SVM_Support_Vector_Machines.ipynb

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        "# **Modelando Máquinas de Vetores de Suporte (SVM)**\n",
        "\n",
        "O modelo de *Support Vector Machines* (SVM) ou Máquina de Vetores de Suporte (SVM) é um algoritmo importante no arsenal do aprendizado de máquina, se destacam por sua capacidade de lidar com dados complexos e realizar classificações precisas.\n",
        "\n",
        "O SVM tem ótimo desempenho em problemas de classificação, especialmente em conjuntos de dados não lineares de pequeno a médio porte. Ele funciona encontrando um hiperplano que separa os dados em diferentes categorias, mesmo quando os dados não são linearmente separáveis.\n",
        "\n",
        "## **Como funciona o SVM?**\n",
        "\n",
        "1. **Mapeamento de Dados para Espaços de Alta Dimensão**\n",
        "\n",
        "Em muitos casos, os dados originais não podem ser separados linearmente em classes distintas. Para lidar com essa limitação, o SVM transforma os dados para um espaço de alta dimensão (espaço de características) onde eles possam ser mais facilmente separados. Por exemplo, imagine pontos distribuídos em uma linha sem separação clara entre classes. Ao projetar esses pontos em um espaço bidimensional, utilizando coordenadas como 𝑥 e 𝑥2, as classes podem se tornar linearmente separáveis. Essa transformação permite ao SVM trabalhar em problemas complexos onde os dados originalmente não apresentam separabilidade linear.\n",
        "\n",
        "2. **Encontrando o Hiperplano Ótimo**\n",
        "\n",
        "Após mapear os dados, o SVM busca o hiperplano que melhor separa as classes. Os dados devem ser transformados de forma que um separador possa ser desenhado como um hiperplano, em que os pontos de dados se enquadram em categorias diferentes, representando um conjunto de dados não separável linearmente.\n",
        "\n",
        "Desta forma é possível transferir esses dados para um espaço de dimensão superior, por exemplo, mapeando-os para um espaço tridimensional. Após a transformação, o limite entre as duas categorias pode ser definido por um hiperplano. Num espaço tridimensional, o separador é mostrado como um plano. Este plano pode ser usado para classificar casos novos ou desconhecidos. Portanto, o algoritmo SVM produz um hiperplano ideal que categoriza novos exemplos.\n",
        "\n",
        "Em outras palavras, se os dados estão dispostos em uma linha no espaço 1D e não são separáveis, podem ser projetados para um espaço 2D (como e ) onde se tornam separáveis. Após o mapeamento, o SVM  identifica o hiperplano ótimo que divide os dados em\n",
        "classes distintas. Em um espaço 2D, esse hiperplano é uma linha, enquanto em um espaço 3D, é um plano.\n",
        "Em dimensões mais altas, é chamado de hiperplano.\n",
        "\n",
        "3. **Maximizando a Margem**\n",
        "\n",
        "O SVM não busca apenas um hiperplano que separa as classes, mas sim aquele que maximiza a margem entre as duas categorias. A margem é definida como a distância entre o hiperplano e os pontos mais próximos de cada classe. Esses pontos são chamados de **vetores de suporte**, pois determinam a posição e a orientação do hiperplano.\n",
        "\n",
        "Esse processo de maximização da margem confere ao SVM uma maior capacidade de generalização, minimizando erros ao classificar novos exemplos.\n",
        "\n",
        "4.  **Kernelização**\n",
        "\n",
        "Como nem sempre os dados podem ser separados linearmente, mesmo em espaços de alta dimensão. Para resolver isso, o SVM utiliza uma técnica chamada kernelização, que aplica funções matemáticas (kernels) para transformar os dados em um novo espaço onde a separação linear seja possível, são eles:\n",
        "\n",
        "* **Linear**: Mantém os dados no espaço original.\n",
        "* **Polinomial**: Projeta os dados em uma dimensão polinomialmente maior.\n",
        "* **RBF (Radial Basis Function)**: Projeta os dados em um espaço infinito com base em distâncias relativas.\n",
        "* **Sigmoide**: Projeta os dados de forma não linear, similar a redes neurais.\n",
        "\n",
        "Em suma, a kernelização permite que o SVM seja aplicado em dados não linearmente separáveis, tornando-o extremamente flexível e eficaz em uma ampla variedade de problemas.\n",
        "\n",
        "Brevemente, resumo suas aplicações e limitações, vantagens e desvantagens, entre outros aspectos.\n",
        "\n",
        "**Aplicações:**\n",
        "\n",
        "SVMs são eficazes em uma variedade de tarefas, incluindo:\n",
        "\n",
        "* **Classificação:**  Desde a categorização de imagens até a detecção de spam, as SVM demonstram excelente performance em problemas de classificação, especialmente quando a separação entre classes não é linear.\n",
        "* **Regressão:**  Embora menos comum, as SVM também podem ser aplicadas em problemas de regressão, prevendo valores contínuos.\n",
        "* **Detecção de Novidades:**  SVM podem identificar padrões incomuns ou *outliers* em conjuntos de dados.\n",
        "\n",
        "**Performance e Escalabilidade**\n",
        "\n",
        "O SVM é particularmente eficaze em conjuntos de dados de pequeno a médio porte (centenas a milhares de instâncias) com relações não lineares entre as variáveis, mas seu desempenho pode ser afetado em conjuntos de dados muito grandes, devido à complexidade computacional. O SVM pode apresentar desafios de escalabilidade.  Nesses casos, algoritmos alternativos como árvores de decisão ou redes neurais podem ser mais eficientes.\n",
        "\n",
        "**Vantagens:**\n",
        "\n",
        "* Eficazes em espaços de alta dimensão.\n",
        "* Robustas a *outliers*.\n",
        "* Versáteis, com diferentes funções kernel para modelar relações complexas.\n",
        "\n",
        "**Desvantagens:**\n",
        "\n",
        "* **Computacionalmente caros para grandes conjuntos de dados**: a complexidade treinamento de um modelo SVM envolve a otimização de um problema quadrático, que se torna computacionalmente caro à medida que o tamanho do conjunto de dados aumenta. O tempo de treinamento pode crescer significativamente com o número de amostras, tornando os SVM poucos eficientes para conjuntos de dados massivos. Embora o tempo de predição seja geralmente menor que o tempo de treinamento, este pode ser afetado pelo tamanho do conjunto de dados e pelo número de vetores de suporte, consumindo muita memória e impactando o desempenho.\n",
        "* **Escolha da função kernel e dos hiperparâmetros pode se mostrar complexa**: o modelo utiliza funções kernel para mapear os dados em um espaço de alta dimensão. Existem diversos tipos de kernels (linear, polinomial, RBF, sigmoide), cada um com características e parâmetros próprios.\n",
        "  * A escolha do kernel ideal depende do problema e dos dados, o que pode exigir experimentação e conhecimento especializado. Além disso, o SVM possui outros hiperparâmetros, como o parâmetro de regularização (C) e a tolerância para o critério de parada.Encontrar a combinação ideal de hiperparâmetros para um bom desempenho do modelo pode ser desafiador e demandar técnicas de otimização e validação cruzada.\n",
        "  * A interpretação do impacto de cada hiperparâmetro no modelo pode ser complexa, exigindo conhecimento teórico e prático sobre SVMs.\n",
        "* Caso se pretenda uma modelagem preditiva, o SVM não fornece diretamente estimativas de probabilidade.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "## **Instalando e importando as bibliotecas**\n"
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          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.6.0)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)\n",
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      ],
      "source": [
        "!pip install scikit-learn\n",
        "!pip install matplotlib\n",
        "!pip install pandas\n",
        "!pip install numpy\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import pylab as pl\n",
        "import numpy as np\n",
        "import scipy.optimize as opt\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "YEgtPc5oNgNE"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "markdown",
      "source": [
        "## **Dataset: amostras de células humanas extraídas de pacientes sob risco de desenvolver Câncer de Mama**\n",
        "\n",
        "O dataset empregado consiste em vários registros de amostras de células humanas, cada um dos quais contém os valores de um conjunto de características celulares. Os dados estão disponíveis no repositório do **[Center for Machine Learning and Intelligent Systems](https://cml.ics.uci.edu/)** da Universidade da Califórnia, Irvine.\n",
        "\n",
        "Os dados de Câncer de Mama de Wisconsin têm sido amplamente utilizado em pesquisas de aprendizado de máquina para classificação de células tumorais como benignas ou malignas. Os campos em cada registro são:\n",
        "\n",
        "\n",
        "| Field name   | Description                       |\n",
        "|--------------|-----------------------------------|\n",
        "| ID           | Patient identifier               |\n",
        "| Clump        | Clump thickness                  |\n",
        "| UnifSize     | Uniformity of cell size          |\n",
        "| UnifShape    | Uniformity of cell shape         |\n",
        "| MargAdh      | Marginal adhesion                |\n",
        "| SingEpiSize  | Single epithelial cell size      |\n",
        "| BareNuc      | Bare nuclei                      |\n",
        "| BlandChrom   | Bland chromatin                  |\n",
        "| NormNucl     | Normal nucleoli                  |\n",
        "| Mit          | Mitoses                          |\n",
        "| Class        | Benign or malignant              |\n",
        "\n",
        "\n",
        "O campo ID contém os identificadores de cada paciente, cujas características das amostras de células estão contidas nos campos Clump a Mit.\n",
        "\n",
        "Os valores foram classificados de 1 a 10, com 1 sendo o mais próximo de benigno. O campo Class contém o diagnóstico, conforme confirmado por procedimentos médicos separados, se as amostras são benignas (valor = 2) ou malignas (valor = 4).\n",
        "\n",
        "Após treinar o modelo SVM, serpa possível classificar novas células com alta precisão, separando as categorias com base no hiperplano calculado.\n",
        "\n",
        "## **Explorando e analisando os dados para modelagem**\n",
        "\n",
        "---\n"
      ],
      "metadata": {
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    {
      "cell_type": "code",
      "source": [
        "# importando e explorando os dados\n",
        "cell_df = pd.read_csv(\"https://raw.githubusercontent.com/SampMark/files/refs/heads/main/cell_samples.csv\")\n",
        "\n",
        "# Imprime o número de linhas e colunas\n",
        "print(\"Número de linhas e colunas:\", cell_df.shape)\n",
        "\n",
        "# Imprime os nomes das colunas/variáveis\n",
        "print(\"Variáveis:\", cell_df.columns.tolist())\n",
        "cell_df.head()"
      ],
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          "text": [
            "Número de linhas e colunas: (699, 11)\n",
            "Variáveis: ['ID', 'Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit', 'Class']\n"
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              "        ID  Clump  UnifSize  UnifShape  MargAdh  SingEpiSize BareNuc  \\\n",
              "0  1000025      5         1          1        1            2       1   \n",
              "1  1002945      5         4          4        5            7      10   \n",
              "2  1015425      3         1          1        1            2       2   \n",
              "3  1016277      6         8          8        1            3       4   \n",
              "4  1017023      4         1          1        3            2       1   \n",
              "\n",
              "   BlandChrom  NormNucl  Mit  Class  \n",
              "0           3         1    1      2  \n",
              "1           3         2    1      2  \n",
              "2           3         1    1      2  \n",
              "3           3         7    1      2  \n",
              "4           3         1    1      2  "
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              "        document.querySelector('#df-2f6e38c1-3b40-47b7-818f-ecc4e72e2ab6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2f6e38c1-3b40-47b7-818f-ecc4e72e2ab6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-88fe5380-9840-4ea6-a903-1e4b04a1c731')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-88fe5380-9840-4ea6-a903-1e4b04a1c731 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cell_df",
              "summary": "{\n  \"name\": \"cell_df\",\n  \"rows\": 699,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 617095,\n        \"min\": 61634,\n        \"max\": 13454352,\n        \"num_unique_values\": 645,\n        \"samples\": [\n          1371920,\n          1277792,\n          1311108\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Clump\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          10,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UnifSize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UnifShape\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          7,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MargAdh\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SingEpiSize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          10,\n          7,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BareNuc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"9\",\n          \"1\",\n          \"8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BlandChrom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6,\n          9,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NormNucl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          5,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar os tipos de dados das colunas do DataFrame\n",
        "cell_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "NE0uGglJWheU",
        "outputId": "71d03025-d99c-45a5-e51e-6afc9f6691a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID              int64\n",
              "Clump           int64\n",
              "UnifSize        int64\n",
              "UnifShape       int64\n",
              "MargAdh         int64\n",
              "SingEpiSize     int64\n",
              "BareNuc        object\n",
              "BlandChrom      int64\n",
              "NormNucl        int64\n",
              "Mit             int64\n",
              "Class           int64\n",
              "dtype: object"
            ],
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              "    </tr>\n",
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              "      <th>Clump</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UnifSize</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UnifShape</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MargAdh</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SingEpiSize</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
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              "      <th>BareNuc</th>\n",
              "      <td>object</td>\n",
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              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A coluna BareNuc inclui alguns valores que não são numéricos, é preciso remover essas linhas."
      ],
      "metadata": {
        "id": "Kt3Gm9fCYjlR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Converter os valores da coluna 'BareNuc' para numéricos, substituindo entradas inválidas por NaN\n",
        "cell_df = cell_df[pd.to_numeric(cell_df['BareNuc'], errors='coerce').notnull()]\n",
        "\n",
        "# Remover as linhas que contêm NaN na coluna 'BareNuc'\n",
        "cell_df = cell_df.dropna(subset=['BareNuc'])\n",
        "\n",
        "# Alterar o tipo de dados da coluna 'BareNuc' para inteiro\n",
        "cell_df['BareNuc'] = cell_df['BareNuc'].astype('int')\n",
        "\n",
        "# Verificar os tipos de dados das colunas do DataFrame\n",
        "cell_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "WbhW0XrdWzxS",
        "outputId": "5da40557-e5c6-4650-a24d-371a275fa798"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID             int64\n",
              "Clump          int64\n",
              "UnifSize       int64\n",
              "UnifShape      int64\n",
              "MargAdh        int64\n",
              "SingEpiSize    int64\n",
              "BareNuc        int64\n",
              "BlandChrom     int64\n",
              "NormNucl       int64\n",
              "Mit            int64\n",
              "Class          int64\n",
              "dtype: object"
            ],
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              "      <th>Clump</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UnifSize</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UnifShape</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MargAdh</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SingEpiSize</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BareNuc</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
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              "      <th>BlandChrom</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NormNucl</th>\n",
              "      <td>int64</td>\n",
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              "    <tr>\n",
              "      <th>Mit</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
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              "      <td>int64</td>\n",
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              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_df = cell_df[['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']]\n",
        "X = np.asarray(feature_df)\n",
        "X[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6cQZPAnXdz5",
        "outputId": "e6d6e57a-8ae5-4902-b25e-7a8a50b0fc2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 5,  1,  1,  1,  2,  1,  3,  1,  1],\n",
              "       [ 5,  4,  4,  5,  7, 10,  3,  2,  1],\n",
              "       [ 3,  1,  1,  1,  2,  2,  3,  1,  1],\n",
              "       [ 6,  8,  8,  1,  3,  4,  3,  7,  1],\n",
              "       [ 4,  1,  1,  3,  2,  1,  3,  1,  1]])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Definir a lista de colunas de interesse\n",
        "interestCols = ['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize',\n",
        "                     'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']\n",
        "\n",
        "# Verificar se todas as colunas de interesse estão presentes no DataFrame\n",
        "for coluna in interestCols:\n",
        "    if coluna not in cell_df.columns:\n",
        "        raise ValueError(f\"A coluna '{coluna}' não está presente no DataFrame.\")\n",
        "\n",
        "# Selecionar as colunas de interesse\n",
        "feature_df = cell_df[interestCols]\n",
        "\n",
        "# Converter o DataFrame selecionado em um array NumPy\n",
        "X = feature_df.to_numpy()\n",
        "\n",
        "# Exibir as primeiras 5 linhas do array NumPy resultante\n",
        "print(\"As primeiras 5 linhas do array NumPy são:\")\n",
        "print(X[:5])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q1E5lOjVX6u_",
        "outputId": "7af77ce8-54ff-46ae-ccb3-5413e556b9c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As primeiras 5 linhas do array NumPy são:\n",
            "[[ 5  1  1  1  2  1  3  1  1]\n",
            " [ 5  4  4  5  7 10  3  2  1]\n",
            " [ 3  1  1  1  2  2  3  1  1]\n",
            " [ 6  8  8  1  3  4  3  7  1]\n",
            " [ 4  1  1  3  2  1  3  1  1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar se a coluna 'Class' existe no DataFrame\n",
        "if 'Class' not in cell_df.columns:\n",
        "    raise ValueError(\"A coluna 'Class' não está presente no DataFrame.\")\n",
        "\n",
        "# Converter a coluna 'Class' para um array NumPy\n",
        "y = cell_df['Class'].to_numpy()\n",
        "\n",
        "# Exibir as primeiras 5 entradas do array resultante\n",
        "print(\"As primeiras 5 entradas do array 'y' são:\", y[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GHBr5-rAZSpq",
        "outputId": "7bcf74f6-1d75-48d2-e26a-7a0709d9ff10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As primeiras 5 entradas do array 'y' são: [2 2 2 2 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Treinando e testando o modelo**\n",
        "\n",
        "---\n",
        "\n",
        "### **Divisão dos dados: função `train_test_split`**\n",
        "\n",
        "A função `train_test_split` do módulo `model_selection` do scikit-learn será utilizada para dividir os arrays (matrizes) em subconjuntos de treinamento e teste de forma aleatória.\n",
        "\n",
        "**Parâmetros:**\n",
        "* `X`: matriz de características (features).\n",
        "* `y`: vetor de rótulos ou variáveis alvo.\n",
        "* `test_size=0.2`: Define que 20% dos dados serão utilizados para o conjunto de teste.\n",
        "* `random_state=4`: garante a reprodutibilidade da divisão dos dados, permitindo que os mesmos subconjuntos sejam obtidos em execuções subsequentes.\n",
        "* `stratify=y`: assegura que a divisão mantenha a mesma proporção de classes em ambos os conjuntos, o que é especialmente importante em problemas de classificação para evitar vieses."
      ],
      "metadata": {
        "id": "0cAj76QQZqwf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dividir os dados em conjuntos de treinamento e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=4, stratify=y\n",
        ")\n",
        "\n",
        "# Exibir as dimensões dos conjuntos resultantes\n",
        "print('Conjunto de treinamento:', X_train.shape, y_train.shape)\n",
        "print('Conjunto de teste:', X_test.shape, y_test.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TN9Y7oyZwSG",
        "outputId": "d8cda82b-ec7d-48ff-ae46-45eeef19290b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conjunto de treinamento: (546, 9) (546,)\n",
            "Conjunto de teste: (137, 9) (137,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Support Vector Machines (SVM) – Explicação Matemática**\n",
        "\n",
        "O Support Vector Machine (SVM) utiliza conceitos geométricos e estatísticos para encontrar o hiperplano ótimo que separa os dados em diferentes classes, maximizando a margem entre as amostras mais próximas de cada classe.\n",
        "\n",
        "1. **Problema Fundamental: Separação Linear**\n",
        "\n",
        "Dado um conjunto de dados de entrada ( $ \\mathbf{x}_i \\in \\mathbb{R}^n \\ $) e seus respectivos rótulos ($ y_i \\in \\{-1, 1\\} \\ $), o SVM busca um hiperplano definido por:\n",
        "$$\n",
        "\\mathbf{w}^\\top \\mathbf{x} + b = 0\n",
        " $$\n",
        "onde:\n",
        "- $ \\mathbf{w} $ é o vetor normal ao hiperplano;\n",
        "- $ b $ é o termo de $ \\textit{bias} $ (deslocamento em relação à origem).\n",
        "\n",
        "O objetivo do hiperplano é separar as classes de tal forma que:\n",
        "\n",
        "$$ \\mathbf{w}^\\top \\mathbf{x}_i + b > 0, \\quad \\text{se } y_i = 1 $$\n",
        "\n",
        "$$ \\mathbf{w}^\\top \\mathbf{x}_i + b < 0, \\quad \\text{se } y_i = -1 $$\n",
        "\n",
        "2. **Margem e Vetores de Suporte**\n",
        "\n",
        "A margem é a distância mínima entre o hiperplano e os pontos mais próximos, conhecidos como **vetores de suporte**. O objetivo do SVM é maximizar essa margem, definida como:\n",
        "\n",
        "$$ \\text{Margem} = \\frac{2}{\\|\\mathbf{w}\\|} $$\n",
        "\n",
        "\n",
        "Maximizar a margem equivale a minimizar ($ \\|\\mathbf{w}\\|^2  $), sujeita às restrições:\n",
        "\n",
        "$$ y_i \\left(\\mathbf{w}^\\top \\mathbf{x}_i + b \\right) \\geq 1, \\quad \\forall i $$\n",
        "\n",
        "\n",
        "3. **Problema de Otimização Primário**\n",
        "\n",
        "O problema pode ser formulado como:\n",
        "\n",
        "$$ \\min_{\\mathbf{w}, b} \\quad \\frac{1}{2} \\|\\mathbf{w}\\|^2 $$\n",
        "\n",
        "sujeito a: $$ \\quad y_i \\left(\\mathbf{w}^\\top \\mathbf{x}_i + b \\right) \\geq 1, \\quad \\forall i $$\n",
        "\n",
        "Trata-se de um problema de otimização convexa, que pode ser resolvido com **multiplicadores de Lagrange**.\n",
        "\n",
        "4. **Kernelização: Dados Não Linearmente Separáveis**\n",
        "\n",
        "Quando os dados não podem ser separados linearmente, o SVM utiliza a técnica de kernelização, que transforma os dados para um espaço de alta dimensão onde a separação linear é possível. Seja ($ \\phi(\\mathbf{x}) $) a função de mapeamento:\n",
        "\n",
        "$$ \\phi: \\mathbb{R}^n \\to \\mathbb{R}^m, \\quad m > n $$\n",
        "\n",
        "O cálculo explícito de ($ \\phi $) é evitado utilizando-se funções de kernel:\n",
        "\n",
        "$$ K(\\mathbf{x}_i, \\mathbf{x}_j) = \\langle \\phi(\\mathbf{x}_i), \\phi(\\mathbf{x}_j) \\rangle $$\n",
        "\n",
        "Os kernels mais comuns são:\n",
        "* Linear: $ K(\\mathbf{x}_i, \\mathbf{x}_j) = \\mathbf{x}_i^\\top \\mathbf{x}_j $\n",
        "* Polinomial: $ K(\\mathbf{x}_i, \\mathbf{x}_j) = \\left(\\mathbf{x}_i^\\top \\mathbf{x}_j + c\\right)^d $\n",
        "* RBF (Radial Basis Function): $ K(\\mathbf{x}_i, \\mathbf{x}_j) = \\exp\\left(-\\gamma \\|\\mathbf{x}_i - \\mathbf{x}_j\\|^2\\right) $\n",
        "* Sigmoidal: $ K(\\mathbf{x}_i, \\mathbf{x}_j) = \\tanh\\left(\\alpha \\mathbf{x}_i^\\top \\mathbf{x}_j + c\\right) $\n",
        "\n",
        "Esses kernels permitem ao SVM encontrar hiperplanos em espaços de alta dimensão sem realizar transformações explícitas, graças ao **truque do kernel**.\n",
        "\n",
        "5. **Dados Não Separáveis e Regularização**\n",
        "\n",
        "Quando os dados não podem ser perfeitamente separados, o SVM permite violações da margem introduzindo variáveis de folga $ \\xi_i $:\n",
        "\n",
        "$$ y_i \\left(\\mathbf{w}^\\top \\mathbf{x}_i + b \\right) \\geq 1 - \\xi_i, \\quad \\xi_i \\geq 0 $$\n",
        "\n",
        "O problema de otimização então se torna:\n",
        "\n",
        "$$ \\min_{\\mathbf{w}, b, \\xi} \\quad \\frac{1}{2} \\|\\mathbf{w}\\|^2 + C \\sum_{i=1}^n \\xi_i $$\n",
        "\n",
        "\n",
        "O parâmetro $ C $ controla o _trade-off_ entre maximizar a margem e penalizar as violações:\n",
        "- $ C $ alto: Menos violações, maior risco de _overfitting_;\n",
        "- $ C $ baixo: Mais violações, maior capacidade de generalização.\n",
        "\n",
        "6. **Interpretação Geométrica**\n",
        "\n",
        "* **Vetores de Suporte** são os pontos mais próximos ao hiperplano, determinando sua posição e orientação.\n",
        "  * Margem Máxima: maximizar a margem reduz o risco de _overfitting_.\n",
        "  * Separação Não Linear: no espaço transformado pelo kernel, a separação pode ser linear, enquanto no espaço original ela pode ser curvada ou complexa.\n",
        "\n",
        "Em suma:\n",
        "\n",
        "O SVM linear busca maximizar a margem entre as classes em um espaço original. O kernel aplicará transformações para permitir separações não lineares em espaços mais complexos. O parâmetro $ C $ controla o _trade-off_ entre regularização e margem, enquanto o kernel define a natureza da separação. Essa abordagem torna o SVM extremamente flexível e poderoso para problemas de classificação complexos e de alta dimensão.\n"
      ],
      "metadata": {
        "id": "5DTEdQpoAuUN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Modelagem em SVM com kernel Radial Basis Function (RBF)**\n",
        "\n",
        "---\n",
        "A modelagem em SVM envolve o seguinte processo abstrato:\n",
        "\n",
        "1. **Transformação inicial**: os dados são projetados para um espaço de alta dimensão (manualmente ou via kernel).\n",
        "2. **Definição do hiperplano ótimo**: um separador é encontrado para dividir as classes.\n",
        "3. **Maximização da margem**: a margem entre os vetores de suporte é maximizada, garantindo uma separação ideal.\n",
        "4. **Classificação**: o modelo final utiliza o hiperplano para prever a classe de novos exemplos.\n",
        "\n",
        "Como explicitado acima, o algoritmo Support Vector Machine utiliza o conceito de \"*kernelling*\" para mapear dados em um espaço dimensional mais alto, permitindo a classificação eficiente mesmo quando os dados não são linearmente separáveis. Essa transformação é realizada por meio de funções kernel, cada uma com suas próprias características e desempenho.\n",
        "\n",
        "Existem diversas funções kernel disponíveis, como:\n",
        "\n",
        "* **Linear**: eficaz para dados linearmente separáveis.\n",
        "* **Polinomial:** mapeia os dados em um espaço polinomial, útil para dados com relações não lineares.\n",
        "* ***Radial Basis Function*** ou Função de Base Radial (RBF): uma função popular que mapeia os dados em um espaço de dimensão infinita, geralmente oferecendo bom desempenho em diversos cenários.\n",
        "* **Sigmoide**: similar à função sigmoide utilizada em redes neurais.\n",
        "\n",
        "A escolha da função kernel ideal depende do conjunto de dados e do problema em questão. Uma vez que não existe uma regra universal para determinar a melhor opção, a experimentação com diferentes funções e a comparação dos resultados são cruciais para encontrar a função mais adequada."
      ],
      "metadata": {
        "id": "-_PGQFJ0bK4Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Acessar o classificador SVM\n",
        "from sklearn import svm\n",
        "# Padroniza os dados\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "# Cria um pipeline para facilitar o processamento sequencial\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "# Supondo que X_train e y_train já estejam definidos\n",
        "\n",
        "# Criar um pipeline que inclui a padronização dos dados e o classificador SVM com kernel RBF\n",
        "clf = make_pipeline(\n",
        "    StandardScaler(),  # Padroniza os dados para ter média 0 e variância 1\n",
        "    svm.SVC(kernel='rbf', C=1.0, gamma='scale')  # Inicializa o classificador SVM\n",
        ")\n",
        "\n",
        "# Treinar o modelo com os dados de treinamento\n",
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "y4-oiNZScI9V",
        "outputId": "10adb828-ecb3-420f-86ab-7122cf3ff653"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()), ('svc', SVC())])"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {\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-10 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 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-10 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-10 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-10 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-10 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-10 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-10 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-10 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-10 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-10 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-10 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-10 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-10 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-10 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-10 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 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-10 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 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-10 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-10 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-10 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-10 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-10 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-10 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-10 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-10 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-10 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-10 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-10 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-10 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-10 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-10 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 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-10 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-10 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-10 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-10 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-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()), (&#x27;svc&#x27;, SVC())])</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-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" 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;standardscaler&#x27;, StandardScaler()), (&#x27;svc&#x27;, SVC())])</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-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" 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-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Realizar previsões no conjunto de teste\n",
        "yhat = clf.predict(X_test)\n",
        "\n",
        "# Exibir as primeiras 5 previsões\n",
        "print(\"As primeiras 5 previsões são:\", yhat[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KbYzs6Ccvh0",
        "outputId": "1fd5cc70-e172-4e3e-b81e-0c8bf092bed9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As primeiras 5 previsões são: [2 2 4 2 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Avaliação da precisão para o kernel Função de Base Radial (RBF)**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "fkQpJZREdhom"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report, jaccard_score\n",
        "\n",
        "# Calcular a matriz de confusão\n",
        "cnf_matrix = confusion_matrix(y_test, yhat, labels=[2, 4])\n",
        "\n",
        "# Exibir o relatório de classificação\n",
        "print(classification_report(y_test, yhat, target_names=['Benigno (2)', 'Maligno (4)'], digits=4))\n",
        "\n",
        "# Imprimir a pontução geral do F1-Score\n",
        "f1_score_overall = f1_score(y_test, yhat, average='weighted')\n",
        "print(f\"Pontuação geral F1-score: {f1_score_overall:.4f}\")\n",
        "\n",
        "# Calcular o Índice de Jaccard para cada classe\n",
        "jaccard_benigno = jaccard_score(y_test, yhat, pos_label=2)\n",
        "jaccard_maligno = jaccard_score(y_test, yhat, pos_label=4)\n",
        "\n",
        "# Exibir os Índices de Jaccard\n",
        "print(f\"Índice de Jaccard para classe Benigno (2): {jaccard_benigno:.4f}\")\n",
        "print(f\"Índice de Jaccard para classe Maligno (4): {jaccard_maligno:.4f}\")\n",
        "\n",
        "# Plotar a matriz de confusão\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cnf_matrix, display_labels=['Benigno (2)', 'Maligno (4)'])\n",
        "\n",
        "# Configurar a plotagem\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "disp.plot(ax=ax, cmap=plt.cm.Blues, colorbar=True)\n",
        "plt.title('Matriz de Confusão')\n",
        "plt.xlabel('Rótulo Previsto')\n",
        "plt.ylabel('Rótulo Real')\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(False)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "id": "SaNeJmK7g-UM",
        "outputId": "079d479c-e9ae-4787-ace8-97710919b538"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            " Benigno (2)     0.9767    0.9438    0.9600        89\n",
            " Maligno (4)     0.9020    0.9583    0.9293        48\n",
            "\n",
            "    accuracy                         0.9489       137\n",
            "   macro avg     0.9394    0.9511    0.9446       137\n",
            "weighted avg     0.9505    0.9489    0.9492       137\n",
            "\n",
            "Pontuação geral F1-score: 0.9492\n",
            "Índice de Jaccard para classe Benigno (2): 0.9231\n",
            "Índice de Jaccard para classe Maligno (4): 0.8679\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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koXCOW13wFlcPklDAZPz9/bM6BAB3kaVD2BzwnW3WV2OShAIAAJiFRZK9k2CTTgthdjwAAACcjkooAACAWVhcbm72btOEzBkVAAAAsjUqoQAAAGZhsThgTKg5B4VSCQUAAIDTUQkFAAAwixw0JpQkFAAAwCzojgcAAAAch0ooAACAaTigO96kNUdzRgUAAIBsjUooAACAWTAmFAAAAHAcKqEAAABmkYOWaDJnVAAAAMjWSEIBAADM4taYUHtvmZCamqqBAweqWLFi8vb2VokSJfTRRx/JMAzrOYZh6MMPP1TBggXl7e2tevXq6c8//8zUfUhCAQAAzOJWd7y9t0z45JNPNHnyZH3xxRc6dOiQPvnkE40aNUqff/659ZxRo0ZpwoQJ+vLLL7V9+3b5+PioQYMGun79eobvw5hQAAAAWG3ZskVNmjRRo0aNJElFixbVt99+q19//VXSzSrouHHjNGDAADVp0kSS9N///ldBQUFavHixWrdunaH7UAkFAAAwCwd2x8fFxdlsSUlJ6YZQrVo1rVmzRn/88Yckae/evdq0aZMaNmwoSTpx4oSio6NVr1496zUBAQGqWrWqtm7dmuFHpRIKAACQA4SGhtp8HjRokAYPHnzHee+++67i4uJUpkwZubq6KjU1VcOHD1e7du0kSdHR0ZKkoKAgm+uCgoKsxzKCJBQAAMAsHLhEU2RkpPz9/a27PT090z39u+++0+zZszVnzhyVL19ee/bs0dtvv62QkBB16NDBbmGRhAIAAOQA/v7+Nkno3fznP//Ru+++ax3b+cgjj+jUqVMaMWKEOnTooODgYElSTEyMChYsaL0uJiZGlStXznA8jAkFAAAwC4vFAbPjM7dE07Vr1+TiYpsiurq6Ki0tTZJUrFgxBQcHa82aNdbjcXFx2r59u8LCwjJ8HyqhAAAAsAoPD9fw4cNVuHBhlS9fXr/99pvGjBmjTp06SZIsFovefvttDRs2TKVKlVKxYsU0cOBAhYSEqGnTphm+D0koAACAWbhYbm72bjMTPv/8cw0cOFDdunXT+fPnFRISojfeeEMffvih9Zz+/fsrISFBr7/+uq5cuaIaNWpo+fLl8vLyyvB9LMbty98jW4mLi1NAQIA8H+kii6tHVocD4DZRW8ZndQgA/iYuLk5FgvMqNjY2Q2Mn7X3vgIAAedZ4Xxa3jCdyGWHcuK6kTR9nyXPdC5VQAAAAs3Dg7HizIQkFAAAwi/t413uG2jQhc6bGAAAAyNaohAIAAJhFDuqON2dUAAAAyNaohAIAAJgFY0IBAAAAx6ESCgAAYBaMCQUAAAAch0ooAACAWeSgMaEkoQAAAGZBdzwAAADgOFRCAQAAzCIHdcdTCQUAAIDTUQkFAAAwDQeMCTVpzdGcUQEAACBboxIKAABgFowJBQAAAByHSigAAIBZWCwOWCfUnJVQklAAAACzYLF6AAAAwHGohAIAAJgFE5MAAAAAx6ESCgAAYBaMCQUAAAAch0ooAACAWTAmFAAAAHAcKqEAAABmkYPGhJKEAgAAmAXd8QAAAIDjUAkFAAAwCYvFIguVUAAAAMAxqIQCAACYBJVQAAAAwIGohAIAAJiF5X+bvds0ISqhAAAAcDoqoQAAACaRk8aEkoQCAACYRE5KQumOBwAAgNNRCQUAADAJKqEAAACAA1EJBQAAMAkqoQAAAIADUQkFAAAwCxarBwAAAByHSigAAIBJMCYUAAAATmex/H8iar8tczEULVo03Xa6d+8uSbp+/bq6d++uwMBA+fr6qkWLFoqJicn0s5KEAgAAwGrHjh2KioqybqtWrZIktWzZUpLUu3dvLV26VPPnz9f69et17tw5NW/ePNP3oTseAADAJCxyQHd8Jmcm5c+f3+bzyJEjVaJECT399NOKjY3VtGnTNGfOHNWpU0eSFBERobJly2rbtm166qmnMnwfKqEAAAA5QFxcnM2WlJT0j9ckJydr1qxZ6tSpkywWi3bt2qWUlBTVq1fPek6ZMmVUuHBhbd26NVPxkIQCAACYhP3Hg/5/ZTU0NFQBAQHWbcSIEf8Yz+LFi3XlyhV17NhRkhQdHS0PDw/lzp3b5rygoCBFR0dn6lnpjgcAAMgBIiMj5e/vb/3s6en5j9dMmzZNDRs2VEhIiN3jIQkFAAAwCwcuVu/v72+ThP6TU6dOafXq1Vq0aJF1X3BwsJKTk3XlyhWbamhMTIyCg4MzFRbd8QAAALhDRESEChQooEaNGln3ValSRe7u7lqzZo1135EjR3T69GmFhYVlqn0qoQAAAGbhgMXqjftoLy0tTREREerQoYPc3P4/XQwICFDnzp3Vp08f5c2bV/7+/urZs6fCwsIyNTNeIgkFAAAwDUe8Mel+2lu9erVOnz6tTp063XFs7NixcnFxUYsWLZSUlKQGDRpo0qRJmb4HSSgAAABsPPvsszIMI91jXl5emjhxoiZOnPiv7kESCgAAYBJmqYQ6AxOTAAAA4HRUQgEAAMzCgUs0mQ2VUAAAADgdlVAAAACTYEwoAAAA4EBUQgEAAEwiJ1VCSUIBAABMIicloXTHAwAAwOmohAIAAJgElVAAAADAgaiEAgAAmAWL1QMAAACOQyUUAADAJBgTCgAAADgQlVAAAACToBIKAAAAOBCVUAAAAJPISZVQklAAAACzYIkmAAAAwHGohAIAAJhETuqOpxIKAAAAp6MSCgAAYBJUQgEAAAAHyjFJaNGiRTVu3LisDsOqVq1amjNnTobPP3jwoAoVKqSEhAQHRoWs5uJi0ftdG2nP4sE6t3GMdn8/SP06P3fX88e821qXd3yhrm2ecV6QAPTp1z+rYLW3bLYarYdndVjIBiyyWKuhdttMOj0+S5PQjh072vyQAgMD9dxzz+n333+3+7127Nih119/3e7t3o8lS5YoJiZGrVu3liRdunRJPXv2VOnSpeXt7a3ChQurV69eio2NtV5Trlw5PfXUUxozZkxWhQ0neLt9fXVqUVP9R89X1VbDNPjzH9TrlXp6/aWn7zi30TMV9fgjRXXu/BXnBwpApYsFa+/Sj6zbD1++ldUhAQ+ULK+EPvfcc4qKilJUVJTWrFkjNzc3NW7c2O73yZ8/v3LlymX3du/HhAkT9Oqrr8rF5eaP/9y5czp37pw+/fRT7d+/X9OnT9fy5cvVuXNnm+teffVVTZ48WTdu3MiKsOEET1Ysrp/W/66Vmw8oMuqSlqzdo1+2H1aV8kVsziuYP0Cf9Gup1wdO140bqVkULZCzubm5qkCgv3ULzO2b1SEhG7B7FdQBY0ztJcuTUE9PTwUHBys4OFiVK1fWu+++q8jISF24cMF6TmRkpFq1aqXcuXMrb968atKkiU6ePGk93rFjRzVt2lSffvqpChYsqMDAQHXv3l0pKSnWc/7eHX/48GHVqFFDXl5eKleunFavXi2LxaLFixdLkk6ePCmLxaJFixapdu3aypUrlypVqqStW7faxL9w4UKVL19enp6eKlq0qD777LN7Pu+FCxe0du1ahYeHW/dVqFBBCxcuVHh4uEqUKKE6depo+PDhWrp0qU3CWb9+fV26dEnr16/PzI8YD5Bffz+up58orRKFC0iSKpR6SE9VKq7VWw5az7FYLPpySHt9PmuNDh+PzqpQgRzveOQFVX5hoKq+OFTdBv9XZ6IvZXVIyA4sDtpMKMuT0NtdvXpVs2bNUsmSJRUYGChJSklJUYMGDeTn56eNGzdq8+bN8vX11XPPPafk5GTrtb/88ouOHTumX375RTNmzND06dM1ffr0dO+Tmpqqpk2bKleuXNq+fbumTJmiDz74IN1zP/jgA/Xr10979uzRww8/rDZt2lgTw127dqlVq1Zq3bq19u3bp8GDB2vgwIF3va8kbdq0Sbly5VLZsmXv+bOIjY2Vv7+/3Nz+fwEDDw8PVa5cWRs3bkz3mqSkJMXFxdlseLCMnbFKi1bt0q/zB+j81vFaP+sdfTl3neYv32k95+0O9XUjNU1fzV2XdYECOdyj5Yto/IC2mjOmq0b2a6nIcxfV9M0JuppwPatDAx4YWb5E07Jly+Tre7MLIyEhQQULFtSyZcusXdXz5s1TWlqavv76a2s5OSIiQrlz59a6dev07LPPSpLy5MmjL774Qq6uripTpowaNWqkNWvWqEuXLnfcc9WqVTp27JjWrVun4OBgSdLw4cNVv379O87t16+fGjVqJEkaMmSIypcvr6NHj6pMmTIaM2aM6tatq4EDB0qSHn74YR08eFCjR49Wx44d033eU6dOKSgoyPp86fnrr7/00UcfpTuGNSQkRKdOnUr3uhEjRmjIkCF3bRfm16zeY2r53BPqMmCGDh+P0iMPP6SP+7yoqAuxmvvjdlUqE6o3Wj+jZ17+JKtDBXK0umHlrP8uV/IhPVa+iJ5oPkRL1v6mtuFhWRgZHnQs0eREtWvX1p49e7Rnzx79+uuvatCggRo2bGhNtPbu3aujR4/Kz89Pvr6+8vX1Vd68eXX9+nUdO3bM2k758uXl6upq/VywYEGdP38+3XseOXJEoaGh1gRUkp588sl0z61YsaJNm5Ks7R46dEjVq1e3Ob969er6888/lZqa/ji9xMREeXl53fXnERcXp0aNGqlcuXIaPHjwHce9vb117dq1dK997733FBsba90iIyPveh+Y09C3mmrc/6qhB4+d07yfd2jSt2vVu+PNP5DCHi2h/Hl8tW/pUF3YOl4Xto5X4ZBADXurufb+wB8gQFYJ8Mul4qH5deLMX1kdCvDAyPJKqI+Pj0qWLGn9/PXXXysgIEBTp07VsGHDdPXqVVWpUkWzZ8++49r8+fNb/+3u7m5zzGKxKC0t7V/Hd3u7t/6S+Dft5suXT5cvX073WHx8vJ577jn5+fnp+++/v+OZpJsz6UuUKJHu9Z6envL09Lzv2JD1vD097vjfV1qaIRfL/3oGftqh9b8esTm+YEJ3fffzr5q9dJvT4gRgK+Fakk6dvagXn/PP6lDwgMtJldAsT0L/zmKxyMXFRYmJiZKkxx57TPPmzVOBAgXk72+fX+7SpUsrMjJSMTExCgoKknRzCafMKlu2rDZv3myzb/PmzXr44YdtqrK3e/TRRxUdHa3Lly8rT5481v1xcXFq0KCBPD09tWTJkrtWS/fv368XX3wx07HiwbB80z71ebWBzkRf1qHjUapYupC6ta2t2UtuJpiXYxN0OdZ2rdgbN1IVczFOR0+lX/kHYH9DPl+s+jUqKDQ4j6L/itOnX/8kF1eLmtavktWhAQ+MLE9Ck5KSFB19c4bv5cuX9cUXX+jq1avW2ePt2rXT6NGj1aRJEw0dOlSFChXSqVOntGjRIvXv31+FChXK9D3r16+vEiVKqEOHDho1apTi4+M1YMAASZn7a6Fv37564okn9NFHH+mll17S1q1b9cUXX2jSpEl3vebRRx9Vvnz5tHnzZutSVHFxcXr22Wd17do1zZo1y2ZSUf78+a0J7cmTJ3X27FnVq1cv08+MB8M7o+fr/a6N9ek7LylfHl9F/xWr6Ys2a9TXP2d1aABuE3X+iroNmqHLsQkKzO2rJysW149T+ihfHpZpwr9jsdzc7N2mGWV5Erp8+XLrWEs/Pz+VKVNG8+fP1zPPPCNJypUrlzZs2KB33nlHzZs3V3x8vB566CHVrVv3viujrq6uWrx4sV577TU98cQTKl68uEaPHq3w8PB7jtf8u8cee0zfffedPvzwQ3300UcqWLCghg4detdJSbfu/eqrr2r27NnWJHT37t3avn27JNkMTZCkEydOqGjRopKkb7/9Vs8++6yKFLFdMxLZx9VrSXp/zEK9P2Zhhq+p1GSQAyMCkJ4vP+qY1SEADzyLYRhGVgdhBps3b1aNGjV09OjRu465tJfo6GiVL19eu3fvznBCmZycrFKlSmnOnDl3TIa6m7i4OAUEBMjzkS6yuHr8m5AB2FnUlvFZHQKAv4mLi1OR4LzWZRKdfe+AgAAV77lALp4+dm07LSlBxz9/MUue616yvBKaVb7//nv5+vqqVKlSOnr0qN566y1Vr17d4QmoJAUHB2vatGk6ffp0hpPQ06dP6/33389wAgoAAB5ADuiON+ti9Tk2CY2Pj9c777yj06dPK1++fKpXr94/vu3Inpo2bZqp80uWLHlHVz0AAMCDKscmoe3bt1f79u2zOgwAAACrnLREU5YvVg8AAICcJ8dWQgEAAMwmJy3RRCUUAAAATkclFAAAwCRcXCxycbFv6dKwc3v2QiUUAAAATkclFAAAwCQYEwoAAACnu7VEk723zDp79qxefvllBQYGytvbW4888oh27txpPW4Yhj788EMVLFhQ3t7eqlevnv78889M3YMkFAAAAFaXL19W9erV5e7urp9//lkHDx7UZ599pjx58ljPGTVqlCZMmKAvv/xS27dvl4+Pjxo0aKDr169n+D50xwMAAJiEGbrjP/nkE4WGhioiIsK6r1ixYtZ/G4ahcePGacCAAWrSpIkk6b///a+CgoK0ePFitW7dOkP3oRIKAACQA8TFxdlsSUlJ6Z63ZMkSPf7442rZsqUKFCigRx99VFOnTrUeP3HihKKjo1WvXj3rvoCAAFWtWlVbt27NcDwkoQAAACbhyDGhoaGhCggIsG4jRoxIN4bjx49r8uTJKlWqlFasWKE333xTvXr10owZMyRJ0dHRkqSgoCCb64KCgqzHMoLueAAAgBwgMjJS/v7+1s+enp7pnpeWlqbHH39cH3/8sSTp0Ucf1f79+/Xll1+qQ4cOdouHSigAAIBJOLIS6u/vb7PdLQktWLCgypUrZ7OvbNmyOn36tCQpODhYkhQTE2NzTkxMjPVYRpCEAgAAwKp69eo6cuSIzb4//vhDRYoUkXRzklJwcLDWrFljPR4XF6ft27crLCwsw/ehOx4AAMAkzDA7vnfv3qpWrZo+/vhjtWrVSr/++qumTJmiKVOm/K89i95++20NGzZMpUqVUrFixTRw4ECFhISoadOmGb4PSSgAAIBJWHR/i8v/U5uZ8cQTT+j777/Xe++9p6FDh6pYsWIaN26c2rVrZz2nf//+SkhI0Ouvv64rV66oRo0aWr58uby8vDJ8H5JQAAAA2GjcuLEaN2581+MWi0VDhw7V0KFD7/seJKEAAAAmYYbueGdhYhIAAACcjkooAACASdy+pJI92zQjKqEAAABwOiqhAAAAJsGYUAAAAMCBqIQCAACYRE4aE0oSCgAAYBJ0xwMAAAAORCUUAADAJHJSdzyVUAAAADgdlVAAAACzcMCYUJmzEEolFAAAAM5HJRQAAMAkGBMKAAAAOBCVUAAAAJPISeuEkoQCAACYBN3xAAAAgANRCQUAADCJnNQdTyUUAAAATkclFAAAwCQYEwoAAAA4EJVQAAAAk6ASCgAAADgQlVAAAACTyEmz40lCAQAATILueAAAAMCBqIQCAACYRE7qjqcSCgAAAKejEgoAAGASjAkFAAAAHIhKKAAAgElY5IAxofZtzm6ohAIAAMDpqIQCAACYhIvFIhc7l0Lt3Z69kIQCAACYBEs0AQAAAA5EJRQAAMAkWKIJAAAAcCAqoQAAACbhYrm52btNM6ISCgAAAKejEgoAAGAWFgeM4aQSCgAAANxEJRQAAMAkWCcUAAAAcCAqoQAAACZh+d9/7N2mGVEJBQAAMIlbSzTZe8uMwYMHWxfNv7WVKVPGevz69evq3r27AgMD5evrqxYtWigmJibzz5rpKwAAAJCtlS9fXlFRUdZt06ZN1mO9e/fW0qVLNX/+fK1fv17nzp1T8+bNM30PuuMBAABMwiyv7XRzc1NwcPAd+2NjYzVt2jTNmTNHderUkSRFRESobNmy2rZtm5566qkM34NKKAAAQA4QFxdnsyUlJd313D///FMhISEqXry42rVrp9OnT0uSdu3apZSUFNWrV896bpkyZVS4cGFt3bo1U/GQhAIAAJjErSWa7L1JUmhoqAICAqzbiBEj0o2hatWqmj59upYvX67JkyfrxIkTqlmzpuLj4xUdHS0PDw/lzp3b5pqgoCBFR0dn6lnpjgcAAMgBIiMj5e/vb/3s6emZ7nkNGza0/rtixYqqWrWqihQpou+++07e3t52i4dKKAAAgEm4WCwO2STJ39/fZrtbEvp3uXPn1sMPP6yjR48qODhYycnJunLlis05MTEx6Y4hvZcMVUJ///33DDdYsWLFTAUAAAAA87p69aqOHTumV155RVWqVJG7u7vWrFmjFi1aSJKOHDmi06dPKywsLFPtZigJrVy5siwWiwzDSPf4rWMWi0WpqamZCgAAAAA3meG1nf369VN4eLiKFCmic+fOadCgQXJ1dVWbNm0UEBCgzp07q0+fPsqbN6/8/f3Vs2dPhYWFZWpmvJTBJPTEiROZix4AAACZZoYlms6cOaM2bdro4sWLyp8/v2rUqKFt27Ypf/78kqSxY8fKxcVFLVq0UFJSkho0aKBJkyZlOq4MJaFFihTJdMMAAAB48MydO/eex728vDRx4kRNnDjxX93nvmfHHzx4UKdPn1ZycrLN/hdeeOFfBQQAAJBTmaE73lkynYQeP35czZo10759+2zGid4q9TImFAAAAP8k00s0vfXWWypWrJjOnz+vXLly6cCBA9qwYYMef/xxrVu3zgEhAgAA5AyOXKLJbDJdCd26davWrl2rfPnyycXFRS4uLqpRo4ZGjBihXr166bfffnNEnAAAAMhGMl0JTU1NlZ+fnyQpX758OnfunKSbk5eOHDli3+gAAAByEIuDNjPKdCW0QoUK2rt3r4oVK6aqVatq1KhR8vDw0JQpU1S8eHFHxAgAAIBsJtNJ6IABA5SQkCBJGjp0qBo3bqyaNWsqMDBQ8+bNs3uAAAAAOYUZ1gl1lkwnoQ0aNLD+u2TJkjp8+LAuXbqkPHnymPYhAQAAHgQulpubvds0o0yPCb3l6NGjWrFihRITE5U3b157xgQAAIBsLtNJ6MWLF1W3bl09/PDDev755xUVFSVJ6ty5s/r27Wv3AAEAAHKKW93x9t7MKNNJaO/eveXu7q7Tp08rV65c1v0vvfSSli9fbtfgAAAAkD1lekzoypUrtWLFChUqVMhmf6lSpXTq1Cm7BQYAAJATmbRwaXeZroQmJCTYVEBvuXTpkjw9Pe0SFAAAALK3TCehNWvW1H//+1/rZ4vForS0NI0aNUq1a9e2a3AAAAA5SU4aE5rp7vhRo0apbt262rlzp5KTk9W/f38dOHBAly5d0ubNmx0RIwAAALKZTFdCK1SooD/++EM1atRQkyZNlJCQoObNm+u3335TiRIlHBEjAABAjnBrnVB7b2aU6UqoJAUEBOiDDz6w2Xf9+nV9+umn6tevn10CAwAAyGly0huTMlUJvXDhgpYtW6aVK1cqNTVVkpSSkqLx48eraNGiGjlypEOCBAAAQPaS4Uropk2b1LhxY8XFxclisejxxx9XRESEmjZtKjc3Nw0ePFgdOnRwZKwAAADZmuV/m73bNKMMV0IHDBig559/Xr///rv69OmjHTt2qFmzZvr444918OBBde3aVd7e3o6MFQAAANlEhpPQffv2acCAAapQoYKGDh0qi8WiUaNG6cUXX3RkfAAAADmGi8XikM2MMpyEXr58Wfny5ZMkeXt7K1euXKpQoYLDAgMAAED2lanZ8QcPHlR0dLQkyTAMHTlyRAkJCTbnVKxY0X7RAQAA5CAWi/1f22nSQmjmktC6devKMAzr58aNG0u6OfXfMAxZLBbrrHkAAADgbjKchJ44ccKRcQAAAOR4OWmd0AwnoUWKFHFkHAAAADleTuqOz/RrOwEAAIB/675e2wkAAAD7c8SSSg/8Ek0AAACAvVAJBQAAMImcNCb0vpPQCxcu6MiRI5Kk0qVLK3/+/HYLCgAAANlbprvjExIS1KlTJ4WEhKhWrVqqVauWQkJC1LlzZ127ds0RMQIAAOQIt5ZosvdmRplOQvv06aP169dryZIlunLliq5cuaIffvhB69evV9++fR0RIwAAALKZTHfHL1y4UAsWLNAzzzxj3ff888/L29tbrVq10uTJk+0ZH+zg9LpP5e/vn9VhALjNG9/9ntUhAPib5GtXszoEucj+s8bNOgs900notWvXFBQUdMf+AgUK0B0PAADwL+SkNyZlOjkOCwvToEGDdP36deu+xMREDRkyRGFhYXYNDgAAANlTpiuh48ePV4MGDVSoUCFVqlRJkrR37155eXlpxYoVdg8QAAAgp7BYJBeWaEpfhQoV9Oeff2r27Nk6fPiwJKlNmzZq166dvL297R4gAAAAsp/7Wic0V65c6tKli71jAQAAyNFcHFAJtXd79pKhJHTJkiUZbvCFF16472AAAACQM2QoCW3atGmGGrNYLEpNTf038QAAAORYOWl2fIaS0LS0NEfHAQAAgBzkvt8dDwAAAPtiTOg9DB069J7HP/zww/sOBgAAICezWOy/pJJJe+Mzn4R+//33Np9TUlJ04sQJubm5qUSJEiShAAAA+EeZTkJ/++23O/bFxcWpY8eOatasmV2CAgAAyIlcLBa52Ll0ae/27MUu77T39/fXkCFDNHDgQHs0BwAAAJMYOXKkLBaL3n77beu+69evq3v37goMDJSvr69atGihmJiYTLVrlyRUkmJjYxUbG2uv5gAAAHIcFwdt92vHjh366quvVLFiRZv9vXv31tKlSzV//nytX79e586dU/PmzTPVdqa74ydMmGDz2TAMRUVFaebMmWrYsGFmmwMAAIAJXb16Ve3atdPUqVM1bNgw6/7Y2FhNmzZNc+bMUZ06dSRJERERKlu2rLZt26annnoqQ+1nOgkdO3aszWcXFxflz59fHTp00HvvvZfZ5gAAAPA/jpwdHxcXZ7Pf09NTnp6ed72ue/fuatSokerVq2eThO7atUspKSmqV6+edV+ZMmVUuHBhbd261XFJ6IkTJzJ7CQAAALJYaGiozedBgwZp8ODB6Z47d+5c7d69Wzt27LjjWHR0tDw8PJQ7d26b/UFBQYqOjs5wPJkeJtCpUyfFx8ffsT8hIUGdOnXKbHMAAAD4HxdZrDPk7bbpZik0MjLSOocnNjb2rj3YkZGReuuttzR79mx5eXk58FkzacaMGUpMTLxjf2Jiov773//aJSgAAICc6FZ3vL036eZqRrdvd+uK37Vrl86fP6/HHntMbm5ucnNz0/r16zVhwgS5ubkpKChIycnJunLlis11MTExCg4OzvCzZrg7Pi4uToZhyDAMxcfH22TGqamp+umnn1SgQIEM3xgAAADmU7duXe3bt89m36uvvqoyZcronXfeUWhoqNzd3bVmzRq1aNFCknTkyBGdPn1aYWFhGb5PhpPQ3Llzy2KxyGKx6OGHH77juMVi0ZAhQzJ8YwAAANgyw7vj/fz8VKFCBZt9Pj4+CgwMtO7v3Lmz+vTpo7x588rf3189e/ZUWFhYhiclSZlIQn/55RcZhqE6depo4cKFyps3r/WYh4eHihQpopCQkAzfGAAAAA+msWPHysXFRS1atFBSUpIaNGigSZMmZaqNDCehTz/9tKSbs+NDQ0Pl4mK3de4BAACgm+M37f2aTXs0t27dOpvPXl5emjhxoiZOnHjfbWZ6iaYiRYroypUrmjZtmg4dOiRJKl++vDp16qSAgID7DgQAAAA5xz+WM48fP27zeefOnSpRooTGjh2rS5cu6dKlSxozZoxKlCih3bt3OyxQAACA7M6Rs+PN5h+T0Llz56pz585KS0uTdPNdoS+88IJOnjypRYsWadGiRTpx4oQaN25s82J7AAAA4G7+MQnt27evXF1d9fzzz0u6WQl955135Ob2/z35bm5u6t+/v3bu3Om4SAEAALK5W7Pj7b2Z0T8moZ6enpoyZYrat28v6eZCp6dPn77jvMjISPn5+dk/QgAAAGQ7GZ7i3rZtW0nSSy+9pM6dO2vevHmKjIxUZGSk5s6dq9dee01t2rRxWKAAAADZncVB/zGjTM+O//TTT2WxWNS+fXvduHFDkuTu7q4333xTI0eOtHuAAAAAOYUZFqt3lkwnoR4eHho/frxGjBihY8eOSZJKlCihXLlypftOeQAAAODv7nvF+Vy5cumRRx7RI488IldXV40ZM0bFihWzZ2wAAAA5ChOT0pGUlKT33ntPjz/+uKpVq6bFixdLkiIiIlSsWDGNHTtWvXv3dlScAAAAyEYy3B3/4Ycf6quvvlK9evW0ZcsWtWzZUq+++qq2bdumMWPGqGXLlnJ1dXVkrAAAANmaxWKRxe6v7TRnKTTDSej8+fP13//+Vy+88IL279+vihUr6saNG9q7d69pHw4AAADmlOEk9MyZM6pSpYokqUKFCvL09FTv3r1JQAEAAOwkJ82Oz/CY0NTUVHl4eFg/u7m5ydfX1yFBAQAAIHvLcCXUMAx17NhRnp6ekqTr16+ra9eu8vHxsTlv0aJF9o0QAAAgh7BYbm72btOMMpyEdujQwebzyy+/bPdgAAAAcjIXi0Uuds4a7d2evWQ4CY2IiHBkHAAAAMhBMv3GJAAAADgGE5MAAAAAB6ISCgAAYBYOmJgkKqEAAADATVRCAQAATMJFFrnYuXRp7/bshUooAAAAnI5KKAAAgEmwWD0AAACcjiWaAAAAAAeiEgoAAGASOem1nVRCAQAA4HRUQgEAAEwiJ01MohIKAAAAp6MSCgAAYBIucsCYUBarBwAAAG6iEgoAAGASOWlMKEkoAACASbjI/t3UZu32NmtcAAAAyMaohAIAAJiExWKRxc795/Zuz16ohAIAAMDpqIQCAACYhOV/m73bNCMqoQAAAHA6KqEAAAAm4WJxwGL1jAkFAAAAbqISCgAAYCLmrFvaH0koAACASeSkNybRHQ8AAACnoxIKAABgEixWDwAAgBxp8uTJqlixovz9/eXv76+wsDD9/PPP1uPXr19X9+7dFRgYKF9fX7Vo0UIxMTGZvg9JKAAAgEm4OGjLjEKFCmnkyJHatWuXdu7cqTp16qhJkyY6cOCAJKl3795aunSp5s+fr/Xr1+vcuXNq3rx5pp+V7ngAAABYhYeH23wePny4Jk+erG3btqlQoUKaNm2a5syZozp16kiSIiIiVLZsWW3btk1PPfVUhu9DEgoAAGASjhwTGhcXZ7Pf09NTnp6e97w2NTVV8+fPV0JCgsLCwrRr1y6lpKSoXr161nPKlCmjwoULa+vWrZlKQumOBwAAyAFCQ0MVEBBg3UaMGHHXc/ft2ydfX195enqqa9eu+v7771WuXDlFR0fLw8NDuXPntjk/KChI0dHRmYqHSigAAIBJWGT/xepvtRcZGSl/f3/r/ntVQUuXLq09e/YoNjZWCxYsUIcOHbR+/Xq7xkUSCgAAYBKO7I6/Nds9Izw8PFSyZElJUpUqVbRjxw6NHz9eL730kpKTk3XlyhWbamhMTIyCg4MzFRfd8QAAALintLQ0JSUlqUqVKnJ3d9eaNWusx44cOaLTp08rLCwsU21SCQUAADCJ+1lSKSNtZsZ7772nhg0bqnDhwoqPj9ecOXO0bt06rVixQgEBAercubP69OmjvHnzyt/fXz179lRYWFimJiVJJKEAAAC4zfnz59W+fXtFRUUpICBAFStW1IoVK1S/fn1J0tixY+Xi4qIWLVooKSlJDRo00KRJkzJ9H5JQAAAAkzDDazunTZt2z+NeXl6aOHGiJk6c+G/CYkwoAAAAnI9KKAAAgEk4cokms6ESCgAAAKejEgoAAGASFsvNzd5tmhFJKAAAgEm4yCIXO3eg27s9e6E7HgAAAE5HJRQAAMAkclJ3PJVQAAAAOB2VUAAAAJOw/O8/9m7TjKiEAgAAwOmohAIAAJgEY0IBAAAAB6ISCgAAYBIWB6wTatYxoSShAAAAJkF3PAAAAOBAVEIBAABMgkooAAAA4EBUQgEAAEyCxeoBAAAAB6ISCgAAYBIulpubvds0IyqhAAAAcDoqoQAAACbBmFAAAADAgaiEAgAAmEROWieUJBQAAMAkLLJ/97lJc1C64wEAAOB8VEIBAABMgiWaAAAAAAeiEgoAAGASLNEEAAAAOFC2TEJPnjwpi8WiPXv2SJLWrVsni8WiK1euZGlctxw5ckTBwcGKj4/P8DXvvvuuevbs6cCoYEZjIlaoTvtRCn26r0o9+67a9ZuiP0/GZHVYQI7XoEx+fdWqolpVLmizv3hgLvV+urgmNK+gcc3Kq1/t4nJ3NWcVCuZ0a4kme29mZIoktGPHjrJYLOratesdx7p37y6LxaKOHTved/vVqlVTVFSUAgIC/kWU9vPee++pZ8+e8vPzu+PY0aNH5efnp9y5c9vs79evn2bMmKHjx487KUqYwZbdR/Vay1pa+U0/Lfqih1JupKp5zy+UkJiU1aEBOVaRPN6qVTxQkVcSbfYXD8ylXjWL6WBMvEas/lMjVh/VL39elGFkUaCAyZkiCZWk0NBQzZ07V4mJ//9Lff36dc2ZM0eFCxf+V217eHgoODhYFhP8KXD69GktW7Ys3aQ6JSVFbdq0Uc2aNe84li9fPjVo0ECTJ092QpQwiwWfd1fb8KdUtkRBPfJwIU0a9LLORF/WnkORWR0akCN5urmo81OFNXPnGV1LTrU51rJyQa09+pdWHL6gqLgkxcQnadeZWN1IIwtFxlkctJmRaZLQxx57TKGhoVq0aJF136JFi1S4cGE9+uijNucuX75cNWrUUO7cuRUYGKjGjRvr2LFjd207ve74qVOnKjQ0VLly5VKzZs00ZswYm+rj4MGDVblyZc2cOVNFixZVQECAWrdubdOFnpSUpF69eqlAgQLy8vJSjRo1tGPHjns+53fffadKlSrpoYceuuPYgAEDVKZMGbVq1Srda8PDwzV37tx7to/sLe7qdUlSHv9cWRwJkDO1eSxE+6LidPj8VZv9fp6uKh7oo/jrN9S/TgmNfqGs+j5TXCXy8buKzHGRRS4WO28mTUNNk4RKUqdOnRQREWH9/M033+jVV1+947yEhAT16dNHO3fu1Jo1a+Ti4qJmzZopLS0tQ/fZvHmzunbtqrfeekt79uxR/fr1NXz48DvOO3bsmBYvXqxly5Zp2bJlWr9+vUaOHGk93r9/fy1cuFAzZszQ7t27VbJkSTVo0ECXLl266703btyoxx9//I79a9eu1fz58zVx4sS7Xvvkk0/qzJkzOnnyZLrHk5KSFBcXZ7Mh+0hLS9N7YxaoaqXiKlcyJKvDAXKcx0MDVDi3t77/PfqOY/l8PCVJjcsHadPxS5qw4YROX0lU76eLq4Cvh7NDBR4IpkpCX375ZW3atEmnTp3SqVOntHnzZr388st3nNeiRQs1b95cJUuWVOXKlfXNN99o3759OnjwYIbu8/nnn6thw4bq16+fHn74YXXr1k0NGza847y0tDRNnz5dFSpUUM2aNfXKK69ozZo1km4mwpMnT9bo0aPVsGFDlStXTlOnTpW3t7emTZt213ufOnVKISG2CcTFixfVsWNHTZ8+Xf7+/ne99tZ1p06dSvf4iBEjFBAQYN1CQ0P/8WeBB0e/Ud/p0LEoTRt+5x9mABwrj7e7Xno0RNO2R6bbvX5rtNfG45e05eRlRV65rvl7ohQTn6RqxfI6OVo8yOiOzyL58+dXo0aNNH36dEVERKhRo0bKly/fHef9+eefatOmjYoXLy5/f38VLVpU0s3xlhlx5MgRPfnkkzb7/v5ZkooWLWozeahgwYI6f/68pJtV0pSUFFWvXt163N3dXU8++aQOHTp013snJibKy8vLZl+XLl3Utm1b1apV655xe3t7S5KuXbuW7vH33ntPsbGx1i0yknGD2cV/Rn2nFRv3a+nkXnooKE9WhwPkOIXzeMvfy10f1C+lSS8+okkvPqLSBXxVu1Q+TXrxEcVdvyFJioq9bnNddFyS8uZyz4qQAdMz3WL1nTp1Uo8ePSTprl3T4eHhKlKkiKZOnaqQkBClpaWpQoUKSk5Otmss7u62/8dhsVgy3OV/N/ny5dPly5dt9q1du1ZLlizRp59+KkkyDENpaWlyc3PTlClT1KlTJ0mydvPnz58/3bY9PT3l6en5r+KDuRiGof6j5+vHdXu19Mu3VOShO/8oA+B4h89f1ZDlR2z2dXgyVNFxSVpx+Lz+SkjW5WspCvK3/f/gAn6eOhCd8eX4AIeULk1aCjVdEvrcc88pOTlZFotFDRo0uOP4xYsXdeTIEU2dOtU6i3zTpk2Zukfp0qXvmED0TxOK/q5EiRLy8PDQ5s2bVaRIEUk3Z7fv2LFDb7/99l2ve/TRR+8YNrB161alpv7/LMsffvhBn3zyibZs2WIzgWn//v1yd3dX+fLlMxUrHlz9PvlOC1bs1JxPX5dvLi/F/HVznK+/r5e8vRhnBjhL0o00nYtLumNfQvIN6/5VRy4ovHyQzly5rsgriQormkfBfp76akv6Q6iAnM50Sairq6u1O9vV1fWO43ny5FFgYKCmTJmiggUL6vTp03r33XczdY+ePXuqVq1aGjNmjMLDw7V27Vr9/PPPmVrCycfHR2+++ab+85//KG/evCpcuLBGjRqla9euqXPnzne9rkGDBnrttdeUmppqfb6yZcvanLNz5065uLioQoUKNvs3btyomjVrWrvlkf19s3CjJKlx1/E2+yd++LLahj+VFSEBuIs1f/4lN1eLWlYuKB8PN525kqhxG47rrwT79tIhe8tJr+00XRIq6Z6Tc1xcXDR37lz16tVLFSpUUOnSpTVhwgQ988wzGW6/evXq+vLLLzVkyBANGDBADRo0UO/evfXFF19kKs6RI0cqLS1Nr7zyiuLj4/X4449rxYoVypPn7mP2GjZsKDc3N61evTrdSu+9zJ07V4MHD87UNXiwXd6Ruf9NAnCeMevufHnIisMXtOLwhSyIBnjwWAyDdzlINycHHT58WBs3bnT4vSZOnKglS5ZoxYoVGb7m559/Vt++ffX777/LzS1jfzvExcUpICBAMRdj75nYA3C+N777PatDAPA3ydeuam6X6oqNdf735q3v7DV7TsvXz773vhofp7qVC2fJc92LKSuhzvDpp5+qfv368vHx0c8//6wZM2Zo0qRJTrn3G2+8oStXrig+Pj7dV3emJyEhQRERERlOQAEAwIMnB81LyrlJ6K+//qpRo0YpPj5exYsX14QJE/Taa6855d5ubm764IMPMnXNiy++6KBoAAAAnC/HJqHfffddVocAAABgKweVQk21WD0AAACy1ogRI/TEE0/Iz89PBQoUUNOmTXXkiO06udevX1f37t0VGBgoX19ftWjRQjExMZm6D0koAACASVgc9J/MWL9+vbp3765t27Zp1apVSklJ0bPPPquEhATrOb1799bSpUs1f/58rV+/XufOnVPz5s0zdZ8c2x0PAACAOy1fvtzm8/Tp01WgQAHt2rVLtWrVUmxsrKZNm6Y5c+aoTp06kqSIiAiVLVtW27Zt01NPZWwdayqhAAAAJmGxOGaTbi4DdfuWlJR072D+JzY2VpKUN29eSdKuXbuUkpKievXqWc8pU6aMChcurK1bt2b4WUlCAQAAcoDQ0FAFBARYtxEjRvzjNWlpaXr77bdVvXp165sco6Oj5eHhody5c9ucGxQUpOjo6AzHQ3c8AACASThycnxkZKTNYvWenp7/eG337t21f/9+bdq0yc5RkYQCAACYhwOzUH9//0y9MalHjx5atmyZNmzYoEKFCln3BwcHKzk5WVeuXLGphsbExCg4ODjD7dMdDwAAACvDMNSjRw99//33Wrt2rYoVK2ZzvEqVKnJ3d9eaNWus+44cOaLTp08rLCwsw/ehEgoAAGAS97OkUkbazIzu3btrzpw5+uGHH+Tn52cd5xkQECBvb28FBASoc+fO6tOnj/LmzSt/f3/17NlTYWFhGZ4ZL5GEAgAA4DaTJ0+WJD3zzDM2+yMiItSxY0dJ0tixY+Xi4qIWLVooKSlJDRo00KRJkzJ1H5JQAAAAk7h9SSV7tpkZhmH84zleXl6aOHGiJk6ceJ9RMSYUAAAAWYBKKAAAgEk4cokms6ESCgAAAKejEgoAAGAWOagUShIKAABgEmZYoslZ6I4HAACA01EJBQAAMAkzLNHkLFRCAQAA4HRUQgEAAEwiB81LohIKAAAA56MSCgAAYBY5qBRKJRQAAABORyUUAADAJHLSOqEkoQAAACbBEk0AAACAA1EJBQAAMIkcNC+JSigAAACcj0ooAACAWeSgUiiVUAAAADgdlVAAAACTyElLNFEJBQAAgNNRCQUAADCJnLROKEkoAACASeSgeUl0xwMAAMD5qIQCAACYRQ4qhVIJBQAAgNNRCQUAADAJlmgCAAAAHIhKKAAAgFk4YIkmkxZCqYQCAADA+aiEAgAAmEQOmhxPEgoAAGAaOSgLpTseAAAATkclFAAAwCRYogkAAABwICqhAAAAJmFxwBJNdl/yyU6ohAIAAMDpqIQCAACYRA6aHE8lFAAAAM5HJRQAAMAsclAplCQUAADAJFiiCQAAAHAgKqEAAAAmYZEDlmiyb3N2QyUUAAAATkclFAAAwCRy0LwkKqEAAABwPpJQAAAAk7j12k57b5m1YcMGhYeHKyQkRBaLRYsXL7Y5bhiGPvzwQxUsWFDe3t6qV6+e/vzzz0zdgyQUAAAANhISElSpUiVNnDgx3eOjRo3ShAkT9OWXX2r79u3y8fFRgwYNdP369QzfgzGhAAAApmGOUaENGzZUw4YN0z1mGIbGjRunAQMGqEmTJpKk//73vwoKCtLixYvVunXrDN2DSigAAEAOEBcXZ7MlJSXdVzsnTpxQdHS06tWrZ90XEBCgqlWrauvWrRluhyQUAADAJBw5JjQ0NFQBAQHWbcSIEfcVY3R0tCQpKCjIZn9QUJD1WEbQHQ8AAGASjuyMj4yMlL+/v3W/p6enne+UOVRCAQAAcgB/f3+b7X6T0ODgYElSTEyMzf6YmBjrsYwgCQUAADAJsyzRdC/FihVTcHCw1qxZY90XFxen7du3KywsLMPt0B0PAAAAG1evXtXRo0etn0+cOKE9e/Yob968Kly4sN5++20NGzZMpUqVUrFixTRw4ECFhISoadOmGb4HSSgAAIBJWP73H3u3mVk7d+5U7dq1rZ/79OkjSerQoYOmT5+u/v37KyEhQa+//rquXLmiGjVqaPny5fLy8srwPUhCAQAAYOOZZ56RYRh3PW6xWDR06FANHTr0vu9BEgoAAGAW5lir3imYmAQAAACnoxIKAABgEjmoEEoSCgAAYBaOWFLJ3u3ZC93xAAAAcDoqoQAAACZhliWanIFKKAAAAJyOSigAAIBZ5KCZSVRCAQAA4HRUQgEAAEwiBxVCqYQCAADA+aiEAgAAmEROWieUJBQAAMA07L9Ek1k75OmOBwAAgNNRCQUAADCJnNQdTyUUAAAATkcSCgAAAKcjCQUAAIDTMSYUAADAJBgTCgAAADgQlVAAAACTsDhgnVD7rztqHyShAAAAJkF3PAAAAOBAVEIBAABMwiL7v2TTpIVQKqEAAABwPiqhAAAAZpGDSqFUQgEAAOB0VEIBAABMIict0UQlFAAAAE5HJRQAAMAkctI6oSShAAAAJpGD5iXRHQ8AAADnoxIKAABgFjmoFEolFAAAAE5HJRQAAMAkWKIJAAAAcCAqoQAAACbBEk3IFgzDkCTFx8VlcSQA/i752tWsDgHA36QkJkj6/+/PrBDngO9sR7RpDySh2Vh8fLwkqWSx0CyOBACAB0d8fLwCAgKcek8PDw8FBwerlIO+s4ODg+Xh4eGQtu+XxcjKdB8OlZaWpnPnzsnPz08Ws9bikWFxcXEKDQ1VZGSk/P39szocAP/D72b2YRiG4uPjFRISIhcX50+buX79upKTkx3StoeHh7y8vBzS9v2iEpqNubi4qFChQlkdBuzM39+fLzrAhPjdzB6cXQG9nZeXl+kSRUdidjwAAACcjiQUAAAATkcSCjwgPD09NWjQIHl6emZ1KABuw+8mcH+YmAQAAACnoxIKAAAApyMJBQAAgNORhAIAAMDpSEIBAADgdCShAACYTEpKSlaHADgcSSiQgw0aNEgHDhzQ6dOnszoUAP/z2Wef6cUXX9TatWt17NixrA4HcBhe2wnkUJcuXdLevXu1a9cunTx5Uj169NALL7ygkJCQrA4NyLHS0tJUpkwZXbx4Ue+//75cXV310ksvqVevXlkdGmB3rBMK5HBHjx7V6tWr9d577+nZZ59Vq1at1KJFi6wOC8hxUlNT5erqav28Z88erV27Vh988IFeeuklDR8+XA899FAWRgjYF0kokMMYhiGLxaK0tDS5uLhYP+/YsUMfffSR4uLi1L59e3Xq1CmrQwVylFtJaFJSks3blzZv3qxGjRqpdu3amjp1qvLly5eFUQL2w5hQIIf566+/dPnyZf3111+Sbn7xpaWl6YknntCoUaMUGhqqb7/9VmvWrMniSIGcY9q0aapRo4bi4+Pl6elpnZhkGIaqV6+uLVu2aO3atRo2bFgWRwrYD0kokIN8++23atmypcLCwlSzZk398ssvcnO7OTTcMAyVKVNG7733nhISErRgwQLrfgCOYRiGUlNTNWbMGG3fvl1169ZVXFyc3N3dlZKSIovFotTUVJUrV06zZs3S119/re+++y6rwwbsgu54IIeYOXOmunbtqs8++0weHh7asWOH5s+fr23btqlkyZKS/r+rfs2aNapfv76WLFmixo0bZ3HkQPZ1a1hMw4YNVblyZe3evVvnz5/X6tWrFRgYqBs3blj/ULx69aoGDRqka9euacKECXJ1dZWLC7UkPLj4Xy+QA+zZs0ejR4/Wl19+qa5du6pTp07q16+fChQooIMHD1rPs1gsMgxDdevW1YABA/Tjjz8qLS2NaijgILeSyCeffFK+vr4aOnSoLBaLGjZsqEuXLmnIkCE6fPiwJMnX11e1a9fWTz/9pJiYGBJQPPD4XzCQA1y6dEl58+ZVWFiYdV+JEiWUO3du6xdcWlqapJuJqCSVKVNGW7duVVJSknUfAPsxDMP6B563t7cOHTqkqlWratq0aZKkoKAgrVy5UmXKlLGOEW3cuLGefvppbdiwIcviBuyFJBTIpsaOHasTJ05IkqpUqaJPPvnE2u1+6wvN09PT2tX396pK27Zt9dJLL+nMmTNOjBrI3nr16qWePXtKuvkH360/8J5//nlduXJFklSuXDldvHhRfn5+un79uhISEqxjRCXplVdeUaVKlbIkfsCeSEKBbCgxMVFTp07V2rVrJUkBAQGqWrWqpJvVl1trEXp6eioxMdG6v0mTJlq5cqW1KtqlSxeVKlUqC54AyJ5q1qxp/f26fZiLp6enoqOj9ccff6hatWp66KGHNH36dPn4+Kh06dLWRFSS6tevr/Lly2dJ/IA9kYQC2UxaWpq8vb3VtWtXbdy4UbGxsTbHLRaLtep5a51QSWrUqJF+/fVX1a5d23qc9QgB+ypcuLBmzJihVatWWcdgp6Wl6aGHHpKfn5/CwsIUEBCgpUuX6oUXXtCQIUPUsGFDeXl5ZXXogN2RhALZzK0Esnz58lq1apV2794t6eZ6oLfc+rfFYpGrq6vatWuno0eP6vTp03J3d9eNGzecHziQA1StWlXdunXTqFGjdPLkSesfhb6+vqpcubKeeuopzZ07VwEBAZKkunXraurUqXJ1dbX5HQayA5JQIJuqW7eumjZtqpdffllnz561+RK7laimpaXpgw8+0MGDB3XgwAFrAnprnCgA+6tbt66SkpI0Y8YMXbx40bp/7Nix+u6771SgQAFJN7vrbx+rffsrPYHsgCQUyIZudbH37dtX5cqVU+vWrRUVFWVNRG9NhqhUqZKqVKmiHTt2kIACTtKgQQPVqVNHc+fO1cyZM3X+/HnrMR8fH+u/WZUC2R2L1QPZ3E8//aTRo0fr6tWrmjdvnooXL249Fh0drQIFCsjFxYUEFHCCW4vTS1KfPn20atUq1a1bV126dLGZbHT7eUB2RRIKZFO33n4kSWvWrNG4ceO0fv16ff7553ryySdVtmxZ67l84QHOk5qaau1anzBhgtasWaPff/9dQ4YM0WOPPaYKFSpkcYSAc5CEAtnY7YlocnKyPvvsM/34449KTk5WzZo11a1bN5UoUSKLowSyl9t/7+627/Y//I4dO6bly5frm2++Ue7cufXCCy+oZ8+e/GGIbI8kFHhA3f4l9vcvuNsrLX936tQpRUdHa+XKlWrWrBlVF8CObv+9vHz5shITExUSEpLu8b//3sbExCglJUVnz561rusLZGckocAD6PYvsqlTp2rHjh1yc3NT+fLl1b17d0l3JqJ//8JLr1oD4P7d/js1ZMgQ/fzzz/rzzz/11FNP6cUXX9Qrr7wiNzc3hr8A/8NvAfAAuvUF9s477+j999+XJB09elSjRo1So0aNJOmOdQX/nnCSgAL2det36qOPPtLnn3+uLl26aOrUqZKkr7/+Wp988olSU1NJQIH/oRIKPKC2b9+uli1baubMmXr66aeVnJysDRs2qHPnznriiSe0YMGCrA4RyFEMw1BMTIzCw8P11ltv6eWXX5YkxcXFaciQIdq4caNGjx6tp59+mp4IQFRCgQfW+fPnlZKSokceeUSS5OHhoaefflpjx47V/v37tWXLliyOEMhZLBaLfHx8dPXqVcXHx0u6OSzG399fI0aMUFxcnJYsWWI9F8jpSEKBB0B6HRalSpWSm5ub1q1bZ93n7u6uxx57TDExMTp37pwTIwRynrS0tHT3+/r6avPmzZJuDp1JTU2Vh4eHqlWrZvOGJCCnIwkFTC4tLc1aNUlLS7OO88yTJ4/KlSunOXPmWL/wJMnPz09FixaVu7t7lsQL5AS3Ty46fPiwYmJidPHiRfn5+Wn06NGaP3++3n//faWlpcnV1VU3btzQgQMHVLBgwSyOHDAPxoQCJnb7uLHRo0frt99+U2xsrIYOHaoqVaro119/1RtvvKECBQqoWrVqqly5sj7//HNdvHhRO3fu5F3TgIN98MEHmj17tlxdXVWpUiV9+OGHqly5smbOnKlOnTqpevXqCggI0JUrV/TXX39p7969vJkM+B8qoYBJ3V4BHT58uEaNGqXcuXMrPj5eNWvW1Jw5c/Tkk0/qm2++UYkSJfTNN99o2LBh8vLy0q+//nrH7HgA/97tdZsVK1Zo+vTp+vLLL9WtWzelpaWpTZs2+u233/TKK69o165dqlSpkoKCglS9enVrAnrjxo0sfALAPKiEAiZ37tw5jR49Wi1atFCNGjUkSb1799akSZM0bdo06wzchIQEXbt2Tfny5ZPFYuFd8IADzZkzR4cPH1a+fPnUq1cvSdLWrVs1atQoHTx4ULNmzdITTzyhlJQUm6Ex93qRBJDTUAkFTGzhwoUqVKiQli5dapNQjh07Vt26dVOXLl307bffKjk5WT4+PsqfP78sFosMwyABBRzkyJEjmjhxosaMGaOUlBTr/rCwML3zzjuqUKGCOnTooG3btt0xNpsEFPh/JKGAifx9tm3Tpk3VqVMnHT9+XJGRkZL+vztw7Nix6t69u9q1a6f169fbXMfyL4D9/L3DsHTp0urbt68qV66syZMn6/jx49ZjTz31lPr3768CBQpo/Pjxzg4VeKDQHQ+Y0E8//aQKFSqocOHCunHjhl5++WWtWrVKixcvVs2aNW3OnTBhgrp160blE3CA22fBjx07VomJida3lC1dulTjxo3TjRs3FBERoeLFi1uvO3DggMqWLcvbkYB74LcDMIHbK6BHjx5V48aNNW7cOJ05c0Zubm6aM2eO6tSpo2bNmmnjxo021/bq1YvJDoAD3J6A7ty5U7t379aAAQP09ddfS5LCw8PVo0cPeXh46NVXX9WJEyes15YvX14uLi53XUsUAEkokOUMw7B+0Q0dOlQLFy5U/vz59cUXX2j48OE6d+6cXFxcNG/ePNWpU0cvvviiVq9efUc7VEIB+7r1e/nuu++qV69eSkpK0kMPPaQePXpo3LhxkqRmzZqpe/fu8vLyUqNGjRQVFZVuGwDuxLcWkMVujd8cOXKkxo0bp4ULF2ru3Lk6evSo3nzzTaWlpWnQoEEKCQnR3Llz9eyzz2rMmDGqV69eFkcOZE+3r887f/58TZw4UcuXL9eTTz6p48eP6+uvv9bAgQPl4uKiXr16qWnTpkpMTNT27dtVoECBLI4eeHCQhAJZYNOmTapWrZq1SpKamqr169frtddeU+3atSVJtWvXVnBwsJo1ayYPDw/1799foaGhWrVqFV18gAP06tVL48ePt5nYd/ToUT3yyCOqXr26pJuTknr16qXY2Fj17t1bPj4+6ty5s9q0aaNWrVpZ1+dlFjzwz+gnAJysR48e+vLLL22+6JKSknThwgXr4vKpqam6ceOGwsPD1b17d02cOFFjx45VXFycLBaLXF1dSUQBOzpw4ICOHDlyx9jqIkWKKDIyUocOHbLuCw0NVfPmzSVJb731liZOnCjp5vJLhmGQgAIZRBIKONknn3yiiIgIWSwW/fHHH7px44Zy5cqlFi1a6JtvvrG+7ehWlbRgwYIKDw/XuHHjNG3aNGs7jDUD7Kd8+fJasWKF3N3dNWvWLOsfeQ8//LAKFCigb775xmYppqCgILVt21Z9+vTR1KlTdeDAAUksjwZkBt9igBOlpaXJx8dH7u7umjlzpsLDw7V06VKlpqaqffv2qlu3rjp16qTt27fLxcVFiYmJ2rJli958802NGDFCn3zyiaKiou5YtxDA/XnppZc0c+ZM6+dz586pa9eueuaZZyRJjz/+uDp06KClS5fqk08+0bJly3Tw4EF98MEH8vLyUsOGDXX8+HGdPHkyax4AeICRhAJOdHv18oUXXlD+/Pk1evRo/fzzz3rooYf0wQcfqEKFCqpRo4aeeOIJVaxYUUePHlX9+vVVsGBBBQYGKiAggGoLYCe5cuXSG2+8oUWLFkmSQkJCtHr1ap09e9aaiPbq1Uu9e/dWZGSkmjdvrqZNmyoqKkqTJ09WxYoVVaRIEXl4eGThUwAPJharB5zg9vUGbxcXF6fw8HAlJiZq8ODBev7555WcnKwff/xRhw4dkr+/v9544w25u7urV69eOnTokBYtWiQ/P78seAoge+rTp48mTZqkWbNm6cUXX5Qkbd++Xa1atVKxYsW0bt06SdKlS5d07tw5GYahChUqyGKxqF+/fvrhhx+0YcMGFSxYMAufAnjwkIQCDnb7ci8zZszQgQMHVK5cOT355JMqV66cYmNj9cILLygxMVEDBw7Uc889Z/O+6dOnT2vUqFGaNWuWNm7cqEceeSSrHgXItnr37q3Jkyenm4iWKFFCa9assemB+OWXXzRt2jStXLlSK1as0KOPPppVoQMPLLrjAQe6PQF9//331bdvX23ZskUff/yx+vXrpw0bNiggIEBLlixRrly5NGLECC1cuNA6KeLKlSv66aefdPz4ca1fv54EFLCD22svt/49duxYvfnmm3r55Zc1f/58SVLVqlU1f/58nTp1SpUqVbK5rnTp0goMDNT69etJQIH7RCUUcILff/9dn332mXr06KEnnnhCP/74o6ZMmaJLly5p2LBhevrppxUbG6tq1aqpevXqmjJlivXaK1euyGKxKCAgIAufAMgebh8ak5KSoqSkJPn6+lqPv/322/ryyy81c+ZMtWzZUtLNdX3Hjh2r7777zroMk8ViYT1Q4F8iCQUcbN68eRo3bpy8vLz0ww8/yN/fX5K0atUqffHFF7p8+bI++ugjPf3007p27Zo8PT1tvugA2MftCehnn32mtWvX6ty5c2rcuLH69u2r3LlzS7qZiH711VeaOXOmtWv+FhJPwH54YxLgYHFxcbpx44b279+vs2fPWpPQ+vXrS5ImT56srl27as6cOdZuPb7oAPu7lYC+//77mj59ut58800VL15cr776qs6fP69+/fqpVKlSGjdunFxcXNSqVSutWbPG+hYzSfxeAnZEEgrYUXqz4Lt06aLAwEB9/PHHeueddzRixAiVL19e0s1ENCkpSZs2bVLFihWt1/BFB9jXrZ6FJUuWaMGCBVq4cKHCwsK0efNmSVJERIRiYmL06aefqmTJkhozZoyKFCmimjVrZnHkQPZFdzxgJ7cnoEuXLlVsbKwuXbqkLl26yNvbWwsWLNDEiROVJ08eDRs2TOXKlbujDSqggH39/vvveuihhxQYGKgbN25oxYoVOnXqlLp166aff/5Zbdu21cSJE1W6dGlVr15dL7/8svr27auyZcta27hx44bc3KjZAPZGEgrYWf/+/TV37lyVKVNGR48eVa5cuTRmzBg9++yzmj17tr755hvlzZtXAwYMUKVKlbI6XCDbWrx4sdq2batOnTpp0KBByp8/vy5duqSEhAT5+vqqcePGCg8P17vvvqsLFy4oLCxMx48fV//+/TVy5MisDh/I9liiCbCj6dOna+bMmVq2bJlWrlypcePG6eDBg0pNTZUktWvXTq+99poOHz6sefPmZXG0QPaVlJSkZcuW6fr16zp+/LiGDRummJgY5c2bV6Ghobpy5YouX75sHYft4uKiJk2aaNeuXRo2bFgWRw/kDCShgB2dOHFCrVq1UsWKFfXtt9+qffv2mjhxoho2bKj4+HilpKSoTZs2+vTTT/XRRx9ldbhAtuXp6Wkdj+3q6qoDBw5o5MiR+uuvvyTdTDrPnj2rJUuWaPHixXr55Ze1bds2Va5cWW5ubrpx40YWPwGQ/dEdD9hBSkqK3N3d1apVKxUuXFitW7dW7dq1NWrUKL355psyDEOjR4+Wh4eH3n77bet1jAEF7C8tLU2GYcjFxUX9+vVTYGCg0tLS9P3336tWrVp69913FRQUpKVLl+qVV17RQw89pLx582rt2rVyd3dneTTASRhpDdyHNWvW6Pfff1dUVJQGDhxofZd7x44d9eabb2rMmDGKiIhQhw4dJEnXrl3TunXr7njjEQkoYD+HDh2Sv7+/HnroIeu+QoUKafbs2dqyZYt8fHz07bffauTIkXr33XcVHh6uQ4cO6caNG3rooYfk4uLCJCTAieiOBzLp66+/Vtu2bfXjjz9q1qxZeuKJJ5SSkiJJKl++vOrXr6+HH35Yrq6uSklJ0cGDB9WyZUvFxMRo+PDhWRw9kD0tXLhQlSpVUo0aNfTtt99q586dkm6+E97f319jx45V7969FR4erq1bt2rUqFGKiopSwYIFFRoaKhcXF6WlpZGAAk7EbxuQCV999ZV69Oih7777TvXr11d0dLSeeeYZ/fbbb3riiSdUpEgR9e7dW4ZhqEePHurbt6+CgoKUJ08ebdu2TW5ubnTBA3aWnJysNWvWqECBAnJ1ddWXX34pf39/63Jo9erV04kTJyRJAwcOlIuLi7755hsVKVJEvXr1srbz9zV+ATgWY0KBDFq8eLGaN2+uH374QeHh4ZKkxMREVa5cWXXr1tWBAwfUsmVLtW3bVv7+/jp27JgOHDig0NBQValSha4+wIGio6M1YsQInT59WgULFlSnTp3Ut29f5cuXT8ePH9fevXu1YMECNW/eXNLNlSxeeeUV/iAEshDfhkAGJCUlacWKFSpevLiOHz9u3d+uXTvFx8fL399ffn5+6t27t86ePatBgwapdOnSKl26tPVcuvoAxwkODtY777yjjz/+WLt27dKuXbu0fv16bdmyRcuWLdOlS5dUpkwZ6/kdO3aUxORAICtRCQUyKCoqSp988om2b9+u1q1ba9OmTTp69KgWLVqkYsWKSZLat2+vlStXav/+/cqXL18WRwzkPFFRUfr444+1detWtWvXTr1795YkXbp0SXnz5k331boAsga/iUAGFSxYUO+++64ef/xxjR8/XmvXrtWyZctUrFgxXbt2TZJUo0YNFS5cWGlpaVkcLZAzFSxYUB988IHCwsI0d+5cjRgxQpKUN29epaamkoACJsJvI5AJwcHBGjBggMLDw1WsWDF9++23kqRcuXLpxo0bWrBggYoXL678+fNncaRAzhUcHKwPPvhAVatW1bJlyzRw4EBJLIkGmA1JKJBJQUFBeu+99xQWFqb58+fr008/lSQ1b95cZ8+e1axZs2SxWMRIFyDrBAcH6/3331eJEiUUExPD7yNgQowJBe5TdHS0dRLE0aNHlTt3bu3fv1/u7u7MggdM4tKlS8qdO7dcXFx4ExJgMiShwL8QHR2td955RxcuXNAPP/xAAgqYFBOSAPMhCQX+pcuXLysgIIB1QAEAyASSUMBOqLQAAJBxJKEAAABwOso2AAAAcDqSUAAAADgdSSgAAACcjiQUAAAATkcSCgAAAKcjCQWQbV2/fl3Dhw/X0aNHszoUAMDfkIQCyLZ69eqlo0ePqmTJknZpz2KxaPHixXZpy9mKFi2qcePGZXUYAGBFEgrggdGxY0dZLBZZLBa5u7urWLFi6t+/v65fv37HubNnz9bJkyc1ZcoUm/3r1q2TxWLRlStXnBT1/zt58qQ1fovFosDAQD377LP67bffHH7vHTt26PXXX8/QuSSsAJyBJBTAA+W5555TVFSUjh8/rrFjx+qrr77SoEGD7jivXbt2Wrlypdzd3bMgyntbvXq1oqKitGLFCl29elUNGza8a1KckpJil3vmz59fuXLlsktbAGAPJKEAHiienp4KDg5WaGiomjZtqnr16mnVqlXW40lJSerVq5cKFCggLy8v1ahRQzt27JB0sxJZu3ZtSVKePHlksVjUsWNHSelX/ypXrqzBgwffNZZ9+/apTp068vb2VmBgoF5//XVdvXr1H58hMDBQwcHBevzxx/Xpp58qJiZG27dvt1ZK582bp6efflpeXl6aPXu2JOnrr79W2bJl5eXlpTJlymjSpEnW9qpVq6Z33nnH5h4XLlyQu7u7NmzYcMfzGYahwYMHq3DhwvL09FRISIh69eolSXrmmWd06tQp9e7d21qxvWXhwoUqX768PD09VbRoUX322Wf/+KwAcDckoQAeWPv379eWLVvk4eFh3de/f38tXLhQM2bM0O7du1WyZEk1aNBAly5dUmhoqBYuXChJOnLkiKKiojR+/Pj7undCQoIaNGigPHnyaMeOHZo/f75Wr16tHj16ZKodb29vSVJycrJ137vvvqu33npLhw4dUoMGDTR79mx9+OGHGj58uA4dOqSPP/5YAwcO1IwZMyTdrPrOnTtXt7+Fed68eQoJCVHNmjXvuOfChQutVeQ///xTixcv1iOPPCJJWrRokQoVKqShQ4cqKipKUVFRkqRdu3apVatWat26tfbt26fBgwdr4MCBmj59eqaeFwCsDAB4QHTo0MFwdXU1fHx8DE9PT0OS4eLiYixYsMAwDMO4evWq4e7ubsyePdt6TXJyshESEmKMGjXKMAzD+OWXXwxJxuXLl23aLlKkiDF27FibfZUqVTIGDRpk/SzJ+P777w3DMIwpU6YYefLkMa5evWo9/uOPPxouLi5GdHR0uvGfOHHCkGT89ttvhmEYxuXLl41mzZoZvr6+RnR0tPX4uHHjbK4rUaKEMWfOHJt9H330kREWFmYYhmGcP3/ecHNzMzZs2GA9HhYWZrzzzjvpPt9nn31mPPzww0ZycnK6cab3s2jbtq1Rv359m33/+c9/jHLlyqXbBgD8EyqhAB4otWvX1p49e7R9+3Z16NBBr776qlq0aCFJOnbsmFJSUlS9enXr+e7u7nryySd16NAhu8Zx6NAhVapUST4+PtZ91atXV1pamo4cOXLPa6tVqyZfX1/lyZNHe/fu1bx58xQUFGQ9/vjjj1v/nZCQoGPHjqlz587y9fW1bsOGDdOxY8ck3Rzv+eyzz1q77k+cOKGtW7eqXbt26d6/ZcuWSkxMVPHixdWlSxd9//33unHjxj8+7+0/11vP++effyo1NfWe1wJAekhCATxQfHx8VLJkSVWqVEnffPONtm/frmnTpv3rdl1cXGy6syX7TQr6u3nz5mnv3r26fPmyjh07pueff97m+O2J7a0xplOnTtWePXus2/79+7Vt2zbree3atdOCBQuUkpKiOXPm6JFHHrF2sf9daGiojhw5okmTJsnb21vdunVTrVq1HPa8AJAeklAADywXFxe9//77GjBggBITE1WiRAl5eHho8+bN1nNSUlK0Y8cOlStXTpKs40f/Xr3Lnz+/dfyjJMXFxenEiRN3vXfZsmW1d+9eJSQkWPdt3rxZLi4uKl269D3jDg0NVYkSJZQ7d+5/fMagoCCFhITo+PHjKlmypM1WrFgx63lNmjTR9evXtXz5cs2ZM+euVdBbvL29FR4ergkTJmjdunXaunWr9u3bJ+nmz+jvP5+yZcva/FxvPe/DDz8sV1fXf3wOAPg7klAAD7SWLVvK1dVVEydOlI+Pj95880395z//0fLly3Xw4EF16dJF165dU+fOnSVJRYoUkcVi0bJly3ThwgVrpbFOnTqaOXOmNm7cqH379qlDhw73TK7atWsnLy8vdejQQfv379cvv/yinj176pVXXrHpWreHIUOGaMSIEZowYYL++OMP7du3TxERERozZoz1HB8fHzVt2lQDBw7UoUOH1KZNm7u2N336dE2bNk379+/X8ePHNWvWLHl7e6tIkSKSbs6k37Bhg86ePau//vpLktS3b1+tWbNGH330kf744w/NmDFDX3zxhfr162fXZwWQg2T1oFQAyKgOHToYTZo0uWP/iBEjjPz58xtXr141EhMTjZ49exr58uUzPD09jerVqxu//vqrzflDhw41goODDYvFYnTo0MEwDMOIjY01XnrpJcPf398IDQ01pk+ffs+JSYZhGL///rtRu3Ztw8vLy8ibN6/RpUsXIz4+/q7x/31iUmaOz54926hcubLh4eFh5MmTx6hVq5axaNEim3N++uknQ5JRq1atO66/fbLR999/b1StWtXw9/c3fHx8jKeeespYvXq19dytW7caFStWtE7+umXBggVGuXLlDHd3d6Nw4cLG6NGj7/qsAPBPLIbxt0FQAAAAgIPRHQ8AAACnIwkFAACA05GEAgAAwOlIQgEAAOB0JKEAAABwOpJQAAAAOB1JKAAAAJyOJBQAAABORxIKAAAApyMJBQAAgNORhAIAAMDp/g9OV+vBnDcpCwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Análise da matriz de confusão**\n",
        "\n",
        "A matriz apresenta os resultados da classificação de células tumorais como benignas ou malignas utilizando um modelo SVM treinado com o dataset \"Câncer de Mama de Wisconsin\".\n",
        "\n",
        "Interpretação da Matriz:\n",
        "\n",
        "* **Verdadeiros Negativos (VN)**: 84 células benignas foram corretamente classificadas como benignas.\n",
        "* **Falsos Negativos (FN)**: 2 células malignas foram incorretamente classificadas como benignas.\n",
        "* **Falsos Positivos (FP)**: 5 células benignas foram incorretamente classificadas como malignas.\n",
        "* **Verdadeiros Positivos (VP)**: 46 células malignas foram corretamente classificadas como malignas.\n",
        "\n",
        "O modelo apresenta uma alta acurácia de 94,89%, indicando bom desempenho na classificação das células. O *recall* de 95,83% para a classe **Maligno** é particularmente importante neste contexto, pois significa que o modelo consegue identificar a maioria das células malignas, minimizando o risco de falsos negativos (células malignas classificadas como benignas), o que poderia ter consequências graves na prática para pacientes.\n",
        "\n",
        "A precisão para a classe **Maligno** é de 90,20%, indicando que a maioria das células classificadas como malignas são de fato malignas. No entanto, o número de falsos positivos (5) mostra que o modelo pode gerar alguns alarmes falsos, classificando células benignas como malignas."
      ],
      "metadata": {
        "id": "MSZlqUnhQmOd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Trade-off* entre Precision e Recall**\n",
        "\n",
        "A curva Precision-Recall fornece uma visão mais detalhada do desempenho em relação à classificação binária, especialmente quando as classes estão desbalanceadas. O *trade-off* entre *Precision* e *Recall* reflete a necessidade de ajustar o desempenho do modelo às prioridades do problema em questão. O gráfico abaixo fornece algumas informações para se encontrar o equilíbrio adequado, considerando-se as implicações dos erros de classificação no domínio da aplicação.\n",
        "\n",
        "Precision e Recall rabalham em oposição. Ao se maximizar a precisão, tende-se a sacrificar a abrangência (recall), pois o modelo torna-se mais conservador nas previsões. E vice-versa, ao se maximizar o recall, a precisão pode cair, pois o modelo tenta classificar mais amostras como positivas. O *threshold* determina o ponto de corte usado para classificar uma amostra como positiva ou negativa:\n",
        "* **Limiar baixo** (ex.: próximo de 0, aumenta o Recall): mais amostras serão classificadas como positivas, incluindo muitas da classe verdadeira, o que também aumenta o número de Falsos Positivos, reduzindo a Precision.\n",
        "* **Limiar alto** (ex.: próximo de 1, aumenta a Precision): apenas as amostras com alta confiança são classificadas como positivas, mas algumas amostras positivas podem ser perdidas (Falsos Negativos), reduzindo o Recall.\n",
        "\n"
      ],
      "metadata": {
        "id": "q168DlbyeMD3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "# Obter pontuações da função de decisão\n",
        "y_scores = clf.decision_function(X_test)\n",
        "\n",
        "# precision_recall_curve utiliza rótulos binários (0/1 or -1/1),\n",
        "# Especificar pos_label para indicar qual classe é considerada \"positiva\" para o cálculo da curva.\n",
        "precisions, recalls, thresholds = precision_recall_curve(y_test, y_scores, pos_label=4)\n",
        "\n",
        "plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
        "plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
        "threshold = 0.6  # Exemplo de valor limite\n",
        "plt.vlines(threshold, 0, 1.0, \"k\", \"dotted\", label=\"threshold\")\n",
        "\n",
        "plt.xlabel(\"Threshold\")\n",
        "plt.ylabel(\"Precision/Recall\")\n",
        "plt.title(\"Precision-Recall Curve\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "1t_MyHH1bU6a",
        "outputId": "56ff46bc-627f-4483-8a60-05d9928f2f21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Modelagem em SVM com `kernel=linear`**\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "xRIOzXMdmByn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Acessar o classificador SVM\n",
        "from sklearn import svm\n",
        "# Padroniza os dados\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "# Cria um pipeline para facilitar o processamento sequencial\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "# Criar um pipeline que inclui a padronização dos dados e o classificador SVM com kernel RBF\n",
        "clf_linear = make_pipeline(\n",
        "    StandardScaler(),  # Padroniza os dados para ter média 0 e variância 1\n",
        "    svm.SVC(kernel='linear', C=1.0, gamma='scale')  # Inicializa o classificador SVM\n",
        ")\n",
        "\n",
        "# Treinar o modelo com os dados de treinamento\n",
        "clf_linear.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "id": "3PZwqScfiK2s",
        "outputId": "c2d14000-775e-4dcb-86d4-19c8fa47aced"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('svc', SVC(kernel='linear'))])"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {\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-11 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11 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-11 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-11 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-11 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 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-11 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11 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-11 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-11 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-11 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-11 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-11 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-11 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-11 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-11 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-11 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svc&#x27;, SVC(kernel=&#x27;linear&#x27;))])</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-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" 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;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svc&#x27;, SVC(kernel=&#x27;linear&#x27;))])</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-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" 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-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Previsões com o modelo linear\n",
        "yhat_linear = clf_linear.predict(X_test)\n",
        "\n",
        "# Exibir as primeiras 5 previsões\n",
        "print(\"As primeiras 5 previsões são:\", yhat_linear[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXH9ZT5aioTX",
        "outputId": "0fd68c71-eb94-4c34-f8e0-b4e14c1ad160"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As primeiras 5 previsões são: [2 2 4 2 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Avaliação da precisão para o kernel Linear**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "ScR4EnMhloHN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the linear model\n",
        "cnf_matrix_linear = confusion_matrix(y_test, yhat_linear, labels=[2, 4])\n",
        "print(classification_report(y_test, yhat_linear, target_names=['Benigno (2)', 'Maligno (4)'], digits=4))\n",
        "\n",
        "# Imprimir a pontução geral do F1-Score\n",
        "f1_score_overall = f1_score(y_test, yhat, average='weighted')\n",
        "print(f\"Pontuação geral F1-score: {f1_score_overall:.4f}\")\n",
        "\n",
        "jaccard_benigno_linear = jaccard_score(y_test, yhat_linear, pos_label=2)\n",
        "jaccard_maligno_linear = jaccard_score(y_test, yhat_linear, pos_label=4)\n",
        "\n",
        "print(f\"Índice de Jaccard para classe Benigno (2): {jaccard_benigno_linear:.4f}\")\n",
        "print(f\"Índice de Jaccard para classe Maligno (4): {jaccard_maligno_linear:.4f}\")\n",
        "\n",
        "disp_linear = ConfusionMatrixDisplay(confusion_matrix=cnf_matrix_linear, display_labels=['Benigno (2)', 'Maligno (4)'])\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "disp_linear.plot(ax=ax, cmap=plt.cm.Blues, colorbar=True)\n",
        "plt.title('Matriz de Confusão (Kernel Linear)')\n",
        "plt.xlabel('Rótulo Previsto')\n",
        "plt.ylabel('Rótulo Real')\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "id": "ouwzJdLWhsyc",
        "outputId": "08b2cdcc-5f22-41c8-8690-7f80bb735783"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            " Benigno (2)     0.9767    0.9438    0.9600        89\n",
            " Maligno (4)     0.9020    0.9583    0.9293        48\n",
            "\n",
            "    accuracy                         0.9489       137\n",
            "   macro avg     0.9394    0.9511    0.9446       137\n",
            "weighted avg     0.9505    0.9489    0.9492       137\n",
            "\n",
            "Pontuação geral F1-score: 0.9492\n",
            "Índice de Jaccard para classe Benigno (2): 0.9231\n",
            "Índice de Jaccard para classe Maligno (4): 0.8679\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Visualização Kernel Linear versus RBF**\n",
        "\n",
        "* **Linear**: uma linha reta separa as duas classes, para dados linearmente separáveis.\n",
        "* **RBF**: separação não linear ajusta-se às curvas formadas pelos dados, adequado para dados mais complexos."
      ],
      "metadata": {
        "id": "_OY0KoEL0EnF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.datasets import make_blobs\n",
        "\n",
        "# 1. Gerar dados sintéticos\n",
        "X, y = make_blobs(n_samples=100, centers=2, random_state=42, cluster_std=1.5)\n",
        "\n",
        "# 2. Treinar o modelo SVM com kernel linear e RBF\n",
        "svm_linear = SVC(kernel=\"linear\", C=1)\n",
        "svm_rbf = SVC(kernel=\"rbf\", C=1, gamma=0.5)\n",
        "\n",
        "svm_linear.fit(X, y)\n",
        "svm_rbf.fit(X, y)\n",
        "\n",
        "# 3. Função para visualização do plano de decisão\n",
        "def plot_decision_boundary(svm, X, y, ax, title):\n",
        "    # Criar grade de pontos para o gráfico\n",
        "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200),\n",
        "                         np.linspace(y_min, y_max, 200))\n",
        "    # Calcular valores da função de decisão\n",
        "    Z = svm.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
        "    Z = Z.reshape(xx.shape)\n",
        "\n",
        "    # Plotar os dados e o plano de decisão\n",
        "    ax.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, edgecolors=\"k\", s=50)\n",
        "    ax.contour(xx, yy, Z, levels=[-1, 0, 1], linestyles=[\"--\", \"-\", \"--\"], colors=\"k\")\n",
        "    ax.scatter(svm.support_vectors_[:, 0], svm.support_vectors_[:, 1], s=200,\n",
        "               facecolors=\"none\", edgecolors=\"red\", label=\"Vetores de Suporte\")\n",
        "    ax.set_title(title)\n",
        "    ax.set_xlabel(\"Feature 1\")\n",
        "    ax.set_ylabel(\"Feature 2\")\n",
        "    ax.legend()\n",
        "\n",
        "# 4. Criar gráficos lado a lado\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharey=True)\n",
        "\n",
        "# Kernel Linear\n",
        "plot_decision_boundary(svm_linear, X, y, axes[0], title=\"SVM com Kernel Linear\")\n",
        "\n",
        "# Kernel RBF\n",
        "plot_decision_boundary(svm_rbf, X, y, axes[1], title=\"SVM com Kernel RBF\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "CZR9qj3Dz4AW",
        "outputId": "7f8f1096-c377-4075-cace-8d3bb6cda724"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Explorando o efeito da regularização em um classificador SVM**"
      ],
      "metadata": {
        "id": "OETU13N30rje"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import LinearSVC\n",
        "iris = load_iris(as_frame=True)\n",
        "X = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
        "y = (iris.target == 2) # Iris virginica\n",
        "svm_clf = make_pipeline(StandardScaler(),\n",
        "LinearSVC(C=1, random_state=42))\n",
        "svm_clf.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "id": "G2s8BWQMiagW",
        "outputId": "ef87f523-908c-4b7b-b811-f02a26b0a718"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('linearsvc', LinearSVC(C=1, random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {\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-12 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12 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-12 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-12 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-12 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 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-12 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12 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-12 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-12 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-12 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-12 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-12 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-12 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-12 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-12 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-12 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvc&#x27;, LinearSVC(C=1, 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-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" 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;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvc&#x27;, LinearSVC(C=1, 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-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" 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-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVC(C=1, random_state=42)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.datasets import load_iris\n",
        "\n",
        "# 1. Carregar o dataset Iris\n",
        "iris = load_iris()\n",
        "X = iris[\"data\"][:, (2, 3)]  # Usar comprimento e largura das pétalas\n",
        "y = (iris[\"target\"] == 2).astype(int)  # Iris virginica como classe positiva\n",
        "\n",
        "# 2. Ajustar dois modelos SVM com diferentes valores de C\n",
        "svm_clf_low_c = SVC(kernel=\"linear\", C=1)\n",
        "svm_clf_high_c = SVC(kernel=\"linear\", C=100)\n",
        "svm_clf_low_c.fit(X, y)\n",
        "svm_clf_high_c.fit(X, y)\n",
        "\n",
        "# 3. Função para calcular margens e suportes\n",
        "def plot_svm_decision_boundary(svm_clf, X, y, ax, title):\n",
        "    # Plotar os dados\n",
        "    ax.scatter(X[:, 0][y == 1], X[:, 1][y == 1], color=\"green\", s=60, marker=\"^\", label=\"Iris virginica\")\n",
        "    ax.scatter(X[:, 0][y == 0], X[:, 1][y == 0], color=\"blue\", s=60, marker=\"s\", label=\"Iris versicolor\")\n",
        "\n",
        "    # Gerar grade para decisão\n",
        "    x0s = np.linspace(X[:, 0].min(), X[:, 0].max(), 200)\n",
        "    x1s = np.linspace(X[:, 1].min(), X[:, 1].max(), 200)\n",
        "    x0, x1 = np.meshgrid(x0s, x1s)\n",
        "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
        "    y_pred = svm_clf.decision_function(X_new).reshape(x0.shape)\n",
        "\n",
        "    # Plotar fronteiras de decisão\n",
        "    ax.contour(x0, x1, y_pred, colors=\"black\", levels=[-1, 0, 1], alpha=0.7, linestyles=[\"--\", \"-\", \"--\"])\n",
        "\n",
        "    # Destacar vetores de suporte\n",
        "    ax.scatter(\n",
        "        svm_clf.support_vectors_[:, 0],\n",
        "        svm_clf.support_vectors_[:, 1],\n",
        "        s=200, facecolors=\"none\", edgecolors=\"gray\", label=\"Vetores de Suporte\"\n",
        "    )\n",
        "    ax.set_title(title)\n",
        "    ax.set_xlabel(\"Petal length\")\n",
        "    ax.set_ylabel(\"Petal width\")\n",
        "    ax.legend()\n",
        "\n",
        "# 4. Criar gráficos lado a lado\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharey=True)\n",
        "\n",
        "plot_svm_decision_boundary(svm_clf_low_c, X, y, axes[0], title=\"C = 1\")\n",
        "plot_svm_decision_boundary(svm_clf_high_c, X, y, axes[1], title=\"C = 100\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "KMVCYmLDkNHH",
        "outputId": "d01d9e78-f5da-4d2f-a4c3-50ee7b56beac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O gráfico acima compara claramente o impacto do parâmetro C, efeito da regularização em aprendizado de máquina, em um classificador SVM.\n",
        "\n",
        "* **C baixo** (margens amplas): Maior flexibilidade, maior número de violações.\n",
        "* **C alto** (margens estreitas): Classificação mais rígida, menor número de violações.\n"
      ],
      "metadata": {
        "id": "RX4G8hFPkvQD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "GÉRON, A. **Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems**. Canada: O’Reilly, 2023."
      ],
      "metadata": {
        "id": "UhAOC5brg6j2"
      }
    }
  ]
}