xgboost_model.ipynb

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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/samuel-haddad/MBAUSP/blob/main/xgboost_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yE-lSQyyPyLt"
      },
      "source": [
        "# **Modelo XGBoost**\n",
        "\n",
        "#### MBA Data Science & Analytics - USP & ESALQ\n",
        "Trabalho de Conclusão de Curso - 02/2022 <br>\n",
        "**Título:** Um estudo comparativo entre modelos Multinível, Logístico e XGBoost na detecção de fraudes ideológicas no setor financeiro. <br>\n",
        "**Autor:** Samuel Haddad Simões Machado\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtZlgNbMQvbZ"
      },
      "source": [
        "## Bibliotecas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ryLGoDUFOkpA"
      },
      "outputs": [],
      "source": [
        "# pacotes básicos\n",
        "import pandas as pd \n",
        "import numpy as np \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# preprocessamento\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# modelagem\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import log_loss\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# bibliotecas google & auth\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# autenticar e criar o cliente com PyDrive\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZaShgLcqRwJT"
      },
      "source": [
        "## Dataset"
      ]
    },
    {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
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        "id": "g2Jxl6rLsJJB",
        "outputId": "598b242e-b072-4b3b-8086-a0f4866883d4"
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      "outputs": [
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "# link do arquivo no Drive\n",
        "link = 'https://drive.google.com/file/d/1U6RV2fdFHoqOyXmNbP6mFMEs23RfmGNG/view?usp=sharing'\n",
        " \n",
        "# extraindo o ID do link\n",
        "id = link.split(\"/\")[-2]\n",
        " \n",
        "downloaded = drive.CreateFile({'id':id})\n",
        "downloaded.GetContentFile('base_tcc_xgboost20220927.csv') \n",
        "\n",
        "# criando o dataframe \n",
        "df = pd.read_csv('base_tcc_xgboost20220927.csv', sep=';')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1G_vEgRhRsZ0"
      },
      "source": [
        "## Treino, teste e validação"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6aGayTSBSkyL",
        "outputId": "8969bb65-9924-42a3-fe34-5905c47d9d3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+ ------------------------- RESUMO ------------------------- +\n",
            "X_train: (15860, 56) | y_train: 15860         \n",
            "X_test:  (5287, 56) | y_test: 5287         \n",
            "X_val:   (5287, 56) | y_val: 5287\n",
            "Train dist: 35.5% | Test dist: 35.26% | Val dist: 34.82%\n",
            "+ ---------------------------------------------------------- +\n"
          ]
        }
      ],
      "source": [
        "# remover a coluna de id\n",
        "df_silver = df.drop(['id'], axis=1)\n",
        "\n",
        "# extrair as variáveis explicativas e resposta\n",
        "X, y = df_silver.drop(['target'], axis=1), df_silver['target']\n",
        "\n",
        "# separar a base de treino\n",
        "X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.6, random_state=42)\n",
        " \n",
        "# separar as bases de teste e validação\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_rem, y_rem, test_size=0.5, random_state=42)\n",
        "\n",
        "# distribuição da variável resposta nas bases\n",
        "y_train_dist = y_train.sum()/len(y_train)\n",
        "y_test_dist = y_test.sum()/len(y_test)\n",
        "y_val_dist = y_val.sum()/len(y_val)\n",
        "\n",
        "# exibir a volumetria das bases\n",
        "print('+', 25*'-','RESUMO', 25*'-','+')\n",
        "print(f'X_train: {X_train.shape} | y_train: {len(y_train)} \\\n",
        "        \\nX_test:  {X_test.shape} | y_test: {len(y_test)} \\\n",
        "        \\nX_val:   {X_val.shape} | y_val: {len(y_val)}'\n",
        "        )\n",
        "print(f'Train dist: {round(y_train_dist*100, 2)}% | Test dist: {round(y_test_dist*100, 2)}% | Val dist: {round(y_val_dist*100, 2)}%')\n",
        "print('+', 58*'-', '+')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K8E8X_OIT-Bt"
      },
      "source": [
        "## Modelagem"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "cHWgibk-V2_p",
        "outputId": "f4cf42d9-cb64-496e-a645-5e60f508c940"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     amostra  acuracia  precisao  recall  auc_roc  f1_score  \\\n",
              "0      teste     77.57     73.28   57.24    81.41     64.28   \n",
              "1  validacao     78.14     74.38   56.76    82.33     64.39   \n",
              "\n",
              "   logloss(normalizada)  \n",
              "0              0.488186  \n",
              "1              0.477099  "
            ],
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              "      <th>amostra</th>\n",
              "      <th>acuracia</th>\n",
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              "      <th>recall</th>\n",
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              "      <th>0</th>\n",
              "      <td>teste</td>\n",
              "      <td>77.57</td>\n",
              "      <td>73.28</td>\n",
              "      <td>57.24</td>\n",
              "      <td>81.41</td>\n",
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              "      <th>1</th>\n",
              "      <td>validacao</td>\n",
              "      <td>78.14</td>\n",
              "      <td>74.38</td>\n",
              "      <td>56.76</td>\n",
              "      <td>82.33</td>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d6229e97-e4b4-494d-95ad-153f62d247ea button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d6229e97-e4b4-494d-95ad-153f62d247ea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# construir o modelo com configuração padrão\n",
        "model = XGBClassifier()\n",
        "\n",
        "# treinar o modelo\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# acurácia de treino\n",
        "acuracy_train = model.score(X_train, y_train)\n",
        "\n",
        "\n",
        "#---------- TESTE ----------#\n",
        "\n",
        "# previsão - teste\n",
        "y_pred_test = model.predict(X_test)\n",
        "\n",
        "# extrair as métricas - test\n",
        "acuracy_test = (metrics.accuracy_score(y_test, y_pred_test)*100).round(2)\n",
        "precision_test = (metrics.precision_score(y_test, y_pred_test)*100).round(2)\n",
        "recall_test = (metrics.recall_score(y_test, y_pred_test)*100).round(2)\n",
        "aucroc_test = (roc_auc_score(y_test, model.predict_proba(X_test)[:,1])*100).round(2)\n",
        "f1_score_test = (f1_score(y_test, y_pred_test)*100).round(2)\n",
        "logloss_test = (metrics.log_loss(y_test, model.predict_proba(X_test), normalize=True))\n",
        "\n",
        "\n",
        "#---------- VALIDAÇÃO ----------#\n",
        "\n",
        "# construir o modelo com parâmetros otimizados\n",
        "model = XGBClassifier(colsample_bytree=0.5317248496346453\n",
        "                      ,learning_rate=0.12023450660570377\n",
        "                      ,max_depth=6\n",
        "                      ,min_child_weight=1\n",
        "                      ,n_estimators=52\n",
        "                      ,n_jobs=100\n",
        "                      ,subsample=0.716121181244957\n",
        "                      ,verbosity=0\n",
        "                      ,random_state=982268122\n",
        "                      )\n",
        "\n",
        "# treinar o modelo\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# previsão - teste\n",
        "y_pred = model.predict(X_val)\n",
        "\n",
        "# extrair as probabilidades\n",
        "yhat = model.predict_proba(X_val)[:, 1]\n",
        "\n",
        "# extrair as métricas - val\n",
        "acuracy_val = (metrics.accuracy_score(y_val, y_pred)*100).round(2)\n",
        "precision_val = (metrics.precision_score(y_val, y_pred)*100).round(2)\n",
        "recall_val = (metrics.recall_score(y_val, y_pred)*100).round(2)\n",
        "aucroc_val = (roc_auc_score(y_val, yhat)*100).round(2)\n",
        "f1_score_val = (f1_score(y_val, y_pred)*100).round(2)\n",
        "logloss_val = (metrics.log_loss(y_val, yhat, normalize=True))\n",
        "\n",
        "\n",
        "# criar o dataframe comparativo\n",
        "columns= ['amostra', 'acuracia', 'precisao', 'recall', 'auc_roc', 'f1_score', 'logloss(normalizada)']\n",
        "data = [('teste', acuracy_test, precision_test, recall_test, aucroc_test, f1_score_test, logloss_test)\n",
        "        ,('validacao', acuracy_val, precision_val, recall_val, aucroc_val, f1_score_val, logloss_val)\n",
        "        ]\n",
        "df_metrics = pd.DataFrame(data=data, columns=columns)\n",
        "\n",
        "# visualizar tabela comparativa\n",
        "df_metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Co_6J6ocwWgJ"
      },
      "source": [
        "## Gráficos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "lq1EKK4u2LMg",
        "outputId": "b824e8c0-a0c9-40db-9798-b559d0a49402"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 3, figsize=(25, 8), gridspec_kw=({'hspace':0.5}))\n",
        "fig.suptitle('XGBoost', fontsize=18, weight='semibold', y=0.98)\n",
        "pad = 15\n",
        "\n",
        "#---------- MATRIZ DE CONFUSÃO ----------#\n",
        "cnf_matrix = confusion_matrix(y_val, y_pred)\n",
        "plt.subplot(1, 3, 1)\n",
        "\n",
        "labels = [0, 1]\n",
        "tick_marks = np.arange(len(labels))\n",
        "plt.xticks(tick_marks, labels)\n",
        "plt.yticks(tick_marks, labels)\n",
        "\n",
        "group_names = ['True Neg','False Pos','False Neg','True Pos']\n",
        "group_counts = ['{0:0.0f}'.format(value) for value in\n",
        "                cnf_matrix.flatten()]\n",
        "group_percentages = ['{0:.2%}'.format(value) for value in\n",
        "                     cnf_matrix.flatten()/np.sum(cnf_matrix)]\n",
        "labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
        "          zip(group_names,group_counts,group_percentages)]\n",
        "labels = np.asarray(labels).reshape(2,2)\n",
        "\n",
        "# criar o heatmap\n",
        "ax = sns.heatmap(data=pd.DataFrame(cnf_matrix), annot=labels, cmap=\"viridis\", fmt='')\n",
        "ax.xaxis.set_label_position(\"bottom\")\n",
        "ax.set_title('Matrix de confusão')\n",
        "plt.ylabel('True')\n",
        "plt.xlabel('Predicted');\n",
        "\n",
        "#---------- AUC ROC ----------#\n",
        "fpr, tpr, thresholds = roc_curve(y_val, yhat)\n",
        "\n",
        "# plot da curva roc\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.plot([0,1], [0,1], linestyle='--', label='No Skill', color='#fde725')\n",
        "plt.plot(fpr, tpr,  color='#4ac16d', marker='.', label='XGBoost (area = {}%)'.format(aucroc_val))\n",
        "\n",
        "# axis labels\n",
        "plt.title('AUC ROC', pad=pad)\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend()\n",
        "\n",
        "#---------- PRECISÃO, RECALL X THRESHOLD ----------#\n",
        "precision, recall, thresholds = precision_recall_curve(y_val, yhat) \n",
        "\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.title('Precisão, Recall x Threshold', fontsize=12, pad=pad)\n",
        "plt.plot(thresholds, precision[: -1], \"b--\", label=\"Precision\", color='#5ec962')\n",
        "plt.plot(thresholds, recall[: -1], \"r--\", label=\"Recall\", color='#440154')\n",
        "plt.ylabel(\"Precision, Recall\")\n",
        "plt.xlabel(\"Threshold\")\n",
        "plt.legend(loc=\"lower left\")\n",
        "plt.ylim([0,1]);"
      ]
    }
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