Lab5_Metrics_Evaluation.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/BrunaFerreira/Coursera_AI_for_Medical_Diagnosis/blob/master/Lab5_Metrics_Evaluation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iV0wFS4e2mmL"
      },
      "source": [
        "# Aluna : Bruna Ferreira"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaQyQN7OY2Ug"
      },
      "source": [
        "# Avaliação de modelos de diagnóstico\n",
        "\n",
        "- conjunto de dados de teste rotulado manualmente de 420 raios-X.\n",
        "- raios-X de 14 condições diferentes diagnosticáveis por um raio-X."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qy3i8yJ9ZzHx"
      },
      "source": [
        "**Pontos a serem analisados:**\n",
        "\n",
        "1. <i> Accuracy </i> - Acurácia\n",
        "1. <i> Prevalence </i>  - Prevalência\n",
        "1. <i> Specificity & Sensitivity </i> - Especificidade e sensibilidade\n",
        "1. <i> PPV and NPV </i>\n",
        "1. <i> ROC curve and AUCROC (c-statistic) </i> - Curva ROC\n",
        "1. <i> Confidence Intervals </i> - Intervalos de Confiança"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "koJJmal4bfWD"
      },
      "source": [
        "## 1. Pacotes\n",
        "\n",
        "- [numpy] (https://docs.scipy.org/doc/numpy/) é uma biblioteca popular para computação científica\n",
        "- [matplotlib] (https://matplotlib.org/3.1.1/contents.html) é uma biblioteca de plotagem compatível com numpy\n",
        "- [pandas] (https://pandas.pydata.org/docs/) é usado para manipular dados\n",
        "- [sklearn] (https://scikit-learn.org/stable/index.html) será usado para medir o desempenho do modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
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          "height": 90
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        "id": "ufSFQm6dbxRP",
        "outputId": "89e69924-1bf8-4b64-cffb-31e385982570"
      },
      "source": [
        "from google.colab import files\n",
        "util = list(files.upload().values())[0]\n",
        "open('util_2w.py','wb').write(util)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8e3f6302-550f-4ef0-be17-1a51fec8ad3f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8e3f6302-550f-4ef0-be17-1a51fec8ad3f\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving util_2w.py to util_2w (1).py\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4807"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXfNSQYfZvXT"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "import pandas as pd \n",
        "import util_2w "
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8n6YWMcK6JYF"
      },
      "source": [
        "train_results = pd.read_csv(\"/content/drive/MyDrive/0_Cursos/1. Coursera/AI Medicine /Code/data/nih/train_preds.csv\")\n",
        "valid_results = pd.read_csv(\"/content/drive/MyDrive/0_Cursos/1. Coursera/AI Medicine /Code/data/nih/valid_preds.csv\")\n",
        "\n",
        "# Labels - Rótulos do dataset\n",
        "class_labels = ['Cardiomegaly',\n",
        " 'Emphysema',\n",
        " 'Effusion',\n",
        " 'Hernia',\n",
        " 'Infiltration',\n",
        " 'Mass',\n",
        " 'Nodule',\n",
        " 'Atelectasis',\n",
        " 'Pneumothorax',\n",
        " 'Pleural_Thickening',\n",
        " 'Pneumonia',\n",
        " 'Fibrosis',\n",
        " 'Edema',\n",
        " 'Consolidation']\n",
        "\n",
        "# Rótulos para prediçao de valores no dataset\n",
        "pred_labels = [l + \"_pred\" for l in class_labels]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_GJmXsaw6hod"
      },
      "source": [
        "## 2. Visão geral\n",
        "\n",
        "- Métricas\n",
        "   - TP, TN, FP, FN\n",
        "   - Acurácia\n",
        "   - Prevalência\n",
        "   - Sensibilidade e especificidade\n",
        "   - PPV e NPV\n",
        "   - AUC\n",
        "- Intervalos de confiança"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l91DEvab8pgd"
      },
      "source": [
        "Extraindo os rótulos (y) e as previsões (pred)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HuE4zDXs6n_l"
      },
      "source": [
        "y = valid_results[class_labels].values\n",
        "pred = valid_results[pred_labels].values"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "Gf5VXSS28kel",
        "outputId": "9fb4614e-3bf4-426b-b49a-a6b53f1a2dac"
      },
      "source": [
        "valid_results[np.concatenate([class_labels, pred_labels])].head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cardiomegaly</th>\n",
              "      <th>Emphysema</th>\n",
              "      <th>Effusion</th>\n",
              "      <th>Hernia</th>\n",
              "      <th>Infiltration</th>\n",
              "      <th>Mass</th>\n",
              "      <th>Nodule</th>\n",
              "      <th>Atelectasis</th>\n",
              "      <th>Pneumothorax</th>\n",
              "      <th>Pleural_Thickening</th>\n",
              "      <th>Pneumonia</th>\n",
              "      <th>Fibrosis</th>\n",
              "      <th>Edema</th>\n",
              "      <th>Consolidation</th>\n",
              "      <th>Cardiomegaly_pred</th>\n",
              "      <th>Emphysema_pred</th>\n",
              "      <th>Effusion_pred</th>\n",
              "      <th>Hernia_pred</th>\n",
              "      <th>Infiltration_pred</th>\n",
              "      <th>Mass_pred</th>\n",
              "      <th>Nodule_pred</th>\n",
              "      <th>Atelectasis_pred</th>\n",
              "      <th>Pneumothorax_pred</th>\n",
              "      <th>Pleural_Thickening_pred</th>\n",
              "      <th>Pneumonia_pred</th>\n",
              "      <th>Fibrosis_pred</th>\n",
              "      <th>Edema_pred</th>\n",
              "      <th>Consolidation_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.714810</td>\n",
              "      <td>0.100277</td>\n",
              "      <td>0.186449</td>\n",
              "      <td>0.812808</td>\n",
              "      <td>0.256020</td>\n",
              "      <td>0.266928</td>\n",
              "      <td>0.312440</td>\n",
              "      <td>0.460342</td>\n",
              "      <td>0.079453</td>\n",
              "      <td>0.271495</td>\n",
              "      <td>0.276861</td>\n",
              "      <td>0.398799</td>\n",
              "      <td>0.015867</td>\n",
              "      <td>0.156320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.613431</td>\n",
              "      <td>0.233874</td>\n",
              "      <td>0.117079</td>\n",
              "      <td>0.840591</td>\n",
              "      <td>0.382199</td>\n",
              "      <td>0.176825</td>\n",
              "      <td>0.465807</td>\n",
              "      <td>0.489424</td>\n",
              "      <td>0.084595</td>\n",
              "      <td>0.377318</td>\n",
              "      <td>0.363582</td>\n",
              "      <td>0.638024</td>\n",
              "      <td>0.025948</td>\n",
              "      <td>0.144419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.007762</td>\n",
              "      <td>0.062787</td>\n",
              "      <td>0.032811</td>\n",
              "      <td>0.158754</td>\n",
              "      <td>0.427727</td>\n",
              "      <td>0.115513</td>\n",
              "      <td>0.249030</td>\n",
              "      <td>0.035105</td>\n",
              "      <td>0.238761</td>\n",
              "      <td>0.167095</td>\n",
              "      <td>0.166389</td>\n",
              "      <td>0.262463</td>\n",
              "      <td>0.007758</td>\n",
              "      <td>0.125790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.006888</td>\n",
              "      <td>0.096380</td>\n",
              "      <td>0.054953</td>\n",
              "      <td>0.655706</td>\n",
              "      <td>0.158596</td>\n",
              "      <td>0.259460</td>\n",
              "      <td>0.334870</td>\n",
              "      <td>0.266489</td>\n",
              "      <td>0.073371</td>\n",
              "      <td>0.229834</td>\n",
              "      <td>0.191281</td>\n",
              "      <td>0.344348</td>\n",
              "      <td>0.008559</td>\n",
              "      <td>0.119153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.053560</td>\n",
              "      <td>0.075098</td>\n",
              "      <td>0.226518</td>\n",
              "      <td>0.228736</td>\n",
              "      <td>0.536762</td>\n",
              "      <td>0.198797</td>\n",
              "      <td>0.273110</td>\n",
              "      <td>0.186771</td>\n",
              "      <td>0.242122</td>\n",
              "      <td>0.309786</td>\n",
              "      <td>0.411771</td>\n",
              "      <td>0.244666</td>\n",
              "      <td>0.126930</td>\n",
              "      <td>0.342409</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cardiomegaly  Emphysema  ...  Edema_pred  Consolidation_pred\n",
              "0             0          0  ...    0.015867            0.156320\n",
              "1             0          0  ...    0.025948            0.144419\n",
              "2             0          0  ...    0.007758            0.125790\n",
              "3             0          0  ...    0.008559            0.119153\n",
              "4             0          0  ...    0.126930            0.342409\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2MN21Hyx85bB"
      },
      "source": [
        "Histograma do número de amostras para cada rótulo no conjunto de dados de validação:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "fT5wWQ1y8yip",
        "outputId": "f5c79a6e-a25a-4124-9dfd-247d644c21db"
      },
      "source": [
        "plt.xticks(rotation=90)\n",
        "plt.bar(x = class_labels, height= y.sum(axis=0));"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vH_j6z0R9EYC"
      },
      "source": [
        "Conjunto de dados com população desequilibrada de amostras. Especificamente, tem um pequeno número de pacientes com diagnóstico de `Hérnia`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N4uAevYF9Okx"
      },
      "source": [
        "## 3. Metricas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcsKsE9Q9Vlr"
      },
      "source": [
        "### 3.1 Verdadeiros positivos, falsos positivos, verdadeiros negativos e falsos negativos\n",
        "\n",
        "As estatísticas mais básicas para calcular a partir das previsões do modelo são os verdadeiros positivos, verdadeiros negativos, falsos positivos e falsos negativos.\n",
        "\n",
        "Como o nome sugere: \n",
        "- verdadeiro positivo (TP): o modelo classifica o exemplo como positivo e o rótulo real também como positivo.\n",
        "- falso positivo (FP): O modelo classifica o exemplo como positivo, ** mas ** o rótulo real é negativo.\n",
        "- verdadeiro negativo (TN): O modelo classifica o exemplo como negativo e o rótulo real também é negativo.\n",
        "- falso negativo (FN): O modelo classifica o exemplo como negativo, ** mas ** o rótulo é realmente positivo.\n",
        "\n",
        "Todas as métricas podem ser construídas a partir dessas quatro estatísticas.\n",
        "\n",
        "Lembre-se de que o modelo produz números reais entre 0 e 1.\n",
        "* Para calcular previsões de classes binárias, é preciso convertê-las em 0 ou 1.\n",
        "* Seá feito usando um valor limite $ th $.\n",
        "* Quaisquer saídas de modelo acima de $ th $ são definidas como 1 e abaixo de $ th $ são definidas como 0.\n",
        "\n",
        "Todas as métricas (exceto para AUC no final) dependerão da escolha deste limite.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5JR9Xlxb88kT"
      },
      "source": [
        "# Verdadeiros Positivos\n",
        "def true_positives(y, pred, th=0.5):\n",
        "    \"\"\"\n",
        "    Contar os verdadeiros positivos.\n",
        "\n",
        "     Args:\n",
        "         y (np.array): valores reais, tamanho (n_exemplos)\n",
        "         pred (np.array): saída do modelo, tamanho (n_examples)\n",
        "         th (float): valor de corte para previsão positiva do modelo\n",
        "     Retorna:\n",
        "         TP (int): verdadeiros positivos\n",
        "    \"\"\"\n",
        "    \n",
        "    # thresholded  - valor de corte\n",
        "    thresholded_preds = pred >= th\n",
        "\n",
        "    # Valores Verdadeiros Positivos\n",
        "    TP = 0\n",
        "    # Valor real é positivo (y == 1) e modelo previu como positivo (=1) \n",
        "    TP = TP + np.sum((y == 1) & (thresholded_preds == 1))\n",
        "    \n",
        "    return TP\n",
        "\n",
        "def true_negatives(y, pred, th=0.5):\n",
        "    \"\"\"\n",
        "    Contar verdadeiros negativos.\n",
        "\n",
        "     Args:\n",
        "         y (np.array): valores reais, tamanho (n_exemplos)\n",
        "         pred (np.array): saída do modelo, tamanho (n_examples)\n",
        "         th (float): valor de corte para previsão positiva do modelo\n",
        "     Retorna:\n",
        "         TN (int): verdadeiros negativos\n",
        "    \"\"\"\n",
        "    \n",
        "    # thresholded  - valor de corte\n",
        "    thresholded_preds = pred >= th\n",
        "    \n",
        "    # Valores Verdadeiros Negativos\n",
        "    TN = 0 \n",
        "\n",
        "    # Valor real é negativo (y == 0) e modelo previu como negativo (=0) \n",
        "    TN = TN + np.sum((y == 0) & (thresholded_preds == 0))\n",
        "    \n",
        "    return TN\n",
        "\n",
        "def false_positives(y, pred, th=0.5):\n",
        "    \"\"\"\n",
        "    Contar Falsos Positivos\n",
        "\n",
        "    Args:\n",
        "         y (np.array): valores reais, tamanho (n_exemplos)\n",
        "         pred (np.array): saída do modelo, tamanho (n_examples)\n",
        "         th (float): valor de corte para previsão positiva do modelo\n",
        "     Retorna:\n",
        "         FP (int): falsos positivos\n",
        "    \"\"\"\n",
        "    #\n",
        "    FP = 0\n",
        "    \n",
        "    # thresholded  - valor de corte\n",
        "    thresholded_preds = pred >= th\n",
        "    \n",
        "    #  Valores Falsos positivos\n",
        "    FP = 0\n",
        "    \n",
        "    # Valor real é negativo (y == 0) e modelo previu como positivo (=1) \n",
        "    FP = FP + np.sum((y == 0) & (thresholded_preds == 1))\n",
        "    \n",
        "    return FP\n",
        "\n",
        "def false_negatives(y, pred, th=0.5):\n",
        "    \"\"\"\n",
        "    Contar Falsos Negativos\n",
        "\n",
        "    Args:\n",
        "         y (np.array): valores reais, tamanho (n_exemplos)\n",
        "         pred (np.array): saída do modelo, tamanho (n_examples)\n",
        "         th (float): valor de corte para previsão positiva do modelo\n",
        "     Retorna:\n",
        "         FN (int): falsos negativos\n",
        "    \"\"\"\n",
        "    #  Valores Falsos negativos\n",
        "    FN = 0\n",
        "    \n",
        "    # thresholded  - valor de corte\n",
        "    thresholded_preds = pred >= th\n",
        "\n",
        "    # Valor real é positivo (y == 1) e modelo previu como negativo (=10)\n",
        "    FN = FN + np.sum((y == 1) & (thresholded_preds == 0))\n",
        "    \n",
        "    return FN"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "id": "5zMFJXdZKvKy",
        "outputId": "7b27d700-f7f0-4672-9444-ecc4d70fed51"
      },
      "source": [
        "# Nota: Import 'display' para que o autograder compile o código enviado\n",
        "\n",
        "from IPython.display import display\n",
        "# Test\n",
        "df = pd.DataFrame({'y_test': [1,1,0,0,0,0,0,0,0,1,1,1,1,1],\n",
        "                   'preds_test': [0.8,0.7,0.4,0.3,0.2,0.5,0.6,0.7,0.8,0.1,0.2,0.3,0.4,0],\n",
        "                   'category': ['TP','TP','TN','TN','TN','FP','FP','FP','FP','FN','FN','FN','FN','FN']\n",
        "                  })\n",
        "\n",
        "display(df)\n",
        "#y_test = np.array([1, 0, 0, 1, 1])\n",
        "y_test = df['y_test']\n",
        "\n",
        "#preds_test = np.array([0.8, 0.8, 0.4, 0.6, 0.3])\n",
        "preds_test = df['preds_test']\n",
        "\n",
        "threshold = 0.5\n",
        "print(f\"threshold: {threshold}\\n\")\n",
        "\n",
        "print(f\"\"\"Funções Calculadas: \n",
        "TP: {true_positives(y_test, preds_test, threshold)}\n",
        "TN: {true_negatives(y_test, preds_test, threshold)}\n",
        "FP: {false_positives(y_test, preds_test, threshold)}\n",
        "FN: {false_negatives(y_test, preds_test, threshold)}\n",
        "\"\"\")\n",
        "\n",
        "print(\"Resultado Esperado\")\n",
        "print(f\"Há {sum(df['category'] == 'TP')} TP\")\n",
        "print(f\"Há {sum(df['category'] == 'TN')} TN\")\n",
        "print(f\"Há {sum(df['category'] == 'FP')} FP\")\n",
        "print(f\"Há {sum(df['category'] == 'FN')} FN\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>y_test</th>\n",
              "      <th>preds_test</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.8</td>\n",
              "      <td>TP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.7</td>\n",
              "      <td>TP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>TN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>TN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>TN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>FP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>FP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>FP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>FP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>FN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>FN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>FN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>FN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>FN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    y_test  preds_test category\n",
              "0        1         0.8       TP\n",
              "1        1         0.7       TP\n",
              "2        0         0.4       TN\n",
              "3        0         0.3       TN\n",
              "4        0         0.2       TN\n",
              "5        0         0.5       FP\n",
              "6        0         0.6       FP\n",
              "7        0         0.7       FP\n",
              "8        0         0.8       FP\n",
              "9        1         0.1       FN\n",
              "10       1         0.2       FN\n",
              "11       1         0.3       FN\n",
              "12       1         0.4       FN\n",
              "13       1         0.0       FN"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "threshold: 0.5\n",
            "\n",
            "Funções Calculadas: \n",
            "TP: 2\n",
            "TN: 3\n",
            "FP: 4\n",
            "FN: 5\n",
            "\n",
            "Resultado Esperado\n",
            "Há 2 TP\n",
            "Há 3 TN\n",
            "Há 4 FP\n",
            "Há 5 FN\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gd3lfuJQSASI"
      },
      "source": [
        "Resumo das métricas avaliativas para as previsões do modelo para cada classe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "gspPJsXxLEL-",
        "outputId": "a2ba6849-f953-4d68-fe9d-e0ba2d2ed0c8"
      },
      "source": [
        "util_2w.get_performance_metrics(y, pred, class_labels)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TP</th>\n",
              "      <th>TN</th>\n",
              "      <th>FP</th>\n",
              "      <th>FN</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Prevalence</th>\n",
              "      <th>Sensitivity</th>\n",
              "      <th>Specificity</th>\n",
              "      <th>PPV</th>\n",
              "      <th>NPV</th>\n",
              "      <th>AUC</th>\n",
              "      <th>F1</th>\n",
              "      <th>Threshold</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cardiomegaly</th>\n",
              "      <td>16</td>\n",
              "      <td>814</td>\n",
              "      <td>169</td>\n",
              "      <td>1</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emphysema</th>\n",
              "      <td>20</td>\n",
              "      <td>869</td>\n",
              "      <td>103</td>\n",
              "      <td>8</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Effusion</th>\n",
              "      <td>99</td>\n",
              "      <td>690</td>\n",
              "      <td>196</td>\n",
              "      <td>15</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hernia</th>\n",
              "      <td>1</td>\n",
              "      <td>743</td>\n",
              "      <td>255</td>\n",
              "      <td>1</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Infiltration</th>\n",
              "      <td>114</td>\n",
              "      <td>543</td>\n",
              "      <td>265</td>\n",
              "      <td>78</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mass</th>\n",
              "      <td>40</td>\n",
              "      <td>789</td>\n",
              "      <td>158</td>\n",
              "      <td>13</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nodule</th>\n",
              "      <td>28</td>\n",
              "      <td>731</td>\n",
              "      <td>220</td>\n",
              "      <td>21</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Atelectasis</th>\n",
              "      <td>64</td>\n",
              "      <td>657</td>\n",
              "      <td>249</td>\n",
              "      <td>30</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumothorax</th>\n",
              "      <td>24</td>\n",
              "      <td>785</td>\n",
              "      <td>183</td>\n",
              "      <td>8</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pleural_Thickening</th>\n",
              "      <td>24</td>\n",
              "      <td>713</td>\n",
              "      <td>259</td>\n",
              "      <td>4</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumonia</th>\n",
              "      <td>14</td>\n",
              "      <td>661</td>\n",
              "      <td>320</td>\n",
              "      <td>5</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fibrosis</th>\n",
              "      <td>10</td>\n",
              "      <td>725</td>\n",
              "      <td>261</td>\n",
              "      <td>4</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Edema</th>\n",
              "      <td>15</td>\n",
              "      <td>767</td>\n",
              "      <td>213</td>\n",
              "      <td>5</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consolidation</th>\n",
              "      <td>36</td>\n",
              "      <td>658</td>\n",
              "      <td>297</td>\n",
              "      <td>9</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     TP   TN   FP  ...          AUC           F1 Threshold\n",
              "                                   ...                                    \n",
              "Cardiomegaly         16  814  169  ...  Not Defined  Not Defined       0.5\n",
              "Emphysema            20  869  103  ...  Not Defined  Not Defined       0.5\n",
              "Effusion             99  690  196  ...  Not Defined  Not Defined       0.5\n",
              "Hernia                1  743  255  ...  Not Defined  Not Defined       0.5\n",
              "Infiltration        114  543  265  ...  Not Defined  Not Defined       0.5\n",
              "Mass                 40  789  158  ...  Not Defined  Not Defined       0.5\n",
              "Nodule               28  731  220  ...  Not Defined  Not Defined       0.5\n",
              "Atelectasis          64  657  249  ...  Not Defined  Not Defined       0.5\n",
              "Pneumothorax         24  785  183  ...  Not Defined  Not Defined       0.5\n",
              "Pleural_Thickening   24  713  259  ...  Not Defined  Not Defined       0.5\n",
              "Pneumonia            14  661  320  ...  Not Defined  Not Defined       0.5\n",
              "Fibrosis             10  725  261  ...  Not Defined  Not Defined       0.5\n",
              "Edema                15  767  213  ...  Not Defined  Not Defined       0.5\n",
              "Consolidation        36  658  297  ...  Not Defined  Not Defined       0.5\n",
              "\n",
              "[14 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4p64R5WFSeNw"
      },
      "source": [
        "No momento, tem apenas TP, TN, FP, FN. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xh9uoN2WSoFB"
      },
      "source": [
        "### 3.2 Acurácia"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eN-ygsitSrVs"
      },
      "source": [
        "- Limite (threshold ) de 0,5 para o corte de probabilidade de previsões para todas as classes.\n",
        "- Acurácia do modelo: \n",
        "\n",
        "$$accuracy = \\frac{\\text{true positives} + \\text{true negatives}}{\\text{true positives} + \\text{true negatives} + \\text{false positives} + \\text{false negatives}}$$\n",
        "\n",
        "Fórmula para calcular acurácia:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vj4ipshESPYF"
      },
      "source": [
        "def get_accuracy(y, pred, th=0.5):\n",
        "    \"\"\"\n",
        "    Calcular a acurácia das previsões no threshold.\n",
        "\n",
        "     Args:\n",
        "         y (np.array): Valores reais, tamanho (n_exemplos)\n",
        "         pred (np.array): saída do modelo, tamanho (n_examples)\n",
        "         th (float): valor de corte para previsão positiva do modelo\n",
        "     Retorna:\n",
        "         precisão (flutuação): precisão das previsões no limite\n",
        "    \"\"\"\n",
        "    accuracy = 0.0\n",
        "    \n",
        "    # Valores de TP, FP, TN, FN usando funções pré-calculadas\n",
        "    TP = true_positives(y, pred, th)\n",
        "    FP = false_positives(y,pred,th)\n",
        "    TN = true_negatives(y,pred,th)\n",
        "    FN = false_negatives(y,pred,th)\n",
        "\n",
        "    # Cálculo de acurácia usando TP, FP, TN, FN\n",
        "    accuracy = accuracy +((TP+TN)/(TP+TN+FP+FN))\n",
        "    \n",
        "    return accuracy"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TGkA6EYQYcYW",
        "outputId": "25b8a022-e945-4a8f-9fb0-ef28b7af35ef"
      },
      "source": [
        "# Test\n",
        "print(\"Caso de teste:\")\n",
        "\n",
        "y_test = np.array([1, 0, 0, 1, 1])\n",
        "print('Rótulos de Teste: {y_test}')\n",
        "\n",
        "preds_test = np.array([0.8, 0.8, 0.4, 0.6, 0.3])\n",
        "print(f'Previsões no teste: {preds_test}')\n",
        "\n",
        "threshold = 0.5\n",
        "print(f\"threshold: {threshold}\")\n",
        "\n",
        "print(f\"Acurácia Calculada: {get_accuracy(y_test, preds_test, threshold)}\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Caso de teste:\n",
            "Rótulos de Teste: {y_test}\n",
            "Previsões no teste: [0.8 0.8 0.4 0.6 0.3]\n",
            "threshold: 0.5\n",
            "Acurácia Calculada: 0.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tr4s6-FlZa7D"
      },
      "source": [
        "<b> Saída Experada: </b>\n",
        "\n",
        "```Python\n",
        "Rótulos de teste: {y_test}\n",
        "Previsões no teste: [0.8 0.8 0.4 0.6 0.3]\n",
        "threshold: 0.5\n",
        "Acurácia Calculada: 0.6\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "Bqgl8nLzX619",
        "outputId": "24e68fee-2615-4a26-b3fb-456aaa5e7ebc"
      },
      "source": [
        "util_2w.get_performance_metrics(y, pred, class_labels, acc=get_accuracy)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TP</th>\n",
              "      <th>TN</th>\n",
              "      <th>FP</th>\n",
              "      <th>FN</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Prevalence</th>\n",
              "      <th>Sensitivity</th>\n",
              "      <th>Specificity</th>\n",
              "      <th>PPV</th>\n",
              "      <th>NPV</th>\n",
              "      <th>AUC</th>\n",
              "      <th>F1</th>\n",
              "      <th>Threshold</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cardiomegaly</th>\n",
              "      <td>16</td>\n",
              "      <td>814</td>\n",
              "      <td>169</td>\n",
              "      <td>1</td>\n",
              "      <td>0.83</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emphysema</th>\n",
              "      <td>20</td>\n",
              "      <td>869</td>\n",
              "      <td>103</td>\n",
              "      <td>8</td>\n",
              "      <td>0.889</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Effusion</th>\n",
              "      <td>99</td>\n",
              "      <td>690</td>\n",
              "      <td>196</td>\n",
              "      <td>15</td>\n",
              "      <td>0.789</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hernia</th>\n",
              "      <td>1</td>\n",
              "      <td>743</td>\n",
              "      <td>255</td>\n",
              "      <td>1</td>\n",
              "      <td>0.744</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Infiltration</th>\n",
              "      <td>114</td>\n",
              "      <td>543</td>\n",
              "      <td>265</td>\n",
              "      <td>78</td>\n",
              "      <td>0.657</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mass</th>\n",
              "      <td>40</td>\n",
              "      <td>789</td>\n",
              "      <td>158</td>\n",
              "      <td>13</td>\n",
              "      <td>0.829</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nodule</th>\n",
              "      <td>28</td>\n",
              "      <td>731</td>\n",
              "      <td>220</td>\n",
              "      <td>21</td>\n",
              "      <td>0.759</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Atelectasis</th>\n",
              "      <td>64</td>\n",
              "      <td>657</td>\n",
              "      <td>249</td>\n",
              "      <td>30</td>\n",
              "      <td>0.721</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumothorax</th>\n",
              "      <td>24</td>\n",
              "      <td>785</td>\n",
              "      <td>183</td>\n",
              "      <td>8</td>\n",
              "      <td>0.809</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pleural_Thickening</th>\n",
              "      <td>24</td>\n",
              "      <td>713</td>\n",
              "      <td>259</td>\n",
              "      <td>4</td>\n",
              "      <td>0.737</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumonia</th>\n",
              "      <td>14</td>\n",
              "      <td>661</td>\n",
              "      <td>320</td>\n",
              "      <td>5</td>\n",
              "      <td>0.675</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fibrosis</th>\n",
              "      <td>10</td>\n",
              "      <td>725</td>\n",
              "      <td>261</td>\n",
              "      <td>4</td>\n",
              "      <td>0.735</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Edema</th>\n",
              "      <td>15</td>\n",
              "      <td>767</td>\n",
              "      <td>213</td>\n",
              "      <td>5</td>\n",
              "      <td>0.782</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consolidation</th>\n",
              "      <td>36</td>\n",
              "      <td>658</td>\n",
              "      <td>297</td>\n",
              "      <td>9</td>\n",
              "      <td>0.694</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     TP   TN   FP  ...          AUC           F1 Threshold\n",
              "                                   ...                                    \n",
              "Cardiomegaly         16  814  169  ...  Not Defined  Not Defined       0.5\n",
              "Emphysema            20  869  103  ...  Not Defined  Not Defined       0.5\n",
              "Effusion             99  690  196  ...  Not Defined  Not Defined       0.5\n",
              "Hernia                1  743  255  ...  Not Defined  Not Defined       0.5\n",
              "Infiltration        114  543  265  ...  Not Defined  Not Defined       0.5\n",
              "Mass                 40  789  158  ...  Not Defined  Not Defined       0.5\n",
              "Nodule               28  731  220  ...  Not Defined  Not Defined       0.5\n",
              "Atelectasis          64  657  249  ...  Not Defined  Not Defined       0.5\n",
              "Pneumothorax         24  785  183  ...  Not Defined  Not Defined       0.5\n",
              "Pleural_Thickening   24  713  259  ...  Not Defined  Not Defined       0.5\n",
              "Pneumonia            14  661  320  ...  Not Defined  Not Defined       0.5\n",
              "Fibrosis             10  725  261  ...  Not Defined  Not Defined       0.5\n",
              "Edema                15  767  213  ...  Not Defined  Not Defined       0.5\n",
              "Consolidation        36  658  297  ...  Not Defined  Not Defined       0.5\n",
              "\n",
              "[14 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPN9UAl4aGiR"
      },
      "source": [
        "Julgando o desempenho modelo com base na métrica de acurácia, o modelo não é muito preciso para detectar os casos de `Infiltração` (acurácia de 0,657), mas bastante preciso para detectar` Enfisema` (acurácia de 0,889).\n",
        "\n",
        "<b> Mas é realmente esse o caso? </b>\n",
        "\n",
        "Um modelo que simplesmente prediz que qualquer paciente <b> Não </b> tem `Enfisema`, independentemente das medidas do paciente. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J6nSO19kZ9jB",
        "outputId": "5708bd2b-f868-4320-b20b-117ef423de9e"
      },
      "source": [
        "get_accuracy(valid_results[\"Emphysema\"].values, np.zeros(len(valid_results)))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.972"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Vr3LUDkc6ex"
      },
      "source": [
        "Esse modelo teria 97% de acurácia! Ainda melhor do que nosso modelo baseado em aprendizado profundo.\n",
        "\n",
        "Mas este é realmente um bom modelo? Este modelo não estaria errado 100% das vezes se o paciente realmente tivesse essa condição?\n",
        "\n",
        "Nas seções a seguir, será abordado essa preocupação com medidas de modelo mais avançadas - <b> sensibilidade e especificidade </b> - que avaliam quão bem o modelo prediz positivos para pacientes com a doença e negativos para casos que realmente não têm a doença."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yx8LDM5Hfj08"
      },
      "source": [
        "### 3.3 Prevalência\n",
        "\n",
        "* Em um contexto médico, a prevalência é a proporção de pessoas na população que têm a doença (ou condição, etc.).\n",
        "* Em termos de aprendizado de máquina, essa é a proporção de exemplos positivos. A expressão para prevalência é:\n",
        "\n",
        "$$ prevalência = \\frac{1}{N} \\sum_{i} y_i$$\n",
        "\n",
        "onde $ y_i = 1 $ quando o exemplo é 'positivo' (tem a doença).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UqXCpSDrc0tG"
      },
      "source": [
        "def get_prevalence(y):\n",
        "    \"\"\"\n",
        "    Calcula Prevalência.\n",
        "\n",
        "    Args:\n",
        "        y (np.array): valores reais, tamanho (n_exemplos)\n",
        "    Retorna:\n",
        "        prevalência (float): prevalência de casos positivos\n",
        "    \"\"\"\n",
        "    prevalence = 0.0\n",
        "    \n",
        "    prevalence = prevalence + (len(y[y==1])/len(y))\n",
        "    \n",
        "    return prevalence"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lbhPxUW_hFCO",
        "outputId": "6c6c222c-3285-4140-989b-9eb8c8165e99"
      },
      "source": [
        "print(\"Caso de teste:\\n\")\n",
        "\n",
        "y_test = np.array([1, 0, 0, 1, 1, 0, 0, 0, 0, 1])\n",
        "print(f'Rótulos de teste: {y_test}')\n",
        "\n",
        "print(f\"cálculo da prevalência: {get_prevalence(y_test)}\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Caso de teste:\n",
            "\n",
            "Rótulos de teste: [1 0 0 1 1 0 0 0 0 1]\n",
            "cálculo da prevalência: 0.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "kgk_3ioshFuA",
        "outputId": "ac2763d3-baaf-4123-cc15-be8037d57ad0"
      },
      "source": [
        "util_2w.get_performance_metrics(y, pred, class_labels, acc=get_accuracy, prevalence=get_prevalence)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TP</th>\n",
              "      <th>TN</th>\n",
              "      <th>FP</th>\n",
              "      <th>FN</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Prevalence</th>\n",
              "      <th>Sensitivity</th>\n",
              "      <th>Specificity</th>\n",
              "      <th>PPV</th>\n",
              "      <th>NPV</th>\n",
              "      <th>AUC</th>\n",
              "      <th>F1</th>\n",
              "      <th>Threshold</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cardiomegaly</th>\n",
              "      <td>16</td>\n",
              "      <td>814</td>\n",
              "      <td>169</td>\n",
              "      <td>1</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.017</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emphysema</th>\n",
              "      <td>20</td>\n",
              "      <td>869</td>\n",
              "      <td>103</td>\n",
              "      <td>8</td>\n",
              "      <td>0.889</td>\n",
              "      <td>0.028</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Effusion</th>\n",
              "      <td>99</td>\n",
              "      <td>690</td>\n",
              "      <td>196</td>\n",
              "      <td>15</td>\n",
              "      <td>0.789</td>\n",
              "      <td>0.114</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hernia</th>\n",
              "      <td>1</td>\n",
              "      <td>743</td>\n",
              "      <td>255</td>\n",
              "      <td>1</td>\n",
              "      <td>0.744</td>\n",
              "      <td>0.002</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Infiltration</th>\n",
              "      <td>114</td>\n",
              "      <td>543</td>\n",
              "      <td>265</td>\n",
              "      <td>78</td>\n",
              "      <td>0.657</td>\n",
              "      <td>0.192</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mass</th>\n",
              "      <td>40</td>\n",
              "      <td>789</td>\n",
              "      <td>158</td>\n",
              "      <td>13</td>\n",
              "      <td>0.829</td>\n",
              "      <td>0.053</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nodule</th>\n",
              "      <td>28</td>\n",
              "      <td>731</td>\n",
              "      <td>220</td>\n",
              "      <td>21</td>\n",
              "      <td>0.759</td>\n",
              "      <td>0.049</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Atelectasis</th>\n",
              "      <td>64</td>\n",
              "      <td>657</td>\n",
              "      <td>249</td>\n",
              "      <td>30</td>\n",
              "      <td>0.721</td>\n",
              "      <td>0.094</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumothorax</th>\n",
              "      <td>24</td>\n",
              "      <td>785</td>\n",
              "      <td>183</td>\n",
              "      <td>8</td>\n",
              "      <td>0.809</td>\n",
              "      <td>0.032</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pleural_Thickening</th>\n",
              "      <td>24</td>\n",
              "      <td>713</td>\n",
              "      <td>259</td>\n",
              "      <td>4</td>\n",
              "      <td>0.737</td>\n",
              "      <td>0.028</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumonia</th>\n",
              "      <td>14</td>\n",
              "      <td>661</td>\n",
              "      <td>320</td>\n",
              "      <td>5</td>\n",
              "      <td>0.675</td>\n",
              "      <td>0.019</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fibrosis</th>\n",
              "      <td>10</td>\n",
              "      <td>725</td>\n",
              "      <td>261</td>\n",
              "      <td>4</td>\n",
              "      <td>0.735</td>\n",
              "      <td>0.014</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Edema</th>\n",
              "      <td>15</td>\n",
              "      <td>767</td>\n",
              "      <td>213</td>\n",
              "      <td>5</td>\n",
              "      <td>0.782</td>\n",
              "      <td>0.02</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consolidation</th>\n",
              "      <td>36</td>\n",
              "      <td>658</td>\n",
              "      <td>297</td>\n",
              "      <td>9</td>\n",
              "      <td>0.694</td>\n",
              "      <td>0.045</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     TP   TN   FP  ...          AUC           F1 Threshold\n",
              "                                   ...                                    \n",
              "Cardiomegaly         16  814  169  ...  Not Defined  Not Defined       0.5\n",
              "Emphysema            20  869  103  ...  Not Defined  Not Defined       0.5\n",
              "Effusion             99  690  196  ...  Not Defined  Not Defined       0.5\n",
              "Hernia                1  743  255  ...  Not Defined  Not Defined       0.5\n",
              "Infiltration        114  543  265  ...  Not Defined  Not Defined       0.5\n",
              "Mass                 40  789  158  ...  Not Defined  Not Defined       0.5\n",
              "Nodule               28  731  220  ...  Not Defined  Not Defined       0.5\n",
              "Atelectasis          64  657  249  ...  Not Defined  Not Defined       0.5\n",
              "Pneumothorax         24  785  183  ...  Not Defined  Not Defined       0.5\n",
              "Pleural_Thickening   24  713  259  ...  Not Defined  Not Defined       0.5\n",
              "Pneumonia            14  661  320  ...  Not Defined  Not Defined       0.5\n",
              "Fibrosis             10  725  261  ...  Not Defined  Not Defined       0.5\n",
              "Edema                15  767  213  ...  Not Defined  Not Defined       0.5\n",
              "Consolidation        36  658  297  ...  Not Defined  Not Defined       0.5\n",
              "\n",
              "[14 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0eZFmhZ9jPJN"
      },
      "source": [
        "`Hérnia` tem uma prevalência de 0,002, que é a mais rara entre o conjunto de dados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XD8tto6Mkg_R"
      },
      "source": [
        "\n",
        "### 3.4 Sensibilidade e especificidade\n",
        "\n",
        "Sensibilidade e especificidade são usados para medir testes de diagnóstico.\n",
        "- A sensibilidade é a probabilidade de que nossos resultados de teste sejam positivos, visto que o caso é realmente positivo.\n",
        "- Especificidade é a probabilidade de que o teste produza resultados negativos, visto que o caso é realmente negativo.\n",
        "\n",
        "Podemos expressar isso facilmente em termos de verdadeiros positivos, verdadeiros negativos, falsos positivos e falsos negativos:\n",
        "\n",
        "$$ sensibilidade = \\frac {\\text {verdadeiros positivos}} {\\text {verdadeiros positivos} + \\text {falsos negativos}} $$\n",
        "\n",
        "$$ especificidade = \\frac {\\text {verdadeiros negativos}} {\\text {verdadeiros negativos} + \\text {falsos positivos}} $$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8RP7XfOjIRf"
      },
      "source": [
        "def get_sensitivity(y, pred, th=0.5):\n",
        "    \"\"\"\n",
        "    Calcula a sensibilidade das previsões no limite (threshold).\n",
        "\n",
        "     Args:\n",
        "         y (np.array): valores reais, tamanho (n_exemplos)\n",
        "         pred (np.array): saída do modelo, tamanho (n_examples)\n",
        "         th (float): valor de corte para previsão positiva do modelo (threshold)\n",
        "     Retorna:\n",
        "         sensibilidade (float): probabilidade de que o resultado do nosso teste seja positivo, dado que o caso é realmente positivo\n",
        "    \"\"\"\n",
        "    sensitivity = 0.0\n",
        "    \n",
        "    # obter TP e FN usando nossas funções definidas anteriormente\n",
        "    TP = true_positives(y,pred,th)\n",
        "    FN = false_negatives(y,pred,th)\n",
        "\n",
        "    # use TP e FN para calcular a sensibilidade\n",
        "    sensitivity = sensitivity + (TP/(TP+FN))\n",
        "    \n",
        "    return sensitivity\n",
        "\n",
        "def get_specificity(y, pred, th=0.5):\n",
        "    \"\"\"\n",
        "    Calcula a especificidade das previsões no limite(threshold).\n",
        "\n",
        "     Args:\n",
        "         y (np.array): valores reais, tamanho (n_exemplos)\n",
        "         pred (np.array): saída do modelo, tamanho (n_examples)\n",
        "         th (float): valor de corte para previsão positiva do modelo (threshold)\n",
        "     Retorna:\n",
        "         especificidade (float): probabilidade de que o teste produza resultados negativos, dado que o caso é realmente negativo\n",
        "    \"\"\"\n",
        "    specificity = 0.0\n",
        "\n",
        "    # obter TN e FP usando nossas funções previamente definidas\n",
        "    TN = true_negatives(y,pred,th)\n",
        "    FP = false_positives(y,pred,th)\n",
        "    \n",
        "    # usar TN e FP para calcular a especificidade\n",
        "    specificity = specificity + (TN/(TN+FP))\n",
        "    \n",
        "    return specificity"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkX1xCouz8QP",
        "outputId": "111543b3-e3af-4845-f5d4-feaa465e9051"
      },
      "source": [
        "# Test\n",
        "print(\"Caso de Teste\")\n",
        "\n",
        "y_test = np.array([1, 0, 0, 1, 1])\n",
        "print(f'Rótulos de teste: {y_test}\\n')\n",
        "\n",
        "preds_test = np.array([0.8, 0.8, 0.4, 0.6, 0.3])\n",
        "print(f'Predições de teste: {preds_test}\\n')\n",
        "\n",
        "threshold = 0.5\n",
        "print(f\"threshold: {threshold}\\n\")\n",
        "\n",
        "print(f\"Sensibilidade: {get_sensitivity(y_test, preds_test, threshold):.2f}\")\n",
        "print(f\"Especificidade: {get_specificity(y_test, preds_test, threshold):.2f}\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Caso de Teste\n",
            "Rótulos de teste: [1 0 0 1 1]\n",
            "\n",
            "Predições de teste: [0.8 0.8 0.4 0.6 0.3]\n",
            "\n",
            "threshold: 0.5\n",
            "\n",
            "Sensibilidade: 0.67\n",
            "Especificidade: 0.50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "h0BmA8aJ0S7O",
        "outputId": "c921ba46-ab61-495b-96d6-d9fe8d7ef4b2"
      },
      "source": [
        "util_2w.get_performance_metrics(y, pred, class_labels, acc=get_accuracy, prevalence=get_prevalence, \n",
        "                        sens=get_sensitivity, spec=get_specificity)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TP</th>\n",
              "      <th>TN</th>\n",
              "      <th>FP</th>\n",
              "      <th>FN</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Prevalence</th>\n",
              "      <th>Sensitivity</th>\n",
              "      <th>Specificity</th>\n",
              "      <th>PPV</th>\n",
              "      <th>NPV</th>\n",
              "      <th>AUC</th>\n",
              "      <th>F1</th>\n",
              "      <th>Threshold</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cardiomegaly</th>\n",
              "      <td>16</td>\n",
              "      <td>814</td>\n",
              "      <td>169</td>\n",
              "      <td>1</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.941</td>\n",
              "      <td>0.828</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emphysema</th>\n",
              "      <td>20</td>\n",
              "      <td>869</td>\n",
              "      <td>103</td>\n",
              "      <td>8</td>\n",
              "      <td>0.889</td>\n",
              "      <td>0.028</td>\n",
              "      <td>0.714</td>\n",
              "      <td>0.894</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Effusion</th>\n",
              "      <td>99</td>\n",
              "      <td>690</td>\n",
              "      <td>196</td>\n",
              "      <td>15</td>\n",
              "      <td>0.789</td>\n",
              "      <td>0.114</td>\n",
              "      <td>0.868</td>\n",
              "      <td>0.779</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hernia</th>\n",
              "      <td>1</td>\n",
              "      <td>743</td>\n",
              "      <td>255</td>\n",
              "      <td>1</td>\n",
              "      <td>0.744</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.744</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Infiltration</th>\n",
              "      <td>114</td>\n",
              "      <td>543</td>\n",
              "      <td>265</td>\n",
              "      <td>78</td>\n",
              "      <td>0.657</td>\n",
              "      <td>0.192</td>\n",
              "      <td>0.594</td>\n",
              "      <td>0.672</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mass</th>\n",
              "      <td>40</td>\n",
              "      <td>789</td>\n",
              "      <td>158</td>\n",
              "      <td>13</td>\n",
              "      <td>0.829</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.755</td>\n",
              "      <td>0.833</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nodule</th>\n",
              "      <td>28</td>\n",
              "      <td>731</td>\n",
              "      <td>220</td>\n",
              "      <td>21</td>\n",
              "      <td>0.759</td>\n",
              "      <td>0.049</td>\n",
              "      <td>0.571</td>\n",
              "      <td>0.769</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Atelectasis</th>\n",
              "      <td>64</td>\n",
              "      <td>657</td>\n",
              "      <td>249</td>\n",
              "      <td>30</td>\n",
              "      <td>0.721</td>\n",
              "      <td>0.094</td>\n",
              "      <td>0.681</td>\n",
              "      <td>0.725</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumothorax</th>\n",
              "      <td>24</td>\n",
              "      <td>785</td>\n",
              "      <td>183</td>\n",
              "      <td>8</td>\n",
              "      <td>0.809</td>\n",
              "      <td>0.032</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.811</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pleural_Thickening</th>\n",
              "      <td>24</td>\n",
              "      <td>713</td>\n",
              "      <td>259</td>\n",
              "      <td>4</td>\n",
              "      <td>0.737</td>\n",
              "      <td>0.028</td>\n",
              "      <td>0.857</td>\n",
              "      <td>0.734</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumonia</th>\n",
              "      <td>14</td>\n",
              "      <td>661</td>\n",
              "      <td>320</td>\n",
              "      <td>5</td>\n",
              "      <td>0.675</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.737</td>\n",
              "      <td>0.674</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fibrosis</th>\n",
              "      <td>10</td>\n",
              "      <td>725</td>\n",
              "      <td>261</td>\n",
              "      <td>4</td>\n",
              "      <td>0.735</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.714</td>\n",
              "      <td>0.735</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Edema</th>\n",
              "      <td>15</td>\n",
              "      <td>767</td>\n",
              "      <td>213</td>\n",
              "      <td>5</td>\n",
              "      <td>0.782</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.783</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consolidation</th>\n",
              "      <td>36</td>\n",
              "      <td>658</td>\n",
              "      <td>297</td>\n",
              "      <td>9</td>\n",
              "      <td>0.694</td>\n",
              "      <td>0.045</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.689</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     TP   TN   FP  ...          AUC           F1 Threshold\n",
              "                                   ...                                    \n",
              "Cardiomegaly         16  814  169  ...  Not Defined  Not Defined       0.5\n",
              "Emphysema            20  869  103  ...  Not Defined  Not Defined       0.5\n",
              "Effusion             99  690  196  ...  Not Defined  Not Defined       0.5\n",
              "Hernia                1  743  255  ...  Not Defined  Not Defined       0.5\n",
              "Infiltration        114  543  265  ...  Not Defined  Not Defined       0.5\n",
              "Mass                 40  789  158  ...  Not Defined  Not Defined       0.5\n",
              "Nodule               28  731  220  ...  Not Defined  Not Defined       0.5\n",
              "Atelectasis          64  657  249  ...  Not Defined  Not Defined       0.5\n",
              "Pneumothorax         24  785  183  ...  Not Defined  Not Defined       0.5\n",
              "Pleural_Thickening   24  713  259  ...  Not Defined  Not Defined       0.5\n",
              "Pneumonia            14  661  320  ...  Not Defined  Not Defined       0.5\n",
              "Fibrosis             10  725  261  ...  Not Defined  Not Defined       0.5\n",
              "Edema                15  767  213  ...  Not Defined  Not Defined       0.5\n",
              "Consolidation        36  658  297  ...  Not Defined  Not Defined       0.5\n",
              "\n",
              "[14 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RLfz2LhK0jSy"
      },
      "source": [
        "A especificidade e a sensibilidade não dependem da prevalência da classe positiva no conjunto de dados.\n",
        "* Isso ocorre porque as estatísticas são calculadas apenas dentro de pessoas da mesma classe.\n",
        "* A sensibilidade considera apenas a resposta de pessoas na classe positiva\n",
        "* Da mesma forma, a especificidade considera apenas a resposta de pessoas na classe negativa."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Idd1VhR405M0"
      },
      "source": [
        "### 3.5 PPV e NPV\n",
        "\n",
        "No diagnóstico, entretanto, a sensibilidade e a especificidade não são úteis. A sensibilidade, diz a probabilidade de o resultado do teste ser positivo, visto que a pessoa já tem a doença. Descobrir se o paciente tem a doença!\n",
        "\n",
        "O que seria mais útil é a probabilidade de a pessoa ter a doença, visto que o resultado do teste é positivo. Isso leva ao <i>positive predictive value (PPV)</i> - valor preditivo positivo (VPP) e ao <i>negative predictive value (NPV)</i> - valor preditivo negativo (NPV).\n",
        "\n",
        "- O valor preditivo positivo (VPP) é a probabilidade de que os indivíduos com um teste de triagem positivo realmente tenham a doença.\n",
        "- O valor preditivo negativo (NPV) é a probabilidade de que os indivíduos com um teste de triagem negativo realmente não tenham a doença.\n",
        "\n",
        "Podendo ser formulados em termos de verdadeiros positivos, verdadeiros negativos, falsos positivos e falsos negativos:\n",
        "\n",
        "$$ PPV = \\frac {\\text {verdadeiros positivos}} {\\text {verdadeiros positivos} + \\text {falsos positivos}} $$\n",
        "\n",
        "$$ NPV = \\frac {\\text {verdadeiros negativos}} {\\text {verdadeiros negativos} + \\text {falsos negativos}} $$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYMG3oUs0YDS"
      },
      "source": [
        "def get_ppv(y, pred, th=0.5):\n",
        "    \"\"\"\n",
        "    Calcula o PPV das previsões no limite (threshold).\n",
        "\n",
        "     Args:\n",
        "         y (np.array): valores reais, tamanho (n_exemplos)\n",
        "         pred (np.array): saída do modelo, tamanho (n_examples)\n",
        "         th (float): valor de corte para previsão positiva do modelo (threshold)\n",
        "     Retorna:\n",
        "         PPV (float): valor preditivo positivo de previsões no limite\n",
        "    \"\"\"\n",
        "    PPV = 0.0\n",
        "    \n",
        "    # obter TP e FP usando nossas funções previamente definidas\n",
        "    TP = true_positives(y,pred,th)\n",
        "    FP = false_positives(y,pred,th)\n",
        "\n",
        "    # use TP e FP para calcular PPV\n",
        "    PPV = PPV + (TP/(TP+FP))\n",
        "    \n",
        "    return PPV\n",
        "\n",
        "def get_npv(y, pred, th=0.5):\n",
        "    \"\"\"\n",
        "    Calcula o VPL das previsões no limite (threshold).\n",
        "\n",
        "     Args:\n",
        "         y (np.array): valores reais, tamanho (n_exemplos)\n",
        "         pred (np.array): saída do modelo, tamanho (n_examples)\n",
        "         th (float): valor de corte para previsão positiva do modelo (threshold)\n",
        "     Retorna:\n",
        "         NPV (float): valor preditivo negativo de previsões no limite\n",
        "    \"\"\"\n",
        "    NPV = 0.0\n",
        "    \n",
        "    # obter TN e FN usando nossas funções previamente definidas\n",
        "    TN = true_negatives(y,pred,th)\n",
        "    FN = false_negatives(y,pred,th)\n",
        "\n",
        "    # use TN e FN pra calcular NPV\n",
        "    NPV = NPV + (TN/(TN+FN))\n",
        "\n",
        "    return NPV"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5MNf29qP3Bi0",
        "outputId": "f28c5fea-01be-4661-e0a5-a71ea7925879"
      },
      "source": [
        "print(\"Caso de Teste:\\n\")\n",
        "\n",
        "y_test = np.array([1, 0, 0, 1, 1])\n",
        "print(f'Rótulos de Teste: {y_test}')\n",
        "\n",
        "preds_test = np.array([0.8, 0.8, 0.4, 0.6, 0.3])\n",
        "print(f'Previsões de teste: {preds_test}\\n')\n",
        "\n",
        "threshold = 0.5\n",
        "print(f\"threshold: {threshold}\\n\")\n",
        "\n",
        "print(f\"PPV calculado: {get_ppv(y_test, preds_test, threshold):.2f}\")\n",
        "print(f\"NPV calculado: {get_npv(y_test, preds_test, threshold):.2f}\")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Caso de Teste:\n",
            "\n",
            "Rótulos de Teste: [1 0 0 1 1]\n",
            "Previsões de teste: [0.8 0.8 0.4 0.6 0.3]\n",
            "\n",
            "threshold: 0.5\n",
            "\n",
            "PPV calculado: 0.67\n",
            "NPV calculado: 0.50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "j5is3RJS3I9c",
        "outputId": "d31c38a8-c597-4045-a840-fe369ed87477"
      },
      "source": [
        "util_2w.get_performance_metrics(y, pred, class_labels, acc=get_accuracy, prevalence=get_prevalence, \n",
        "                        sens=get_sensitivity, spec=get_specificity, ppv=get_ppv, npv=get_npv)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TP</th>\n",
              "      <th>TN</th>\n",
              "      <th>FP</th>\n",
              "      <th>FN</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Prevalence</th>\n",
              "      <th>Sensitivity</th>\n",
              "      <th>Specificity</th>\n",
              "      <th>PPV</th>\n",
              "      <th>NPV</th>\n",
              "      <th>AUC</th>\n",
              "      <th>F1</th>\n",
              "      <th>Threshold</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cardiomegaly</th>\n",
              "      <td>16</td>\n",
              "      <td>814</td>\n",
              "      <td>169</td>\n",
              "      <td>1</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.941</td>\n",
              "      <td>0.828</td>\n",
              "      <td>0.086</td>\n",
              "      <td>0.999</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emphysema</th>\n",
              "      <td>20</td>\n",
              "      <td>869</td>\n",
              "      <td>103</td>\n",
              "      <td>8</td>\n",
              "      <td>0.889</td>\n",
              "      <td>0.028</td>\n",
              "      <td>0.714</td>\n",
              "      <td>0.894</td>\n",
              "      <td>0.163</td>\n",
              "      <td>0.991</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Effusion</th>\n",
              "      <td>99</td>\n",
              "      <td>690</td>\n",
              "      <td>196</td>\n",
              "      <td>15</td>\n",
              "      <td>0.789</td>\n",
              "      <td>0.114</td>\n",
              "      <td>0.868</td>\n",
              "      <td>0.779</td>\n",
              "      <td>0.336</td>\n",
              "      <td>0.979</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hernia</th>\n",
              "      <td>1</td>\n",
              "      <td>743</td>\n",
              "      <td>255</td>\n",
              "      <td>1</td>\n",
              "      <td>0.744</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.744</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.999</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Infiltration</th>\n",
              "      <td>114</td>\n",
              "      <td>543</td>\n",
              "      <td>265</td>\n",
              "      <td>78</td>\n",
              "      <td>0.657</td>\n",
              "      <td>0.192</td>\n",
              "      <td>0.594</td>\n",
              "      <td>0.672</td>\n",
              "      <td>0.301</td>\n",
              "      <td>0.874</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mass</th>\n",
              "      <td>40</td>\n",
              "      <td>789</td>\n",
              "      <td>158</td>\n",
              "      <td>13</td>\n",
              "      <td>0.829</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.755</td>\n",
              "      <td>0.833</td>\n",
              "      <td>0.202</td>\n",
              "      <td>0.984</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nodule</th>\n",
              "      <td>28</td>\n",
              "      <td>731</td>\n",
              "      <td>220</td>\n",
              "      <td>21</td>\n",
              "      <td>0.759</td>\n",
              "      <td>0.049</td>\n",
              "      <td>0.571</td>\n",
              "      <td>0.769</td>\n",
              "      <td>0.113</td>\n",
              "      <td>0.972</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Atelectasis</th>\n",
              "      <td>64</td>\n",
              "      <td>657</td>\n",
              "      <td>249</td>\n",
              "      <td>30</td>\n",
              "      <td>0.721</td>\n",
              "      <td>0.094</td>\n",
              "      <td>0.681</td>\n",
              "      <td>0.725</td>\n",
              "      <td>0.204</td>\n",
              "      <td>0.956</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumothorax</th>\n",
              "      <td>24</td>\n",
              "      <td>785</td>\n",
              "      <td>183</td>\n",
              "      <td>8</td>\n",
              "      <td>0.809</td>\n",
              "      <td>0.032</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.811</td>\n",
              "      <td>0.116</td>\n",
              "      <td>0.99</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pleural_Thickening</th>\n",
              "      <td>24</td>\n",
              "      <td>713</td>\n",
              "      <td>259</td>\n",
              "      <td>4</td>\n",
              "      <td>0.737</td>\n",
              "      <td>0.028</td>\n",
              "      <td>0.857</td>\n",
              "      <td>0.734</td>\n",
              "      <td>0.085</td>\n",
              "      <td>0.994</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumonia</th>\n",
              "      <td>14</td>\n",
              "      <td>661</td>\n",
              "      <td>320</td>\n",
              "      <td>5</td>\n",
              "      <td>0.675</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.737</td>\n",
              "      <td>0.674</td>\n",
              "      <td>0.042</td>\n",
              "      <td>0.992</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fibrosis</th>\n",
              "      <td>10</td>\n",
              "      <td>725</td>\n",
              "      <td>261</td>\n",
              "      <td>4</td>\n",
              "      <td>0.735</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.714</td>\n",
              "      <td>0.735</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.995</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Edema</th>\n",
              "      <td>15</td>\n",
              "      <td>767</td>\n",
              "      <td>213</td>\n",
              "      <td>5</td>\n",
              "      <td>0.782</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.783</td>\n",
              "      <td>0.066</td>\n",
              "      <td>0.994</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consolidation</th>\n",
              "      <td>36</td>\n",
              "      <td>658</td>\n",
              "      <td>297</td>\n",
              "      <td>9</td>\n",
              "      <td>0.694</td>\n",
              "      <td>0.045</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.689</td>\n",
              "      <td>0.108</td>\n",
              "      <td>0.987</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     TP   TN   FP  ...          AUC           F1 Threshold\n",
              "                                   ...                                    \n",
              "Cardiomegaly         16  814  169  ...  Not Defined  Not Defined       0.5\n",
              "Emphysema            20  869  103  ...  Not Defined  Not Defined       0.5\n",
              "Effusion             99  690  196  ...  Not Defined  Not Defined       0.5\n",
              "Hernia                1  743  255  ...  Not Defined  Not Defined       0.5\n",
              "Infiltration        114  543  265  ...  Not Defined  Not Defined       0.5\n",
              "Mass                 40  789  158  ...  Not Defined  Not Defined       0.5\n",
              "Nodule               28  731  220  ...  Not Defined  Not Defined       0.5\n",
              "Atelectasis          64  657  249  ...  Not Defined  Not Defined       0.5\n",
              "Pneumothorax         24  785  183  ...  Not Defined  Not Defined       0.5\n",
              "Pleural_Thickening   24  713  259  ...  Not Defined  Not Defined       0.5\n",
              "Pneumonia            14  661  320  ...  Not Defined  Not Defined       0.5\n",
              "Fibrosis             10  725  261  ...  Not Defined  Not Defined       0.5\n",
              "Edema                15  767  213  ...  Not Defined  Not Defined       0.5\n",
              "Consolidation        36  658  297  ...  Not Defined  Not Defined       0.5\n",
              "\n",
              "[14 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkTM6Byd3HRs"
      },
      "source": [
        "Apesar de ter sensibilidade e acurácia muito altas, o PPV das previsões ainda pode ser muito baixo.\n",
        "\n",
        "É o caso do `Edema`, por exemplo.\n",
        "* A sensibilidade para `Edema` é de 0,75.\n",
        "* Porém, dado que o modelo previu positivo, a probabilidade de uma pessoa ter edema (seu VPP) é de apenas 0,066!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4oLn1vY3e-O"
      },
      "source": [
        "### 3.6 Curva ROC\n",
        "\n",
        "Até então a previsão do modelo de \"0,5\" e acima deve ser tratada como positiva e, caso contrário, deve ser tratada como negativa. No entanto, esta foi uma escolha bastante arbitrária. Uma maneira de ver isso é observar uma visualização muito informativa chamada curva de característica de operação do receptor (ROC) - <i> Receiver operating characteristic (ROC) curve </i>.\n",
        "\n",
        "A curva ROC é criada traçando a taxa de verdadeiro positivo (TPR - <i>true positive rate </i>) contra a taxa de falso positivo (FPR <i>false positive rate</i>) em várias configurações de limite (<i> threshold </i>). O ponto ideal está no canto superior esquerdo, com uma taxa de verdadeiro positivo de 1 e uma taxa de falso positivo de 0. Os vários pontos na curva são gerados pela mudança gradual do limite (threshold)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3ESHRY43iwy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "cdcfbb97-f579-4ca7-8b99-8d0802b4fe93"
      },
      "source": [
        "util_2w.get_curve(y, pred, class_labels)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMm-0VRA3qNY"
      },
      "source": [
        "A área sob a curva ROC também é chamada de AUCROC (<i>area under the ROC curve </i>) ou estatística C e é uma medida da qualidade do ajuste (fit). Na literatura médica, esse número também dá a probabilidade de que um paciente selecionado aleatoriamente, que experimentou uma condição, tenha um score de risco mais alto do que um paciente que não experimentou o evento. Isso resume a saída do modelo em todos os limites (threshold) e fornece uma boa noção do poder discriminativo de um determinado modelo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AAtROJGf3vB5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "ed965f06-00f2-47d7-f62c-c44759c76e70"
      },
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "util_2w.get_performance_metrics(y, pred, class_labels, acc=get_accuracy, prevalence=get_prevalence, \n",
        "                        sens=get_sensitivity, spec=get_specificity, ppv=get_ppv, npv=get_npv, auc=roc_auc_score)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TP</th>\n",
              "      <th>TN</th>\n",
              "      <th>FP</th>\n",
              "      <th>FN</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Prevalence</th>\n",
              "      <th>Sensitivity</th>\n",
              "      <th>Specificity</th>\n",
              "      <th>PPV</th>\n",
              "      <th>NPV</th>\n",
              "      <th>AUC</th>\n",
              "      <th>F1</th>\n",
              "      <th>Threshold</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cardiomegaly</th>\n",
              "      <td>16</td>\n",
              "      <td>814</td>\n",
              "      <td>169</td>\n",
              "      <td>1</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.941</td>\n",
              "      <td>0.828</td>\n",
              "      <td>0.086</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.933</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emphysema</th>\n",
              "      <td>20</td>\n",
              "      <td>869</td>\n",
              "      <td>103</td>\n",
              "      <td>8</td>\n",
              "      <td>0.889</td>\n",
              "      <td>0.028</td>\n",
              "      <td>0.714</td>\n",
              "      <td>0.894</td>\n",
              "      <td>0.163</td>\n",
              "      <td>0.991</td>\n",
              "      <td>0.935</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Effusion</th>\n",
              "      <td>99</td>\n",
              "      <td>690</td>\n",
              "      <td>196</td>\n",
              "      <td>15</td>\n",
              "      <td>0.789</td>\n",
              "      <td>0.114</td>\n",
              "      <td>0.868</td>\n",
              "      <td>0.779</td>\n",
              "      <td>0.336</td>\n",
              "      <td>0.979</td>\n",
              "      <td>0.891</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hernia</th>\n",
              "      <td>1</td>\n",
              "      <td>743</td>\n",
              "      <td>255</td>\n",
              "      <td>1</td>\n",
              "      <td>0.744</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.744</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.644</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Infiltration</th>\n",
              "      <td>114</td>\n",
              "      <td>543</td>\n",
              "      <td>265</td>\n",
              "      <td>78</td>\n",
              "      <td>0.657</td>\n",
              "      <td>0.192</td>\n",
              "      <td>0.594</td>\n",
              "      <td>0.672</td>\n",
              "      <td>0.301</td>\n",
              "      <td>0.874</td>\n",
              "      <td>0.696</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mass</th>\n",
              "      <td>40</td>\n",
              "      <td>789</td>\n",
              "      <td>158</td>\n",
              "      <td>13</td>\n",
              "      <td>0.829</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.755</td>\n",
              "      <td>0.833</td>\n",
              "      <td>0.202</td>\n",
              "      <td>0.984</td>\n",
              "      <td>0.888</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nodule</th>\n",
              "      <td>28</td>\n",
              "      <td>731</td>\n",
              "      <td>220</td>\n",
              "      <td>21</td>\n",
              "      <td>0.759</td>\n",
              "      <td>0.049</td>\n",
              "      <td>0.571</td>\n",
              "      <td>0.769</td>\n",
              "      <td>0.113</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.745</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Atelectasis</th>\n",
              "      <td>64</td>\n",
              "      <td>657</td>\n",
              "      <td>249</td>\n",
              "      <td>30</td>\n",
              "      <td>0.721</td>\n",
              "      <td>0.094</td>\n",
              "      <td>0.681</td>\n",
              "      <td>0.725</td>\n",
              "      <td>0.204</td>\n",
              "      <td>0.956</td>\n",
              "      <td>0.781</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumothorax</th>\n",
              "      <td>24</td>\n",
              "      <td>785</td>\n",
              "      <td>183</td>\n",
              "      <td>8</td>\n",
              "      <td>0.809</td>\n",
              "      <td>0.032</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.811</td>\n",
              "      <td>0.116</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.826</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pleural_Thickening</th>\n",
              "      <td>24</td>\n",
              "      <td>713</td>\n",
              "      <td>259</td>\n",
              "      <td>4</td>\n",
              "      <td>0.737</td>\n",
              "      <td>0.028</td>\n",
              "      <td>0.857</td>\n",
              "      <td>0.734</td>\n",
              "      <td>0.085</td>\n",
              "      <td>0.994</td>\n",
              "      <td>0.868</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumonia</th>\n",
              "      <td>14</td>\n",
              "      <td>661</td>\n",
              "      <td>320</td>\n",
              "      <td>5</td>\n",
              "      <td>0.675</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.737</td>\n",
              "      <td>0.674</td>\n",
              "      <td>0.042</td>\n",
              "      <td>0.992</td>\n",
              "      <td>0.762</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fibrosis</th>\n",
              "      <td>10</td>\n",
              "      <td>725</td>\n",
              "      <td>261</td>\n",
              "      <td>4</td>\n",
              "      <td>0.735</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.714</td>\n",
              "      <td>0.735</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.995</td>\n",
              "      <td>0.801</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Edema</th>\n",
              "      <td>15</td>\n",
              "      <td>767</td>\n",
              "      <td>213</td>\n",
              "      <td>5</td>\n",
              "      <td>0.782</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.783</td>\n",
              "      <td>0.066</td>\n",
              "      <td>0.994</td>\n",
              "      <td>0.856</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consolidation</th>\n",
              "      <td>36</td>\n",
              "      <td>658</td>\n",
              "      <td>297</td>\n",
              "      <td>9</td>\n",
              "      <td>0.694</td>\n",
              "      <td>0.045</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.689</td>\n",
              "      <td>0.108</td>\n",
              "      <td>0.987</td>\n",
              "      <td>0.799</td>\n",
              "      <td>Not Defined</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     TP   TN   FP  FN  ...    NPV    AUC           F1 Threshold\n",
              "                                       ...                                     \n",
              "Cardiomegaly         16  814  169   1  ...  0.999  0.933  Not Defined       0.5\n",
              "Emphysema            20  869  103   8  ...  0.991  0.935  Not Defined       0.5\n",
              "Effusion             99  690  196  15  ...  0.979  0.891  Not Defined       0.5\n",
              "Hernia                1  743  255   1  ...  0.999  0.644  Not Defined       0.5\n",
              "Infiltration        114  543  265  78  ...  0.874  0.696  Not Defined       0.5\n",
              "Mass                 40  789  158  13  ...  0.984  0.888  Not Defined       0.5\n",
              "Nodule               28  731  220  21  ...  0.972  0.745  Not Defined       0.5\n",
              "Atelectasis          64  657  249  30  ...  0.956  0.781  Not Defined       0.5\n",
              "Pneumothorax         24  785  183   8  ...   0.99  0.826  Not Defined       0.5\n",
              "Pleural_Thickening   24  713  259   4  ...  0.994  0.868  Not Defined       0.5\n",
              "Pneumonia            14  661  320   5  ...  0.992  0.762  Not Defined       0.5\n",
              "Fibrosis             10  725  261   4  ...  0.995  0.801  Not Defined       0.5\n",
              "Edema                15  767  213   5  ...  0.994  0.856  Not Defined       0.5\n",
              "Consolidation        36  658  297   9  ...  0.987  0.799  Not Defined       0.5\n",
              "\n",
              "[14 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0PoLxXo3pFt"
      },
      "source": [
        "## 4. Intervalos de confiança"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLh5BnPv37f7"
      },
      "source": [
        "O conjunto de dados é apenas uma amostra do mundo real, e valores calculados para todas as métricas são estimativas dos valores do mundo real. Seria bom quantificar essa incerteza devido à amostragem do conjunto de dados. Isso é feito usando intervalos de confiança - <i>confidence intervals</i>. Um intervalo de confiança de 95\\% para uma estimativa $\\hat {s} $ de um parâmetro $ s $,  é um intervalo $I = (a, b) $ tal que 95\\% do tempo quando o experimento é executado, o valor real $ s $ está contido em $ I $. Mais concretamente, executando o experimento muitas vezes, a fração desses experimentos para os quais $ I $ que contém o parâmetro verdadeiro, tenderia para 95\\%.\n",
        "\n",
        "Embora algumas estimativas venham com métodos para calcular o intervalo de confiança analiticamente, estatísticas mais complicadas, como a AUC por exemplo, são difíceis. Para isso,existe um método chamado <b> bootstrap </b>. O bootstrap estima a incerteza por meio da reamostragem do conjunto de dados com substituição. Para cada reamostragem $ i $, uma nova estimativa é obtida, $ \\hat {s} _i $. Sendo possível estimar a distribuição de $ \\hat {s} $ usando a distribuição de $ \\hat {s} _i $ para os exemplos de bootstrap.\n",
        "\n",
        "A seguir, são criadas amostras de bootstrap e calculdas as  AUCs das amostras. Observe que é usado a  amostragem aleatória estratificada (amostragem das classes positiva e negativa separadamente) para garantir que os membros de cada classe sejam representados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2kR49_94EoK"
      },
      "source": [
        "def bootstrap_auc(y, pred, classes, bootstraps = 100, fold_size = 1000):\n",
        "    statistics = np.zeros((len(classes), bootstraps))\n",
        "\n",
        "    for c in range(len(classes)):\n",
        "        df = pd.DataFrame(columns=['y', 'pred'])\n",
        "        df.loc[:, 'y'] = y[:, c]\n",
        "        df.loc[:, 'pred'] = pred[:, c]\n",
        "        # get positive examples for stratified sampling\n",
        "        df_pos = df[df.y == 1]\n",
        "        df_neg = df[df.y == 0]\n",
        "        prevalence = len(df_pos) / len(df)\n",
        "        for i in range(bootstraps):\n",
        "            # stratified sampling of positive and negative examples\n",
        "            pos_sample = df_pos.sample(n = int(fold_size * prevalence), replace=True)\n",
        "            neg_sample = df_neg.sample(n = int(fold_size * (1-prevalence)), replace=True)\n",
        "\n",
        "            y_sample = np.concatenate([pos_sample.y.values, neg_sample.y.values])\n",
        "            pred_sample = np.concatenate([pos_sample.pred.values, neg_sample.pred.values])\n",
        "            score = roc_auc_score(y_sample, pred_sample)\n",
        "            statistics[c][i] = score\n",
        "    return statistics\n"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86Y5yWza3Bhe"
      },
      "source": [
        "statistics = bootstrap_auc(y, pred, class_labels)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kndVFZj04HcP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "159a5f29-bd92-41f3-c783-cd0ac7cfca3b"
      },
      "source": [
        "util_2w.print_confidence_intervals(class_labels, statistics)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Mean AUC (CI 5%-95%)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cardiomegaly</th>\n",
              "      <td>0.94 (0.90-0.96)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emphysema</th>\n",
              "      <td>0.93 (0.91-0.96)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Effusion</th>\n",
              "      <td>0.89 (0.87-0.92)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hernia</th>\n",
              "      <td>0.68 (0.30-0.98)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Infiltration</th>\n",
              "      <td>0.69 (0.65-0.73)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mass</th>\n",
              "      <td>0.89 (0.85-0.92)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nodule</th>\n",
              "      <td>0.74 (0.68-0.79)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Atelectasis</th>\n",
              "      <td>0.78 (0.74-0.82)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumothorax</th>\n",
              "      <td>0.82 (0.75-0.90)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pleural_Thickening</th>\n",
              "      <td>0.87 (0.83-0.92)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumonia</th>\n",
              "      <td>0.76 (0.70-0.84)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fibrosis</th>\n",
              "      <td>0.81 (0.73-0.87)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Edema</th>\n",
              "      <td>0.86 (0.83-0.89)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consolidation</th>\n",
              "      <td>0.80 (0.76-0.85)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Mean AUC (CI 5%-95%)\n",
              "Cardiomegaly           0.94 (0.90-0.96)\n",
              "Emphysema              0.93 (0.91-0.96)\n",
              "Effusion               0.89 (0.87-0.92)\n",
              "Hernia                 0.68 (0.30-0.98)\n",
              "Infiltration           0.69 (0.65-0.73)\n",
              "Mass                   0.89 (0.85-0.92)\n",
              "Nodule                 0.74 (0.68-0.79)\n",
              "Atelectasis            0.78 (0.74-0.82)\n",
              "Pneumothorax           0.82 (0.75-0.90)\n",
              "Pleural_Thickening     0.87 (0.83-0.92)\n",
              "Pneumonia              0.76 (0.70-0.84)\n",
              "Fibrosis               0.81 (0.73-0.87)\n",
              "Edema                  0.86 (0.83-0.89)\n",
              "Consolidation          0.80 (0.76-0.85)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iScKdzwn4Qzx"
      },
      "source": [
        "Os intervalos de confiança são muito maiores para algumas classes do que para outras. A hérnia, por exemplo, tem um intervalo em torno de (0,30 - 0,98), indicando que não se pode ter certeza de que é melhor do que o acaso (em 0,5)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cb6T5eNH4Xrz"
      },
      "source": [
        "<a name='5'></a>\n",
        "## 5. Precision-Recall Curve"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ed0Bvssb4hQC"
      },
      "source": [
        "Precision-Recall é uma medida útil quando as classes são muito desequilibradas.\n",
        "\n",
        "- A precisão é uma medida de relevância do resultado, equivalente ao PPV.\n",
        "- Recall é uma medida de quantos resultados verdadeiramente relevantes são retornados, equivalente a medida de sensibilidade.\n",
        "\n",
        "A curva de recuperação de precisão (<i>PRC - precision-recall curve </i>) mostra a compensação entre precisão e recall para diferentes limites (threshold). Uma área alta sob a curva representa alta recuperação e alta precisão, em que alta precisão se relaciona a uma taxa baixa de falsos positivos e alta recuperação está relacionada a uma baixa taxa de falsos negativos.\n",
        "\n",
        "Altas pontuações para ambos mostram que o classificador está retornando resultados precisos (alta precisão), bem como retornando a maioria de todos os resultados positivos (alto recall).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5TiKda694kCp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "4840e934-f533-4162-9cbc-8147a895456b"
      },
      "source": [
        "util_2w.get_curve(y, pred, class_labels, curve='prc')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKSQG8k84q8t"
      },
      "source": [
        "<a name='6'></a>\n",
        "## 6. F1 Score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEfrZWO_4zsM"
      },
      "source": [
        "A pontuação de F1 é a média harmônica de precisão e recall, onde uma pontuação de F1 atinge seu melhor valor em 1 (precisão perfeita e recall) e pior em 0.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkZ0EVv044K6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "b0d1a96c-d0eb-4343-e816-2eb121a38cf4"
      },
      "source": [
        "from sklearn.metrics import f1_score\n",
        "util_2w.get_performance_metrics(y, pred, class_labels, acc=get_accuracy, prevalence=get_prevalence, \n",
        "                        sens=get_sensitivity, spec=get_specificity, ppv=get_ppv, npv=get_npv, auc=roc_auc_score,f1=f1_score)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TP</th>\n",
              "      <th>TN</th>\n",
              "      <th>FP</th>\n",
              "      <th>FN</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Prevalence</th>\n",
              "      <th>Sensitivity</th>\n",
              "      <th>Specificity</th>\n",
              "      <th>PPV</th>\n",
              "      <th>NPV</th>\n",
              "      <th>AUC</th>\n",
              "      <th>F1</th>\n",
              "      <th>Threshold</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cardiomegaly</th>\n",
              "      <td>16</td>\n",
              "      <td>814</td>\n",
              "      <td>169</td>\n",
              "      <td>1</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.941</td>\n",
              "      <td>0.828</td>\n",
              "      <td>0.086</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.933</td>\n",
              "      <td>0.158</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emphysema</th>\n",
              "      <td>20</td>\n",
              "      <td>869</td>\n",
              "      <td>103</td>\n",
              "      <td>8</td>\n",
              "      <td>0.889</td>\n",
              "      <td>0.028</td>\n",
              "      <td>0.714</td>\n",
              "      <td>0.894</td>\n",
              "      <td>0.163</td>\n",
              "      <td>0.991</td>\n",
              "      <td>0.935</td>\n",
              "      <td>0.265</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Effusion</th>\n",
              "      <td>99</td>\n",
              "      <td>690</td>\n",
              "      <td>196</td>\n",
              "      <td>15</td>\n",
              "      <td>0.789</td>\n",
              "      <td>0.114</td>\n",
              "      <td>0.868</td>\n",
              "      <td>0.779</td>\n",
              "      <td>0.336</td>\n",
              "      <td>0.979</td>\n",
              "      <td>0.891</td>\n",
              "      <td>0.484</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hernia</th>\n",
              "      <td>1</td>\n",
              "      <td>743</td>\n",
              "      <td>255</td>\n",
              "      <td>1</td>\n",
              "      <td>0.744</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.744</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.644</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Infiltration</th>\n",
              "      <td>114</td>\n",
              "      <td>543</td>\n",
              "      <td>265</td>\n",
              "      <td>78</td>\n",
              "      <td>0.657</td>\n",
              "      <td>0.192</td>\n",
              "      <td>0.594</td>\n",
              "      <td>0.672</td>\n",
              "      <td>0.301</td>\n",
              "      <td>0.874</td>\n",
              "      <td>0.696</td>\n",
              "      <td>0.399</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mass</th>\n",
              "      <td>40</td>\n",
              "      <td>789</td>\n",
              "      <td>158</td>\n",
              "      <td>13</td>\n",
              "      <td>0.829</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.755</td>\n",
              "      <td>0.833</td>\n",
              "      <td>0.202</td>\n",
              "      <td>0.984</td>\n",
              "      <td>0.888</td>\n",
              "      <td>0.319</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nodule</th>\n",
              "      <td>28</td>\n",
              "      <td>731</td>\n",
              "      <td>220</td>\n",
              "      <td>21</td>\n",
              "      <td>0.759</td>\n",
              "      <td>0.049</td>\n",
              "      <td>0.571</td>\n",
              "      <td>0.769</td>\n",
              "      <td>0.113</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.745</td>\n",
              "      <td>0.189</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Atelectasis</th>\n",
              "      <td>64</td>\n",
              "      <td>657</td>\n",
              "      <td>249</td>\n",
              "      <td>30</td>\n",
              "      <td>0.721</td>\n",
              "      <td>0.094</td>\n",
              "      <td>0.681</td>\n",
              "      <td>0.725</td>\n",
              "      <td>0.204</td>\n",
              "      <td>0.956</td>\n",
              "      <td>0.781</td>\n",
              "      <td>0.314</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumothorax</th>\n",
              "      <td>24</td>\n",
              "      <td>785</td>\n",
              "      <td>183</td>\n",
              "      <td>8</td>\n",
              "      <td>0.809</td>\n",
              "      <td>0.032</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.811</td>\n",
              "      <td>0.116</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.826</td>\n",
              "      <td>0.201</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pleural_Thickening</th>\n",
              "      <td>24</td>\n",
              "      <td>713</td>\n",
              "      <td>259</td>\n",
              "      <td>4</td>\n",
              "      <td>0.737</td>\n",
              "      <td>0.028</td>\n",
              "      <td>0.857</td>\n",
              "      <td>0.734</td>\n",
              "      <td>0.085</td>\n",
              "      <td>0.994</td>\n",
              "      <td>0.868</td>\n",
              "      <td>0.154</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pneumonia</th>\n",
              "      <td>14</td>\n",
              "      <td>661</td>\n",
              "      <td>320</td>\n",
              "      <td>5</td>\n",
              "      <td>0.675</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.737</td>\n",
              "      <td>0.674</td>\n",
              "      <td>0.042</td>\n",
              "      <td>0.992</td>\n",
              "      <td>0.762</td>\n",
              "      <td>0.079</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fibrosis</th>\n",
              "      <td>10</td>\n",
              "      <td>725</td>\n",
              "      <td>261</td>\n",
              "      <td>4</td>\n",
              "      <td>0.735</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.714</td>\n",
              "      <td>0.735</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.995</td>\n",
              "      <td>0.801</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Edema</th>\n",
              "      <td>15</td>\n",
              "      <td>767</td>\n",
              "      <td>213</td>\n",
              "      <td>5</td>\n",
              "      <td>0.782</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.783</td>\n",
              "      <td>0.066</td>\n",
              "      <td>0.994</td>\n",
              "      <td>0.856</td>\n",
              "      <td>0.121</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consolidation</th>\n",
              "      <td>36</td>\n",
              "      <td>658</td>\n",
              "      <td>297</td>\n",
              "      <td>9</td>\n",
              "      <td>0.694</td>\n",
              "      <td>0.045</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.689</td>\n",
              "      <td>0.108</td>\n",
              "      <td>0.987</td>\n",
              "      <td>0.799</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     TP   TN   FP  FN  ...    NPV    AUC     F1 Threshold\n",
              "                                       ...                               \n",
              "Cardiomegaly         16  814  169   1  ...  0.999  0.933  0.158       0.5\n",
              "Emphysema            20  869  103   8  ...  0.991  0.935  0.265       0.5\n",
              "Effusion             99  690  196  15  ...  0.979  0.891  0.484       0.5\n",
              "Hernia                1  743  255   1  ...  0.999  0.644  0.008       0.5\n",
              "Infiltration        114  543  265  78  ...  0.874  0.696  0.399       0.5\n",
              "Mass                 40  789  158  13  ...  0.984  0.888  0.319       0.5\n",
              "Nodule               28  731  220  21  ...  0.972  0.745  0.189       0.5\n",
              "Atelectasis          64  657  249  30  ...  0.956  0.781  0.314       0.5\n",
              "Pneumothorax         24  785  183   8  ...   0.99  0.826  0.201       0.5\n",
              "Pleural_Thickening   24  713  259   4  ...  0.994  0.868  0.154       0.5\n",
              "Pneumonia            14  661  320   5  ...  0.992  0.762  0.079       0.5\n",
              "Fibrosis             10  725  261   4  ...  0.995  0.801   0.07       0.5\n",
              "Edema                15  767  213   5  ...  0.994  0.856  0.121       0.5\n",
              "Consolidation        36  658  297   9  ...  0.987  0.799   0.19       0.5\n",
              "\n",
              "[14 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AwhCkDdG5A-5"
      },
      "source": [
        "<a name='7'> </a>\n",
        "## 7. Calibração"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfOdsAUW5Gr3"
      },
      "source": [
        "Ao realizar a classificação, geralmente queremos não apenas prever o rótulo da classe, mas também obter uma probabilidade de cada rótulo. Idealmente, essa probabilidade nos daria algum tipo de confiança na previsão. Para observar como as probabilidades geradas pelo nosso modelo estão alinhadas com as probabilidades reais, podemos plotar o que é chamado de <b>curva de calibração </b>.\n",
        "\n",
        "A fim de gerar um gráfico de calibração, primeiro dividimos nossas previsões em um número fixo de caixas separadas (por exemplo, 5) entre 0 e 1. Em seguida, calculamos um ponto para cada caixa: o valor x para cada ponto é a média para o probabilidade de que nosso modelo atribuiu a esses pontos e o valor y para cada fração de pontos de verdadeiros positivos nesse compartimento. Em seguida, representamos esses pontos em um gráfico linear. Um modelo bem calibrado tem uma curva de calibração que quase se alinha com a linha y = x.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QERyVJk75KzL"
      },
      "source": [
        "from sklearn.calibration import calibration_curve\n",
        "def plot_calibration_curve(y, pred):\n",
        "    plt.figure(figsize=(20, 20))\n",
        "    for i in range(len(class_labels)):\n",
        "        plt.subplot(4, 4, i + 1)\n",
        "        fraction_of_positives, mean_predicted_value = calibration_curve(y[:,i], pred[:,i], n_bins=20)\n",
        "        plt.plot([0, 1], [0, 1], linestyle='--')\n",
        "        plt.plot(mean_predicted_value, fraction_of_positives, marker='.')\n",
        "        plt.xlabel(\"Predicted Value\")\n",
        "        plt.ylabel(\"Fraction of Positives\")\n",
        "        plt.title(class_labels[i])\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JKZf_l_l5NxY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1a218f70-eab3-4014-bdf0-10920edbd66a"
      },
      "source": [
        "plot_calibration_curve(y, pred)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 14 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_bGwPtVb5WKG"
      },
      "source": [
        "Existe um método muito útil chamado [Platt scaling] (https://en.wikipedia.org/wiki/Platt_scaling) que funciona ajustando um modelo de regressão logística às pontuações do modelo. Para construir este modelo, usaremos a parte de treinamento de nosso conjunto de dados para gerar o modelo linear e, em seguida, usaremos o modelo para calibrar as previsões para nossa parte de teste."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HREG1ZbT5W-G"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression as LR \n",
        "\n",
        "y_train = train_results[class_labels].values\n",
        "pred_train = train_results[pred_labels].values\n",
        "pred_calibrated = np.zeros_like(pred)\n",
        "\n",
        "for i in range(len(class_labels)):\n",
        "    lr = LR(solver='liblinear', max_iter=10000)\n",
        "    lr.fit(pred_train[:, i].reshape(-1, 1), y_train[:, i])    \n",
        "    pred_calibrated[:, i] = lr.predict_proba(pred[:, i].reshape(-1, 1))[:,1]"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yy_KIaZd5bAL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2ef66394-236d-47ae-96ad-1570da3264b0"
      },
      "source": [
        "plot_calibration_curve(y[:,], pred_calibrated)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 14 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NuC2-da23JFl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}