Naive_Bayes_Cogumelos.ipynb

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        "# Naive Bayes\n",
        "um método de classificação muito simples, mas poderoso. Para um determinado objeto x, Naive Bayes calcula a probabilidade de x pertencer a cada\n",
        "classe yi (i = 1, ⋯, k), usando o teorema de Bayes:\n",
        "\n",
        "P (yi| x) = P(yi) P(x1, ⋯, xm| yi) / P (x1, ⋯, xm)\n",
        "\n",
        "Além disso, assume que os recursos são independentes uns dos outros (razão pela qual é chamado de Bayes).\n",
        "\n"
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          "text": [
            "Requirement already satisfied: ucimlrepo in /usr/local/lib/python3.10/dist-packages (0.0.7)\n",
            "Requirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ucimlrepo) (2.0.3)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (2024.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (1.25.2)\n",
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      "source": [
        "# instalando o repositório da base de dados\n",
        "!pip install ucimlrepo"
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      "source": [
        "# importando a base e as bibliotecas necessárias\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn.metrics import accuracy_score, roc_curve, auc\n",
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "cogumelos = fetch_ucirepo(id=73)\n",
        "\n",
        "# definindo a semente aleatório\n",
        "seed = 10\n",
        "\n",
        "df = pd.DataFrame(cogumelos.data.features)\n",
        "label = cogumelos.data.targets\n",
        "\n",
        "df['label'] = label\n",
        "df.head()\n",
        "\n"
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              "  cap-shape cap-surface cap-color bruises odor  ... ring-type spore-print-color population habitat label\n",
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      "source": [
        "Pesquisando, li em um artigo que o scikit-learn não implementa o algoritmo clássico Naive Bayes, porém, o scikit-learn contém o BernoulliNB\n",
        "classe que assume que os dados são distribuídos de acordo com distribuições multivariadas de Bernoulli.\n",
        "Portanto, para o conjunto de dados Cogumelos, podemos transformar cada recurso categórico em variáveis ​​fictícias, mas com essa conversão claramente\n",
        "violamos a suposição de independência entre recursos. No entanto, foi comprovado que Naive Bayes alcança um bom desempenho em\n",
        "diversas aplicações onde a independência é violada (por exemplo, na classificação de textos)."
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      "cell_type": "code",
      "source": [
        "# criando variáveis binárias de acordo com a variável alvo\n",
        "dummies = [pd.get_dummies(df[c]) for c in df.drop('label', axis=1).columns]\n",
        "\n",
        "# concatenando os dados com as variáveis dummies\n",
        "dados_binarios = pd.concat(dummies, axis=1)\n",
        "\n",
        "X = dados_binarios.values\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJ5Cwint-OWi",
        "outputId": "066d7319-5959-4569-da7b-615d1659d3a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[False, False, False, ..., False,  True, False],\n",
              "       [False, False, False, ..., False, False, False],\n",
              "       [ True, False, False, ..., False, False, False],\n",
              "       ...,\n",
              "       [False, False,  True, ..., False, False, False],\n",
              "       [False, False, False, ..., False, False, False],\n",
              "       [False, False, False, ..., False, False, False]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtendo os rótulos\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(df['label'].values)\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LtYec-yS7Zlg",
        "outputId": "a702bb66-559c-443e-d074-6a26f1d93235"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, ..., 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dividindo o conjunto de dados binários em conjunto de treinamento e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, stratify=y, random_state=seed)\n",
        "\n",
        "# utilizando a classe Bernoulli e indicando que não se faz necessário binarizar os dados\n",
        "nb = BernoulliNB(binarize=None)\n",
        "nb.fit(X_train, y_train)\n",
        "y_pred = nb.predict(X_test)\n",
        "acc = accuracy_score(y_test, y_pred)\n",
        "print(f'Acurácia: {acc}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wYxPHQO1Shzg",
        "outputId": "a678ca92-b77b-4279-e5ec-b51e5e8e89a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia: 0.9419005416051206\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtendo as probabilidades de cada classe\n",
        "y_prob = nb.predict_proba(X_test)\n",
        "\n",
        "# calcucando a curva ROC e ROC AUC\n",
        "falso_positivo, verdadeiro_positivo, limite = roc_curve(y_test, y_prob[:, 1])\n",
        "roc_auc = auc(falso_positivo, verdadeiro_positivo)\n",
        "\n",
        "# plotando o gráfico\n",
        "lw = 2\n",
        "plt.plot(falso_positivo, verdadeiro_positivo, color='green', lw=lw, label=f'Curva ROC (area = {roc_auc: .4f})')\n",
        "plt.plot([0,1], [0,1], color='red', lw=lw, linestyle='--', label='Random Classifier')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('Falso Positivo')\n",
        "plt.ylabel('Verdadeiro Positivo')\n",
        "plt.title('ROC')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "RS24nu68TZWK",
        "outputId": "7fb99263-d3ce-411a-82f3-43b75780de6b"
      },
      "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": "code",
      "source": [],
      "metadata": {
        "id": "K0Z9PeUIWKMt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}