SVM_Applied_to_Transaction_Fraud_Detection.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/SampMark/Machine-Learn/blob/main/SVM_Applied_to_Transaction_Fraud_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oHnVCZQO4O6a"
      },
      "source": [
        "# **Modelagem *Support Vector Machines* (SVM) para detecção de fraudes em transações de crédito/bancária**.\n",
        "\n",
        "\n",
        "Modelos de aprendizado de máquina, como [Árvores de Decisão](https://github.com/SampMark/Machine-Learn/blob/main/Decision_Trees.ipynb) e [Máquinas de Vetores de Suporte (SVM)](https://github.com/SampMark/Machine-Learn/blob/main/SVM_Support_Vector_Machines.ipynb) têm sido amplamente utilizados na detecção de fraudes em transações com cartão de crédito ou bancárias, trata-se de um desafio devido ao volume significativo de dados e à natureza dinâmica das fraudes.\n",
        "\n",
        "Em especial, o SVM, é uma ferramenta poderosa na detecção de fraudes, mas a modelagem exige uma atenção especial para garantir a eficácia de sua aplicação, devido aos seguintes desafios:\n",
        "\n",
        "1. **O Desafio dos Dados Desbalanceados:**\n",
        "\n",
        "  Em cenários de detecção de fraudes, os conjuntos de dados geralmente são desbalanceados, com uma quantidade muito maior de transações legítimas em comparação com as fraudulentas.  Isso pode levar a um viés do modelo, que tende a classificar a maioria das transações como legítimas, ignorando as fraudes.\n",
        "\n",
        "2. **Mitigando o Desbalanceamento:**\n",
        "\n",
        "Para lidar com esse problema, são empregadas técnicas como:\n",
        "\n",
        "  * **Pesos Ajustados:** atribuir pesos maiores às instâncias fraudulentas durante o treinamento, tornando-as mais relevantes para o modelo.\n",
        "  * **Reamostragem:**  balancear o conjunto de dados através de técnicas como _oversampling_ (replicar instâncias da classe minoritária) ou _undersampling_ (remover instâncias da classe majoritária).\n",
        "\n",
        "3. **Escolha do Kernel e Otimização:**\n",
        "\n",
        "  A escolha da função kernel adequada é crucial para o desempenho do SVM.  Kernels não lineares, como o RBF (Radial Basis Function), podem ser mais eficazes para capturar relações complexas nos dados de fraude.  Além disso, a otimização de hiperparâmetros, como o custo de regularização (C), é essencial para evitar overfitting e garantir a generalização do modelo.\n",
        "\n",
        "4. **Considerações Adicionais:**\n",
        "\n",
        "  * **Interpretabilidade:**  como os SVM são modelos complexos, técnicas como a análise de vetores de suporte podem fornecer insights sobre as características mais relevantes para a detecção de fraudes.\n",
        "  * **Escalabilidade:**  em conjuntos de dados muito grandes, os SVM são computacionalmente intensivos.  Nesses casos, otimizações e algoritmos alternativos podem ser considerados.\n",
        "\n",
        "O modelo SVMs é uma ferramenta valiosa na detecção de fraudes, mas sua aplicação requer cuidado na escolha do kernel, otimização de hiperparâmetros e tratamento de dados desbalanceados.  Ao abordar esses desafios, as SVMs podem auxiliar na identificação de padrões complexos e na proteção contra atividades fraudulentas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rFBAxtPFHS9h"
      },
      "source": [
        "## **Instalando e importando bibliotecas Python**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "pUaLi7YRipPd",
        "outputId": "c0c23af7-4467-4889-8f7f-be48d1fa34bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "# Aprendizado de Máquina\n",
        "!pip install scikit-learn\n",
        "!pip install snapml # Instala SnapML para aprendizado de máquina em alta velocidade (aceleração de hardware)\n",
        "\n",
        "# Manipulação de Dados\n",
        "!pip install pandas\n",
        "!pip install numpy\n",
        "\n",
        "# Visualização de Dados\n",
        "!pip install matplotlib\n",
        "%matplotlib inline\n",
        "\n",
        "#Aceleração por GPU\n",
        "!pip install -U cupy-cuda11x\n",
        "\n",
        "# Integração com Google Drive\n",
        "!pip install PyDrive\n",
        "\n",
        "!pip install dask[dataframe]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "hTV5W-k7kASd"
      },
      "outputs": [],
      "source": [
        "# Implementação do ambiente para análise de dados e aprendizado de máquina.\n",
        "# Importa a função de impressão compatível com versões futuras do Python (Python 3+)\n",
        "from __future__ import print_function\n",
        "from google.colab import drive\n",
        "\n",
        "# Importa bibliotecas matemáticas e de análise de dados\n",
        "import numpy as np  # Para se trabalhar com arrays e operações matemáticas\n",
        "import pandas as pd  # Para manipulação e análise de dados em tabelas\n",
        "\n",
        "import scipy.optimize as opt\n",
        "# Importa a biblioteca de visualização\n",
        "import matplotlib.pyplot as plt  # Para criar gráficos e visualizações\n",
        "\n",
        "# Importa bibliotecas adicionais\n",
        "import time  # Medir o tempo de execução\n",
        "import warnings  # Gerenciar e suprimir avisos\n",
        "# import cupy as cp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZBN_yyLHp5J"
      },
      "source": [
        "## **Importando e explorando a base de dados**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lCdf-lr3BrvE",
        "outputId": "3152465a-e6da-4e33-ac0f-df894a568250"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Há \u001b[1;34m284,807\u001b[0m linhas de observação nos dados de fraude em cartão de crédito.\n",
            "Há 31 colunas de variáveis na base de dados.\n"
          ]
        }
      ],
      "source": [
        "# Download arquivo CSV do Colab\n",
        "raw_data = pd.read_csv('/content/drive/MyDrive/my_colab_notebooks/data/creditcard.csv', sep=';', encoding='UTF-8')\n",
        "# Imprimir a estrutura do arquivo\n",
        "print(f\"Há \\033[1;34m{len(raw_data):,}\\033[0m linhas de observação nos dados de fraude em cartão de crédito.\")\n",
        "print(\"Há \" + str(len(raw_data.columns)) + \" colunas de variáveis na base de dados.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "wJX5m0afBrbA",
        "outputId": "43dc4b6d-9602-4c64-9751-12a8a380ef85"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
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              "    0% {\n",
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              "    20% {\n",
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      ],
      "source": [
        "# Visualiza no formato de tabela o cabeçalho e as primeiras 5 colunas do dataset 'raw_data'\n",
        "display(raw_data.head())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar os tipos de dados das colunas do DataFrame\n",
        "raw_data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LQ5Y0op-qCKB",
        "outputId": "00b76d3d-b51f-43fb-945e-e4f35be40f7d"
      },
      "execution_count": 6,
      "outputs": [
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              "      <th>V13</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V14</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V15</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V16</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V17</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V18</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V19</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V20</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V21</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V22</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V23</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V24</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V25</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V26</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V27</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V28</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Amount</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "82cJXBpYkKcQ",
        "outputId": "472c3974-8dc5-474e-ba7a-2b5469c669da"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Obtém os rótulos únicos da variável de destino (classe) e seus tamanhos correspondentes\n",
        "labels = raw_data.Class.unique()  # Rótulos únicos da variável \"Class\"\n",
        "sizes = raw_data.Class.value_counts().values  # Contagem de observações por classe\n",
        "\n",
        "# Ordena os rótulos e tamanhos para consistência (opcional)\n",
        "labels, sizes = zip(*sorted(zip(labels, sizes), key=lambda x: x[0]))\n",
        "\n",
        "# Cria o gráfico de pizza\n",
        "fig, ax = plt.subplots()\n",
        "colors = ['#e63946']\n",
        "ax.pie(\n",
        "    sizes,\n",
        "    labels=[f\"{label} ({size})\" for label, size in zip(labels, sizes)],  # Rótulos incluem contagem\n",
        "    autopct='%1.3f%%',  # Formato das porcentagens com 2 casas decimais\n",
        "    colors=colors,\n",
        "    startangle=90,  # Inicia o gráfico no ângulo de 90º para consistência\n",
        "    wedgeprops={'linewidth': 1.0, 'edgecolor': 'white'}  # Melhora o design\n",
        ")\n",
        "\n",
        "# Configurações adicionais\n",
        "ax.set_title('Distribuição da Variável Alvo', fontsize=12)\n",
        "plt.tight_layout()  # Ajusta o layout para evitar sobreposição\n",
        "plt.show()  # Exibe o gráfico"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "MEa8yS4Fn9VQ",
        "outputId": "b46a56d4-68a4-4767-d2fb-cd9c063ef14f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valor Mínimo das Transações: 0.00\n",
            "Valor Máximo das Transações: 25691.1600\n",
            "Valor Médio das Transações: 88.3496\n",
            "Desvio Padrão: 250.1201\n",
            "90º Percentil: 203.00\n"
          ]
        }
      ],
      "source": [
        "# Plota um histograma dos valores das transações\n",
        "plt.figure(figsize=(8, 4))  # Define o tamanho da figura\n",
        "plt.hist(raw_data['Amount'], bins=30, color='#e63946')\n",
        "plt.xlabel('Valor da Transação')\n",
        "plt.ylabel('Frequência')\n",
        "plt.title('Distribuição dos Valores das Transações')\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Calcula e imprime estatísticas descritivas\n",
        "min_amount = raw_data['Amount'].min()  # Valor mínimo\n",
        "max_amount = raw_data['Amount'].max()  # Valor máximo\n",
        "mean_amount = raw_data['Amount'].mean()  # Média\n",
        "std_amount = raw_data['Amount'].std()  # Desvio padrão\n",
        "percentile_90 = raw_data['Amount'].quantile(0.9)  # 90º percentil\n",
        "\n",
        "print(f\"Valor Mínimo das Transações: {min_amount:.2f}\")\n",
        "print(f\"Valor Máximo das Transações: {max_amount:.4f}\")\n",
        "print(f\"Valor Médio das Transações: {mean_amount:.4f}\")\n",
        "print(f\"Desvio Padrão: {std_amount:.4f}\")\n",
        "print(f\"90º Percentil: {percentile_90:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "p_Xd3qWgrd8O",
        "outputId": "6781a322-872d-4ccb-f61d-e7b6e55bd758"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valor mínimo: 0.0\n",
            "Valor máximo: 25691.16\n",
            "98% das transações têm um valor menor ou igual a: 691.0576000000001\n"
          ]
        }
      ],
      "source": [
        "# Plota um histograma dos valores das transações\n",
        "plt.hist(raw_data.Amount.values, 6, histtype='bar', facecolor='#e63946')\n",
        "plt.hist(raw_data.Amount.values, bins=50, histtype='bar', facecolor='#e63946')\n",
        "plt.xlabel(\"Valor da Transação\")\n",
        "plt.ylabel(\"Frequência\")\n",
        "plt.title(\"Distribuição dos Valores das Transações\")\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Calcula e imprime estatísticas descritivas\n",
        "print(\"Valor mínimo:\", np.min(raw_data.Amount.values))\n",
        "print(\"Valor máximo:\", np.max(raw_data.Amount.values))\n",
        "print(\"98% das transações têm um valor menor ou igual a:\", np.percentile(raw_data.Amount.values, 98))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DP8oRkhbosaL"
      },
      "source": [
        "## **Pré-processamento do Conjunto de Dados**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "Nesta subseção, os dados serão preparados para o treinamento. O pré-processamento serve para melhorar o desempenho do modelo, especialmente modelos lineares, o escalonamento/normalização é normalmente útil para acelerar a convergência de treinamento."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rlEDJy3QuvjJ",
        "outputId": "f965dc96-5edb-4dc1-9052-046967a7f2a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X.shape= (284807, 29) y.shape= (284807,)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import normalize\n",
        "\n",
        "# Padroniza as features (colunas de 1 a 29) para terem média 0 e desvio padrão 1\n",
        "raw_data.iloc[:, 1:30] = StandardScaler().fit_transform(raw_data.iloc[:, 1:30])\n",
        "raw_data.isnull().sum()\n",
        "\n",
        "# Converte o DataFrame em uma matriz NumPy\n",
        "data_matrix = raw_data.values\n",
        "\n",
        "# Cria a matriz de features X (excluindo a coluna Time)\n",
        "X = data_matrix[:, 1:30]\n",
        "\n",
        "# Cria o vetor de labels y\n",
        "y = data_matrix[:, 30]\n",
        "\n",
        "# Normaliza as features usando a norma L1\n",
        "X = normalize(X, norm=\"l1\")\n",
        "\n",
        "# Imprime as dimensões da matriz de features e do vetor de labels\n",
        "print('X.shape=', X.shape, 'y.shape=', y.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETc1RsVm0C6e"
      },
      "source": [
        "**1. Padronização dos dados do DataFrame** `raw_data`:\n",
        "* `raw_data.iloc[:, 1:30]`: Seleciona todas as linhas e as colunas de 1 a 29 (excluindo a primeira coluna dos identificadores).\n",
        "* `StandardScaler()`: Cria um objeto StandardScaler  usado para padronização, isto é, transforma os dados para que tenham média zero e desvio padrão igual a 1.\n",
        "* .`fit_transform()`: Ajusta o StandardScaler aos dados selecionados (calcula a média e o desvio padrão) e, em seguida, transforma os dados aplicando a padronização.\n",
        "* `data_matrix = raw_data.values`: Converte o DataFrame `raw_data` em uma matriz NumPy (`data_matrix`) para facilitar a manipulação dos dados no aprendizado de máquina.\n",
        "\n",
        "**2. Separação de features e labels**:  As *features* são as variáveis ​​de entrada usadas para treinar o modelo e os os *labels* são os valores que o modelo de aprendizado de máquina deve prever.\n",
        "\n",
        "* `X = data_matrix[:, 1:30]`: Cria a matriz de features X, selecionando todas as linhas e as colunas de 1 a 29 da data_matrix.\n",
        "* `y = data_matrix[:, 30]`: Cria o vetor de labels y selecionando todas as linhas e a coluna 30 da data_matrix.\n",
        "\n",
        "**3. Normalização dos dados**:\n",
        "\n",
        "* `X = normalize(X, norm=\"l1\")`: Aplica a normalização L1 às features X.\n",
        "* `normalize(X, norm=\"l1\")`: Aplica a função do scikit-learn que normaliza os dados, `norm=\"l1\"` especifica que a norma L1 deve ser usada, o que significa que a soma dos valores absolutos de cada linha será igual a 1.\n",
        "\n",
        "**4. Impressão das dimensões**: para verificar se os dados foram processados ​​corretamente é feita a impressão das dimensões (número de linhas e colunas) da matriz de features X e do vetor de labels y."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCMbBq3HpC20"
      },
      "source": [
        "## **Divisão do Conjunto de Dados em Treinamento/Teste**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldvSDmhqpLYI",
        "outputId": "148064b9-ffff-4b76-be9b-5f1adcd087be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conjunto de treino X = (199364, 29) Conjunto de treino y = (199364,)\n",
            "Conjunto de teste X = (85443, 29) Conjunto de teste y = (85443,)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Divide os dados em conjuntos de treinamento e teste\n",
        "# Especifica que 30% dos dados serão usados para teste e os 70% restantes para treinamento\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
        "\n",
        "# Imprime as dimensões dos conjuntos de treinamento\n",
        "# O número de linhas e colunas da matriz de features de treinamento (X_train) e do vetor de labels de treinamento (y_train)\n",
        "print('Conjunto de treino X =', X_train.shape, 'Conjunto de treino y =', y_train.shape)\n",
        "\n",
        "# Imprime as dimensões dos conjuntos de teste, da matriz de features de teste (X_test) e do vetor de labels de teste (y_test)\n",
        "print('Conjunto de teste X =', X_test.shape, 'Conjunto de teste y =', y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwVQ46xDxfb_"
      },
      "source": [
        "Nesta etapa crucial no desenvolvimento de modelos, os dados foram divididos em conjuntos de treinamento e teste, o que nos permite avaliar o desempenho do modelo em dados não vistos durante o treinamento.\n",
        "\n",
        "* `train_test_split(X, y, ...)`: Esta função do scikit-learn divide os dados em conjuntos de treinamento e teste.\n",
        "* `X`: A matriz de features (variáveis de entrada).\n",
        "* `y`: O vetor de labels (variável de saída).\n",
        "* `test_size=0.3`: Especifica que 30% dos dados serão usados para teste e os 70% restantes para treinamento.\n",
        "* `random_state=42`: Define uma semente para o gerador de números aleatórios, garantindo que a divisão dos dados seja reproduzível, ao executar o código novamente com a mesma semente, se obterá a mesma divisão.\n",
        "* `stratify=y`: garante que a proporção de classes (valores únicos em y) seja mantida nos conjuntos de treinamento e teste. É especialmente útil quando se lida com dados desbalanceados, quando uma classe é muito mais frequente do que as outras.\n",
        "\n",
        "As variáveis que armazenam os conjuntos de dados resultantes são:\n",
        "* `X_train`: Matriz de features para treinamento.\n",
        "* `X_test`: Matriz de features para teste.\n",
        "* `y_trai`n: Vetor de labels para treinamento.\n",
        "* `y_test`: Vetor de labels para teste."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWrRVPXntT1u"
      },
      "source": [
        "## **Modelagem do _Support Vector Machine_ (SVM) com o Snap ML**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from snapml import SupportVectorMachine\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Pipeline com pré-processamento e modelagem SVM\n",
        "fraudDetection = Pipeline([\n",
        "    ('scaler', StandardScaler()),  # Etapa de padronização dos dados\n",
        "    ('pca', PCA(n_components=0.95)),  # Preserva 95% da variância\n",
        "    ('svm', SupportVectorMachine(\n",
        "        kernel='rbf',              # Kernel radial basis function\n",
        "        gamma=0.001,               # Controla a influência dos pontos de dados\n",
        "        # penalty='l2',              # Regularização L2\n",
        "        # loss='hinge',              # Perda hinge (ótima para SVM)\n",
        "        # dual=False,                # Resolve o problema primal (mais eficiente para conjuntos pequenos)\n",
        "        # C=1.0,                     # Regularização (maior = menos regularização)\n",
        "        # tol=1e-4,                  # Critério de tolerância para convergência\n",
        "        fit_intercept=False,       # Não calcula o intercepto; os dados devem ser centrados\n",
        "        class_weight='balanced',   # Balanceia automaticamente classes desbalanceadas\n",
        "        random_state=42,           # Semente para reprodutibilidade\n",
        "        # max_iter=1000,             # Número máximo de iterações\n",
        "        # verbose=0,                 # Desativa logs de execução\n",
        "        # use_gpu=True,              # Aceleração via GPU, se disponível\n",
        "        # enable_sparse=False        # Desativa representações esparsas\n",
        "    ))\n",
        "])\n",
        "\n",
        "# Exemplo de ajuste do pipeline com dados de treinamento\n",
        "fraudDetection.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "wFa2yrmnt62G",
        "outputId": "4f37308c-d4b9-4e58-ea40-c587c36e18ef"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('scaler', StandardScaler()), ('pca', PCA(n_components=0.95)),\n",
              "                ('svm',\n",
              "                 SupportVectorMachine(class_weight='balanced',\n",
              "                                      device_ids=array([], dtype=uint32),\n",
              "                                      gamma=0.001, kernel='rbf',\n",
              "                                      random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;pca&#x27;, PCA(n_components=0.95)),\n",
              "                (&#x27;svm&#x27;,\n",
              "                 SupportVectorMachine(class_weight=&#x27;balanced&#x27;,\n",
              "                                      device_ids=array([], dtype=uint32),\n",
              "                                      gamma=0.001, kernel=&#x27;rbf&#x27;,\n",
              "                                      random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;pca&#x27;, PCA(n_components=0.95)),\n",
              "                (&#x27;svm&#x27;,\n",
              "                 SupportVectorMachine(class_weight=&#x27;balanced&#x27;,\n",
              "                                      device_ids=array([], dtype=uint32),\n",
              "                                      gamma=0.001, kernel=&#x27;rbf&#x27;,\n",
              "                                      random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PCA</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.decomposition.PCA.html\">?<span>Documentation for PCA</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>PCA(n_components=0.95)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SupportVectorMachine</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>SupportVectorMachine(class_weight=&#x27;balanced&#x27;,\n",
              "                     device_ids=array([], dtype=uint32), gamma=0.001,\n",
              "                     kernel=&#x27;rbf&#x27;, random_state=42)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prever os dados de teste, aplicando o pipeline completo\n",
        "y_pred = fraudDetection.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wWfTLSEpHDWv",
        "outputId": "0533b533-5e09-4a32-caa7-74f5f4837cb0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 0., 0., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"As primeiras 100 previsões são:\", y_pred[:100])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JeU7ZKfIXaE",
        "outputId": "0bfc0381-0f24-4db7-befc-0bad6d3c375f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As primeiras 100 previsões são: [1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 0. 1. 0. 1.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 1.\n",
            " 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1.\n",
            " 0. 0. 1. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Análise da Acurácia do Modelo e da curva ROC-AUC (Área Sob a Curva ROC)**"
      ],
      "metadata": {
        "id": "a_Z946MyTBW3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "\n",
        "# A acurácia é a proporção de predições corretas em relação ao total de predições\n",
        "accuracy = fraudDetection.score(X_test, y_test)\n",
        "print(\"Acurácia do modelo:   {0:.4f}\".format(accuracy))\n",
        "\n",
        "# Calcula a área sob a curva ROC (Receiver Operating Characteristic),\n",
        "# para avalia a capacidade do modelo de distinguir entre as classes (fraude e não fraude).\n",
        "acc_snapml  = roc_auc_score(y_test, fraudDetection.decision_function(X_test))\n",
        "print(\"ROC-AUC score:   {0:.4f}\".format(acc_snapml))\n",
        "\n",
        "# Obter os scores da função de decisão\n",
        "y_scores = fraudDetection.decision_function(X_test)\n",
        "\n",
        "# Calcular a curva ROC e a área sob a curva (AUC)\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_scores)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plotar a curva ROC\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2,\n",
        "         label='Curva ROC (área = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--',\n",
        "         label='Classificador Aleatório')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('Taxa de Falsos Positivos', fontsize=12)\n",
        "plt.ylabel('Taxa de Verdadeiros Positivos', fontsize=12)\n",
        "plt.title('Curva ROC - Receiver Operating Characteristic', fontsize=14)\n",
        "plt.legend(loc='lower right')\n",
        "plt.grid(alpha=0.3)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "id": "-CgDuW7J55Ks",
        "outputId": "1cf161f0-d89c-4e9a-a83b-abf3faf063e3"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia do modelo:   0.8112\n",
            "ROC-AUC score:   0.8926\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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5XI6TJ0/m+rPl1U8//aR2hb1Dhw5wcXHBxYsXlW0pKSnYsWMH7O3tMWnSJLXHDxkyBNWrV8/z8xX09ejr64sWLVoo1w0MDDB48GAAUMsMZP97dHJyQq9evfDPP//g0aNHWbY3aNAAQ4YMyfa583O8FStWQKFQYPbs2VmulkokEpQtWzbnHzKT/L4PZDZv3jyYmprm6XkAwMjICEZGRlnabWxs8nyM7OzZswfPnj2Dn58fPD09s2zXlfdmyhm7RlCx1qRJk2zbq1WrhiZNmuDw4cOIjo5W3lWeURhn7hYBAKmpqfjll1+wdetW3L17F2/fvoUgCMrtz58/z1euP/74I0t3ij59+mDbtm2QSCT5OlZR2r17N65evarW1rZt2zwPX3XixIksd8K3aNECJ06cUPsq+Ny5cwDSR9TIbgzYu3fvKv/v5uaGCxcuAIDa19k5yTj2+fPn8eDBgyzbk5OTER0drfZ3kR1/f3/MnTsXQUFByg8vCoUCmzdvho2NDbp27Vrg56xbt+4Hu0IUllatWmVpK126NOrVq4fg4GCEh4ejSpUqyp8rLCws2/MUGRkJhUKBe/fuoVGjRsr2ihUr5vi7TUhIwPz587F79248ePAAiYmJatvz+1rLiZWVVbbFXfny5RESEqJcDwsLQ0pKCho1apSlu4JEIoG7uzvCwsK0kik3DRs2zNKWUUjFxsaqtYeHh2Pu3Ln4888/8ezZM6SkpKhtf/78eZYPZdmNOKPJ8fLzevyQ/L4PZDAxMUHt2rXz/Dz9+/fHl19+CTc3N/j6+qJdu3Zo2bKlsrtOQWjrd0HiYSFMWuXo6Ii7d+/i2bNn+bqSoqkP9Qvz9/fHhQsXsG3bNowePRqCIGDTpk2wtrZGt27d1Pbt3bs39u3bh2rVqqFfv36wt7eHkZERYmNjsXjx4iz/KORmy5Yt6N+/P9LS0hAWFoYvvvgCO3bsQPXq1TFr1iy1fR0dHQEAT548yfW4GftkDN+T8dhnz57lK19Odu/ejfXr12dpz2shnDF0kUKhQEREBAIDAxEUFIThw4djw4YNyv3evHkDADhw4ECWK66ZZRRJGWMo52VIpIxjL1269IP7JSYmfrAQ/uijj9CwYUMcPHgQMTExsLa2RnBwMJ4+fYrPPvtM7eqSps+ZWz/6/yrI30pmOT1vRnvG7zvj59q0adMHn+u/xWxOx09NTUXbtm1x+fJl1K9fH/7+/rCxsYGhoSEiIiKwfv36fL/WcmJpaZltu6GhofKKNwDl1Wx7e/ts98/POSro6zG7wiyjb71cLle23b9/H02aNEF8fDzatWsHLy8vWFhYQCqVIjg4GKdOncr295jTz5Lf48XFxUEikRR4GLH8vg9ksLe3z9cFhS+++AI2NjZYtmwZFixYgPnz58PQ0BDdunXDTz/9lO0HprzKz3sT6SZ2jSCtyvhaL7/jY0ql6X+KaWlpWbZ9aCKJD70Z9u/fH0ZGRsqrwKdPn8ajR4/Qt29ftSs/Fy9exL59++Dh4YHbt29j1apVmDNnDgIDA9G/f/98/Rz/ZWhoiFq1amHXrl2oUqUK5syZk2WyA3d3dwC5/85iY2Nx+fJlyGQy5ZWjxo0bQyaT4dKlS7lO1pEX69atg5A+mozyP01m7ZJKpahUqRLWr1+P1q1bIygoCLt371Zuz/gH/+eff87yfJn/y/haOOPmmrwUGBnHvnHjxgeP/d+rZdnx9/dHamoqtm/fDkDVLeK/3yho+pz5/XagUaNGMDIyQmhoaK4TrGT8PTVv3jzLtpcvX2b7mIz2jCIy4+fat2/fB3+uNm3a5Onn2rNnDy5fvoxPPvkEly9fxrJlyzB79mwEBgZm+7VyUcj4GV+9epXt9px+V9nR9P0vv3766SfExMRg3bp1OHbsGBYtWqQch7xGjRo5Pi6n85Lf41lZWUEQhGy73eRHft8Hcvs5ciKRSDB06FBcvHgRUVFR2LVrFz7++GPs2bMH3bt3V/uQkV/5eW8i3cRCmLQqICAABgYGWLlyZa4zmWW+wpBxl/p/30wUCgWuXbumURZbW1t4enri3LlzuH//vrIgzty3E4Dyq+xu3bpl6dN45swZjZ77v0xMTDB//nwIgoApU6aobevduzdKly6NnTt3Kr8KzM6CBQuQnJyMfv36KSd/MDMzQ//+/ZGUlKS82z4naWlpalfCioJEIsHixYshkUgwdepU5fNn3AWe+SvqD8noAnP06NFc983vsT9kwIABMDQ0xMaNG5GUlISdO3eiSpUqaNasWaE954eUKlUKffr0QXJy8gfP9507d7Br1y6Ym5ujd+/eWbZn93f99u1bXL16FRYWFspRILT9c2W81rIb0SCn15pUKi1QoZKb6tWrw9jYGKGhoVmuogqCkK+fvV27dqhUqRL+/vvvXPs6F+TKd06/R0EQ8NdffxX68fLzesx4T83uHBbV6yYzGxsb+Pj4YNu2bWjfvj1u376N+/fv55o1J/n5XZBuYiFMWlWlShV8+eWXiI6ORpcuXfDw4cMs+yQnJ2PhwoVqVxoz+q79dw73hQsXZnuMvMq4cvfbb79hx44dqFixotrNKACUV+n+OzzRrVu3MHfuXI2f+7+8vb3RoEEDHDt2TO0ffWtra8yZMwepqanw8vLCvXv3sjx29erVmDt3LmxsbDBnzhy1bXPmzIGdnR3mzJmDJUuWZFvsXr9+HW3bttXKVeP8qlevHnx8fHD37l3lV+xNmjRB06ZNsWXLFmzbti3LYxQKBU6dOqVcb9y4MRo3bozTp09j1apVWfbP/AFqyJAhMDc3x7Rp03Dr1q0s+757907ZNzE39vb26Ny5M/766y8sWrQI8fHxWT5Iafs5c/Pdd9/B2toa3333HX777bcs2//55x94e3sjNTUV33//vdpQVRmOHz+OI0eOqLXNmTMHsbGxGDRokPIbGm9vb1SoUAELFy7E6dOnsxzn/fv3WV43H5LTa+3UqVPZnlcAKFOmDJ4+fZrn58gvY2Nj9O7dGy9fvsSiRYvUtm3YsOGDH07/y8DAAEuXLoVUKkXfvn3x559/Zrvfvn37sv2Aklc5/R6///77bG8s0/bxRo4cCQMDA0yfPj3LTXmCIKj18864KTm77jz5fR/QVHBwsNo9H0D6325G14yMITatra0hkUjy1PUoQ48ePVC+fHls3Lgxy2sK4JXi4oB9hEnrZs+ejeTkZPz000+oXr062rdvDzc3NxgZGeHhw4c4fvw4Xr9+jdmzZysfM2TIEMybNw+BgYG4evUqKleujEuXLuHmzZto06aNxm+GXl5esLS0xMKFC/H+/XuMHTs2y9dqTZo0QZMmTbB9+3a8ePECzZo1w+PHj7F3715069YNv//+e4F+H5kFBgaiR48emDFjhtoVo7FjxyI6OhqzZs1C7dq14enpiY8++gjJyckIDg7GtWvX4ODggL1792YZMaB8+fI4evQofHx8MG7cOPz000/o0KGDcorlCxcu4OLFi7CwsMj2rumi8M0332D37t349ttvlVdZt2zZgnbt2qF///5YtGgRGjRoAFNTUzx+/BghISGIiopCcnKy8hibNm1C27Zt8emnnyIoKAjNmzdHcnIybt26hStXruD169cAADs7O2zZsgV9+vRB3bp14enpiRo1aiAlJQURERE4deoU3N3dcfjw4Txl9/f3x8GDB5XTFWdXCGv7OT/ExcUFBw8ehLe3N4YPH46ff/4Zbdu2VU6xfOjQIbx//145Ykl2unfvDi8vL/Tu3Ruurq44d+4cTp48icqVK+Pbb79V7mdsbIzff/8dXbp0QZs2bdC+fXvUrl0bEokEjx49wpkzZ2BjY5PnYtHLywuurq6YN28ebt68CTc3N4SFhWH//v3o2bNntq+19u3bY/v27fDx8UH9+vVhYGCAHj16oE6dOpr9ArMxd+5cHD9+HFOmTMGpU6eU4wjv378fnp6eOHz4sPLDQW48PT0RFBSEYcOGoUOHDmjUqBGaN28Oc3NzvHz5EsHBwXjw4EG2o93k1ciRI7F27Vr06tULffv2hY2NDc6dO4fLly+jW7duH+xvq43j1a5dG4sWLcLYsWNRq1Yt+Pj4wMXFBZGRkTh9+jS6deum/FDRrl07SCQSfPXVV7h16xYsLS1hZWWFMWPGAEC+3wc04ePjAwsLCzRr1gwuLi54//49jh07htu3b6N3797KDwKlS5dWfuD29/dH1apVIZVK4e/vn2NXKmNjY2zfvh2enp7o0qULPD09UbduXcTHx+Pq1at49+4drly5UqD8VMi0PyIbUbqLFy8KQ4cOFapUqSKYmpoKxsbGgqurq+Dr65vt2L9Xr14VOnToIJiZmQkWFhaCt7e38M8//+Q6s1xuhg0bphxDMywsLNt9Xr16JQwdOlQoW7asYGJiItSuXVtYunSpEB4enq8pNz80s1yGjPFwT5w4kWXbpUuXhEGDBilndzI3Nxfq168vBAYG5jo9cmJiorBo0SKhTZs2gq2trWBoaChYWVkJzZs3F+bMmZPt7FTa8qGZ5TL06tVLACCsXr1a2fbmzRth+vTpgpubm2BqaiqULl1aqFq1quDr6yvs3LkzyzEiIyOFcePGCZUqVRJkMplQpkwZoWnTpsLChQuz7Hv37l3hk08+EVxcXASZTCZYW1sLtWvXFsaOHStcuHBBbV98YKzad+/eCRYWFgIAoXnz5h/8PeT1ObUxleubN2+EwMBAoUGDBoKFhYUgk8mEChUqCIMGDRIuXbqU7WNymlnOxsZGCAgIEF68eJHt454+fSqMGzdOqFq1qmBsbCxYWFgIH330kTBs2LAsf8cf+l0KQvp4tb169RLs7OwEMzMzoXHjxsLWrVtzHBP4xYsXQt++fQVbW1tBKpXma2a57OQ0Znl4eLjQp08fwdLSUjAzMxNatWolnDp1ShgzZowAQLhy5UqOP1N2nj59KkyePFmoX7++YGFhIRgaGgoODg6Cp6ensHbtWrUZKz/0fpbTz3jy5EmhRYsWgrm5uWBlZSV07dpVCA0NzXbc3pyOoenxMj+me/fuQpkyZZQzy/Xq1Uv466+/1PZbt26dULt2bcHY2DjbmeXy8z7woXMrCNmPI/zrr78qZ80zMTERbGxshCZNmgjLli3LMnNoWFiY0LVrV8HKykqQSCR5nlnu/v37wieffCKUL19eMDIyEuzt7YW2bdsKGzZsyDEr6QaJIPzn+wIiIiqR1q1bhyFDhmDt2rWc7SqPWrZsiZCQEMTFxaF06dJixyEiLWMfYSIi0nvZjYCwceNG/PXXX+jYsSOLYKISin2EiYhI77m5uaF+/fqoWbMmDAwMcPXqVQQHB8Pc3Bzz588XOx4RFRIWwkREpPdGjhyJffv24dKlS0hMTISdnR18fX3x9ddff3BsXiIq3thHmIiIiIj0EvsIExEREZFeYiFMRERERHpJ7/sIKxQKPH/+HObm5vmev5yIiIiICp8gCEhISEDZsmXzPMFNXuh9Ifz8+fMsM3URERERke558uQJypcvr7Xj6X0hbG5uDgB49OgRrKysxA1DhU6hUCAqKgp2dnZa/URJuonnW7/wfOsXnm/9EhsbCxcXF2Xdpi16XwhndIewsLCAhYWFyGmosCkUCiQnJ8PCwoJvnHqA51u/8HzrF55v/aJQKABA691Y+ZdDRERERHqJhTARERER6SUWwkRERESkl1gIExEREZFeYiFMRERERHqJhTARERER6SUWwkRERESkl1gIExEREZFeYiFMRERERHqJhTARERER6SUWwkRERESkl1gIExEREZFeYiFMRERERHqJhTARERER6SUWwkRERESkl3SqED59+jS8vLxQtmxZSCQS7N69O9fHBAcHo0GDBjA2NkaVKlWwbt26Qs9JRERERMWfThXCiYmJqFu3LpYuXZqn/R8+fIhu3bqhXbt2uHr1KsaPH49hw4bhyJEjhZyUiIiIiIo7Q7EDZNalSxd06dIlz/svX74cFStWxIIFCwAAH330Ec6ePYuffvoJHh4ehRWTiIiI9JFCDlxaALy8KHYSvaJQALfDJIVybJ0qhPMrJCQEHTt2VGvz8PDA+PHjc3xMSkoKUlJSlOvx8fEAAIVCAYVCUSg5SXcoFAoIgsBzrSd4vvULz7d+EeV8RxyB9Mzkons+wov40hiyzQenHjgWyvGLdSEcGRkJBwcHtTYHBwfEx8cjKSkJpqamWR4zd+5czJw5M0t7VFQUUlNTCy0r6QaFQoG4uDgIggCpVKd6BlEh4PnWLzzf+kWM8236/C4si+SZCAD23KyOYTt6IDqxFIDkQnmOYl0Ia2Lq1KmYOHGicj0+Ph7Ozs6ws7ODlZWVeMGoSCgUCkgkEtjZ2fEfSj3A861f9Pp8JzyB5MJc4O0zsZMUGUEAyqSmQCYzhqRwvjXPKv6xclHR8nugxoAiemL9kpj4Hl9Mv4iV68KUbfZ2pngVpf3nKtaFsKOjI16+fKnW9vLlS1hYWGR7NRgAjI2NYWxsnKVdKpXq3xunnpJIJDzfeoTnW7/o7fm+9CNwfYXYKYqUBICJiM8vLe0IWFYQMUHJFBr6HAMH7kVY2Gtlm49PDcyf3wpVqnyj9ecr1u8UzZs3x4kTJ9Tajh07hubNm4uUiIiISASJL8ROoF8sKwIVu4qdokSRyxX44YezaNZstbIINjMzwsqV3bFzZ1/Y2JgVyvPq1BXht2/f4v79+8r1hw8f4urVqyhTpgwqVKiAqVOn4tmzZ9iwYQMAYOTIkfjll1/w5ZdfYujQofjzzz+xfft2HDhwQKwfgYiICAjbAdxaByjSiub5oq6qlgddA0oVzo1FukShUCA6Ohq2trZF/w2AiQ0gNSja5yzhkpPT8NtvV5CWln7zY8OGTti8uReqVbMp1OfVqUL40qVLaNeunXI9oy/v4MGDsW7dOrx48QKPH6v651SsWBEHDhzAhAkTsHjxYpQvXx6//fYbh04jIiLxvH8HHA4A0t6J8/ylnAAzO3GeuygpFFCYADCzB/StK0wJVKqUDJs3f4yWLddi0qTmCAxsC5ms8D9s6FQh3LZtWwiCkOP27GaNa9u2La5cuVKIqYiIiPLhfaJ4RfBHA/WjCKZiLyEhBfHxKShXzkLZ1rhxOYSHj1VrK2w6VQgTEREVe4+OqpZdPYHuW4vmeSUGgKx00TwXUQGEhDyBn98uODqWxqlTATA0VF3RL8oiGGAhTEREpF1/jlMtG5UCjDnyLBEApKUpMGfOacyadRpyuYDw8Bj88MNZTJvWWrRMLISJiIi0SZ5p4P/aw8TLQaRDwsNj4Oe3EyEhT5Vt7u7O8PWtLWIqFsJERFQSvb4NnJ0GJL7MfV9te/9v/+AyHwEVPYv++Yl0iCAICAq6jjFjDiIhIX0GXwMDCb75pg2mTm2l1i1CDCyEiYio5Dn/HXB/t7gZjApn3FOi4iImJgkjRx7A9u23lG2VKllj06aP0axZeRGTqbAQJiKikifpde77FCZjS6DBeHEzEIkoPj4F9eqtwOPHccq2gIB6WLLEE+bmWWf4FQsLYSIi0g1RN4AzU4DESI0eLgFgk/YeEkMjIPYf1YbRbwBjK61EzF8gSdE/J5GOsLAwRs+eNbB48XlYW5tgxYru6NOnltixsmAhTEREuuHiD8DDgxo/XALAKEujFDCQsSglEsH333dEcnIapk1rBWdn3Rw9hYUwERHphuQ3qmWpIdJL27zLPB2TBACkRkD9z9OHMCOiQiMIAlatugwDAwk++aSBst3ExBDLl3cXMVnuWAgTEZH4EiOBh4dU66NeASbW+TqEoFDg1atXsLe3h4RT7hIViaioRAwfvg979oTB1NQQ7u7O+Oij4jO7Id8piIhIfDfXqa8byESJQUR5d/ToA9Spsxx79oQBAJKS0rB//z2RU+UPrwgTEZH4UmJVy+VasTsDkQ5LTk7D1KnHsWjReWWbra0Z1qzpAS+v6iImyz8WwkREVDTOzQZubwQEedZtSdGq5Razii4TEeXLjRsvMXDgTty48UrZ5ulZBWvXesPRsbSIyTTDQpiIiApfYiTw19d525dXg4l0jiAI+PnnC/jyy2NISUn/MGtsbIAff+yEMWOaQFJMR2ZhIUxERIUv9a1q2UAGGJln3UciASp2BRwaFl0uIsqTt29TsWBBiLIIrlPHAZs2fQw3N3uRkxUMC2EiIn0R8w9wdBgQ97Don1vxXrVcrS/QNajoMxCRxszNjbFxY0+0a7ceY8c2xXffdYCJSfEvI4v/T0BERHlzfSXw9LTYKQBZNleDiUinJCamIjHxPeztVV2VWrVywb17n6NSpfwNbajLWAgTEemL1ATVsqkdYJBlHrbCZ14BqDuq6J+XiPIsNPQ5Bg7ciXLlLHDsmD+kUlX/35JUBAMshImICubeH8CZKUBqvNhJcpc5Y+9jgH1d8bIQkc6RyxWYP/9vTJ9+EmlpCoSFvcZPP4Vg0iR3saMVGhbCREQFcW4WEHtf7BT5Z2whdgIi0iFPnsRh0KDdCA6OULY1bOhU7MYFzi8WwkREBfE+YzQECWDpKmaSvJFIgWp9AMuKYichIh2xffstjBixH7GxyQDSB3CZMqUlAgPbQiYzEDld4WIhTES5e30bOOgHxIWLnSRfJADsFQIk0kIc3zLl3+4GpjbAsOL1+yEi/RYfn4KxYw9h/fpryjZnZwsEBfVEmzau4gUrQiyEiSh3N9YAr66InSLfJP/+VySMrYrqmYiICiwuLhkNGqxEeHiMsq1fv1pYtqwbrK1NRUxWtFgIE1Hu0t6pr5epIU6OfBIAyNPSYGBoWLgFsVEpoOm0wnwGIiKtsrQ0Qfv2rggPj4G5uQxLl3aFn1+dYjtDnKZYCBPRh138Ebi2TLXudxlwqC9ennwQFApEv3oFe3t7SKRSseMQEemUn37yRFJSGr79tl2JGxYtr1gIE9GHnZulvm5iJUoMIiLSjCAICAq6DiMjKQYMqK1sL11aho0bPxYxmfhYCBPRh6UlqZZbzOZoA0RExUhMTBJGjjyA7dtvoXRpGZo0KYfKlcuIHUtnsBAm0kVXlgJnpwKpb3Pft9AJ6f9zaAg0Yz9YIqLiIjg4Av7+u/D0afroNm/fpuL3329j8uSWIifTHSyEiXTRpR/Vp8PVBSY2YicgIqI8SE2VY8aMk5g37y8I/17LsLIywcqV3dGnTy1xw+kYFsJEYlKkQfkulVlGdwQDGWBXr0gjZcukDOA+U+wURESUi7CwaPj67sTlyy+UbW3bumLDBh84O1uKmEw3sRAmEssBX+Dulg/vU6osMPB80eQhIqJiSxAErFwZigkTjiApKQ0AYGQkxZw57TFpkjukhTmxUDHGQphIDPGPci+CAcDMrvCzEBFRsRcXl4LAwFPKIrh6dRts3twLDRo4iZxMt7EQJsqvtBRA8b5gx0h6o1o2swesq2fdx9gCaDylYM9DRER6wcrKBOvWecPTcxNGjmyIBQs8YGZmJHYsncdCmCg/Li0Azn4FyFO1d0xXT6DLeu0dj4iISrzk5DS8e/ceZcqopkP28KiCmzdHoVYtexGTFS+caokoP678ot0iGEi/IkxERJRHN268ROPGqzBo0C4I/7nhmkVw/vCKMFFeJccC8uT0ZQMZUK5VwY9p4QI0GF/w4xARUYmnUAj4+efzmDz5OFJS5Lh58xWWL7+EUaMaix2t2GIhTJQbQQB2dgUiDqvazByAPsfFy0RERHrlxYsEDBmyB0eOPFC21anjgFatXERMVfyxECbKTex99SIYAEo5ipOFiIj0zp49dzFs2D5ER79Ttk2Y0AzffdcBJiYs5QqCvz3ST4IAJEZmP5nFf719rlouXQ4o1xJoOLHwshEREQFITEzFpElHsWJFqLLNyak01q/3QadOlUVMVnKwECb9o5BDsq0l8OJc/h/r0hnwXKP9TERERJnExCShefPVCAt7rWzz8amBVau8YGtrJmKykoWjRpDeMYy9DYkmRTAAmJfTbhgiIqJsWFubomHDsgAAMzMjrFrlhZ07+7II1jJeEaaSQxCAhKeAIM95H4UCBonPVOuWlQC7unk7vqUrUH9sgSISERHl1dKlXZGU9B7ff98R1arZiB2nRGIhTCWDIAA72gNPgj+4mxSAdeaGSt2A9ksKLxcREVEebN9+C8bGBvD2rqFss7Iywc6d/URMVfKxEKaSIT4i1yI4W6XLazkIERFR3sXHp2Ds2ENYv/4arK1NcP16WZQvbyF2LL3BQpiKN0EA4h6mD3GWwbwCUNY9+90hIDk5BSYmxpBYVQHqjiiioEREROpCQp5g4MCdePgwFgAQE5OMjRuvY8qUluIG0yMshKl429MTeLBHva18K6Drxmx3FxQKxL16BWN7e0ikvFeUiIiKXlqaArNnn8bs2achl6cP42luLsPSpV3h51dH5HT6hYUwFV+pb7MWwUD6tMVEREQ6KDw8Bn5+OxES8lTZ5u7ujI0be6JiResPPJIKAwthKn7SkoGYe+mFcIZSToBr5/RuEQ3GiZeNiIgoG4IgYMOGaxgz5hDevk0FABgYSDBjRht89VUrGBryW0oxsBCm4uVdFLCuJpAUrd5uWxvwXCdKJCIiotzExCRj0qSjyiK4UiVrbNr0MZo1403bYuLHDypengRnLYIBwLJiUSchIiLKszJlTPHbbz0AAAEB9XD16ggWwTqAV4Sp+EiJB2L/Ua07NQPs6gBmDkC90eLlIiIi+o/UVDlSUtJgbm6sbPPxqYFLl4YrZ4wj8bEQpuIh7iGwvjbwPlHVVr0f0HC8aJGIiIiyExYWDV/fnahSpQy2bu0FiUSi3MYiWLewawQVD4+OqxfBAGBVWZwsRERE2RAEAStWXEL9+itw+fILbN9+C0FB18WORR/AK8IkrrgIIOFprrsh5p5quUJ7oIYvULFrocUiIiLKj6ioRAwbtg9794Yp26pXt4Gbm72IqSg3LIRJPHe3AgcG5P9xNQYCtYdqPw8REZEGjhy5j4CAPYiMVA3rOXJkQyxY4AEzMyMRk1FuWAiTeB4e0uxx1tW0m4OIiEgDyclpmDr1OBYtOq9ss7U1w5o1PeDlVV3EZJRXLIRJHAnPgMfHVeuVvQHrqrk/zrEJUK5F4eUiIiLKgzdvktC27TrcuPFK2ebpWQVr13rD0bG0iMkoP1gIU9F7cQHY4g4IclVbmx/zVggTERHpAGtrE1SqZI0bN17B2NgAP/7YCWPGNFEbIYJ0HwthKnqPT6gXwTLz9CmSiYiIigmJRILffuuBpKSdWLCgM2+KK6ZYCFPReX0XiAsH3txVtdnVAzx+A2T8GomIiHTX3r1hMDY2gIdHFWWbra0ZjhzxEzEVFRQLYSoa/+wE9vbK2u4+E3BoWPR5iIiI8iAxMRWTJh3FihWhsLcvhRs3RsHevpTYsUhLOKEGFY3Hf2bfbvNR0eYgIiLKo9DQ52jQYCVWrAgFALx6lYg1a66InIq0iVeEqfAIAvDiHJAYCcQ+ULXXGQGULgc4t+ENckREpHPkcgXmz/8b06efRFqaAgBgZmaERYs8MGxYA5HTkTaxEKbCc/FH4MzkrO11hrM7BBER6aQnT+Lg778Lp049UrY1bOiEzZt7oVo1GxGTUWFgIUyF59nprG2GpoBl5aLPQkRElIvt229hxIj9iI1NBgBIJMCUKS0RGNgWMpmByOmoMLAQJu3K6A4R/yh90owMzWakjwzh6gmYWIkWj4iIKDvR0e8wfPg+xMenAACcnS0QFNQTbdq4ihuMChULYdKuG6uAYyOytjcYC5jyKyUiItJNtrZmWLasGwYO3Il+/Wph2bJusLY2FTsWFTIWwqRdz//O2mbhAhhbFXkUIiKinKSlKZCaKoeZmZGyzde3NsqXt0CrVhU4Q5yeYCFM2iV/r1puMhUwLw9U8gKk7FtFRES6ITw8Bn5+O1Gjhi3WrPFW29a6tYtIqUgMLIRJu+5uVi3XGgyUqS5eFiIiokwEQUBQ0HWMHn0Qb9+mIiTkKbp0qYI+fWqJHY1Ewgk1SLscG6uWLfipmoiIdENMTBL69/8Dgwfvxtu3qQCASpWs4exsKXIyEhOvCJP2pCUDkRdV64Ym4mUhIiL6V3BwBPz9d+Hp03hlW0BAPSxZ4glzc2MRk5HYWAiT9vz5eaYV3mRARETiSk2VY8aMk5g37y8IQnqbtbUJVqzozu4QBICFMGlT5CXVslMT8XIQEZHee/36HTp33ojLl18o29q1c8WGDT1RvryFiMlIl7AQpoJ5nwTc3w0kvwaSolTtvY6IFomIiMja2hS2tmYAACMjKebMaY9Jk9whlfIbS1JhIUwFc+oL4Nqv6m0GxoAxbz4gIiLxSKUSrFvnjb59f8fixZ5o0MBJ7Eikg1gIU8G8upK1rWzzos9BRER67ejRBzAxMVQbB9jJyRxnzgwRMRXpOp0bPm3p0qVwdXWFiYkJmjZtigsXLnxw/0WLFqF69eowNTWFs7MzJkyYgOTk5CJKq6eibwGXFgKXFgBvn6nauwQBPf4Aeu4XLxsREemV5OQ0TJhwGB4eGzFw4E7ExCSJHYmKEZ26Irxt2zZMnDgRy5cvR9OmTbFo0SJ4eHggLCwM9vb2WfbfvHkzpkyZgjVr1sDd3R337t1DQEAAJBIJFi5cKMJPoAeSY4BNjdKHSvuvjwYCnJKSiIiKyJ07rzFu3C7cuPEKAPD0aTxWrgzF5MktRU5GxYVOXRFeuHAhhg8fjiFDhqBmzZpYvnw5zMzMsGbNmmz3//vvv9GiRQv4+vrC1dUVnTt3xoABA3K9ikwFEHs/+yK4bAsWwUREVCQUCgFLlpxHly6qItjY2ABLlnjiyy9biJyOihOduSKcmpqK0NBQTJ06VdkmlUrRsWNHhISEZPsYd3d3bNy4ERcuXECTJk0QHh6OgwcPwt/fP8fnSUlJQUpKinI9Pj59cG2FQgGFQqGln6YEe5+k/PQkuHpAqDU0feKMCh2AYvD7UygUEASB51pP8HzrF55v/fDiRQKGDt2Ho0cfKNtq17bHxo094eZmD0EQIGQMGkwlRmG9rrVWCL979w5bt25FSkoKunbtCheX/E2vGx0dDblcDgcHB7V2BwcH3L17N9vH+Pr6Ijo6Gi1btoQgCEhLS8PIkSPx1Vdf5fg8c+fOxcyZM7O0R0VFITU1NV+Z9ZHlxV9g+u/yO1k5JFi1Tl95kwAgQaxYeaZQKBAXFwdBECCV6tQXIlQIeL71C893yXf4cAQmTTqFN29U30wOH+6Gr75qChMT4NWrVyKmo8IUFxdXKMfVqBD+5JNPcP78edy8eRNA+tXcZs2aKdctLS3x559/on79+tpLmo3g4GB89913+PXXX9G0aVPcv38f48aNw6xZs/D1119n+5ipU6di4sSJyvX4+Hg4OzvDzs4OVlZWhZq3JJCYqKZNNrWvCtNs+m7rMoVCAYlEAjs7O/5DqQd4vvULz3fJFhWViDFj/kRi4nsAgJNTaSxY0Bp9+tTn+dYDMpmsUI6rUSF88uRJ+Pn5Kdc3b96MmzdvYtOmTahbty569eqFmTNnYvfu3Xk+pq2tLQwMDPDy5Uu19pcvX8LR0THbx3z99dfw9/fHsGHDAAC1a9dGYmIiPv30U0ybNi3bF4axsTGMjbPOKy6VSvlCyk1aMnAnSLkqrfoxUAx/ZxKJhOdbj/B86xee75LLwcEcixZ5YvjwffD2ro6VK7tDoXjL860nCusca3TUyMhIuLq6Ktd3796NRo0aYcCAAahZsyaGDx+O8+fP5+uYMpkMDRs2xIkTJ5RtCoUCJ06cQPPm2Y9L++7duyy/GAMDAwBg/6DCcH2F2AmIiEhPyOUKpKSkqbV98kl9HDo0ELt29VPOGkdUEBoVwqVKlUJsbCwAIC0tDcHBwfDw8FBuNzc316gvx8SJE7Fq1SqsX78ed+7cwahRo5CYmIghQ9IHwx40aJDazXReXl5YtmwZtm7diocPH+LYsWP4+uuv4eXlpSyISYte31Fft6osTg4iIirRnjyJQ8eOQfjii6Nq7RKJBJ6eVSDhKEWkJRp1jWjQoAFWrVqFdu3aYe/evUhISICXl5dy+4MHD7Lc9JYX/fr1Q1RUFGbMmIHIyEjUq1cPhw8fVh7r8ePHaleAp0+fDolEgunTp+PZs2ews7ODl5cX5syZo8mPRbl5+1y1POBvQMoPG0REpF3bt9/CiBH7ERubjODgCHTpUhVdu1YVOxaVUBJBgz4Ely5dgoeHB2JjYyEIAnr37o3t27crt1evXh2NGzfGxo0btRq2MMTHx8PS0hIxMTG8WS43CzJ9Ah90DbCrI14WDSkUCrx69Qr29vbsU6YHeL71C8938RYfn4KxYw9h/fpryjZnZwts2vQxWrXKOhIVz7d+iY2NhbW1NeLi4mBhYaG142p0RbhRo0a4e/cu/v77b1hZWaFNmzZqQT/77DO1NiohrKsBMffSl8t8JG4WIiIqMUJCnsDPbxfCw2OUbf361cKyZd1gbW36gUcSFYzG4wjb2dnB29s7S7uVlRXGjRtXoFCkg+Ifq4pgQ1PAwEjcPEREVOylpSkwZ85pzJp1GnJ5+hfU5uYyLF3aFX5+ddgXmApdgSbUOHXqFA4cOIBHjx4BAFxcXNC9e3e0bt1aK+FIhxwfqVo25KdzIiIqmNev38HLawtCQp4q29zdnbFxY09UrGgtYjLSJxoVwqmpqRgwYAB2794NQRCUfWtjY2OxYMEC9OzZE1u2bIGREa8alhix91XLlbN+E0BERJQfVlYmMDRM79trYCDBjBlt8NVXrZRtREVBo7+2mTNnYteuXZg0aRJevHiBN2/e4M2bN4iMjMQXX3yBnTt34ttvv9V2VhLLw0NAzD+qdY/V4mUhIqISwcBAiqCgnmjQwAlnzw7FjBltWARTkdNo1IiKFSuibdu2WLt2bbbbAwICEBwcjIiIiILmK3QcNSIXiZHASmdA8e+g5qa2wGdR4mYqAN5lrF94vvULz7duO3UqAqamRmjSpJxauyAIGvUF5vnWL4U1aoRGfzkvXrxA06ZNc9zetGlTREZGahyKdEjCU1URDABVe4mXhYiIip3UVDmmTj2Odu3WY8CAP5CQkKK2nTfEkZg0KoTLly+P4ODgHLefOnUK5cuX1zQT6ZLnf6mWXToDnZaLl4WIiIqVsLBoNG++Gt9//xcEAQgPj8GyZZfEjkWkpFEhPHjwYGzfvh0jR45EWFgY5HI5FAoFwsLCMGrUKOzYsQMBAQFajkpFTpEGnByvWi9TQ7QoRERUfAiCgJUrQ1G//gpcvvwCAGBkJMW8eR3xxRfuIqcjUtFo1IivvvoKDx48wMqVK7Fq1Spl3xyFQgFBEDB48GB89dVXWg1KIkhLVl+v4iNKDCIiKj6iohIxfPg+7NkTpmyrXt0Gmzf3QoMGTiImI8pKo0LYwMAA69atw8SJE3Hw4EG1cYS7du2KOnWK39S7lI37u1XLDo2ACu1Ei0JERLrvyJH7CAjYg8jIt8q2kSMbYsECD5iZcUhV0j0FmlCjTp06LHpLqnfRwCF/1bqpjXhZiIhI5718+RY+PtuQnJx+g7WtrRnWrOkBL6/qIicjyplGfYSbNGmCn376CU+fPs19Zyqe3r1UX68xQJwcRERULDg4lMb333cAAHh4VMaNG6NYBJPO06gQNjAwwKRJk+Dq6oqWLVvil19+4XBpJU3CE9VylZ5ArcHiZSEiIp2jUAh4/16u1vb5503x++99cPDgQDg6lhYpGVHeaVQIh4SEICIiAnPnzkVKSgrGjh0LZ2dntG/fHitXrkR0dLS2c1JRu/KzatmEc74TEZHKixcJ6NJlE6ZP/1OtXSqVoFevmpBKOTYwFQ8aT8VSoUIF/O9//8PFixdx//59fPvtt4iJicHIkSNRtmxZeHp6ajMnFbXMA5xX7CJeDiIi0il79txF7drLcPToA/z449/488+HYkci0phW5iSsVKkSpk6disuXL2PFihUwNTXFsWPHtHFoKgoRR4C9vYFdPVT/RV5UbXfmaBFERPouMTEVI0fuh4/PNrx+nQQgvV8wUXFWoFEjMpw7dw7bt2/Hjh078Pz5c5QuXRq+vr7aODQVNoUcOOgHJH2gO4tUK38mRERUTIWGPoev707cu/da2ebtXR2//dYDtrZmIiYjKhiNK5zQ0FBs27YN27dvx5MnT2Bqaoru3bujX79+6Nq1K4yNjbWZkwqLIu3DRfBHfoCxZdHlISIinSGXKzB//t+YPv0k0tIUAAAzMyMsWuSBYcMaQCJhX2Aq3jQqhCtXroyIiAjIZDJ06dIFP/zwA7y8vGBmxk+Fxc6ttaplp2aAzx7VusQQMC1T9JmIiEh00dHv0KfPDgQHRyjbGjZ0wubNvVCtGseWp5JBo0K4Zs2amDlzJry9vWFubq7tTFRU3r0Cjo9SrRuVAszsxctDREQ6w9LSGG/fpgJIv396ypSWCAxsC5nMQORkRNqjUSG8b98+becgMSS9Vl+v86k4OYiISOcYGRlg06aP4eOzFcuWdUObNq5iRyLSujwVwo8fPwaQPmRa5vXcZOxPOujWBuDsV6r1Gr5A9b7i5SEiIlGFhDyBmZkR6tZ1VLZVq2aDmzc/47jAVGLlqRB2dXWFRCJBUlISZDKZcj03crk8131IBCnxwLHhgDxV1SZjFxciIn2UlqbAnDmnMWvWaVSrZoNLlz6FmZmRcjuLYCrJ8lQIr1mzBhKJBEZGRmrrVEylxqsXwaUcOYUyEZEeCg+PgZ/fToSEPAUA3LkTjV9/vYgvvnAXORlR0chTIRwQEPDBdSpm4sJVy5W6Ad57AClvfiAi0heCICAo6DrGjDmIhIT0CyMGBhJ8800bjB/fTOR0REVHo5nlhg4divPnz+e4/cKFCxg6dKjGoaiQXV+lWpYasQgmItIjMTFJ6N//DwwevFtZBFeubI2zZ4fi66/bwNBQK5POEhULGv21r1u3Dg8ePMhx+8OHD7F+/XqNQ1EhU6Splqv4iBaDiIiKVnBwBOrUWY7t228p24YMqYcrV0agWbPyIiYjEkehzJ37/PlzmJqaFsahqSBe3wFO/w949peqrXwb8fIQEVGRefEiAR4eG5Gamn4ju7W1CVas6I4+fWqJnIxIPHkuhPfs2YM9e1Szjq1cuRLHjx/Psl9sbCyOHz+Oxo0baychac/FH4HwA+pthibiZCEioiLl5GSOb75pg2nT/kS7dq7YsKEnype3EDsWkajyXAjfvn0bO3bsAABIJBKcP38eoaGhavtIJBKUKlUKrVu3xsKFC7WblAou+Y1q2dAEqDU0fcQIIiIqcQRBgEIhwMBA1Qty8uQWcHa2wMCBdTgsGhEAiSAIQn4fJJVKsXHjRvj6+hZGpiIVHx8PS0tLxMTEwMrKSuw4hSPqBnBqEvA8BHj/Nr1tZCRQykHcXCJQKBR49eoV7O3tIZXyhpCSjudbv/B8q0RFJWL48H2oX98R33zTVuw4hYLnW7/ExsbC2toacXFxsLDQ3jcZGvURVigUWgtARSB0AfDoWKYGCWBgLFocIiIqPEeO3EdAwB5ERr7F/v330LlzZTRv7ix2LCKdVCg3y5GOSY5VLRtbAnVGAiZWYqUhIqJCkJychqlTj2PRItXwptbWpsoh0ogoqzwVwlKpFFKpFO/evYNMJoNUKs11ZjmJRIK0tLQP7kNF4PVd4IHqJkcMCdPLLhFERCXZjRsvMXDgTty48UrZ5uFRGevW+cDRsbSIyYh0W54K4RkzZkAikcDQ0FBtnYqBq7+or3OUCCKiEkOhEPDzz+cxefJxpKSkD4tmbGyAefM6YcyYJrwhjigXeSqEAwMDP7hOOiwlVrVcwze9awQRERV7r1+/w8CBO3HkiGqCq9q17bF5cy+4udmLmIyo+OBtliVd5kFBWnwrXg4iItKqUqVkePYsQbk+YUIzXLgwnEUwUT5oVAifOHECP/74o1rbmjVrUKFCBTg4OGDChAmQy+VaCUgFlakQlvBzDxFRSWFiYojNmz9GxYpWOHLEDwsXesDEhPfAE+WHRq+YwMBAuLi4KNdv3LiBESNGoE6dOqhSpQqWLFkCR0dHTJ48WWtBSUNC5qHu2FeMiKi4Cg19jlKlZKhRw1bZVru2A+7d+xyGhrzQQaQJjV45d+7cQaNGjZTrQUFBsLCwwJkzZ7Bt2zYMHz4cGzZs0FpIKoCwbapl3uBIRFTsyOUK/PDDWTRrthoDBvyBlBT1EZlYBBNpTqNXT2JiotqsHocPH4anpyfMzMwAAI0bN8ajR4+0k5A0F/+fc2DEIXSIiIqTJ0/i0KHDBkyZcgJpaQpcvRqJX3+9KHYsohJDo0LY2dkZFy+mvxDv37+PmzdvonPnzsrtb968gbExZy4TXWqC+rqpjTg5iIgo37Zvv4U6dZbj1Kn0ixoSCTB1akuMHt1E5GREJYdGfYQHDhyIb7/9Fs+ePcOtW7dgbW0Nb29v5fbQ0FBUq1ZNayFJQ5n7B9f5VLwcRESUZ/HxKRg79hDWr7+mbHN2tkBQUE+0aeMqXjCiEkijQnjatGlITU3FwYMHUaFCBaxbtw5WVlYA0q8GBwcHY9y4cdrMSZrIXAhzxAgiIp0XEvIEfn67EB4eo2zr168Wli3rBmtrUxGTEZVMGhXChoaGmDNnDubMmZNlW5kyZRAZGVngYKQFaiNGsBAmItJlz57Fo23b9UhNTR9+1NxchqVLu8LPrw5ncyUqJAWujt6+fYs7d+7gzp07ePv2rTYykba8uata5hVhIiKdVq6cBb74ojkAwN3dGdeujYS/f10WwUSFSOPq6OLFi2jXrh2sra3h5uYGNzc3WFtbo3379rh06ZI2M5Kmnv+tWk5LEi8HERFlIQgChMyzfwIIDGyL5cu74dSpAFSsaC1SMiL9oVHXiPPnz6Nt27aQyWQYNmwYPvroIwDp4wtv2bIFrVu3RnBwMJo04Z2tojIwUS27dBIvBxERqYmJScLIkQfQuHFZfPGFu7LdyMgAI0Y0+sAjiUibNL5Zrly5cjh79iwcHR3VtgUGBqJFixaYNm0ajh07ppWQlE/vk4A/PIBnZ1Rt5uXFy0NERErBwRHw99+Fp0/jsWvXHXToUBH16zuJHYtIL2nUNeL8+fMYMWJEliIYABwcHPDpp5/i3LlzBQ5HGnp8Qr0IBgATjiFMRCSm1FQ5pkw5jvbt1+Pp03gAQOnSMkRG8v4aIrFodEVYKpUiLS0tx+1yuRxSKW/OEo08WX29xSzApoY4WYiICGFh0fD13YnLl18o29q1c8WGDT1RvrzFBx5JRIVJo2rV3d0dS5cuzXYa5cePH+PXX39FixYtChyONBT3ULXcZj7QbLp4WYiI9JggCFix4hLq11+hLIKNjKSYN68jjh8fxCKYSGQaXRH+7rvv0Lp1a9SoUQM9e/ZUziIXFhaGPXv2wNDQEHPnztVqUMqH5Deq5ZQ48XIQEemxN2+SMGTIHuzdG6Zsq17dBps390KDBuwTTKQLNCqE69evj3PnzmH69OnYu3cv3r17BwAwMzODp6cnZs+ejZo1a2o1KOWDoZlq2ba2eDmIiPSYsbEB7t6NVq6PGtUI8+d3hpmZkYipiCgzjQphAKhVqxZ27doFhUKBqKgoAICdnR37BuuCR0dVy0alxMtBRKTHSpWSYdOmj+HtvRXLl3eDl1d1sSMR0X/kq2pduXIlPvroI5iYmKBcuXKYMGEC3r9/DwcHBzg4OLAI1hWGptkvExFRoblx4yXCw2PU2ho1Kovw8LEsgol0VJ4r1927d2PkyJF4+vQp6tSpA4VCgSVLlmDkyJGFmY80Ic30tZsNu6gQERUmhULA4sXn0LjxKgwcuBNpaQq17cbGGn/5SkSFLM+F8MKFC1G5cmXcv38fFy5cwJMnT9C3b19s2rQJ8fHxhZmR8iP6FvDwoGqdc9QTERWaFy8S0KXLJowffwQpKXKcO/cUy5ZdFDsWEeVRngvhsLAwjBgxAg4ODgAAQ0NDTJ06FWlpabhz506hBaR8+ucP9XWj0uLkICIq4fbsuYvatZfh6NEHyrYJE5ph+PCGIqYiovzI8/c1UVFRKFu2rFpbuXLlAEA5agSJQBCAdy/T/w+oD51WdxRgZJb944iISCOJiamYNOkoVqwIVbY5OZXGunU+6Ny5sojJiCi/8tVxScKv2XWLIg3Y3Ax4GZr99qofF20eIqISLjT0OXx9d+LevdfKNh+fGli1ygu2trzwQFTc5KsQnj9/PrZs2aJcf//+PQBg2rRpsLW1VdtXIpFgz549WohIOXoZmnMRDACly+a8jYiI8uXJkzi4u69BaqocAGBmZoTFiz3xySf1eaGIqJjKcyFcoUIFvHnzBm/evFFrd3FxwYsXL/DixQu1dr4pFLK0ZCDhiWrdqor65BmuHhwxgohIi5ydLfHZZ42waNF5NGzohM2be6FaNRuxYxFRAeS5EI6IiCjEGJQvr+8CW1uo9weu7A20nS9eJiKiEkgQBLULO3PndkSFCpYYPboJZDIDEZMRkTZwBoziKHyfehEMAOblxclCRFQCxcenICBgN5Ytu6TWbmJiiAkTmrMIJiohOMp3caSQq5YNTYFGXwBuQ8XLQ0RUgoSEPMHAgTvx8GEstm27hXbtXPHRR3ZixyKiQsArwsVRxGHVcrctQItvAWML8fIQEZUAaWkKBAYGo1WrtXj4MBYAYGQkxYMHMR9+IBEVW7wiXByZ2auWDYzFy0FEVEKEh8fAz28nQkKeKtvc3Z2xcWNPVKxoLWIyIipMLISLE0Ua8PoO8P6tqs26mnh5iIiKOUEQsGHDNYwZcwhv36YCAAwMJJgxow2++qoVDA35xSlRScZCuLiQvwfW1wZiwsROQkRUIsTGJmPEiP3Yvv2Wsq1SJWts2vQxmjXjDchE+kBrhbAgCDh58iRSUlLQsmVLmJuba+vQBADRN7IWwYZmgBlv4CAi0oREApw/r+oKERBQD0uWeMLcnF3OiPSFRt/5TJs2De3atVOuC4KAzp07o1OnTujWrRtq166NBw8eaC0kARAUqmWbmkDdUUDPfYCMHziIiDRhaWmCoKCesLU1w/btvbF2rTeLYCI9o1Eh/Mcff6BJkybK9d9//x0nTpzA7NmzsX//fsjlcgQGBmorIwHAX9NVy87tgY6/AhXai5eHiKiYCQuLxtOn8WptrVq5ICJiHPr0qSVSKiISk0aF8LNnz1ClShXl+s6dO1GzZk1MnToVXbt2xahRoxAcHKytjAQAcQ9Vy1aVxctBRFTMCIKAFSsuoX79FRg0aBcUCkFte6lSMpGSEZHYNCqEDQ0NkZKSAiD9DebEiRPw9PRUbndwcEB0dLR2ElI6SaZZjOp8Kl4OIqJiJCoqET4+2zBy5AEkJaXh5MkIrFwZKnYsItIRGhXCbm5u2LhxI2JiYrB27Vq8fv0a3bp1U25/9OgRbG1ttRaSMpFZAEZmYqcgItJ5R47cR506y7F3r+pG45EjG2LQoLoipiIiXaJRITxjxgxcvXoVtra2GD58OFq0aKF289yBAwfQuHFjjQItXboUrq6uMDExQdOmTXHhwoUP7h8bG4vRo0fDyckJxsbGqFatGg4ePKjRcxMRUfGXnJyGCRMOw9NzEyIj08ddt7U1w969/bFsWXeYmRmJnJCIdIVGw6d16tQJly9fxrFjx2BlZYV+/fopt8XExKB169bw9vbO93G3bduGiRMnYvny5WjatCkWLVoEDw8PhIWFwd7ePsv+qamp6NSpE+zt7fH777+jXLlyePToEaysrDT5sXSbPFnsBEREOu/OndcYN24Xbtx4pWzz8KiMdet84OhYWsRkRKSLJIIgCLnvVjSaNm2Kxo0b45dffgEAKBQKODs74/PPP8eUKVOy7L98+XL8+OOPuHv3LoyMNPuEHx8fD0tLS8TExOhuAS0ogIX/9hGWWQCfx4mbpxhTKBR49eoV7O3tIZVyxqiSjudbvzx8+AYfffQrUlLkAABjYwPMm9cJY8Y0gVQqETkdaRtf3/olNjYW1tbWiIuLg4WFhdaOW6AJNR4+fIhDhw7h0aNHAAAXFxd06dIFFStWzPexUlNTERoaiqlTpyrbpFIpOnbsiJCQkGwfs3fvXjRv3hyjR4/Gnj17YGdnB19fX0yePBkGBgbZPiYlJUV5ox+QXggD6S8ohUKR7WNEFxeh6sOSGq+7OYsBhUIBQRD4O9QTPN/6xdnZAr17V8WmTXdRu7Y9Nm7sCTc3ewBClpEiqPjj61u/FNZ51rgQnjRpEhYvXpwlmFQqxfjx4zF//vx8HS86OhpyuRwODg5q7Q4ODrh79262jwkPD8eff/6JgQMH4uDBg7h//z4+++wzvH//Ht988022j5k7dy5mzpyZpT0qKgqpqan5ylxUDN6+Rsb8cam2jfDm1asP7k85UygUiIuLgyAIvIKgB3i+9YtCocCECTVRrlxpjBpVFyYmwCu+X5ZYfH3rl7i4wvk2XKNCeMGCBfjpp5/Qu3dvTJo0CR999BEA4M6dO/jpp5/w008/oVy5cpgwYYJWw/6XQqGAvb09Vq5cCQMDAzRs2BDPnj3Djz/+mGMhPHXqVEycOFG5Hh8fD2dnZ9jZ2elw1wjVFKBGNpWz7S9NeaNQKCCRSGBnZ8c3Tj3A811yJSam4osvjqFp0/IICEgfBSLjfM+Z8xHPtx7g61u/yGSFM963RoXwqlWr0KNHD2zfvl2tvWnTpti6dSuSk5OxYsWKfBXCtra2MDAwwMuXL9XaX758CUdHx2wf4+TkBCMjI7VuEB999BEiIyORmpqa7S/N2NgYxsZZp9CUSqW6+0JKjlIuSmLuQaKrOYsJiUSi2+ebtIrnu+QJDX2OgQN3IizsNTZvvok2bVxQuXIZADzf+obnW38U1jnW6KgRERHw8PDIcbuHhwciIiLydUyZTIaGDRvixIkTyjaFQoETJ06gefPm2T6mRYsWuH//vlr3jHv37sHJyanQPjmIIvP9jK45/96JiEoyuVyBH344i2bNViMs7DUAQKEQcPMmuz8QkWY0KoTt7e1x7dq1HLdfu3YNdnZ2OW7PycSJE7Fq1SqsX78ed+7cwahRo5CYmIghQ4YAAAYNGqR2M92oUaPw5s0bjBs3Dvfu3cOBAwfw3XffYfTo0fn/oXRapkLYkJNpEJH+efIkDh06bMCUKSeQlpZ+8aNhQydcuTIC3t41RE5HRMWVRl0j+vTpg8WLF8PV1RWff/45SpUqBQBITEzEL7/8gt9++w3jx4/P93H79euHqKgozJgxA5GRkahXrx4OHz6svIHu8ePHapfGnZ2dceTIEUyYMAF16tRBuXLlMG7cOEyePFmTH0t3Zb4iLOHXP0SkX7Zvv4URI/YjNjZ9PHWJBJgypSUCA9tCJst+hCAiorzQaBzhd+/ewcvLCydPnoShoSHKli0LAHj+/DnS0tLQrl077Nu3D2Zmun/1sliMI3x/D7DHJ3255Ryg6VeixinOOO6kfuH5Lt4SElLw+eeHsH696htIZ2cLBAX1RJs2rln25/nWLzzf+kWnxhE2MzPDiRMnsGfPHrVxhD09PdG1a1d4eXlBIuHg5VpzbXmmFf5eiUg/pKTIcfToA+V6v361sGxZN1hbm4qYiohKknwXwu/evYOfnx969eqFgQMHajSVMuWTUaYr66WyH0GDiKiksbU1w/r1Pujdewd++aUL/Pzq8CILEWlVvr9LMDMzw/Hjx/Hu3bvCyEPZUchVyy6dxMtBRFSIwsNj8PLlW7W2Tp0q49Gj8fD3r8simIi0TqNONS1btsxx2mMqBA/2ZFrhPwREVLIIgoD166+ibt3lGDp0L/5764qVlYlIyYiopNOoEP7ll19w5swZTJ8+HU+fPs39AVQw5Vqqlk3KiJeDiEjLYmKS0L//HwgI2IO3b1Nx8OA/WLv2qtixiEhPaHSzXN26dZGWloa5c+di7ty5MDQ0zDJbm0QiKbR5ofVO5q4RhllnxSMiKo6CgyPg778LT5/GK9sCAuqhT5+aIqYiIn2iUSHcq1cv9tUqSkLavwsSjiNMRMVeaqocM2acxLx5fymHSbe2NsGKFd3Rp08tccMRkV7RqBBet26dlmPQB2VcEZZy4HgiKt7u3o3GwIE7cfnyC2Vbu3au2LChJ8qX197YoEREeaFRIUxF7NXl9P9LebqIqPgKD49BgwYrkJSU/i2XkZEUc+a0x6RJ7pBK+S0jERW9PFVWGzZsAAD4+/tDIpEo13MzaNAgzZNRuvjHqmUJrwgTUfFVqZI1Pv74I2zadAPVq9tg8+ZeaNDASexYRKTH8lQIBwQEQCKRoH///pDJZAgICMj1MRKJhIWwNkSpphbF+0TxchARacHSpV3h4mKJadNaw8zMSOw4RKTn8lQIP3z4EAAgk8nU1qkIKNJUy82/ES8HEVE+JCenYerU43B3d1a7Ac7S0gRz5nQQMRkRkUqeCmEXF5cPrlMhylwIy3gjCRHpvhs3XmLgwJ24ceMV1q27hmbNysPZ2VLsWEREWRRoLK6UlBSEhIRgz549iI6O1lYmyixzIcyb5YhIhykUAhYvPofGjVfhxo1XAICkpPe4dOm5yMmIiLKncSG8ZMkSODk5oWXLlvj4449x/fp1AEB0dDRsbW2xZs0arYXUa6mZJiVhIUxEOurFiwR07boJ48cfQUpK+pCPtWvb49KlT9Gz50cipyMiyp5GhfDatWsxfvx4eHp6YvXq1Wrzwtva2qJ9+/bYunWr1kLqtesrVcucTIOIdNCePXdRp85yHDnyQNk2YUIzXLgwHG5u9iImIyL6MI0uMS5YsADe3t7YvHkzXr9+nWV7w4YNsWTJkgKHIwClHFXLdnXFy0FE9B+JiamYNOkoVqwIVbY5OZXGunU+6Ny5sojJiIjyRqNLjPfv30eXLl1y3F6mTJlsC2TSQMQR1XKZGuLlICL6j/j4FPzxxx3luo9PDVy/PopFMBEVGxoVwlZWVh+8Oe727dtwdHTMcTvl0aurgKAQOwURUbacnMzx229eMDMzwqpVXti5sy9sbc3EjkVElGcaFcJdu3bFypUrERsbm2XbrVu3sGrVKvTo0aOg2ejNXfV1YytRYhARAcCTJ3F48yZJrc3buwYePhyHYcMaQCLhNMlEVLxoVAjPnj0bcrkcbm5umD59OiQSCdavXw8/Pz80atQI9vb2mDFjhraz6p8He1XLrecB/EeGiESyffst1KmzHCNG7Fe7QRoA7O1LiZSKiKhgNCqEy5Yti9DQUHh6emLbtm0QBAFBQUHYt28fBgwYgHPnzsHW1lbbWfXLy1Dg7hbVuoGxeFmISG/Fx6cgIGA3+vX7HbGxyfj999vYvPmG2LGIiLRC44Fp7e3t8dtvv+G3335DVFQUFAoF7OzsIJVyiC+teHNPfb0CpyQloqIVEvIEAwfuxMOHscq2fv1qoWvXquKFIiLSIq3M0GBnZ6eNw1BmmSfSaPoVYFtLvCxEpFfS0hSYM+c0Zs06Dbk8vRuEubkMS5d2hZ9fHfYFJqISI0+F8LfffpvvA0skEnz99df5fhz9K2ybatmU3UyIqGiEh8fAz28nQkKeKtvc3Z2xcWNPVKxoLWIyIiLty1MhHBgYmKUt44rAf2+akEgkEASBhXBBmWaajcmmpng5iEhv3L//Bg0arEBCQioAwMBAghkz2uCrr1rB0JDd3oio5MnTO5tCoVD778mTJ6hduzYGDBiACxcuIC4uDnFxcTh//jz69++PunXr4smTJ4WdvWRKiQcuzAPubVe1lflIvDxEpDcqV7ZGhw6VAACVKlnj7NmhmDGjDYtgIiqxNOojPHr0aFStWhUbN25Ua2/cuDE2bdqE3r17Y/To0di1a5dWQuqVq0uBs1+pt0n4jxARFT6JRIJVq7zg4mKJWbPawdyco9UQUcmmUYX1559/on379jlu79ChA06cOKFxKL0WF66+7tQUKF1OnCxEVGKlpsoxZcpxHDigPkKNra0ZFi3yZBFMRHpBo0LYxMQEISEhOW7/+++/YWJionEovfY80++16yZgwN+cSIOItCosLBrNm6/GDz/8haFD9+Lly7diRyIiEoVGhfDAgQOxadMmjB07Fv/884+y7/A///yDzz//HJs3b8bAgQO1nbXkU8iB17dU6/b12C2CiLRGEASsWHEJ9euvwOXLLwAAMTFJ+Osv3tNBRPpJoz7CP/zwA6Kjo/HLL79g6dKlykk0FAoFBEHAgAED8MMPP2g1qF6Qp6qvl6khTg4iKnGiohIxbNg+7N0bpmyrXt0Gmzf3QoMGTiImIyISj0aFsEwmQ1BQEP73v//h4MGDePToEQDAxcUFXbp0Qd26dbUaUi+Vb8OrwUSkFUeO3EdAwB5ERqq6QIwa1Qjz53eGmZmRiMmIiMRVoJnl6tSpgzp16mgrCyVFq5YFuXg5iKhESE5Ow9Spx7Fo0Xllm62tGdas6QEvr+oiJiMi0g1amWKZtCQlRrX85q54OYioRHj1KhFr115Vrnt6VsHatd5wdCwtXigiIh2i8Xfvhw4dQqdOnWBjYwNDQ0MYGBhk+Y/yKS1JtVz1Y/FyEFGJUKGCJZYt6wZjYwMsWeKJgwd9WQQTEWWi0RXhP/74A3379kWtWrXQv39/LFu2DL6+vhAEAXv27EHVqlXh4+Oj5ah64NFx1fJ/b5wjIsrFixcJKFVKBgsL1RjAAwbURsuWFeDsbCliMiIi3aTRFeG5c+eiSZMmuHLlCmbOnAkAGDp0KDZt2oSbN2/ixYsXqFixolaD6oXM4wWb2IiXg4iKnT177qJOneUYO/ZQlm0sgomIsqdRIXz79m30798fBgYGMDRMv6j8/v17AICrqys+++wzDp+mCUFQLTu3ES8HERUbiYmpGDlyP3x8tiE6+h3Wr7+GP/64LXYsIqJiQaOuEWZmZpDJZAAAKysrGBsb48WLF8rtDg4OePjwoXYS6i3OJkdEHxYa+hy+vjtx795rZZuPTw20aeMqXigiomJEoyvC1atXx+3bqisO9erVQ1BQENLS0pCcnIzNmzejQoUKWgupP4TcdyEivSeXK/DDD2fRrNlqZRFsZmaEVau8sHNnX9jamomckIioeNDoinDPnj2xZMkSzJ8/H8bGxpg2bRq8vb1hZWUFiUSCxMRErFmzRttZS743qhmf1PoLExH968mTOPj778KpU4+UbQ0bOmHz5l6oVo33FhAR5YdGhfAXX3yBL774QrnevXt3BAcHY+fOnTAwMEC3bt3Qrl07rYXUGy9CVMsSDj9HROru3XuNpk1/Q2xsMoD0z8tTprREYGBbyGR8zyAiyi+tTajRqlUrtGrVSluH00/mzkDsg/Rlp2biZiEinVOlShk0bVoOR448gLOzBYKCerI/MBFRAWg8oQZpWUoc8CRYtW5oIlYSItJRUqkEa9d649NPG+DatZEsgomICihPV4QrVqwIST77rEokEjx48ECjUHrpzib1dQk/oxDps7Q0BebMOY1WrVzQvr1qXHYnJ3OsWOElYjIiopIjT4VwmzZtshTCly5dwq1bt1CzZk1Ur14dABAWFobbt2/Dzc0NDRs21H7akuxdlGrZpiZgIBMvCxGJKjw8Bn5+OxES8hTlypnj+vVRKFPGVOxYREQlTp4K4XXr1qmt7969G7t378axY8fQoUMHtW3Hjh1D3759MWvWLK2F1AtPT6mW2ywQLwcRiUYQBAQFXceYMQeRkJA+zXpk5FucPPkQvXrVFDkdEVHJo9H37zNmzMDnn3+epQgGgE6dOmHMmDGYPn16gcPplWdnVMsGxuLlICJRxMQkoX//PzB48G5lEVypkjXOnh3KIpiIqJBoNGrEP//8AxubnMertLGxYf/g/CpdDoj/d1zQci3FzUJERSo4OAL+/rvw9Gm8si0goB6WLPGEuTk/GBMRFRaNrghXrlwZa9euxdu3b7NsS0hIwJo1a1CpUqUCh9MbDw+riuBSjoCBkbh5iKhIpKbKMXXqcbRvv15ZBFtZmWD79t5Yu9abRTARUSHT6Irw7Nmz0bt3b9SoUQMBAQGoUqUKgPQrxevXr8fLly+xY8cOrQYt0U6pJieBIW+IIdIXT5/G4+efL0D4d3b1tm1dsWGDD5ydLcUNRkSkJzQqhH18fHDw4EFMnjwZ3333ndq2evXqYfXq1fDw8NBKQL2Q/Ea13HiyeDmIqEhVqmSNxYs9MWrUAcyZ0x6TJrlDKuX06kRERSXfhbAgCEhISEDr1q1x5coVREZG4tGj9K/1XVxc4OjoqPWQJV7ii/T/m1cA6o4QNwsRFZro6HcwMzOCmZmq+9PQofXRpo0rqlQpI2IyIiL9lO8+wqmpqShTpgyWLFkCAHB0dETTpk3RtGlTFsGayDx+cGqceDmIqFAdOXIftWsvw//+d1StXSKRsAgmIhJJvgthY2NjODo6wtiYN3FoRWpC9stEVCIkJ6dhwoTD8PTchMjIt/j110s4cOCe2LGIiAgajhoREBCADRs2IDU1Vdt59E9KpqvA1fuLl4OItO7GjZdo0mQVFi06r2zz9KyChg3LipiKiIgyaHSzXO3atbF7927UqlULAQEBcHV1halp1tEOPv744wIHLPGenFQt84owUYmgUAj4+efzmDz5OFJS5AAAY2MD/PhjJ4wZ0yTLlPVERCQOjQrhAQMGKJe//vrrbPeRSCSQy+WapdIngkK1bFdHvBxEpBUvXiRgyJA9OHJENalQ7dr22Ly5F9zc7EVMRkRE/6VRIXzy5Mncd6K8ETJ9WHBoKF4OIiqwsLBotGy5FtHR75RtEyY0w3ffdYCJiUZvt0REVIg0emdu06aNtnPor8yFsJT/UBIVZ1WqlEHNmnY4ffoRnJxKY906H3TuXFnsWERElAONbpbLkJKSgpCQEOzZswfR0dHayqRfFGmqZYmBeDmIqMAMDKQICuoJf/86uH59FItgIiIdp3EhvGTJEjg5OaFly5b4+OOPcf36dQBAdHQ0bG1tsWbNGq2FLNFCF6qWpSyEiYoLuVyBH344i7//fqLWXqGCJTZs6AlbWzORkhERUV5pVAivXbsW48ePh6enJ1avXg1BEJTbbG1t0b59e2zdulVrIUssQaE+fJqxlWhRiCjvnjyJQ4cOGzBlygn4+e1EfHyK2JGIiEgDGhXCCxYsgLe3NzZv3gwvL68s2xs2bIhbt24VOFyJl+kDBADAsYk4OYgoz7Zvv4U6dZbj1Kn0qeUjImJx9OiDXB5FRES6SKNC+P79++jSpUuO28uUKYPXr19rHEpvKN6rlsu2ADi2KJHOio9PQUDAbvTr9ztiY5MBAM7OFjh5cjB6964pcjoiItKERsMUWFlZffDmuNu3b8PR0VHjUHrj2VnVctIr8XIQ0QeFhDyBn98uhIfHKNv69auFZcu6wdo662RCRERUPGh0Rbhr165YuXIlYmNjs2y7desWVq1ahR49ehQ0W8mXlqRatq4uXg4iylZamgIzZwajVau1yiLY3FyGDRt8sGVLLxbBRETFnEaF8OzZsyGXy+Hm5obp06dDIpFg/fr18PPzQ6NGjWBvb48ZM2ZoO2vJE52pH3XZ5uLlIKJsPXjwBnPnnoVcnt6f393dGdeujYS/f11Ok0xEVALkuRBWKFRTAZctWxahoaHw9PTEtm3bIAgCgoKCsG/fPgwYMADnzp2Dra1toQQuUWLuqpbTeNc5ka6pXt0W8+Z1goGBBDNntsWpUwGoWNFa7FhERKQlee4jXK5cOfTv3x++vr5o3Lgx7O3t8dtvv+G3335DVFQUFAoF7OzsIJUWaI4O/WJUWrVcvpV4OYgIABATkwQzMyMYG6veGj//vAnat68INzd7EZMREVFhyHPVWqlSJSxZsgTNmjVDtWrV8O233+LBg/Qhg+zs7ODg4MAiOD+uLgOuLlWtm/AqE5GYgoMjUKfOckyf/qdau0QiYRFMRFRC5bly/euvvxAeHo7Zs2fDxMQEgYGBqFatGpo2bYqff/4Zr15x1IN8uTRffd2kjDg5iPRcaqocU6ceR/v26/H0aTzmzw/BiRPhYsciIqIikK9LuC4uLpg6dSquX7+O69ev48svv0RUVBTGjRuH8uXLo0uXLti4cSMSExMLK2/JIU9WLbf6AbCsKF4WIj0VFhaN5s1X4/vv/1LOb9OunSuqV+c9DkRE+kDjvgxubm6YO3cuwsPDcebMGQwfPhyXL1/G4MGD4eDgAF9fX23mLHnePk//v7kz0ORLcbMQ6RlBELBixSXUr78Cly+/AAAYGUkxb15HHD8+COXLW4ickIiIioJWOvW2aNECS5cuxY0bN9CjRw+8e/cO27Zt08ahSyZBNQIHEp6Il4NID0VFJcLHZxtGjjyApKQ0AED16jY4d24Y/ve/FpBKOSwaEZG+0GhmucySkpKwZ88ebN68GUePHkVqairKly+PAQMGaCNfyZTxHSwRFamwsGi0bbsekZFvlW2jRjXC/PmdYWZmJGIyIiISg0ZXhOVyOQ4ePAg/Pz9lN4gzZ87A398ff/75Jx49eoQffvhB41BLly6Fq6srTExM0LRpU1y4cCFPj9u6dSskEgl8fHw0fu6ikakQLttCvBhEeqZSJWs4O6d3e7C1NcPevf3x66/dWAQTEempfF0RPnv2LDZv3ozff/8dr1+/hkwmQ9euXTFw4EB0794dMpmswIG2bduGiRMnYvny5WjatCkWLVoEDw8PhIWFwd4+5yGMIiIi8MUXX6BVq2IwHm/sA9VyfIRoMYj0jZGRATZt+hhTppzA0qVd4ehYOvcHERFRiZXnQtjV1RVPnqT3Z23dujUGDhyI3r17w8rKSquBFi5ciOHDh2PIkCEAgOXLl+PAgQNYs2YNpkyZku1j5HI5Bg4ciJkzZ+LMmTOIjY3VaiatU7xXLZtXEC8HUQmmUAj4+ecLqFmzNDp0UH2IrlrVBn/80VfEZEREpCvyXAhbWFhg7ty58PX1Rfny5QslTGpqKkJDQzF16lRlm1QqRceOHRESEpLj47799lvY29vjk08+wZkzZz74HCkpKUhJUU1nHB8fDyB9CunM00gXqpj7yj4pgk1NCEX1vASFQgFBEIruXJMoXrxIwNCh+3D06ANUqWKF0NDKKF3aWOxYVMj4+tYvPN/6pbDOc54L4evXrxdKgMyio6Mhl8vh4OCg1u7g4IC7d+9m+5izZ89i9erVuHr1ap6eY+7cuZg5c2aW9qioKKSmpuY7syZKPToH83+XU+JeIJaTkRQZhUKBuLg4CILAmRBLqMOHIzBp0im8eZM+Vvf9+7HYtu0yvLwqi5yMChtf3/qF51u/xMXFFcpxCzxqhJgSEhLg7++PVatWwdY2bwPgT506FRMnTlSux8fHw9nZGXZ2dlrv5pEjc9XzyKp2+2DfZ9IuhUIBiUQCOzs7vnGWMImJqfjii2NYufKyss3JqTQWLGiNPn3q83zrAb6+9QvPt37Rxn1o2dGpQtjW1hYGBgZ4+fKlWvvLly/h6OiYZf8HDx4gIiICXl5eyraMS+eGhoYICwtD5crqV4GMjY1hbJz1K1KpVFo0LyRBAZzN1PWjtBPAF3CRkkgkRXe+qUiEhj6Hr+9O3Lv3Wtnm41MDK1Z0g0Lxludbj/D1rV94vvVHYZ1jnfrLkclkaNiwIU6cOKFsUygUOHHiBJo3b55l/xo1auDGjRu4evWq8r8ePXqgXbt2uHr1KpydnYsyft68vq2+XrqcODmISgC5XIHvvz+LZs1WK4tgMzMjrFzZHTt39oWtrZnICYmISJfp1BVhAJg4cSIGDx6MRo0aoUmTJli0aBESExOVo0gMGjQI5cqVw9y5c2FiYgI3Nze1x2d0b/hvu85QpKmvOzYWJwdRCXD3bjS+/vok0tLSvwlq2NAJmzf3QrVqNgDSp1ImIiLKic4Vwv369UNUVBRmzJiByMhI1KtXD4cPH1beQPf48ePi/RXIlV9Uy3VHAhJO50qkqVq17DFrVjt89dUJTJnSEoGBbSGTGYgdi4iIigmJoOElE7lcjh07duDkyZN49eoVvv32W9SuXRtxcXE4ceIEWrRokWX0B10UHx8PS0tLxMTEFM3NchvqAlH/jsDRYjbQbFrhPycpKRQKvHr1Cvb29sX7A5WeSkhIgampEQwNVedOLlfgypVINGpUNsv+PN/6hedbv/B865fY2FhYW1sjLi4OFhYWWjuuRn85sbGxaNGiBXx9fbFlyxbs3bsXUVFRAIDSpUtj7NixWLx4sdZCliiSTBfh630mXg6iYiYk5Anq1VuB2bNPq7UbGEizLYKJiIhyo1EhPGXKFNy6dQtHjhxBeHi4Wj88AwMD9O7dGwcPHtRayBLl1b9DO0kNARNrcbMQFQNpaQrMnBmMVq3WIjw8BrNmncbffz8ROxYREZUAGhXCu3fvxueff45OnTpBkk0f12rVqiEiIqKg2Uqe1Leq5f/eNEdEWYSHx6B167UIDDwFuTz9A3ezZuXh5FRa5GRERFQSaHSzXFxcHCpWrJjj9vfv3yMtjYVeFkKm6QFNyoiXg0jHCYKAoKDrGDPmIBIS0md8NDCQYMaMNvjqq1ZqfYSJiIg0pVEhXLlyZVy+fDnH7UePHkXNmjU1DlViyTNN4ezQSLwcRDosJiYJo0YdwLZtt5RtlSpZY9Omj9GsWXkRkxERUUmj0WWVYcOGYc2aNdi2bZuyf7BEIkFKSgqmTZuGw4cPY8SIEVoNWiLE/qNafnNXvBxEOiosLBp16y5XK4IDAurh6tURLIKJiEjrNLoiPG7cONy6dQsDBgxQDjnm6+uL169fIy0tDSNGjMAnn3yizZwlT7mWYicg0jkuLlawsjLBkyfxsLY2wYoV3dGnTy2xYxERUQmlUSEskUiwatUqDB48GL///jv++ecfKBQKVK5cGX379kXr1q21nbPkKaX7YywTFTUTE0Ns3twLkycfx4oV3VG+vPbGiiQiIvqvAs0s17JlS7RsySubecbpXomUBEHAqlWX0bJlBdSsaadsd3Ozx4EDviImIyIifcFbr0XDqZVJf0VFJcLHZxtGjNgPX98/kJLCUWaIiKjo5emKcMWKFbMdL/hDJBIJHjx4oFGoEis+QuwERKI7cuQ+AgL2IDIyfVzta9deYv/+e+jViyPNEBFR0cpTIdymTZsshfClS5dw69Yt1KxZE9WrVwcAhIWF4fbt23Bzc0PDhg21n7a4e5+oWk54Kl4OIhEkJ6dhypTjWLz4vLLN1tYMa9b0gJdXdRGTERGRvspTIbxu3Tq19d27d2P37t04duwYOnTooLbt2LFj6Nu3L2bNmqW1kCWGJFNPlPKtxMtBVMRu3HgJX9+duHnzlbLNw6My1q3zgaMjZ4kjIiJxaNRHeMaMGfj888+zFMEA0KlTJ4wZMwbTp08vcLgS594O1bKBiXg5iIqIQiFg8eJzaNx4lbIINjY2wOLFnjh4cCCLYCIiEpVGo0b8888/sLGxyXG7jY0N+wdnJ/O0yjIWAFTy3bjxEhMnHoVCkT5iSu3a9ti8uRfc3OxFTkZERKThFeHKlStj7dq1ePv2bZZtCQkJWLNmDSpVqlTgcCUOp1gmPVO3riO++ip9iMUJE5rhwoXhLIKJiEhnaHRFePbs2ejduzdq1KiBgIAAVKlSBUD6leL169fj5cuX2LFjRy5H0TOCAPzzh2o9n6NwEBUH7969h4mJIaRS1d/3jBlt0LlzZbRq5SJiMiIioqw0KoR9fHxw8OBBTJ48Gd99953atnr16mH16tXw8PDQSsASIzVBfb2Ukzg5iApJaOhz+PruxLBh9fG//7VQthsZGbAIJiIinaTxzHKdO3dG586dERkZiUePHgEAXFxc4OjoqLVwJUrmodNKlwWMzMTLQqRFcrkC8+f/jenTTyItTYFp0/5Ehw6V0KABP+wREZFuK9AUywDg6OjI4jc3ggBsbqZat3ETLwuRFj15Egd//104deqRsq1OHQeULi0TMRUREVHeFLgQpjxIeg0kPFat29YWLwuRlmzffgsjRuxHbGwygPRu71OmtERgYFvIZAYipyMiIsodC+GiIKSpr7f4VpwcRFoQH5+CsWMPYf36a8o2Z2cLBAX1RJs2ruIFIyIiyicWwkVBkakQrtab/YOp2AoLi0bXrpsRHh6jbOvXrxaWL+8OKytOEkNERMULC+GioHivWpbwV07FV/nyFjA0TB9+3NxchqVLu8LPrw4kHA6QiIiKIY0m1KB8ynxF2MBIvBxEBVSqlAybN3+Mtm1dce3aSPj712URTERExVaBL08mJCQgLi4OCoUiy7YKFSoU9PAlw9vnqmVeEaZiQhAEBAVdR4sWzqhcWTU9eMOGZfHnn4NYABMRUbGncVW2bNkyLFy4EOHh4TnuI5fLNT18yZJ5Mo2Ye+LlIMqjmJgkjBx5ANu330LTpuVw5swQGBmpRoJgEUxERCWBRl0jli9fjtGjR6NKlSqYPXs2BEHA+PHjMWXKFDg6OqJu3bpYvXq1trMWX0Kmq+WunHGPdFtwcATq1FmO7dtvAQDOn3+G/fv5AY6IiEoejQrhn3/+GR4eHjh06BA+/fRTAEC3bt0wZ84c3L59GwkJCXj9+rVWgxZrmQthKfsIk25KTZVjypTjaN9+PZ4+jQcAWFubYMeOPujZ8yOR0xEREWmfRoXwgwcP4OXlBQAwMkov7FJTUwEAlpaWGDZsGH799VctRSwJBNWihPcnku4JC4tG8+ar8cMPf0H498+1XTtXXL8+Cr171xQ3HBERUSHRqI+wpaUl0tLSR0KwsLCAmZkZnjx5otxubm6OyMhI7SQsCTKPGsG+laRDBEHAypWhmDDhCJKS0v9OjYykmDOnPSZNcodUyr9XIiIquTQqhN3c3HDtmmpWqWbNmmHZsmXo2rUrFAoFVqxYgWrVqmktZLEXcTjTCgsL0h1XrkRi5MgDyvXq1W2weXMvNGjgJGIqIiKioqHR9/R+fn64efMmUlJSAAAzZ87EnTt3UKFCBbi6uiIsLAyzZ8/WatBizdhKtcxxhEmHNGjghIkTmwEARo1qhMuXR7AIJiIivaHRFeEhQ4ZgyJAhyvUWLVrg1q1b2LdvHwwMDNC5c2deEc6JQ2OxE5AeS0lJg0xmoDb82XffdYCnZxV06lRZxGRERERFT2uzO1SqVAnjxo3T1uFKGCH3XYgK2Y0bL+HruxOjRjXCZ5+pPpAZGxuyCCYiIr2kUdeIFStWfHB7UlISxowZo1GgEo83y1ERUygELF58Do0br8LNm68wadJR3L4dJXYsIiIi0WlUCI8aNQqenp549uxZlm1nzpxB7dq1sWrVqgKHKzEyzyxHVIRevEhA166bMH78EaSkpM/0WLVqmVweRUREpB80KoQ3btyIS5cuwc3NDevXrwcAJCcnY/z48WjXrh1MTU0REhKi1aDF2j87VcsCu0lQ0diz5y7q1FmOI0ceKNsmTGiGCxeGo2ZNOxGTERER6QaNCmFfX1/cuHEDLVq0wNChQ9G1a1fUrVsXS5cuxZdffonLly+jQYMG2s5afNnVVS1b8yZCKlyJiakYOXI/fHy2ITr6HQDAyak0jhzxw8KFHjAx0dqtAURERMWaxv8iOjk5YefOnWjdujUOHz4MiUSC+fPnY8KECdrMVzK8vq1aNiolXg4q8e7dew0vry24d081xbmPTw2sWuUFW1szEZMRERHpHo3n+7169SoaNWqE0NBQjB49GjVr1sSXX36JL7/8UjndMgGQvwfevRQ7BekJB4dSSE1N7wtsZmaEVau8sHNnXxbBRERE2dCoEA4MDETTpk2RnJyMU6dO4eeff0ZoaCgmTZqEn376CQ0aNEBoaKi2sxZPyW/U141MxclBesHS0gQbN/ZE06blcOXKCAwb1kBtzGAiIiJS0agQnjVrFj799FNcu3YN7u7uAACZTIbvv/8eZ8+eRVpaGpo3b67VoMWWIFctV/ISLweVSDt23MKTJ3FqbS1aVEBIyCeoVs1GpFRERETFg0aF8PHjx/Hzzz/D1DTr1c2mTZvi2rVr+PzzzwscrkRQpKmWDU3Ey0ElSnx8CgICdqNv398xaNBuyOUKte28CkxERJQ7jQrhdu3afXC7sbExFixYoFGgEifzFWGJgXg5qMQICXmC+vVXYP36awCA4OAI7N9/T+RURERExU+Bx1FKSEhAXFwcFApFlm0VKlQo6OGLt7fPgb+/Ua1LOWwVaS4tTYE5c05j1qzTkMvTx6M2N5dh6dKu6NGjusjpiIiIih+NK7Nly5Zh4cKFCA8Pz3EfuVye4za98OdY4J8/VOsshElD4eEx8PPbiZCQp8o2d3dnbNzYExUrWouYjIiIqPjSqGvE8uXLMXr0aFSpUgWzZ8+GIAgYP348pkyZAkdHR9StWxerV6/WdtbiJybz19USoFJ30aJQ8SQIAjZsuIZ69ZYri2ADAwlmzmyLU6cCWAQTEREVgEaF8M8//wwPDw8cOnQIn376KQCgW7dumDNnDm7fvo2EhAS8fv06l6Pogcz9gz99DFTrJV4WKpYuXXqOwYN3IyEhfWzuSpWscfbsUMyY0QaGhhoPA05ERETQsBB+8OABvLzShwIzMjICAOUkGpaWlhg2bBh+/fVXLUUsxhT/FsLGloB5eXGzULHUuHE5jBjREAAQEFAPV6+OQLNm/FsiIiLSBo06rVpaWiItLX1YMAsLC5iZmeHJkyfK7ebm5oiMjNROwuIs44owR4ugPHr/Xg5DQ6na8GcLFnRG165VeUMcERGRluX5ivDp06cRFRUFAHBzc8O1a9eU25o1a4Zly5bh2bNnePLkCVasWIFq1appP21xE3s//f8shCkPwsKi0azZauWwaBlKlZKxCCYiIioEeS6E27Vrh2PHjgEA/Pz8cPPmTaSkpAAAZs6ciTt37qBChQpwdXVFWFgYZs+eXTiJi4vkmEwrgmgxSPcJgoAVKy6hfv0VuHz5BT7//BDu33+T+wOJiIioQPLcNUIQVMXckCFDMGTIEOV6ixYtcOvWLezbtw8GBgbo3LkzrwgnvlQtJ0WLl4N0WlRUIoYN24e9e8OUbeXKmSMp6b2IqYiIiPSD1ga2rVSpEsaNG6etwxVvacnAhbmq9VoBokUh3XXkyH0EBOxBZORbZdvIkQ2xYIEHzMyMRExGRESkH/JVCGe+gYc+4N4O4PYG1Tr7CFMmyclpmDr1OBYtOq9ss7U1w5o1PeDlxb7ARERERSVfw6f5+fnBwMAgT/8ZGurxLGrxj9XXq/cRJwfpnPv336BJk1VqRbCnZxXcuDGKRTAREVERy1e12rFjR/b9zYu3qmlw0XUj4OohXhbSKdbWJnj9OgkAYGxsgB9/7IQxY5rw2xYiIiIR5KsQHjx4MHx9fQsrS8nx7C/VspG5eDlI59jYmGHdOm/873/HsHHjx3Bzsxc7EhERkd7iHK2FwcJVtWxXR7QYJL59+8LUboYDgE6dKiM09FMWwURERCJjIVwYwveplo1KiZeDRJOYmIqRI/ejR4+tGDp0j9rwgwBgYMCXHhERkdj4r7G2papf/YNUj28a1FOhoc/RoMFKrFgRCgA4dOg+9u+/J3IqIiIi+q88V2kKhaIwc5QcKXHq6ybW4uSgIieXKzB//t+YPv0k0tLSXy9mZkZYvNgT3bvzJlMiIiJdw8uVhalqL7ETUBF58iQO/v67cOrUI2Vbw4ZO2Ly5F6pVsxExGREREeWEhTBRAW3bdhMjRx5AbGwyAEAiAaZMaYnAwLaQyTiZChERka5iIUxUAOfOPUX//n8o152dLRAU1BNt2riKF4qIiIjyhDfLaUvkRWBPT2AfZ5HTJ82alYe/f/oQef361cK1ayNZBBMRERUTvCKsLae/BJ4Eq7cZyMRIQoVIoRAglarPAvfLL13RrVtV9O1bizPEERERFSO8Iqwt76LU101tAbdPxMlChSI8PAYtW67B9u231NotLIzRr58bi2AiIqJihleEtUEhB17/WxxJDYHPEwCpESDljVIlgSAICAq6jjFjDiIhIRV37uxH8+bl4exsKXY0IiIiKgBeEdaGx8dVy4o0wNCERXAJEROThP79/8DgwbuRkJAKAChTxhSvXyeJnIyIiIgKileEtSHptWq5dHnxcpBWBQdHwN9/F54+jVe2BQTUw5IlnjA3NxYxGREREWkDC2FNvX8HnBwHvLwMJL9RtTf+UrxMpBWpqXLMmHES8+b9BUFIb7OyMsHKld3Rp08tccMRERGR1rAQ1tSDvcCN37K2G5oWfRbSmvDwGPTpswOXL79QtrVt64oNG3zYJ5iIiKiEYSGsqcxXgSVSQGIA2NcHqviIFokKztTUEI8fxwEAjIykmDOnPSZNcs8yZBoREREVfyyENZXwRLXsuQ6o6S9aFNIeJydzrF7dA5MnH8emTR+jQQMnsSMRERFRIWEhrKn4R6rltGTxclCBHD8ejvr1HWFjY6Zs69GjOrp0qQIjI478QUREVJLp5PBpS5cuhaurK0xMTNC0aVNcuHAhx31XrVqFVq1awdraGtbW1ujYseMH99caUzvVslWVwn8+0qrk5DRMmHAYnToFYcSI/RAy7or7F4tgIiKikk/nCuFt27Zh4sSJ+Oabb3D58mXUrVsXHh4eePXqVbb7BwcHY8CAATh58iRCQkLg7OyMzp0749mzZ4UX8tU14MoS1bpRqcJ7LtK6O3deo1mz1Vi06DwA4I8/7uDw4fsipyIiIqKipnOF8MKFCzF8+HAMGTIENWvWxPLly2FmZoY1a9Zku/+mTZvw2WefoV69eqhRowZ+++03KBQKnDhxovBChi5UX2chXCwoFAKWLDmPLl124caN9A9WxsYGWLLEE56evKpPRESkb3Sqj3BqaipCQ0MxdepUZZtUKkXHjh0REhKSp2O8e/cO79+/R5kyZbLdnpKSgpSUFOV6fHz6ZAkKhQIKhSJPzyFJiUPGGAJCxW4QrGsAeXwsiePFiwQMHboPR48+ULbVrm2PjRt7ws3NHoIgZOkeQcWfQqGAIAh5fm1T8cbzrV94vvVLYZ1nnSqEo6OjIZfL4eDgoNbu4OCAu3fv5ukYkydPRtmyZdGxY8dst8+dOxczZ87M0h4VFYXU1NRcj1/q5hKYP9ijelz976CIispTNhLHkSMRmDjxFN68Ud3UOHy4G776qilMTJBjtxsq/hQKBeLi4iAIAqRSnfsCjLSM51u/8Hzrl7i4uEI5rk4VwgX1/fffY+vWrQgODoaJiUm2+0ydOhUTJ05UrsfHx8PZ2Rl2dnawsrL68BMkx0JyfZ5yVZBIYevoDBhzogVd9ddfTxAQcES57uhYCgsXtkGfPvX5xqkHFAoFJBIJ7OzseL71AM+3fuH51i8ymaxQjqtThbCtrS0MDAzw8uVLtfaXL1/C0dHxg4+dP38+vv/+exw/fhx16tTJcT9jY2MYGxtnaZdKpbm/kOTvAEGuXJW4fwuJqfWHH0OiatmyAnr2rIFdu+7C27s6Vq7sDoXibd7ON5UIEomE51uP8HzrF55v/VFY51in/nJkMhkaNmyodqNbxo1vzZs3z/Fx8+bNw6xZs3D48GE0atSo8AIq3quWq/cHmk0rvOcijfy3n69EIsGqVV5Yu9Ybu3b1g62tWQ6PJCIiIn2jU4UwAEycOBGrVq3C+vXrcefOHYwaNQqJiYkYMmQIAGDQoEFqN9P98MMP+Prrr7FmzRq4uroiMjISkZGRePv2rfbDKdJUy1KduphOAJ48iUP79huwf/89tXYbGzMEBNSDRMJpkomIiEhF56q5fv36ISoqCjNmzEBkZCTq1auHw4cPK2+ge/z4sdrl8WXLliE1NRW9e/dWO84333yDwMBA7YZTK4SNtHtsKpDt229hxIj9iI1Nxq1br3D9+ig4OpYWOxYRERHpMJ0rhAFgzJgxGDNmTLbbgoOD1dYjIiIKP1CGuHDVMq8I64T4+BSMHXsI69dfU7aZmBji+fMEFsJERET0Qazm8uPZWdVy2jvxchAAICTkCQYO3ImHD2OVbf361cKyZd1gbW0qXjAiIiIqFlgI50fmq8Dl24oWQ9+lpSkwe/ZpzJ59GnJ5+s1x5uYyLF3aFX5+ddgXmIiIiPKEhXB+CJlmNbGsKF4OPRYREQtf3z8QEvJU2ebu7oyNG3uiYkUOZUdERER5p3OjRui0zENzSfirE4NUKsHt2+kz+RkYSDBzZlucOhXAIpiIiIjyjdVcfmS+Isyv30VRoYIlli/vjkqVrHH27FDMmNEGhob8MyYiIqL8YwWRH4+Pq5Z5RbhInDnzCPHxKWpt/fu74datz9CsWXmRUhEREVFJwGouPwwzjURgyBnKClNqqhxTphxHmzbr8Pnnh7JsNzFh93YiIiIqGBbC+ZG5+LWqIl6OEi4sLBrNm6/GDz/8BUEANmy4hqNHH4gdi4iIiEoYXlbLqxfngUdHxU5RogmCgJUrQzFhwhEkJaXP4mdkJMWcOe3RsWMlkdMRERFRScNCOK9urVdfNzQRJ0cJFRWViGHD9mHv3jBlW/XqNti8uRcaNHASMRkRERGVVCyE80qe6YatZtNZCGvRkSP3ERCwB5GRb5Vto0Y1wvz5nWFmZiRiMiIiIirJWAjn1c01quUaA8TLUcKcOfMInp6blOu2tmZYs6YHvLyqi5iKiIiI9AFvlssrA2PVsomNeDlKmJYtK8DTM/3GQ0/PKrhxYxSLYCIiIioSvCKcV4r3quVSDuLlKGEkEgnWrvXGrl13MHJkI0g4UQkREREVEV4RzovDAapZ5ax5tVJTkZFv0a3bZpw4Ea7W7uhYGqNGNWYRTEREREWKV4RzkxyrPmJEKUfRohRne/eG4ZNP9iI6+h2uXYvEtWsjYWPDSUmIiIhIPLwinBu5+vS+aPuTODmKqcTEVIwcuR/e3lsRHf0OAKBQCIiIiBU3GBEREek9XhHOjSJNtVz1Y8ChvnhZipnQ0OcYOHAnwsJeK9t8fGpg1Sov2NryajARERGJi4VwboRMhbCUY9rmhVyuwPz5f2P69JNIS0vvW21mZoTFiz3xySf12ReYiIiIdAIL4dy8e6ValvLXlZunT+Ph778LwcERyraGDZ2weXMvVKvGYeeIiIhId7CP8IdcWw5saqJaZyGcq6Sk97h48RkAQCIBpk5tib///oRFMBEREekcFsIfcnuj+rq5izg5ipGqVW2wZEkXODtb4OTJwfjuuw6QyQzEjkVERESUBQvhDxHkquXGk4EGY8XLoqMuXHiGd+/eq7UNGVIPt2+PRps2ruKEIiIiIsoDFsIf8uKcarn194Apv97PkJamwMyZwXB3X40vvjiqtk0ikaB0aZlIyYiIiIjyhoXwh5iUETuBTgoPj0Hr1msRGHgKcrmAZcsu4eTJh2LHIiIiIsoX3v31IclvxE6gUwRBQFDQdYwZcxAJCakAAAMDCWbMaINWrdh/moiIiIoXFsI5ubxYtWzhKloMXRETk4RRow5g27ZbyrZKlayxadPHaNasvIjJiIiIiDTDQjgnYTtUy5auosXQBadORcDffxeePIlXtgUE1MOSJZ4wNzcWMRkRUeGSy+V4//597jtSkVMoFHj//j2Sk5MhlbKnZ0kgk8mK/FyyEM6JIlW13GmleDlEdupUBNq1Ww9BSF+3tjbBihXd0adPLXGDEREVIkEQEBkZidjYWLGjUA4EQYBCoUBCQgJnLC0hpFIpKlasCJms6G64ZyGcE8W/Q6dJjQDrquJmEVHLlhXQurULTp16hHbtXLFhQ0+UL28hdiwiokKVUQTb29vDzMyMhZYOEgQBaWlpMDQ05PkpARQKBZ4/f44XL16gQoUKRXZOWQjnREhL/79UvyeDMDCQIiioJ3bsuI3x45tBKuWbDRGVbHK5XFkE29hw2ExdxUK45LGzs8Pz58+RlpYGIyOjInlOdqrJSdK/I0ZI9OezQlRUInr12o6//nqs1u7sbImJE5uzCCYivZDRJ9jMzEzkJET6JaNLhFwuz2VP7WEhnJ13r4C3T9OXpfpRCB85ch916izHzp134Oe3C/HxKWJHIiISFa8y0n8dOXIE69atEztGiSXGa46FcHaenlYtp8SKFqMoJCenYfz4w/D03ITIyLcAgLdvU3Hv3muRkxERERWt1NRUVKlSBX///XeWbXfv3sWwYcPQtGlTEZIVf1OmTMHnn38udowsWAhnR55pxIj/t3ffYVFcXx/Av0vZZekoIEUEEcQuEaKA7YdBMWLBEjEaRSNqFDWKJRiNYI8VYzT2LtbYIypEJTYUGzaw0NS8AjaKIErZ8/5hGF1Yqggo5/M8+zy7d+7MnDt3Vw8zd+60+LHy4vjIbt5MwpdfrsVvv10Uyjp3tsLNmyNhb29SiZExxhgri8TERIwZMwaWlpaQSCQwMzNDt27dcOLEicoOTaH4+HiIRCLhVaNGDbRv3x5nzpwpUPfFixcYN24czM3NIRaLYWpqimHDhuHhw4cF6pb1OKxatQp169aFk5OTXPnr168xaNAgbNu2DQ0bNvywRley0NBQtGjRAhKJBFZWViU6w338+HE4ODhAS0sLBgYG6N27N+Lj4+XqrFixAg0bNoRUKoWNjQ22bNkit3zixInYvHkzYmNjy7E1H44TYUVkOe/e69WvvDg+EpmM8NtvF/Dll2tx69YTAIBEooxlyzojKKg/jIw0KzlCxhhjpRUfHw87OzucPHkSCxcuxM2bN3Hs2DE4OzvD29u7zNvNuyntY/r777+RkJCA06dPw8TEBF27dkVSUpKw/MWLF3BwcMDff/+NVatWITo6Gjt27EBMTAxatmwpl1yV9TgQEZYvX46hQ4cWWKampobw8HC0b9++2LZkZWUVW6eyxMXFwc3NDc7OzoiIiMC4cePg5eWF48ePF7lOjx490KFDB0REROD48eN49uwZevXqJdRZuXIlpkyZAn9/f9y+fRszZsyAt7c3Dh8+LNTR19eHq6srVq5c+VHbWGpUzaWmphIASk5Ofld4Yz3RIrx9XV9TabF9DI8fp5Gr61YC/IVX06Z/0M2bSZUdWoXIzc2lhIQEys3NrexQWAXg/q5eyqu/MzMzKTIykjIzM8spsorx9ddfk6mpKaWnpxdYlvd/XFxcHAGga9euyS0DQKdOnSIiolOnThEACgoKohYtWpCqqiqtXr2aAFBUVJTcdpcsWUKWlpZERJSTk0Pff/89WVhYkJqaGtWvX5+WLl1aZMyK4rlx4wYBoIMHDwplP/zwA2loaFBCQoJQJpPJKDU1lUxNTalz586lOg6KXLp0iZSUlCgtLU2ufPLkyWRtbU1SqZTq1q1L06ZNo6ysLGG5n58fNW/enNauXUsWFhYkEomEfQ0dOpT09fVJS0uLnJ2dKSIiQlgvOjqaunfvToaGhqShoUH29vYUEhJS5PH6UJMnT6bGjRvLlXl4eJCrq2uh6+zZs4dUVFTkfleHDh0ikUgkHAdHR0eaOHGi3Ho+Pj7UunVrubLNmzdT7dq1C91XUb+9vO9pampq4Q0sAz4jrEju63fvP7Ob5V68yERoaLzwefx4B4SHD0OTJoaVFxRjjLEP8uLFCxw7dgze3t7Q0NAosFxXV7fU2/T19cWvv/6KqKgo9OnTB/b29ggMDJSrExgYiP79+wN4Ow9s7dq1sWfPHkRGRmL69On4+eefsXv37hLvMzMzU7iknjeDgEwmw86dOzFgwAAYGRnJ1ZdKpRg5ciSOHz+OFy9efNBxOHPmDOrXrw8tLS25ci0tLWzatAmRkZFYtmwZ1q9fj4CAALk60dHR2Lt3L/bt24eIiAgAwDfffIMnT57g6NGjuHLlClq0aIGvvvoKL168nZUqPT0dXbp0wYkTJ3Dt2jV07twZ3bp1UzjU4/0YNTU1i3zl76P3hYWFwcXFRa7M1dUVYWFhha5jZ2cHJSUlbNy4Ebm5uUhNTcXWrVvh4uIiTHH25s0bqKmpya0nlUoRHh4u92TGli1b4t9//y0wrKIyfV5ZXnk58d6lE6WKmceuojRubIiFCzti7tyz2LzZHZ061avskBhjrOrbZg9kJFb8fjWMgO8uF1stOjoaRIQGDRqU265nzpyJjh07Cp8HDBiA5cuXY9asWQCAe/fu4cqVK9i2bRsAQFVVFTNmzBDq161bF2FhYdi9ezf69u1b5L6cnJygpKSEV69egYhgZ2eHr776CgDw9OlTpKSkFDo2t2HDhiAiREdHA0CZj8ODBw9gYlLw/php06YJ7y0sLDBhwgTs2LEDkydPFsqzsrKwZcsWGBgYAADOnj2L8PBwPHnyBBKJBACwaNEiHDhwAH/++SeGDx+O5s2bo3nz5sI2Zs2ahf379+PQoUMYPXq0whjt7e2FRLswtWrVKnRZYmJigeW1atVCWloaMjMzIZVKC6xTt25dBAcHo2/fvhgxYgRyc3Ph6OiIoKAgoY6rqyvWrVsHd3d3tGjRAleuXMG6deuQnZ2NZ8+ewdjYGACE4/vgwQNYWFgU2Y6KwomwImJtICvt7Xv9JpUbywe6fj0RDRroQyJ519WjR7fEd981g55ewS88Y4wxBTISgfT/q+woCkVE5b5Ne3t7uc/9+vXDxIkTceHCBTg4OCAwMBAtWrSQSzpXrFiBDRs24OHDh8jMzERWVhZsbW2L3deuXbvQoEED3Lp1C5MnT8amTZsKPFChJG38kOOQmZlZ4KwmAGzevBkBAQGIjo5GRkYGAAgJbx5zc3O5suvXryM9Pb3AA1kyMzMRExMD4O0ZYX9/fxw5cgQJCQnIyclBZmZmkWeEpVIprKysytzGskhMTMSwYcPg6emJb7/9Fi9fvsT06dPRp08fhISEQCQS4ZdffkFiYiIcHBxARKhVqxY8PT2xYMECKCm9G3yQl2i/evWqQttQFE6EFXl/HjtD20oL40Pk5sqwaNF5TJt2Cj/+2AqLFnUSlolEIk6CGWOsNDSMiq9Tifu1traGSCTCnTt3iqyXl5S8nzC+f+labtf5hhYYGRmhQ4cO2L59OxwcHLB9+3aMHDlSWL5z505MnDgRixcvhqOjI7S0tLBw4UJcvHgx/6YLMDMzg7W1NaytrZGTk4OePXvi1q1bkEgkMDAwgK6uLqKiohSuGxUVBZFIJCSIJTkOiujr6+PmzZtyZWfPnoWXlxc2bdqELl26QFdXF6tWrcKUKVPk6uU/Vunp6TA2NkZoaGiB/eQNz5g4cSJCQkKwaNEiWFlZQSqVok+fPkXebHfmzBl8/fXXRbZj9erVGDBggMJlRkZGcjchAkBSUhK0tbUVng0G3v5xo6OjgwULFghl27Ztg5mZGS5evAgHBwdIpVJs2LABq1evRlJSEoyNjbFmzRphlok8ecNC8v8hUZk4ES6Knk1lR1Amjx6lYuDA/fjnnwcAgMWLw+Du3gBt2tSp5MgYY+wTVYLhCZWpRo0acHV1xYoVKzB27NgCiVlKSgp0dXWFBCQhIQFffPEFABR7qf19AwYMwOTJk/Htt98iNjYW/fr1E5adO3cOTk5OGDVqlFCWd/azNPr06YPp06fjjz/+wPjx46GkpIS+ffsiMDAQM2fOlBsnnJmZiZUrV8LV1RU1atQAgBIdB0W++OILrFy5EkQkPNjhwoULsLCwkEssFc0xnF+LFi2QmJgIFRWVQocAnDt3DoMHD0bPnj0BvE2eixs7+6FDI/IPaQCAkJAQODo6FrrOq1ev5M7qAoCysjKAt+O336eqqoratWsDePuHUdeuXeXWvXXrFlRVVdG4ceMi21CR+Ga5z8zu3bfRrNkqIQkWiYApU9qgZUvTSo6MMcbYx7RixQrk5uaiZcuW2Lt3L+7fv4+oqCgsW7ZMSHSkUikcHByEm+D++ecfuTGwxenVqxdevnyJkSNHwtnZWW5MrbW1NS5fvozjx4/j3r17+OWXX3Dp0qVSt0MkEmHs2LH49ddfhUvoc+fOhZGRETp27IijR4/i0aNHOH36NNzc3JCdnY0VK1aU6jgo4uzsjPT0dNy+fVsos7GxQWxsLAIDAxETE4MlS5YUSCQVcXFxgaOjI9zd3REcHIz4+HicP38eU6dOxeXLl4XjlXdz3fXr19G/f/8CiWV+eUMjinrlv9nvfT/88ANiY2MxefJk3LlzB3/88Qd2796N8ePHC3WWL18ujM8GADc3N1y6dAkzZ87E/fv3cfXqVQwZMgTm5ubCH1P37t3Dtm3bcP/+fYSHh6Nfv364desW5s6dK7f/M2fOoG3btoWefa4U5ToHxSdI4fRpeVOnrbeptLhKKzX1NXl67pebFs3MbAmFhsZVdmhVCk+nVb1wf1cv1X36NCKix48fk7e3N5mbm5NYLCZTU1Pq3r27MDUaEVFkZCQ5OjqSVColW1tbCg4OVjh9WmFTjfXt25cA0IYNG+TKX79+TYMHDyYdHR3S1dWlkSNHkq+vLzVv3rzQeBVNn0ZElJGRQXp6ejR//nyh7OnTpzRmzBgyMzMjVVVVqlWrFg0aNIji4+PLdBwKa5uvr69cma+vL+nr65OmpiZ5eHhQQEAA6ejoCMvzpk/LLy0tjcaMGUMmJiakqqpKZmZmNGDAAHr48KHQdmdnZ5JKpWRmZkbLly+n9u3b048//lhkjB/q1KlTZGtrS2KxmCwtLWnjxo1yy/38/Mjc3FyubMeOHfTFF1+QhoYGGRgYUPfu3eWm0ouMjCRbW1uSSqWkra1NPXr0oDt37hTYt42NDe3YsaPQ2Cpj+jQR0UcYYf8JSUtLg46ODpKTk99eLklPAFb/9xeung3wfenHGVW0sLBH+O67/YiNTRbKPDwaY+VKNx4LnI9MJsOTJ09gaGhY4FIP+/xwf1cv5dXfr1+/RlxcHOrWravw5ilWNdB/D/pQUVERhjJ8qBs3bqBjx46IiYmBpiY/XKo8HT16FBMmTMCNGzegoqJ4ZG5Rv72UlBTo6ekhNTUV2tra5RYX/8+QX9J748CS71ZeHCUUGhqPtm03CkmwlpYYW7a4Y8eO3pwEM8YYY6XQrFkzzJ8/H3FxcZUdymcnIyMDGzduLDQJrixVK5qq4P0T5F/+VHlxlFDr1mawszNBePj/wcnJDNu29UTdunqVHRZjjDH2SRo8eHBlh/BZ6tOnT2WHoBAnwvnFH3v3Xlz4gPOqQlVVGYGBvbBr1y389FMbqKjwSX7GGGOMsZLgrCk/1femWqGi796saMnJmRgwYB+uXHksV25lVQNTp7bjJJgxxhhjrBT4jHB+aQ/evTdrX3lx5BMaGo+BA/fj33/TcOXKY1y9OgLq6p/X458ZY4wxxioSn0J8X9ZL4N6e9wrK5y7UD5GVlQtf37/RocNm/Pvv28c+P3mSgdu3n1RyZIwxxhhjnzY+I/y+5Hvyn/WbVE4c/7l79xn699+Hq1cThDJnZwts2dITtWuX39QhjDHGGGPVESfC78vOePfe0g1Qq5zZF4gIa9Zcwfjxx5GZmQMAUFVVwpw5HTBhghOUlCr/TDVjjDHG2KeOE+H3hfq8e69tUSkhPH2aAS+vwzh06N0cxjY2NbF9e2+0aGFcKTExxhhjjH2OeIzw+2TZ796bFP488o/p0aM0BAXdFz6PHGmPq1dHcBLMGGOMsRJ7/fo15syZg+jo6MoOpUrjRPh9z26/e9+gf6WE0KKFMWbPdoa+vjoOHeqHP/5w49khGGOMlRuRSIQDBw589P2EhoZCJBIhJSVFKDtw4ACsrKygrKyMcePGYdOmTdDV1a3wOCpSZe1/7NixiI6OhpWVVbF1/f39YWtr+/GDqoI4Ec7zOgWg3LfvVdSAcnpueXHu3HmG7OxcubKJE51w+/YodOtmUyExMMYY+zwkJiZizJgxsLS0hEQigZmZGbp164YTJ05UeCxOTk5ISEiAjo6OUDZixAj06dMHjx49wqxZs+Dh4YF79+4VsZVPQ1hYGJSVleHm5lYh+ysuuQ4MDER8fDzWrFlTou1NnDixUr4jVQEnwnlSY9+9z3n90XcnkxF+++0CbG1XYfbs03LLlJWVYGioUciajDHGWEHx8fGws7PDyZMnsXDhQty8eRPHjh2Ds7MzvL29KzwesVgMIyMjiP47sZSeno4nT57A1dUVJiYm0NLSglQqhaGhYYXHVlpZWVlFLl+/fj3GjBmD06dP4/Hjx0XWrQgDBgxAcHAwVFWLvqJMRMjJyYGmpiZq1qxZQdFVLZwIK9J02EfdfELCS3TpEohx447jzZtczJ59BuHh//dR98kYY+zzNmrUKIhEIoSHh6N3796oX78+GjduDB8fH1y4cKHQ9X766SfUr18f6urqsLS0xC+//ILs7Hf3zFy/fh3Ozs7Q0tKCtrY27OzscPnyZQDAgwcP0K1bN+jp6UFDQwONGzdGUFAQAPmzlqGhodDS0gIAdOjQASKRCKGhoQqHRhw+fBhffvkl1NTUoK+vj549ewrLtm7dCnt7e2hpacHY2BgDBw7Ekyfy8+oHBQWhfv36kEqlcHZ2Rnx8fIE27927F40bN4ZEIoGFhQUWL14st9zCwgKzZs3CoEGDoK2tjeHDhxd6/NLT07Fr1y6MHDkSbm5u2LRpU6F185w9exZt27aFVCqFmZkZxo4di4yMdzNXvd9OIyMj9O/fX2hnfHw8nJ2dAQB6enoQiUQYPHgwAODNmzcYO3YsDA0NoaamhjZt2uDSpUvCdvP65OjRo7Czs4NEIsHZs2cLDI2QyWSYOXMmateuDYlEAltbWxw7dqzYdn2KeNaIPAnh794riz/abg4evAMvr8N49uyVUDZ2bEs0a1bro+2TMcbYh1uyJAxLloQVW69FC2McOvStXFn37jvk5oQvjI+PI3x8Sn+z9osXL3Ds2DHMmTMHGhoFrygWNQ5XS0sLmzZtgomJCW7evIlhw4ZBS0sLkydPBvD27OIXX3yBlStXQllZGREREcKZRm9vb2RlZeH06dPQ0NBAZGQkNDU1C+zDyckJd+/ehY2NDfbu3QsnJyfUqFGjQJJ65MgR9OzZE1OnTsWWLVuQlZUlJNYAkJ2djVmzZsHGxgZJSUnw8fHBkCFDhDqPHj1Cr1694O3tjeHDh+Py5cuYMGGC3D6uXLmCvn37wt/fHx4eHjh//jxGjRqFmjVrCgklACxatAjTp0+Hn59fkcd+9+7daNCgAWxsbPDdd99h3LhxmDJlinAmPL+YmBh07twZs2fPxoYNG/D06VOMHj0ao0ePxsaNGwu088mTJ/Dx8cHgwYMRFBQEMzMz7N27F71798bdu3ehra0NqVQKAJg8eTL27t2LzZs3w9zcHAsWLICrqyuio6NRo0YNIQZfX18sWrQIlpaW0NPTQ2hoqFyMv/32GxYvXozVq1fjiy++wIYNG9C9e3fcvn0b1tbWRR6PTw5Vc6mpqQSAkk/NJVqEt6+gQeW+n/T0NzRixGEC/IWXkdEiOn48utz3xQqXm5tLCQkJlJubW9mhsArA/V29lFd/Z2ZmUmRkJGVmZsqV+/mdkvs3vLCXg8O6Att0cFhXonX9/E6VKeaLFy8SANq3b1+xdQHQ/v37C12+cOFCsrOzEz5raWnRpk2bFNZt2rQp+fv7K1x26tSpt/+/JicTEVFycjIBoFOnTgl1Nm7cSDo6OsJnR0dHGjBgQLFtICKSyWQUFhZGAOjly5dERDRlyhRq1KiRXL2ffvpJLo7+/ftTx44d5epMmjRJbj1zc3Nyd3cvURxOTk60dOlSIiLKzs4mfX19uTbmPw5Dhw6l4cOHy23jzJkzpKSkVOA7l+fSpUty7cy/TSKi9PR0UlVVpcDAQKEsKyuLTExMaMGCBXLrHThwQG77fn5+1Lx5c+GziYkJzZkzR67Ol19+SaNGjSr+gHyAwn57RO++P6mpqeW6Tz4j/B9Rxntjeup2KddtX7nyGP3778O9e8+Fsh49bLBuXXfo66uX674YY4x9HNraEpiaahVbz8Cg4L/rBgbqJVpXW1tSptiIqEzrAcCuXbuwbNkyxMTEID09HTk5OdDWfvf0Uh8fH3h5eWHr1q1wcXHBN998g3r16gF4OzPByJEjERwcDBcXF/Tu3RvNmjUrcywREREYNqzw4YlXrlyBv78/rl+/juTkZMhkMgDAw4cP0ahRI0RFRaFVq1Zy6zg6yp9hj4qKQo8ePeTKWrdujaVLlyI3NxfKysoAAHt7+2LjvXv3LsLDw7F//34AgIqKCjw8PLB+/Xr873//U7jO9evXcePGDQQGBgplRASZTIa4uDg0bNiw2HYqEhMTg+zsbLRu3VooU1VVRcuWLREVFSVXt6i2paWl4fHjx3LbAd4eo+vXrxd+MD5RnAj/RxSxHFAr/+2ePBkHV9dtyMl5+yVWV1fF0qWu8PJqUehlE8YYY1VPWYctACgwVKK8WVtbQyQS4c6dO6VaLywsDAMGDMCMGTPg6uoKHR0d7Ny5U27MrL+/P/r3748jR47g6NGj8PPzw86dO9GzZ094eXnB1dUVR44cQXBwMObNm4fFixdjzJgxZWpH3iV+RTIyMuDq6gpXV1cEBgZCX18fcXFxcHNzK/ZmtrJQNMQkv/Xr1yMnJwcmJiZCGRFBIpFg+fLlcjNm5ElPT8eIESMwduzYAsvq1KlToJ0GBgZ4+PAhXF1dy62dJWlbdcE3y/2HpO/dtWrapty227q1GRo1MgAA2NkZ49q1ERg2zI6TYMYYY+WmRo0acHV1xYoVK+RuuspT2DRb58+fh7m5OaZOnQp7e3tYW1vjwYMHBerVr18f48ePR3BwMHr16iWMZQUAMzMz/PDDD9i3bx8mTJiAtWvXlrkdzZo1K3Qarzt37uD58+f49ddf0bZtWzRo0KDAjXINGzZEeHi4XFn+GwUbNmyIc+fOyZWdO3cO9evXF84Gl0ROTg62bNmCxYsXIyIiQnhdv34dJiYm2LFjh8L1WrRogcjISFhZWRV4icXiErVTLH57L1Nu7rvpV+vVqwexWCzXtuzsbFy6dKnQs8iKaGtrw8TEROExKs12PhWcCP9HlPnfl0xVA9AyLbftSiQq2L69F6ZObYvz54eifv3qOT0JY4yxj2vFihXIzc1Fy5YtsXfvXty/fx9RUVFYtmxZgeEBeaytrfHw4UPs3LkTMTExWLZsmXCZHwAyMzMxevRohIaG4sGDBzh37hwuXbqEhg0bAgDGjRuH48ePIy4uDlevXsWpU6eEZWXh5+eHHTt2wM/PD1FRUbh58ybmz58P4O3ZUrFYjN9//x2xsbE4dOgQ5s6dK7f+Dz/8gPv372PSpEm4e/cutm/fXmAWhwkTJuDEiROYNWsW7t27h82bN2P58uWYOHFiqWL966+/kJycjKFDh6JJkyZyr969e2P9+vUK1/vpp59w/vx5jB49GhEREbh//z4OHjyI0aNHF9rOWbNmyW3D3NwcIpEIf/31F54+fYr09HRoaGhg5MiRmDRpEo4dO4bIyEgMGzYMr169wtChQ0vVtkmTJmH+/PnYtWsX7t69C19fX0RERODHH38s1XY+CeU64vgTlHezXOrs/26UW13nA7b1mry8DtKtW0nlGCErT3zzVPXC/V29fOyb5T4Fjx8/Jm9vbzI3NyexWEympqbUvXt3uZu3kO9muUmTJlHNmjVJU1OTPDw8KCAgQLiB7c2bN9SvXz8yMzMjsVhMJiYmNHr0aOHYjB49murVq0cSiYQMDAxo4MCB9OzZMyIq281yRER79+4lW1tbEovFpK+vT7169RKWbd++nSwsLEgikZCjoyPt27ePANC1a9eEOocPHyYrKyuSSCTUtm1b2rBhQ4Eby/78809q1KgRqaqqUp06dWjhwoVyMZibm1NAQECRx7pr167UpUsXhcvybl68fv26whvbwsPDqWPHjqSpqUkaGhrUrFkzuZvT8rfz0KFDBdo5c+ZMMjIyIpFIRJ6enkT09rs7ZswY0tfXJ4lEQq1bt6bw8HBhHUWxEBW8WS43N5f8/f3J1NSUVFVVqXnz5nT06NEij0d5qIyb5UREHzDC/jOQlpYGHR0dpM4GtPPGCE8o/SEJC3uE777bj9jYZDRrVgvh4V6QSHgIdlUjk8nw5MkTGBoaQkmJL4h87ri/q5fy6u/Xr18jLi4OdevWhZraR7h5hJUL+u9hECoqKjzc8DNR1G8vJSUFenp6SE1NlbuZ80Px/wz5Oc0sVfWcHBlmzAhF27YbERubDACIi0vGjRtJHyM6xhhjjDFWTviUZX5KJR8oHxubjO++24ewsH+FMicnM2zb1hN16+p9jOgYY4wxxlg54UQ4P1HxiTARYevWGxg9OggvX76dykRZWYTp09vj55/bQkWFT7QzxhhjjFV1nAjnV0winJyciZEjj2DXrttCmaWlHgIDe8HBofbHjo4xxhhjjJUTToTzExV9Njcq6hn27IkUPg8ebItlyzpDS6tsTwNijDFWNVXze8kZq3CV8Zvja/j5FTNG2MnJDFOntoWurhp27+6DjRt7cBLMGGOfEVVVVQDAq1evKjkSxqqXvCfnlebBJh+Kzwjnl29oRFxcMurU0YGy8ru/GX75pR1GjLCDqWn5Td/BGGOsalBWVoaurq7wNC91dXWenqsK4unTPi8ymQxPnz6Furo6VFQqLj3lRDg/pbeHhIiwZs0VjB9/HH5+7fHTT+8eu6yqqsxJMGOMfcaMjIwAoMCjbVnVQUSQyWRQUlLiRPgzoaSkhDp16lRof3IinJ9FZzx9mgEvr8M4dOguAGDatFPo1KkevvjCuJKDY4wxVhFEIhGMjY1haGiI7Ozsyg6HKSCTyfD8+XPUrFmTH5jzmRCLxRXel5wIv6/tfBy/kIPBg1chMTFdKPby+gI2NvqVGBhjjLHKoKysXKHjFVnJyWQyqKqqQk1NjRNhVmZV8puzYsUKWFhYQE1NDa1atUJ4eHiR9ffs2YMGDRpATU0NTZs2RVBQUKn3+TpbGeOWytC5c6CQBOvrq+PQoX5YubIr1NVVy9QWxhhjjDFWNVW5RHjXrl3w8fGBn58frl69iubNm8PV1bXQcVrnz5/Ht99+i6FDh+LatWtwd3eHu7s7bt26Var9/m/lYPy2443wuXNnK9y8ORLdutl8UHsYY4wxxljVJKIqNlFiq1at8OWXX2L58uUA3l76MDMzw5gxY+Dr61ugvoeHBzIyMvDXX38JZQ4ODrC1tcWqVauK3V9aWhp0dHQA+AJQg0SijIULO2L06JY8+P4zJJPJ8OTJExgaGvKltGqA+7t64f6uXri/q5eUlBTo6ekhNTUV2trlN2FBlRojnJWVhStXrmDKlClCmZKSElxcXBAWFqZwnbCwMPj4+MiVubq64sCBAwrrv3nzBm/evDvzm5qamrcEjRroYe36nmjUyOC9cvY5kclkSEtLq5QB+azicX9XL9zf1Qv3d/WSkpICoPwfulGlEuFnz54hNzcXtWrVkiuvVasW7ty5o3CdxMREhfUTExMV1p83bx5mzJihYEkAIu8ArVv/VKbYGWOMMcbYx/X8+fP/ruSXjyqVCFeEKVOmyJ1BTklJgbm5OR4+fFiuB5ZVTWlpaTAzM8OjR4/K9dIKq5q4v6sX7u/qhfu7eklNTUWdOnVQo0aNct1ulUqE9fX1oaysjKSkJLnypKQkYXLz/IyMjEpVXyKRQCIp+EhkHR0d/iFVI9ra2tzf1Qj3d/XC/V29cH9XL+U9DKZKDaoRi8Wws7PDiRMnhDKZTIYTJ07A0dFR4TqOjo5y9QEgJCSk0PqMMcYYY4wBVeyMMAD4+PjA09MT9vb2aNmyJZYuXYqMjAwMGTIEADBo0CCYmppi3rx5AIAff/wR7du3x+LFi+Hm5oadO3fi8uXLWLNmTWU2gzHGGGOMVXFVLhH28PDA06dPMX36dCQmJsLW1hbHjh0Tboh7+PCh3GlxJycnbN++HdOmTcPPP/8Ma2trHDhwAE2aNCnR/iQSCfz8/BQOl2CfH+7v6oX7u3rh/q5euL+rl4/V31VuHmHGGGOMMcYqQpUaI8wYY4wxxlhF4USYMcYYY4xVS5wIM8YYY4yxaokTYcYYY4wxVi1Vi0R4xYoVsLCwgJqaGlq1aoXw8PAi6+/ZswcNGjSAmpoamjZtiqCgoAqKlJWH0vT32rVr0bZtW+jp6UFPTw8uLi7Ffj9Y1VLa33eenTt3QiQSwd3d/eMGyMpVafs7JSUF3t7eMDY2hkQiQf369fnf9E9Iaft76dKlsLGxgVQqhZmZGcaPH4/Xr19XULTsQ5w+fRrdunWDiYkJRCIRDhw4UOw6oaGhaNGiBSQSCaysrLBp06bS75g+czt37iSxWEwbNmyg27dv07Bhw0hXV5eSkpIU1j937hwpKyvTggULKDIykqZNm0aqqqp08+bNCo6clUVp+7t///60YsUKunbtGkVFRdHgwYNJR0eH/v333wqOnJVFafs7T1xcHJmamlLbtm2pR48eFRMs+2Cl7e83b96Qvb09denShc6ePUtxcXEUGhpKERERFRw5K4vS9ndgYCBJJBIKDAykuLg4On78OBkbG9P48eMrOHJWFkFBQTR16lTat28fAaD9+/cXWT82NpbU1dXJx8eHIiMj6ffffydlZWU6duxYqfb72SfCLVu2JG9vb+Fzbm4umZiY0Lx58xTW79u3L7m5ucmVtWrVikaMGPFR42Tlo7T9nV9OTg5paWnR5s2bP1aIrByVpb9zcnLIycmJ1q1bR56enpwIf0JK298rV64kS0tLysrKqqgQWTkqbX97e3tThw4d5Mp8fHyodevWHzVOVv5KkghPnjyZGjduLFfm4eFBrq6updrXZz00IisrC1euXIGLi4tQpqSkBBcXF4SFhSlcJywsTK4+ALi6uhZan1UdZenv/F69eoXs7GzUqFHjY4XJyklZ+3vmzJkwNDTE0KFDKyJMVk7K0t+HDh2Co6MjvL29UatWLTRp0gRz585Fbm5uRYXNyqgs/e3k5IQrV64IwydiY2MRFBSELl26VEjMrGKVV75W5Z4sV56ePXuG3Nxc4al0eWrVqoU7d+4oXCcxMVFh/cTExI8WJysfZenv/H766SeYmJgU+HGxqqcs/X327FmsX78eERERFRAhK09l6e/Y2FicPHkSAwYMQFBQEKKjozFq1ChkZ2fDz8+vIsJmZVSW/u7fvz+ePXuGNm3agIiQk5ODH374AT///HNFhMwqWGH5WlpaGjIzMyGVSku0nc/6jDBjpfHrr79i586d2L9/P9TU1Co7HFbOXr58iYEDB2Lt2rXQ19ev7HBYBZDJZDA0NMSaNWtgZ2cHDw8PTJ06FatWrars0NhHEBoairlz5+KPP/7A1atXsW/fPhw5cgSzZs2q7NBYFfZZnxHW19eHsrIykpKS5MqTkpJgZGSkcB0jI6NS1WdVR1n6O8+iRYvw66+/4u+//0azZs0+ZpisnJS2v2NiYhAfH49u3boJZTKZDACgoqKCu3fvol69eh83aFZmZfl9GxsbQ1VVFcrKykJZw4YNkZiYiKysLIjF4o8aMyu7svT3L7/8goEDB8LLywsA0LRpU2RkZGD48OGYOnUqlJT43N/npLB8TVtbu8Rng4HP/IywWCyGnZ0dTpw4IZTJZDKcOHECjo6OCtdxdHSUqw8AISEhhdZnVUdZ+hsAFixYgFmzZuHYsWOwt7eviFBZOShtfzdo0AA3b95ERESE8OrevTucnZ0REREBMzOzigyflVJZft+tW7dGdHS08AcPANy7dw/GxsacBFdxZenvV69eFUh28/4Ienv/FfuclFu+Vrr7+D49O3fuJIlEQps2baLIyEgaPnw46erqUmJiIhERDRw4kHx9fYX6586dIxUVFVq0aBFFRUWRn58fT5/2CSltf//6668kFovpzz//pISEBOH18uXLymoCK4XS9nd+PGvEp6W0/f3w4UPS0tKi0aNH0927d+mvv/4iQ0NDmj17dmU1gZVCafvbz8+PtLS0aMeOHRQbG0vBwcFUr1496tu3b2U1gZXCy5cv6dq1a3Tt2jUCQEuWLKFr167RgwcPiIjI19eXBg4cKNTPmz5t0qRJFBUVRStWrODp0wrz+++/U506dUgsFlPLli3pwoULwrL27duTp6enXP3du3dT/fr1SSwWU+PGjenIkSMVHDH7EKXpb3NzcwJQ4OXn51fxgbMyKe3v+32cCH96Stvf58+fp1atWpFEIiFLS0uaM2cO5eTkVHDUrKxK09/Z2dnk7+9P9erVIzU1NTIzM6NRo0ZRcnJyxQfOSu3UqVMK/z/O62NPT09q3759gXVsbW1JLBaTpaUlbdy4sdT7FRHx9QLGGGOMMVb9fNZjhBljjDHGGCsMJ8KMMcYYY6xa4kSYMcYYY4xVS5wIM8YYY4yxaokTYcYYY4wxVi1xIswYY4wxxqolToQZY4wxxli1xIkwY4wxxhirljgRZowxBQYPHgwLC4vKDqOATZs2QSQSIT4+vrJDqVT+/v4QiUQlqsvHjDFWGE6EGWPlSiQSlegVGhpa2aFWqLzETdFr1apVlR3eB7OwsJBrk6GhIdq2bYv9+/dXWAxz587FgQMHKmx/jLFPn0plB8AY+7xs3bpV7vOWLVsQEhJSoLxhw4YVGVaVsXLlSmhqasqVtWrVqpKiKV+2traYMGECAODx48dYvXo1evXqhZUrV+KHH34o131NmzYNvr6+cmVz585Fnz594O7uLlc+cOBA9OvXDxKJpFxjYIx9+jgRZoyVq++++07u84ULFxASElKgvLrq06cP9PX1KzuMj8LU1FSunwcNGgQrKysEBASUeyKsoqICFZWS/RemrKwMZWXlct0/Y+zzwEMjGGMVbuPGjejQoQMMDQ0hkUjQqFEjrFy5Uq7OyZMnoaSkhOnTp8uVb9++HSKRSK5+SbZXlAMHDqBJkyZQU1NDkyZNCr2cL5PJsHTpUjRu3BhqamqoVasWRowYgeTk5FK0XrEbN25g8ODBsLS0hJqaGoyMjPD999/j+fPnxa57+fJluLq6Ql9fH1KpFHXr1sX3338vVycjIwMTJkyAmZkZJBIJbGxssGjRIhCRXL2QkBC0adMGurq60NTUhI2NDX7++ecytcnIyAgNGzZEXFycUHbt2jV8/fXX0NbWhqamJr766itcuHBBbr3s7GzMmDED1tbWUFNTQ82aNdGmTRuEhIQIdfKPERaJRMjIyMDmzZuF4RmDBw8GUHCMcNeuXWFpaakwZkdHR9jb2wufc3JyMGvWLNSrVw8SiQQWFhb4+eef8ebNG7n1StIHjLGqh88IM8Yq3MqVK9G4cWN0794dKioqOHz4MEaNGgWZTAZvb28AQIcOHTBq1CjMmzcP7u7uaNGiBRISEjBmzBi4uLjInWEsyfYKExwcjN69e6NRo0aYN28enj9/jiFDhqB27doF6o4YMQKbNm3CkCFDMHbsWMTFxWH58uW4du0azp07B1VV1WLb/uLFC7nPysrK0NPTQ0hICGJjYzFkyBAYGRnh9u3bWLNmDW7fvo0LFy4UemPYkydP0KlTJxgYGMDX1xe6urqIj4/Hvn37hDpEhO7du+PUqVMYOnQobG1tcfz4cUyaNAn/93//h4CAAADA7du30bVrVzRr1gwzZ86ERCJBdHQ0zp07V2y7FMnOzsajR49Qs2ZNYftt27aFtrY2Jk+eDFVVVaxevRr/+9//8M8//whDRPz9/TFv3jx4eXmhZcuWSEtLw+XLl3H16lV07NhR4b62bt0q1B8+fDgAoF69egrrenh4YNCgQbh06RK+/PJLofzBgwe4cOECFi5cKJR5eXlh8+bN6NOnDyZMmICLFy9i3rx5iIqKEv5gKkkfMMaqKGKMsY/I29ub8v9T8+rVqwL1XF1dydLSUq4sIyODrKysqHHjxvT69Wtyc3MjbW1tevDgQZm2p4itrS0ZGxtTSkqKUBYcHEwAyNzcXCg7c+YMAaDAwEC59Y8dO6awPD8/Pz8CUOCVtw9FbdixYwcBoNOnTwtlGzduJAAUFxdHRET79+8nAHTp0qVC933gwAECQLNnz5Yr79OnD4lEIoqOjiYiooCAAAJAT58+LbItipibm1OnTp3o6dOn9PTpU7p+/Tr169ePANCYMWOIiMjd3Z3EYjHFxMQI6z1+/Ji0tLSoXbt2Qlnz5s3Jzc2tyP3lHc/3aWhokKenZ4G6+Y9ZamoqSSQSmjBhgly9BQsWkEgkEr5fERERBIC8vLzk6k2cOJEA0MmTJ4moZH3AGKuaeGgEY6zCSaVS4X1qaiqePXuG9u3bIzY2FqmpqcIydXV1bNq0CVFRUWjXrh2OHDmCgIAA1KlTp0zbyy8hIQERERHw9PSEjo6OUN6xY0c0atRIru6ePXugo6ODjh074tmzZ8LLzs4OmpqaOHXqVInavnfvXoSEhAivwMDAAm14/fo1nj17BgcHBwDA1atXC92erq4uAOCvv/5Cdna2wjpBQUFQVlbG2LFj5conTJgAIsLRo0fltnXw4EHIZLISted9wcHBMDAwgIGBAZo3b449e/Zg4MCBmD9/PnJzcxEcHAx3d3e5YQnGxsbo378/zp49i7S0NCGO27dv4/79+6WOoSS0tbXx9ddfY/fu3XJDQ3bt2gUHBwfh+xUUFAQA8PHxkVs/74bAI0eOCPECRfcBY6xq4kSYMVbhzp07BxcXF2hoaEBXVxcGBgbCONT8iWvr1q0xcuRIhIeHw9XVVeG4y9Js730PHjwAAFhbWxdYZmNjI/f5/v37SE1NhaGhoZDs5b3S09Px5MmTErW9Xbt2cHFxEV6tW7cG8HbIxI8//ohatWpBKpXCwMAAdevWLbYN7du3R+/evTFjxgzo6+ujR48e2Lhxo9wY1gcPHsDExARaWlpy6+bN3JF3HDw8PNC6dWt4eXmhVq1a6NevH3bv3l3ipLhVq1YICQnB33//jfPnz+PZs2fYsmULpFIpnj59ilevXhU4rnlxyGQyPHr0CAAwc+ZMpKSkoH79+mjatCkmTZqEGzdulCiGkvLw8MCjR48QFhYGAIiJicGVK1fg4eEh1Hnw4AGUlJRgZWUlt66RkRF0dXWF41aSPmCMVU08RpgxVqFiYmLw1VdfoUGDBliyZAnMzMwgFosRFBSEgICAAknXmzdvhDmHY2Ji8OrVK6irq5d5e2Ulk8lgaGgonMHNz8DA4IO237dvX5w/fx6TJk2Cra0tNDU1IZPJ0Llz5yLbIBKJ8Oeff+LChQs4fPgwjh8/ju+//x6LFy/GhQsXCkzVVhSpVIrTp0/j1KlTOHLkCI4dO4Zdu3ahQ4cOCA4OLnbmBX19fbi4uJR4f4Vp164dYmJicPDgQQQHB2PdunUICAjAqlWr4OXl9cHbB4Bu3bpBXV0du3fvhpOTE3bv3g0lJSV88803BeoW9+CO8uwDxljF4jPCjLEKdfjwYbx58waHDh3CiBEj0KVLF7i4uMgNDXifn58foqKisGjRIsTFxRWYO7a023ufubk5ACi8BH/37l25z/Xq1cPz58/RunVruTO6ea/mzZuX9BAUkJycjBMnTsDX1xczZsxAz5490bFjx0JnNlDEwcEBc+bMweXLlxEYGIjbt29j586dQjsfP36Mly9fyq1z584dYXkeJSUlfPXVV1iyZAkiIyMxZ84cnDx5ssRDPwpjYGAAdXX1Asc1Lw4lJSWYmZkJZTVq1MCQIUOwY8cOPHr0CM2aNYO/v3+R+yjpk+YAQENDA127dsWePXsgk8mwa9cutG3bFiYmJkIdc3NzyGSyAt+PpKQkpKSkyB03oOg+YIxVTZwIM8YqVN5ZxffHZqampmLjxo0F6l68eBGLFi3CuHHjMGHCBEyaNAnLly/HP//8U6bt5WdsbAxbW1ts3rxZbvhBSEgIIiMj5er27dsXubm5mDVrVoHt5OTkICUlpdj9FUZRGwBg6dKlxa6bnJxcYD1bW1sAEC7Nd+nSBbm5uVi+fLlcvYCAAIhEInz99dcACs5ooWhbZaWsrIxOnTrh4MGDco86TkpKwvbt29GmTRtoa2sDQIEp4zQ1NWFlZVVsDBoaGqXqBw8PDzx+/Bjr1q3D9evX5YZFAG+PG1CwH5YsWQIAcHNzA1CyPmCMVU08NIIxVqE6deoEsViMbt26YcSIEUhPT8fatWthaGiIhIQEod7r16/h6ekJa2trzJkzBwAwY8YMHD58GEOGDMHNmzehoaFR4u0VZt68eXBzc0ObNm3w/fff48WLF/j999/RuHFjpKenC/Xat2+PESNGYN68eYiIiECnTp2gqqqK+/fvY8+ePfjtt9/Qp0+fMh0TbW1ttGvXDgsWLEB2djZMTU0RHBwsN/9uYTZv3ow//vgDPXv2RL169fDy5UusXbsW2traQiLXrVs3ODs7Y+rUqYiPj0fz5s0RHByMgwcPYty4ccI0YzNnzsTp06fh5uYGc3NzPHnyBH/88Qdq166NNm3alKlt75s9e7YwT/GoUaOgoqKC1atX482bN1iwYIFQr1GjRvjf//4HOzs71KhRA5cvX8aff/6J0aNHF7l9Ozs7/P3331iyZAlMTExQt27dIp/a16VLF2hpaWHixIlQVlZG79695ZY3b94cnp6eWLNmDVJSUtC+fXuEh4dj8+bNcHd3h7OzM4CS9QFjrIqqxBkrGGPVgKLp0w4dOkTNmjUjNTU1srCwoPnz59OGDRvkprgaP348KSsr08WLF+XWvXz5MqmoqNDIkSNLtb2i7N27lxo2bEgSiYQaNWpE+/btI09PT7np0/KsWbOG7OzsSCqVkpaWFjVt2pQmT55Mjx8/LnIfedN9FTY12b///ks9e/YkXV1d0tHRoW+++YYeP35MAMjPz0+ol38qsKtXr9K3335LderUIYlEQoaGhtS1a1e6fPmy3PZfvnxJ48ePJxMTE1JVVSVra2tauHAhyWQyoc6JEyeoR48eZGJiQmKxmExMTOjbb7+le/fuFXsMzc3Ni53yLC9eV1dX0tTUJHV1dXJ2dqbz58/L1Zk9eza1bNmSdHV1SSqVUoMGDWjOnDmUlZUl1FE0fdqdO3eoXbt2JJVKCYAwlVr+Y/a+AQMGEABycXFRGG92djbNmDGD6tatS6qqqmRmZkZTpkyh169fy7WpJH3AGKt6RET5rucwxhhjjDFWDfAYYcYYY4wxVi1xIswYY4wxxqolToQZY4wxxli1xIkwY4wxxhirljgRZowxxhhj1RInwowxxhhjrFriRJgxxhhjjFVLnAgzxhhjjLFqiRNhxhhjjDFWLXEizBhjjDHGqiVOhBljjDHGWLXEiTBjjDHGGKuW/h/IUUFcizBYXwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A curva _Receiver Operating Characteristic_ (ROC) reflete a relação entre a Taxa de Verdadeiros Positivos (Recall) e a Taxa de Falsos Positivos. A curva laranja está significativamente acima da linha azul (classificador aleatório), sugerindo um bom desempenho.\n",
        "\n",
        "* **Acurácia do Modelo** = 81,12%, representa a proporção de previsões corretas em relação ao total de previsões. Embora pareça razoável, em problemas de classes desbalanceadas, como na detecção de fraudes, a acurácia por si só pode se mostrar uma métrica enganosa. Porque a classe \"Sem-Fraude\" é significativamente maior, inflando a \"acurácia\", já que o modelo pode estar prevendo a classe majoritária com maior frequência.\n",
        "\n",
        "* **ROC-AUC** (Área Sob a Curva ROC) = 89,26%: o ROC-AUC mede a capacidade do modelo em distinguir entre as classes, indicando uma boa habilidade na separação entre as classes \"Fraude\" e \"Sem-Fraude\". Quanto mais próximo de 1, melhor."
      ],
      "metadata": {
        "id": "HfG9NFOOTnyh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, f1_score, jaccard_score, hinge_loss\n",
        "import seaborn as sns\n",
        "\n",
        "def plot_confusion_matrix(cm, labels, title=\"Matriz de Confusão\"):\n",
        "    \"\"\"\n",
        "    Plota a matriz de confusão.\n",
        "    \"\"\"\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=labels, yticklabels=labels, cbar=True)\n",
        "    plt.xlabel('Previsto', fontsize=12)\n",
        "    plt.ylabel('Real', fontsize=12)\n",
        "    plt.title(title, fontsize=14)\n",
        "    plt.show()\n",
        "\n",
        "def evaluate_model(y_test, y_pred, labels=[\"Sem-Fraude\", \"Fraude\"], model=fraudDetection):\n",
        "    \"\"\"\n",
        "    Avalia o modelo com matriz de confusão, classification report,\n",
        "    F1 score e Jaccard score.\n",
        "    \"\"\"\n",
        "    # Matriz de Confusão\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    print(\"Matriz de Confusão:\")\n",
        "    print(cm)\n",
        "    plot_confusion_matrix(cm, labels)\n",
        "\n",
        "    # Classification Report\n",
        "    print(\"\\nRelatório de Classificação:\")\n",
        "    print(classification_report(y_test, y_pred, target_names=labels, digits=4))\n",
        "\n",
        "    # F1 Score (overall)\n",
        "    f1 = f1_score(y_test, y_pred, average='binary')\n",
        "    print(f\"\\nOverall F1 Score: {f1:.4f}\")\n",
        "\n",
        "    # Jaccard Score\n",
        "    jaccard = jaccard_score(y_test, y_pred)\n",
        "    print(f\"Jaccard Score: {jaccard:.4f}\")\n",
        "\n",
        "    # Hinge loss for Snap ML SVM\n",
        "    # snapml_hinge_loss = hinge_loss(y_test, snapml_svm.decision_function(X_test))\n",
        "\n",
        "    X_test_pca = model.named_steps['pca'].transform(X_test)  # Apply PCA transformation\n",
        "    snapml_hinge_loss = hinge_loss(y_test, model.named_steps['svm'].decision_function(X_test_pca))\n",
        "    print(\"Hinge Loss:   {0:.4f}\".format(snapml_hinge_loss))\n",
        "\n",
        "# Avaliação do modelo\n",
        "evaluate_model(y_test, y_pred, labels=[\"Sem-Fraude\", \"Fraude\"])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "i0L041niw-lt",
        "outputId": "976e8d13-516a-4cc9-986a-5cd81f5be2c4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matriz de Confusão:\n",
            "[[69180 16115]\n",
            " [   17   131]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Relatório de Classificação:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "  Sem-Fraude     0.9998    0.8111    0.8956     85295\n",
            "      Fraude     0.0081    0.8851    0.0160       148\n",
            "\n",
            "    accuracy                         0.8112     85443\n",
            "   macro avg     0.5039    0.8481    0.4558     85443\n",
            "weighted avg     0.9980    0.8112    0.8941     85443\n",
            "\n",
            "\n",
            "Overall F1 Score: 0.0160\n",
            "Jaccard Score: 0.0081\n",
            "Hinge Loss:   0.9910\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Matriz de Confusão**\n",
        "\n",
        "* **Falsos Positivos** (16115): o modelo gerou um número significativo de transações \"Sem-Fraude\"  classificadas erroneamente como \"Fraude\". Esse comportamento é indesejável em sistemas de detecção de fraude, pois pode levar a alarmes falsos, impactando negativamente a experiência do cliente.\n",
        "\n",
        "* **Falsos Negativos** (17): apesar de pequeno, cada caso de fraude não detectado pode ser um evento crítico, representando ainda que há falha do modelo na previsão de transações fraudulentas.\n",
        "\n",
        "## **Desempenho Geral do Modelo**\n",
        "\n",
        "* **Accuracy** = 81.12%, apesar de alta, a Acurácia Geral pode ser enganosa em cenários de classes desbalanceadas. A classe \"Sem-Fraude\" domina o conjunto de dados, logo o modelo tenderá a priorizar prever corretamente esta classe, enquanto negligencia \"Fraude\".\n",
        "\n",
        "* **Overall F1 Score** = 0.0160, F1 Score está extremamente baixo para a classe de interesse \"Fraude\", logo o modelo tem grande dificuldade em equilibrar precisão e recall para esta classe.\n",
        "\n",
        "### **Classe \"Sem-Fraude\"**\n",
        "\n",
        "* **Precision** = 99.98%, a maioria das previsões feitas para \"Sem-Fraude\" está correta, o que é esperado, devido o desbalanceamento.\n",
        "\n",
        "* **Recall** (Revocação) = 81.11%, o modelo está capturando apenas 81% dos casos \"Sem-Fraude\", indicando que uma fração considerável destas está sendo erroneamente classificada como \"Fraude\".\n",
        "\n",
        "### **Classe \"Fraude\"**\n",
        "\n",
        "* **Precision** = 0.81%, muitas previsões de \"Fraude\" estão incorretas, indicando alto número de falsos positivos.\n",
        "\n",
        "* **Recall** (Revocação) = 88.51%, o modelo consegue capturar a maioria dos casos de fraude (131 de 148), o que é positivo.\n",
        "\n",
        "### **Pontuações Complementares**\n",
        "\n",
        "* **Jaccard Score** (Fraude) = 0.0081, a interseção entre previsões corretas e totais é muito baixa, indicando dificuldade do modelo no alinhamento das previsões com os rótulos verdadeiros.\n",
        "\n",
        "* **Hinge Loss** = 0.9910, a perda hinge elevada sugere que o modelo está deixando ainda a desejar em separar adequadamente as classes.\n",
        "\n",
        "### **Conclusões**\n",
        "\n",
        "O alto número de falsos positivos sugere que o modelo não está separando bem as classes. A classe \"Fraude\" é extremamente sub-representada em comparação com \"Sem-Fraude\", por isto o modelo está priorizando o _recall_ (revocação) da classe \"Fraude\" em detrimento da precisão."
      ],
      "metadata": {
        "id": "TGzKREIpKGKS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Otimização de Hiperparâmetros**\n",
        "\n",
        "A otimização visa encontrar os melhores valores para os hiperparâmetros de um modelo, buscar valores \"corretos\" pode melhorar significativamente o seu desempenho.\n",
        "\n",
        "Os hiperparâmetros são diferentes dos parâmetros do modelo (como pesos ou coeficientes), que são aprendidos durante o treinamento. A otimização de hiperparâmetros envolve testar diferentes combinações para determinar quais fornecem o melhor desempenho.\n",
        "\n",
        "A função `RandomizedSearchCV` (Busca Aleatória) testa combinações aleatórias de hiperparâmetros dentro de um espaço definido. Não garantindo encontrar a melhor combinação, mas é uma boa aproximação para problemas com muitas variáveis. Sendo mais rápido e eficiente para espaços de busca grandes.\n",
        "\n"
      ],
      "metadata": {
        "id": "GJZuNH80Wob1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from scipy.stats import uniform\n",
        "from snapml import SupportVectorMachine\n",
        "\n",
        "# Definir o espaço de busca (valores contínuos são suportados)\n",
        "param_distributions = {\n",
        "    'regularizer': uniform(0.1, 100),     # 'regularizer' for Snap ML, instead of 'C'\n",
        "    'gamma': uniform(0.001, 0.1),  # Valores contínuos de gamma entre 0.001 e 0.1\n",
        "    'kernel': ['rbf']\n",
        "}\n",
        "\n",
        "# Inicializar o RandomizedSearchCV\n",
        "random_search = RandomizedSearchCV(estimator=SupportVectorMachine(), param_distributions=param_distributions,\n",
        "                                   n_iter=50, cv=5, verbose=1, scoring='accuracy', random_state=42)\n",
        "\n",
        "# Treinar com diferentes combinações\n",
        "random_search.fit(X_train, y_train)\n",
        "\n",
        "# Melhor combinação de hiperparâmetros\n",
        "print(\"Melhores Hiperparâmetros:\", random_search.best_params_)\n",
        "print(\"Melhor Pontuação:\", random_search.best_score_)\n",
        "\n",
        "# Avaliação no conjunto de teste\n",
        "best_model = random_search.best_estimator_\n",
        "accuracy = best_model.score(X_test, y_test)\n",
        "print(\"Acurácia no Teste:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HSspFeFNVpuh",
        "outputId": "67404868-ef3d-4ba5-eb97-342c1961e386"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
            "Melhores Hiperparâmetros: {'gamma': 0.03845401188473625, 'kernel': 'rbf', 'regularizer': 95.17143064099162}\n",
            "Melhor Pontuação: 0.998274512045246\n",
            "Acurácia no Teste: 0.9982678510820079\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pipeline com pré-processamento e modelagem SVM\n",
        "fraudDetectionOptimized = Pipeline([\n",
        "    ('scaler', StandardScaler()),  # Etapa de padronização dos dados\n",
        "    ('pca', PCA(n_components=0.95)),  # Preserva 95% da variância\n",
        "    ('svm', SupportVectorMachine(\n",
        "        kernel='rbf',              # Kernel radial basis function\n",
        "        gamma=0.03845401188473625,   # Controla a influência dos pontos de dados\n",
        "        # penalty='l2',              # Regularização L2\n",
        "        # loss='hinge',              # Perda hinge (ótima para SVM)\n",
        "        # dual=False,                # Resolve o problema primal (mais eficiente para conjuntos pequenos)\n",
        "        regularizer=95.17143064099162, # Regularização (maior = menos regularização)\n",
        "        # tol=1e-4,                  # Critério de tolerância para convergência\n",
        "        fit_intercept=False,       # Não calcula o intercepto; os dados devem ser centrados\n",
        "        class_weight='balanced',   # Balanceia automaticamente classes desbalanceadas\n",
        "        random_state=42,           # Semente para reprodutibilidade\n",
        "        # max_iter=1000,             # Número máximo de iterações\n",
        "        # verbose=0,                 # Desativa logs de execução\n",
        "        # use_gpu=True,              # Aceleração via GPU, se disponível\n",
        "        # enable_sparse=False        # Desativa representações esparsas\n",
        "    ))\n",
        "])\n",
        "\n",
        "# Exemplo de ajuste do pipeline com dados de treinamento\n",
        "fraudDetectionOptimized.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "bVrHtiO7YWsl",
        "outputId": "1a58bb56-52e4-4e9e-d159-741abdb0a4b7"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('scaler', StandardScaler()), ('pca', PCA(n_components=0.95)),\n",
              "                ('svm',\n",
              "                 SupportVectorMachine(class_weight='balanced',\n",
              "                                      device_ids=array([], dtype=uint32),\n",
              "                                      gamma=0.03845401188473625, kernel='rbf',\n",
              "                                      random_state=42,\n",
              "                                      regularizer=95.17143064099162))])"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;pca&#x27;, PCA(n_components=0.95)),\n",
              "                (&#x27;svm&#x27;,\n",
              "                 SupportVectorMachine(class_weight=&#x27;balanced&#x27;,\n",
              "                                      device_ids=array([], dtype=uint32),\n",
              "                                      gamma=0.03845401188473625, kernel=&#x27;rbf&#x27;,\n",
              "                                      random_state=42,\n",
              "                                      regularizer=95.17143064099162))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;pca&#x27;, PCA(n_components=0.95)),\n",
              "                (&#x27;svm&#x27;,\n",
              "                 SupportVectorMachine(class_weight=&#x27;balanced&#x27;,\n",
              "                                      device_ids=array([], dtype=uint32),\n",
              "                                      gamma=0.03845401188473625, kernel=&#x27;rbf&#x27;,\n",
              "                                      random_state=42,\n",
              "                                      regularizer=95.17143064099162))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PCA</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.decomposition.PCA.html\">?<span>Documentation for PCA</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>PCA(n_components=0.95)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SupportVectorMachine</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>SupportVectorMachine(class_weight=&#x27;balanced&#x27;,\n",
              "                     device_ids=array([], dtype=uint32),\n",
              "                     gamma=0.03845401188473625, kernel=&#x27;rbf&#x27;, random_state=42,\n",
              "                     regularizer=95.17143064099162)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "\n",
        "# A acurácia é a proporção de predições corretas em relação ao total de predições\n",
        "accuracy = fraudDetectionOptimized.score(X_test, y_test)\n",
        "print(\"Acurácia do modelo:   {0:.4f}\".format(accuracy))\n",
        "\n",
        "# Calcula a área sob a curva ROC (Receiver Operating Characteristic),\n",
        "# para avalia a capacidade do modelo de distinguir entre as classes (fraude e não fraude).\n",
        "acc_snapml  = roc_auc_score(y_test, fraudDetectionOptimized.decision_function(X_test))\n",
        "print(\"ROC-AUC score:   {0:.4f}\".format(acc_snapml))\n",
        "\n",
        "# Obter os scores da função de decisão\n",
        "y_scores = fraudDetectionOptimized.decision_function(X_test)\n",
        "\n",
        "# Calcular a curva ROC e a área sob a curva (AUC)\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_scores)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plotar a curva ROC\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2,\n",
        "         label='Curva ROC (área = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--',\n",
        "         label='Classificador Aleatório')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('Taxa de Falsos Positivos', fontsize=12)\n",
        "plt.ylabel('Taxa de Verdadeiros Positivos', fontsize=12)\n",
        "plt.title('Curva ROC - Receiver Operating Characteristic', fontsize=14)\n",
        "plt.legend(loc='lower right')\n",
        "plt.grid(alpha=0.3)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "id": "SKS0mcGxY6uz",
        "outputId": "0874fce1-aec7-4ebe-8453-cb4e237c9868"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia do modelo:   0.8193\n",
            "ROC-AUC score:   0.9188\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, f1_score, jaccard_score, hinge_loss\n",
        "import seaborn as sns\n",
        "\n",
        "def plot_confusion_matrix(cm, labels, title=\"Matriz de Confusão\"):\n",
        "    \"\"\"\n",
        "    Plota a matriz de confusão.\n",
        "    \"\"\"\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=labels, yticklabels=labels, cbar=True)\n",
        "    plt.xlabel('Previsto', fontsize=12)\n",
        "    plt.ylabel('Real', fontsize=12)\n",
        "    plt.title(title, fontsize=14)\n",
        "    plt.show()\n",
        "\n",
        "def evaluate_model(y_test, y_pred, labels=[\"Sem-Fraude\", \"Fraude\"], model=fraudDetectionOptimized):\n",
        "    \"\"\"\n",
        "    Avalia o modelo com matriz de confusão, classification report,\n",
        "    F1 score e Jaccard score.\n",
        "    \"\"\"\n",
        "    # Matriz de Confusão\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    print(\"Matriz de Confusão:\")\n",
        "    print(cm)\n",
        "    plot_confusion_matrix(cm, labels)\n",
        "\n",
        "    # Classification Report\n",
        "    print(\"\\nRelatório de Classificação:\")\n",
        "    print(classification_report(y_test, y_pred, target_names=labels, digits=4))\n",
        "\n",
        "    # F1 Score (overall)\n",
        "    f1 = f1_score(y_test, y_pred, average='binary')\n",
        "    print(f\"\\nOverall F1 Score: {f1:.4f}\")\n",
        "\n",
        "    # Jaccard Score\n",
        "    jaccard = jaccard_score(y_test, y_pred)\n",
        "    print(f\"Jaccard Score: {jaccard:.4f}\")\n",
        "\n",
        "    # Hinge loss for Snap ML SVM\n",
        "    # snapml_hinge_loss = hinge_loss(y_test, snapml_svm.decision_function(X_test))\n",
        "\n",
        "    X_test_pca = model.named_steps['pca'].transform(X_test)  # Apply PCA transformation\n",
        "    snapml_hinge_loss = hinge_loss(y_test, fraudDetectionOptimized.named_steps['svm'].decision_function(X_test_pca))\n",
        "    print(\"Hinge Loss:   {0:.4f}\".format(snapml_hinge_loss))\n",
        "\n",
        "# Avaliação do modelo\n",
        "evaluate_model(y_test, y_pred, labels=[\"Sem-Fraude\", \"Fraude\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "80RzRUl_ZCFR",
        "outputId": "26ec3d4f-ae08-4151-9ce2-acff64c3e4b0"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matriz de Confusão:\n",
            "[[69180 16115]\n",
            " [   17   131]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Relatório de Classificação:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "  Sem-Fraude     0.9998    0.8111    0.8956     85295\n",
            "      Fraude     0.0081    0.8851    0.0160       148\n",
            "\n",
            "    accuracy                         0.8112     85443\n",
            "   macro avg     0.5039    0.8481    0.4558     85443\n",
            "weighted avg     0.9980    0.8112    0.8941     85443\n",
            "\n",
            "\n",
            "Overall F1 Score: 0.0160\n",
            "Jaccard Score: 0.0081\n",
            "Hinge Loss:   1.7199\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ka4v8qcz7tFm"
      },
      "source": [
        "## **Referências**\n",
        "\n",
        "1. **\"Fraud Detection in Credit Card Transactions Using SVM and Random Forest Algorithms\"**  \n",
        "   HUSSAIN, SK Saddam et al. Fraud detection in credit card transactions using SVM and random forest algorithms. In: 2021 Fifth international conference on I-SMAC (IoT in social, mobile, analytics and cloud)(I-SMAC). IEEE, 2021. p. 1013-1017. Disponível em: https://ieeexplore.ieee.org/abstract/document/9640631. Acesso em: 8 nov. 2024.\n",
        "\n",
        "2. **Machine learning model for credit card fraud detection - a comparative analysis**\n",
        "\n",
        "   SHARMA, Pratyush et al. Machine learning model for credit card fraud detection - a comparative analysis. Int. Arab J. Inf. Technol., v. 18, n. 6, p. 789-796, 2021. Disponível em: https://iajit.org/portal/images/year2021/no6/19792.pdf Acesso em: 8 nov. 2024.\n",
        "\n",
        "3. **Snap ML-Accelerated Machine Learning for Big Data**\n",
        "   POZIDIS, Haris. Snap ML-Accelerated Machine Learning for Big Data (Keynote Abstract). In: 23rd International Conference on Principles of Distributed Systems (OPODIS 2019). Schloss-Dagstuhl-Leibniz Zentrum für Informatik, 2020.\n",
        "   \n",
        "3. **\"A Comparative Analysis of Credit Card Detection Models\"**  \n",
        "   KIM, Kimberly Chan Li et al. A Comparative Analysis of Credit Card Detection Models. In: International Conference on Educational Technology and Administration. Singapore: Springer Nature Singapore, 2022. p. 201-211.. Disponível em: https://viacarreira.com/referencias-bibliograficas-abnt/. Acesso em: 8 nov. 2024.\n",
        "\n",
        "3. **\"A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised\"**  \n",
        "   Autores não identificados. A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised. Disponível em: https://www.academia.edu/12345678/A_Comparison_Study_of_Credit_Card_Fraud_Detection_Supervised_versus_Unsupervised. Acesso em: 8 nov. 2024.\n",
        "\n",
        "4. **\"Fraud Detection in Credit Card Transaction Using ANN and SVM\"**  \n",
        "   Autores não identificados. Fraud Detection in Credit Card Transaction Using ANN and SVM. Disponível em: https://blog.fastformat.co/referencias-bibliograficas-normas-abnt/. Acesso em: 8 nov. 2024.\n",
        "\n",
        "5. **\"Credit Card Fraud Detection Using SVM, Decision Tree and Random Forest Supervised Machine Learning Algorithms\"**  \n",
        "   Autores não identificados. Credit Card Fraud Detection Using SVM, Decision Tree and Random Forest Supervised Machine Learning Algorithms. Disponível em: https://www.normasabnt.org/referencias-abnt/. Acesso em: 8 nov. 2024.\n",
        "\n"
      ]
    }
  ],
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      "mount_file_id": "155WIJS0uVPYohi0TM7AXevI4LS_eCJ7J",
      "authorship_tag": "ABX9TyMw8RqgIGQY2ik+fhaHK53Q",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
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  "nbformat_minor": 0
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