Base Covid.ipynb

base-covid.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/metehus/3eef42bf8312a725f1439dcc44ec93da/base-covid.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Base professor abaixo"
      ],
      "metadata": {
        "id": "qVtaBoG2l5jJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import StratifiedKFold, GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from imblearn.pipeline import Pipeline\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Carregar dados\n",
        "lbp_test_url = \"https://gist.github.com/metehus/315e5fd46adbc2dc3433d2e5b5728409/raw/28204aa227b80d03a443988652feb06ba12f0729/lbp-test.csv\"\n",
        "lbp_train_fold0_url = \"https://gist.github.com/metehus/315e5fd46adbc2dc3433d2e5b5728409/raw/28204aa227b80d03a443988652feb06ba12f0729/lbp-train-fold_0.csv\"\n",
        "lbp_train_fold1_url = \"https://gist.github.com/metehus/315e5fd46adbc2dc3433d2e5b5728409/raw/28204aa227b80d03a443988652feb06ba12f0729/lbp-train-fold_1.csv\"\n",
        "lbp_train_fold2_url = \"https://gist.github.com/metehus/315e5fd46adbc2dc3433d2e5b5728409/raw/28204aa227b80d03a443988652feb06ba12f0729/lbp-train-fold_2.csv\"\n",
        "lbp_train_fold3_url = \"https://gist.github.com/metehus/315e5fd46adbc2dc3433d2e5b5728409/raw/28204aa227b80d03a443988652feb06ba12f0729/lbp-train-fold_3.csv\"\n",
        "lbp_train_fold4_url = \"https://gist.github.com/metehus/315e5fd46adbc2dc3433d2e5b5728409/raw/28204aa227b80d03a443988652feb06ba12f0729/lbp-train-fold_4.csv\"\n",
        "\n",
        "test_df = pd.read_csv(lbp_test_url)\n",
        "lbp_train_fold0_df = pd.read_csv(lbp_train_fold0_url)\n",
        "lbp_train_fold1_df = pd.read_csv(lbp_train_fold1_url)\n",
        "lbp_train_fold2_df = pd.read_csv(lbp_train_fold2_url)\n",
        "lbp_train_fold3_df = pd.read_csv(lbp_train_fold3_url)\n",
        "lbp_train_fold4_df = pd.read_csv(lbp_train_fold4_url)\n",
        "\n",
        "# Juntar os dfs\n",
        "df = pd.concat([lbp_train_fold0_df, lbp_train_fold1_df, lbp_train_fold2_df, lbp_train_fold3_df, lbp_train_fold4_df])\n",
        "\n",
        "# Preprocessar\n",
        "df['class'] = df['class'].str.split('/').str[-1]\n",
        "X = df.drop(columns=['class'])\n",
        "y = df['class']\n",
        "\n",
        "X_test = test_df.drop(columns=['class'])\n",
        "y_test = test_df['class'].str.split('/').str[-1]\n",
        "\n",
        "print(df['class'].value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBSyXcwwGTeS",
        "outputId": "eebed61d-4e8e-4a28-c086-6b815e49936b"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class\n",
            "Normal           700\n",
            "COVID-19          63\n",
            "Streptococcus      9\n",
            "SARS               8\n",
            "Pneumocystis       8\n",
            "MERS               7\n",
            "Varicella          7\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "jydi6GMNxNrA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.pipeline import Pipeline\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score\n",
        "\n",
        "smote = SMOTE(sampling_strategy='auto', k_neighbors=3)\n",
        "\n",
        "pipeline = Pipeline([\n",
        "    ('scaler', StandardScaler()),  # Normalizar dados\n",
        "    ('smote', smote),              # Balanceamento\n",
        "    ('model', LogisticRegression(max_iter=1000, C=0.1))\n",
        "])\n",
        "\n",
        "param_grid = {\n",
        "    'model__C': [0.1, 1, 10],\n",
        "    'model__max_iter': [1000],\n",
        "    'model__class_weight': ['balanced'],\n",
        "    'smote__k_neighbors': [3, 2]\n",
        "}\n",
        "\n",
        "cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n",
        "grid_search = GridSearchCV(pipeline, param_grid, scoring='f1_weighted', cv=cv, n_jobs=-1)\n",
        "\n",
        "grid_search.fit(X, y)\n",
        "print(\"Best parameters found:\", grid_search.best_params_)\n",
        "\n",
        "y_pred = grid_search.predict(X_test)\n",
        "\n",
        "print(\"* Classificação:\")\n",
        "print(classification_report(y_test, y_pred, zero_division=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-mVkVPcF011D",
        "outputId": "214ae154-f9d9-4b5f-cbc1-19c68ad5d516"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters found: {'model__C': 10, 'model__class_weight': 'balanced', 'model__max_iter': 1000, 'smote__k_neighbors': 3}\n",
            "* Classificação:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     COVID-19       0.64      0.52      0.57        27\n",
            "         MERS       0.75      1.00      0.86         3\n",
            "       Normal       0.98      0.98      0.98       300\n",
            " Pneumocystis       0.00      0.00      0.00         3\n",
            "         SARS       1.00      1.00      1.00         3\n",
            "Streptococcus       0.33      0.33      0.33         3\n",
            "    Varicella       0.12      0.33      0.18         3\n",
            "\n",
            "     accuracy                           0.93       342\n",
            "    macro avg       0.55      0.60      0.56       342\n",
            " weighted avg       0.93      0.93      0.93       342\n",
            "\n"
          ]
        }
      ]
    }
  ]
}