KNN_Iris.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "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": "4G9p2T7i6qtA",
        "outputId": "977e3e72-d176-4349-a0df-b17d2d8a7707"
      },
      "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')\n"
      ],
      "metadata": {
        "id": "uLf4qNou6xtS"
      },
      "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": "shK3O1voDCxK",
        "outputId": "8d55e76b-2e7f-4dd5-ad16-f80fb9cbe0d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "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": "HX5LAC2JTWs7",
        "outputId": "7220a216-4f42-4b76-8966-9cdafb54a295"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
              "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
              "        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
              "        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
              "        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
              "        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
              "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
              "        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
              "       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
              "       117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
              "       130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
              "       143, 144, 145, 146, 147, 148, 149])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "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": "UkMetzt7TqgR",
        "outputId": "74dd010d-1d3a-4d3d-cb38-b102c1854ef4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 72, 112, 132,  88,  37, 138,  87,  42,   8,  90, 141,  33,  59,\n",
              "       116, 135, 104,  36,  13,  63,  45,  28, 133,  24, 127,  46,  20,\n",
              "        31, 121, 117,   4, 130, 119,  29,   0,  62,  93, 131,   5,  16,\n",
              "        82,  60,  35, 143, 145, 142, 114, 136,  53,  19,  38, 110,  23,\n",
              "         9,  86,  91,  89,  79, 101,  65, 115,  41, 124,  95,  21,  11,\n",
              "       103,  74, 122, 118,  44,  51,  81, 149,  12, 129,  56,  50,  25,\n",
              "       128, 146,  43,   1,  71,  54, 100,  14,   6,  80,  26,  70, 139,\n",
              "        30, 108,  15,  18,  77,  22,  10,  58, 107,  75,  64,  69,   3,\n",
              "        40,  76, 134,  34,  27,  94,  85,  97, 102,  52,  92,  99, 105,\n",
              "         7,  48,  61, 120, 137, 125, 147,  39,  84,   2,  67,  55,  49,\n",
              "        68, 140,  78, 144, 111,  32,  73,  47, 148, 113,  96,  57, 123,\n",
              "       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": "bbjeJbPAEYds"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X_train[y_train == 0, 0],\n",
        "            X_train[y_train == 0, 1],\n",
        "            marker='o',\n",
        "            label='class 0 (Setosa)')\n",
        "\n",
        "plt.scatter(X_train[y_train == 1, 0],\n",
        "            X_train[y_train == 1, 1],\n",
        "            marker='^',\n",
        "            label='class 1 (Versicolor)')\n",
        "\n",
        "plt.scatter(X_train[y_train == 2, 0],\n",
        "            X_train[y_train == 2, 1],\n",
        "            marker='s',\n",
        "            label='class 2 (Virginica)')\n",
        "\n",
        "plt.xlabel('petal length [cm]')\n",
        "plt.ylabel('petal width [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "plt.title(\"Pontos de Treinamento\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "MfIfny1VErPR",
        "outputId": "9f150bd7-b6cc-4fd9-d7bc-b4f623694783"
      },
      "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": [
        "class KNN:\n",
        "  def __init__(self, n_neighbors):\n",
        "    self.n_neighbors = n_neighbors\n",
        "\n",
        "  def fit(self, X, y):\n",
        "    self.X = X\n",
        "    self.y = y\n",
        "\n",
        "  def predict(self, X_test):\n",
        "    predictions = []\n",
        "    for x_test in X_test:\n",
        "      distances = []\n",
        "      for x_train in self.X:\n",
        "        distance = ((x_test[0] - x_train[0]) ** 2 + (x_test[1] - x_train[1]) ** 2) ** 0.5\n",
        "        distances.append(distance)\n",
        "      sorted_indices = sorted(range(len(distances)), key=distances.__getitem__)\n",
        "      k_nearest_indices = sorted_indices[:self.n_neighbors]\n",
        "      k_nearest_labels = [self.y[i] for i in k_nearest_indices]\n",
        "      prediction = max(set(k_nearest_labels), key=k_nearest_labels.count)\n",
        "      predictions.append(prediction)\n",
        "    return predictions\n",
        "\n",
        "knn_model = KNN(n_neighbors=3)\n",
        "knn_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = knn_model.predict(X_test)\n",
        "num_correct_predictions = (y_pred == y_test).sum()\n",
        "accuracy = (num_correct_predictions / y_test.shape[0]) * 100\n",
        "print('Test set accuracy: %.2f%%' % accuracy)\n",
        "\n",
        "# Criando 5 novos objetos para serem classificados em uma das 3 classes, primeiramente imprimindo os objetos e seus valores depois sua classificação\n",
        "\n",
        "new_objects = [[4.7, 1.4], [1.2, 2.3], [6.3, 2.9], [5.8, 2.9], [4.1, 2.0]]\n",
        "print('Novos Objetos:')\n",
        "for obj in new_objects:\n",
        "  print(obj)\n",
        "\n",
        "y_pred = knn_model.predict(new_objects)\n",
        "\n",
        "print('\\nClassificações:')\n",
        "for i, pred in enumerate(y_pred):\n",
        "  print('Object {}: Class {}'.format(i+1, pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLS8k_91Fl0m",
        "outputId": "1a6b5f37-110c-45ab-ad4b-6a27a63ebf9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set accuracy: 93.33%\n",
            "Novos Objetos:\n",
            "[4.7, 1.4]\n",
            "[1.2, 2.3]\n",
            "[6.3, 2.9]\n",
            "[5.8, 2.9]\n",
            "[4.1, 2.0]\n",
            "\n",
            "Classificações:\n",
            "Object 1: Class 1\n",
            "Object 2: Class 0\n",
            "Object 3: Class 2\n",
            "Object 4: Class 2\n",
            "Object 5: Class 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import numpy as np\n",
        "class KNN_melhorado:\n",
        "  def __init__(self, n_neighbors):\n",
        "    self.n_neighbors = n_neighbors\n",
        "\n",
        "  def fit(self, X, y):\n",
        "    self.X = X\n",
        "    self.y = y\n",
        "\n",
        "  def predict(self, X_test):\n",
        "    predictions = []\n",
        "    for x_test in X_test:\n",
        "      distances = np.sqrt(((self.X - x_test) ** 2).sum(axis=1))\n",
        "      sorted_indices = np.argsort(distances)\n",
        "      k_nearest_indices = sorted_indices[:self.n_neighbors]\n",
        "      k_nearest_labels = self.y[k_nearest_indices]\n",
        "      prediction = np.argmax(np.bincount(k_nearest_labels))\n",
        "      predictions.append(prediction)\n",
        "    return predictions\n",
        "\n",
        "knn_model_melhorado = KNN_melhorado(n_neighbors=3)\n",
        "knn_model_melhorado.fit(X_train, y_train)\n",
        "\n",
        "y_pred = knn_model_melhorado.predict(X_test)\n",
        "num_correct_predictions = (y_pred == y_test).sum()\n",
        "accuracy = (num_correct_predictions / y_test.shape[0]) * 100\n",
        "print('Test set accuracy: %.2f%%' % accuracy)\n",
        "# Criando 5 novos objetos para serem classificados em uma das 3 classes, primeiramente imprimindo os objetos e seus valores depois sua classificação\n",
        "\n",
        "new_objects = [[4.7, 1.4], [1.2, 2.3], [6.3, 2.9], [5.8, 2.9], [4.1, 2.0]]\n",
        "\n",
        "print('Novos Objetos:')\n",
        "for obj in new_objects:\n",
        "  print(obj)\n",
        "\n",
        "y_pred = knn_model_melhorado.predict(new_objects)\n",
        "\n",
        "print('\\nClassificações:')\n",
        "for i, pred in enumerate(y_pred):\n",
        "  print('Object {}: Class {}'.format(i+1, pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6YT-dVF_LFT",
        "outputId": "73074824-10ed-4fd3-aefe-4906715766c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set accuracy: 93.33%\n",
            "Novos Objetos:\n",
            "[4.7, 1.4]\n",
            "[1.2, 2.3]\n",
            "[6.3, 2.9]\n",
            "[5.8, 2.9]\n",
            "[4.1, 2.0]\n",
            "\n",
            "Classificações:\n",
            "Object 1: Class 1\n",
            "Object 2: Class 0\n",
            "Object 3: Class 2\n",
            "Object 4: Class 2\n",
            "Object 5: Class 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "# Gerando um vetor de teste grande\n",
        "X_test_large = np.random.rand(100000, 2)\n",
        "\n",
        "# Medindo o tempo de execução do KNN\n",
        "start_time = time.time()\n",
        "y_pred_knn = knn_model.predict(X_test_large)\n",
        "end_time = time.time()\n",
        "knn_execution_time = end_time - start_time\n",
        "\n",
        "# Medindo o tempo de execução do KNN_melhorado\n",
        "start_time = time.time()\n",
        "y_pred_knn_melhorado = knn_model_melhorado.predict(X_test_large)\n",
        "end_time = time.time()\n",
        "knn_melhorado_execution_time = end_time - start_time\n",
        "\n",
        "# Comparando os tempos de execução\n",
        "print(\"Tempo de execução do KNN:\", knn_execution_time)\n",
        "print(\"Tempo de execução do KNN_melhorado:\", knn_melhorado_execution_time)\n",
        "\n",
        "# Comparando os vetores de predição\n",
        "if np.array_equal(y_pred_knn, y_pred_knn_melhorado):\n",
        "  print(\"Os vetores de predição são iguais.\")\n",
        "else:\n",
        "  print(\"Os vetores de predição são diferentes.\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YT8J5rGv_lCd",
        "outputId": "ec8ca499-3186-42c5-aa39-84907c609a78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tempo de execução do KNN: 16.61157989501953\n",
            "Tempo de execução do KNN_melhorado: 2.689091682434082\n",
            "Os vetores de predição são iguais.\n"
          ]
        }
      ]
    }
  ]
}