11(6317), dec. 2020. DOI: https://doi.org/10.1038/s41467-020-19741-6 . Disponível em: https://www.nature.com/articles/s41467-020-19741-6#citeas . Acesso em: 25 jan. 2022.\n",
        "\n",
        "SUBUDHI, S. et al. Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19. **NPJ Digit**. Med. 4, v. 87, may 2021. DOI: https://doi.org/10.1038/s41746-021-00456-x . Disponível em: https://www.nature.com/articles/s41746-021-00456-x#citeas . Acesso em: 29 jan. 2022\n",
        "\n"
      ],
      "metadata": {
        "id": "Wql-Gcs5RWuw"
      }
    }
  ],
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