sklearn6_KnnM.ipynb

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      "source": [
        "## Importações e Informações do dataset"
      ],
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          "text": [
            "Primeiros registros do dataset:\n",
            "\n"
          ]
        },
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              "   USMER  MEDICAL_UNIT  SEX  PATIENT_TYPE   DATE_DIED  INTUBED  PNEUMONIA  \\\n",
              "0      2             1    1             1  03/05/2020       97          1   \n",
              "1      2             1    2             1  03/06/2020       97          1   \n",
              "2      2             1    2             2  09/06/2020        1          2   \n",
              "3      2             1    1             1  12/06/2020       97          2   \n",
              "4      2             1    2             1  21/06/2020       97          2   \n",
              "\n",
              "   AGE  PREGNANT  DIABETES  ...  ASTHMA  INMSUPR  HIPERTENSION  OTHER_DISEASE  \\\n",
              "0   65         2         2  ...       2        2             1              2   \n",
              "1   72        97         2  ...       2        2             1              2   \n",
              "2   55        97         1  ...       2        2             2              2   \n",
              "3   53         2         2  ...       2        2             2              2   \n",
              "4   68        97         1  ...       2        2             1              2   \n",
              "\n",
              "   CARDIOVASCULAR  OBESITY  RENAL_CHRONIC  TOBACCO  CLASIFFICATION_FINAL  ICU  \n",
              "0               2        2              2        2                     3   97  \n",
              "1               2        1              1        2                     5   97  \n",
              "2               2        2              2        2                     3    2  \n",
              "3               2        2              2        2                     7   97  \n",
              "4               2        2              2        2                     3   97  \n",
              "\n",
              "[5 rows x 21 columns]"
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      ],
      "source": [
        "# Importações necessárias\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_selection import SelectKBest, chi2, f_classif\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "# Carregamento dos dados\n",
        "file_path = '/content/drive/MyDrive/covid_data.csv'\n",
        "data = pd.read_csv(file_path)\n",
        "\n",
        "# Visualização dos dados\n",
        "print(\"Primeiros registros do dataset:\\n\")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pré-processamento"
      ],
      "metadata": {
        "id": "dJQQLiDdJ-pi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tratamento de valores faltantes (se necessário)\n",
        "data = data.dropna()\n",
        "\n",
        "# Transformação de atributos categóricos para numéricos\n",
        "categorical_columns = data.select_dtypes(include=['object']).columns\n",
        "data = pd.get_dummies(data, columns=categorical_columns)\n",
        "\n",
        "# Amostragem do dataset para evitar estouro de memória\n",
        "data_sampled = data.sample(n=10000, random_state=42)  # Ajuste o valor conforme necessário\n",
        "\n",
        "# Separação entre atributos (X) e rótulos (y)\n",
        "X = data_sampled.drop(columns=['CLASIFFICATION_FINAL'])\n",
        "y = data_sampled['CLASIFFICATION_FINAL']\n",
        "\n",
        "# Divisão do conjunto de dados em treinamento e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Normalização dos dados\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Seleção de atributos\n",
        "select_k_best = SelectKBest(f_classif, k=50)  # Mantém 50 atributos mais relevantes\n",
        "X_train = select_k_best.fit_transform(X_train, y_train)\n",
        "X_test = select_k_best.transform(X_test)\n",
        "\n",
        "# Redução da dimensionalidade usando PCA\n",
        "pca = PCA(n_components=0.95)  # Mantém 95% da variância explicada\n",
        "X_train = pca.fit_transform(X_train)\n",
        "X_test = pca.transform(X_test)"
      ],
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        "outputId": "9186161c-d7ac-4a00-c5cd-12ae86839f35"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 19  20  21  22  24  26  29  30  31  32  34  36  38  42  43  44  45  46\n",
            "  47  48  53  54  55  56  57  59  63  64  65  66  67  68  69  70  71  75\n",
            "  76  77  78  79  80  81  82  83  85  89  90  91  92  93  94  95  96  97\n",
            "  98 104 105 106 108 109 110 111 116 117 118 119 120 121 122 124 128 129\n",
            " 130 131 132 133 134 135 136 137 141 142 143 144 145 146 147 148 149 150\n",
            " 154 155 157 158 161 166 167 168 169 170 172 173 177 178 179 180 181 182\n",
            " 183 184 185 187 191 192 193 194 195 196 197 198 200 204 205 206 207 209\n",
            " 210 211 213 217 218 219 220 222 223 224 225 227 231 233 234 235 236 237\n",
            " 239 241 245 246 247 248 250 251 252 253 254 256 260 261 262 263 264 265\n",
            " 266 267 268 270 274 275 276 277 279 281 286 287 288 289 290 291 292 293\n",
            " 295 299 300 301 302 303 304 305 306 307 308 313 314 315 316 317 318 319\n",
            " 320 321 322 324 328 329 330 331 332 333 334 335 336 338 343 344 345 346\n",
            " 347 348 349 350 351 352 354 357 358 359 360 361 362 363 364 365 366 367\n",
            " 368 372 373 374 375 376 377 378 379 385 386 387 388 389 390 391 392 398\n",
            " 399 400 402 404 408 409 410 411 412 416 417 418] are constant.\n",
            "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
            "  f = msb / msw\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Knn (mudando a medida de distãncia)\n"
      ],
      "metadata": {
        "id": "AWJThPJ9KAex"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "distance_metrics = ['manhattan', 'euclidean', 'minkowski']\n",
        "\n",
        "for metric in distance_metrics:\n",
        "    print(f\"\\n## K-Nearest Neighbors (distância={metric}) ##\")\n",
        "\n",
        "    # Criação e treinamento do classificador KNN\n",
        "    clf = KNeighborsClassifier(metric=metric)\n",
        "    clf.fit(X_train, y_train)\n",
        "\n",
        "    # Predição e avaliação do modelo\n",
        "    y_pred = clf.predict(X_test)\n",
        "\n",
        "    # Avaliação do modelo\n",
        "    print(\"Matriz de Confusão:\\n\\n\")\n",
        "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "    print(conf_matrix)\n",
        "\n",
        "    print(\"\\nRelatório de Classificação:\\n\\n\")\n",
        "    print(classification_report(y_test, y_pred))\n",
        "    print(\"\\nAcurácia:\")\n",
        "    print(accuracy_score(y_test, y_pred))\n",
        "\n",
        "    # Visualização da matriz de confusão\n",
        "    plt.figure(figsize=(7, 4))\n",
        "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
        "    plt.title(f'Matriz de Confusão (distância={metric})')\n",
        "    plt.xlabel('Predicted')\n",
        "    plt.ylabel('Actual')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "X_U8rjEZJXx6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ae302ec4-4bca-4412-ddef-35deab4e8fe6"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "## K-Nearest Neighbors (distância=manhattan) ##\n",
            "Matriz de Confusão:\n",
            "\n",
            "\n",
            "[[  0   0   7   0   0   2   7]\n",
            " [  0   0   6   0   0   0   0]\n",
            " [  3   0 323   0   1  54 303]\n",
            " [  0   0   0   0   0   0   4]\n",
            " [  0   0  34   0   0   5  28]\n",
            " [  0   0 110   0   0  44  87]\n",
            " [  2   0 366   0   0  54 560]]\n",
            "\n",
            "Relatório de Classificação:\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00        16\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.38      0.47      0.42       684\n",
            "           4       0.00      0.00      0.00         4\n",
            "           5       0.00      0.00      0.00        67\n",
            "           6       0.28      0.18      0.22       241\n",
            "           7       0.57      0.57      0.57       982\n",
            "\n",
            "    accuracy                           0.46      2000\n",
            "   macro avg       0.17      0.18      0.17      2000\n",
            "weighted avg       0.44      0.46      0.45      2000\n",
            "\n",
            "\n",
            "Acurácia:\n",
            "0.4635\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "## K-Nearest Neighbors (distância=euclidean) ##\n",
            "Matriz de Confusão:\n",
            "\n",
            "\n",
            "[[  0   0   8   0   0   1   7]\n",
            " [  0   0   6   0   0   0   0]\n",
            " [  2   0 332   0   1  51 298]\n",
            " [  0   0   1   0   0   0   3]\n",
            " [  0   0  33   0   0   7  27]\n",
            " [  0   0 107   0   0  46  88]\n",
            " [  2   0 379   0   5  54 542]]\n",
            "\n",
            "Relatório de Classificação:\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00        16\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.38      0.49      0.43       684\n",
            "           4       0.00      0.00      0.00         4\n",
            "           5       0.00      0.00      0.00        67\n",
            "           6       0.29      0.19      0.23       241\n",
            "           7       0.56      0.55      0.56       982\n",
            "\n",
            "    accuracy                           0.46      2000\n",
            "   macro avg       0.18      0.18      0.17      2000\n",
            "weighted avg       0.44      0.46      0.45      2000\n",
            "\n",
            "\n",
            "Acurácia:\n",
            "0.46\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "## K-Nearest Neighbors (distância=minkowski) ##\n",
            "Matriz de Confusão:\n",
            "\n",
            "\n",
            "[[  0   0   8   0   0   1   7]\n",
            " [  0   0   6   0   0   0   0]\n",
            " [  2   0 332   0   1  51 298]\n",
            " [  0   0   1   0   0   0   3]\n",
            " [  0   0  33   0   0   7  27]\n",
            " [  0   0 107   0   0  46  88]\n",
            " [  2   0 379   0   5  54 542]]\n",
            "\n",
            "Relatório de Classificação:\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00        16\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.38      0.49      0.43       684\n",
            "           4       0.00      0.00      0.00         4\n",
            "           5       0.00      0.00      0.00        67\n",
            "           6       0.29      0.19      0.23       241\n",
            "           7       0.56      0.55      0.56       982\n",
            "\n",
            "    accuracy                           0.46      2000\n",
            "   macro avg       0.18      0.18      0.17      2000\n",
            "weighted avg       0.44      0.46      0.45      2000\n",
            "\n",
            "\n",
            "Acurácia:\n",
            "0.46\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}