Nearest Centroid Classifier for Iris Dataset in Colab.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNlFP-O9kURy",
        "outputId": "cf0d297f-8983-43ac-d389-da5d02ef1eb7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_iris = pd.read_csv('/content/drive/MyDrive/2024_01/Aprendizado de Máquina/iris.csv')"
      ],
      "metadata": {
        "id": "5DXR5Uq4kbCN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_iris[['PetalLength[cm]', 'PetalWidth[cm]']].values\n",
        "y = df_iris['Species'].values\n",
        "\n",
        "\n",
        "label_dict = {'Iris-setosa': 0,\n",
        "              'Iris-versicolor': 1,\n",
        "              'Iris-virginica': 2}\n",
        "\n",
        "df_iris['ClassLabel'] = df_iris['Species'].map(label_dict)\n",
        "\n",
        "y = df_iris['ClassLabel'].values\n",
        "y[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ANtCPeiikdge",
        "outputId": "a41902d8-5a79-4df2-f9f5-27419b967e48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "indices = np.arange(y.shape[0])\n",
        "indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qU7sfTD0kpW_",
        "outputId": "762a763f-4763-4a4a-f645-1b93e4c6c881"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "       143, 144, 145, 146, 147, 148, 149])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rnd = np.random.RandomState(123)\n",
        "shuffled_indices = rnd.permutation(indices)\n",
        "shuffled_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQUizxqIkq4o",
        "outputId": "1f49c32b-b8b0-418c-848f-4e7e65bc3437"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       106,  83,  17,  98,  66, 126, 109])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_shuffled, y_shuffled = X[shuffled_indices], y[shuffled_indices]\n",
        "X_train, y_train = X_shuffled[:105], y_shuffled[:105]\n",
        "X_test, y_test = X_shuffled[105:], y_shuffled[105:]"
      ],
      "metadata": {
        "id": "9WatU_gKkuEy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "class NearestCentroidClassifier:\n",
        "    def __init__(self):\n",
        "        pass\n",
        "\n",
        "    def fit(self, X_train, y_train):\n",
        "        self.X_train = X_train\n",
        "        self.y_train = y_train\n",
        "        self.centroids = {}\n",
        "\n",
        "        # Calcula o centróide para cada classe\n",
        "        for label in np.unique(y_train):\n",
        "            self.centroids[label] = np.mean(X_train[y_train == label], axis=0)\n",
        "\n",
        "    def predict(self, X_test):\n",
        "        y_pred = [self._predict(x) for x in X_test]\n",
        "        return np.array(y_pred)\n",
        "\n",
        "    def _predict(self, x):\n",
        "        # Calcula as distâncias para todos os centróides e encontra o mais próximo\n",
        "        distances = [np.linalg.norm(x - centroid) for centroid in self.centroids.values()]\n",
        "        nearest_label = min(self.centroids, key=lambda label: distances[label])\n",
        "\n",
        "        return nearest_label\n",
        "\n",
        "# Cria uma instância do classificador\n",
        "classifier = NearestCentroidClassifier()\n",
        "\n",
        "# Treina o classificador\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Faz previsões para os dados de teste\n",
        "y_pred_test = classifier.predict(X_test)\n",
        "\n",
        "# Calcula a acurácia do classificador nos dados de teste\n",
        "accuracy_test = np.mean(y_pred_test == y_test) * 100\n",
        "print('Acurácia Teste: %.2f%%' % accuracy_test)\n",
        "\n",
        "# Faz previsões para os dados de treinamento\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "\n",
        "# Calcula a acurácia do classificador nos dados de treinamento\n",
        "accuracy_train = np.mean(y_pred_train == y_train) * 100\n",
        "print('Acurácia Treino: %.2f%%' % accuracy_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-YM3apjNk6RI",
        "outputId": "9db872cb-f1a2-4fde-bf2a-18a598f94ac4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia Teste: 93.33%\n",
            "Acurácia Treino: 98.10%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotar os pontos de treino e teste\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Plotar pontos de treino\n",
        "for label in np.unique(y_train):\n",
        "    plt.scatter(X_train[y_train == label][:, 0], X_train[y_train == label][:, 1], marker='o', label=f'Treino Classe {label}', alpha=0.5)\n",
        "\n",
        "# Plotar pontos de teste\n",
        "for label in np.unique(y_test):\n",
        "    plt.scatter(X_test[y_test == label][:, 0], X_test[y_test == label][:, 1], marker='^', label=f'Teste Classe {label}', alpha=0.5)\n",
        "\n",
        "# Plotar centróides\n",
        "for label, centroid in classifier.centroids.items():\n",
        "    plt.scatter(centroid[0], centroid[1], marker='x', color='black', s=200, label=f'Centróide Classe {label}')\n",
        "\n",
        "plt.title('Pontos de Treino e Teste com Centróides')\n",
        "plt.xlabel('Feature 1')\n",
        "plt.ylabel('Feature 2')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "WC-hS1A-UcBb",
        "outputId": "214aae95-171c-4d68-94e2-19a3539a2ada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}