3_6_classification_ex_1.ipynb

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        "<a href=\"https://colab.research.google.com/github/minyoung347/python101/blob/main/3_6_classification_ex_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V_F9vLCil27H"
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      "source": [
        "### 데이터 전처리"
      ]
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          "text": [
            "dataset shape: (76020, 371)\n"
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          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "url = \"https://drive.google.com/file/d/1AXz0aql6rr5MauddMMCfCWx6KaysHgN1/view?usp=sharing\"\n",
        "path = 'https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "cust_df = pd.read_csv(path, encoding='latin-1')\n",
        "print('dataset shape:', cust_df.shape)\n",
        "cust_df.head(3)"
      ]
    },
    {
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      "execution_count": 3,
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 76020 entries, 0 to 76019\n",
            "Columns: 371 entries, ID to TARGET\n",
            "dtypes: float64(111), int64(260)\n",
            "memory usage: 215.2 MB\n"
          ]
        }
      ],
      "source": [
        "cust_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "HxlvWXX1l27K",
        "outputId": "dc3d18f7-7217-4a87-b81d-06961e3c9875",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    73012\n",
            "1     3008\n",
            "Name: TARGET, dtype: int64\n",
            "unsatisfied 비율은 0.04\n"
          ]
        }
      ],
      "source": [
        "print(cust_df['TARGET'].value_counts())\n",
        "unsatisfied_cnt = cust_df[cust_df['TARGET'] == 1].TARGET.count()\n",
        "total_cnt = cust_df.TARGET.count()\n",
        "print('unsatisfied 비율은 {0:.2f}'.format((unsatisfied_cnt / total_cnt)))"
      ]
    },
    {
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      "execution_count": 5,
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        "colab": {
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          "height": 394
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      "outputs": [
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          "output_type": "execute_result",
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              "                  ID           var3         var15  imp_ent_var16_ult1  \\\n",
              "count   76020.000000   76020.000000  76020.000000        76020.000000   \n",
              "mean    75964.050723   -1523.199277     33.212865           86.208265   \n",
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              "\n",
              "       imp_op_var39_comer_ult1  imp_op_var39_comer_ult3  \\\n",
              "count             76020.000000             76020.000000   \n",
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              "\n",
              "       imp_op_var40_comer_ult1  imp_op_var40_comer_ult3  \\\n",
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              "\n",
              "       imp_op_var40_efect_ult1  imp_op_var40_efect_ult3  ...  \\\n",
              "count             76020.000000             76020.000000  ...   \n",
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              "\n",
              "       saldo_medio_var33_hace2  saldo_medio_var33_hace3  \\\n",
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              "\n",
              "       saldo_medio_var33_ult1  saldo_medio_var33_ult3  \\\n",
              "count            76020.000000            76020.000000   \n",
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              "\n",
              "       saldo_medio_var44_hace2  saldo_medio_var44_hace3  \\\n",
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              "\n",
              "       saldo_medio_var44_ult1  saldo_medio_var44_ult3         var38  \\\n",
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              "      <th>saldo_medio_var44_hace2</th>\n",
              "      <th>saldo_medio_var44_hace3</th>\n",
              "      <th>saldo_medio_var44_ult1</th>\n",
              "      <th>saldo_medio_var44_ult3</th>\n",
              "      <th>var38</th>\n",
              "      <th>TARGET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>76020.000000</td>\n",
              "      <td>7.602000e+04</td>\n",
              "      <td>76020.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>75964.050723</td>\n",
              "      <td>-1523.199277</td>\n",
              "      <td>33.212865</td>\n",
              "      <td>86.208265</td>\n",
              "      <td>72.363067</td>\n",
              "      <td>119.529632</td>\n",
              "      <td>3.559130</td>\n",
              "      <td>6.472698</td>\n",
              "      <td>0.412946</td>\n",
              "      <td>0.567352</td>\n",
              "      <td>...</td>\n",
              "      <td>7.935824</td>\n",
              "      <td>1.365146</td>\n",
              "      <td>12.215580</td>\n",
              "      <td>8.784074</td>\n",
              "      <td>31.505324</td>\n",
              "      <td>1.858575</td>\n",
              "      <td>76.026165</td>\n",
              "      <td>56.614351</td>\n",
              "      <td>1.172358e+05</td>\n",
              "      <td>0.039569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>43781.947379</td>\n",
              "      <td>39033.462364</td>\n",
              "      <td>12.956486</td>\n",
              "      <td>1614.757313</td>\n",
              "      <td>339.315831</td>\n",
              "      <td>546.266294</td>\n",
              "      <td>93.155749</td>\n",
              "      <td>153.737066</td>\n",
              "      <td>30.604864</td>\n",
              "      <td>36.513513</td>\n",
              "      <td>...</td>\n",
              "      <td>455.887218</td>\n",
              "      <td>113.959637</td>\n",
              "      <td>783.207399</td>\n",
              "      <td>538.439211</td>\n",
              "      <td>2013.125393</td>\n",
              "      <td>147.786584</td>\n",
              "      <td>4040.337842</td>\n",
              "      <td>2852.579397</td>\n",
              "      <td>1.826646e+05</td>\n",
              "      <td>0.194945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-999999.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.163750e+03</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>38104.750000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.787061e+04</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>76043.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.064092e+05</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>113748.750000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.187563e+05</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>151838.000000</td>\n",
              "      <td>238.000000</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>210000.000000</td>\n",
              "      <td>12888.030000</td>\n",
              "      <td>21024.810000</td>\n",
              "      <td>8237.820000</td>\n",
              "      <td>11073.570000</td>\n",
              "      <td>6600.000000</td>\n",
              "      <td>6600.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>50003.880000</td>\n",
              "      <td>20385.720000</td>\n",
              "      <td>138831.630000</td>\n",
              "      <td>91778.730000</td>\n",
              "      <td>438329.220000</td>\n",
              "      <td>24650.010000</td>\n",
              "      <td>681462.900000</td>\n",
              "      <td>397884.300000</td>\n",
              "      <td>2.203474e+07</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 371 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0dc7af50-ab55-4cd8-b4b7-fa1d6263c045')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </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-0dc7af50-ab55-4cd8-b4b7-fa1d6263c045 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-0dc7af50-ab55-4cd8-b4b7-fa1d6263c045');\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": 5
        }
      ],
      "source": [
        "cust_df.describe( )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "-HcD9BVfl27K",
        "outputId": "cccd070f-16e3-411b-aac5-19888ae6b558",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 2         74165\n",
              " 8           138\n",
              "-999999      116\n",
              " 9           110\n",
              " 3           108\n",
              "           ...  \n",
              " 231           1\n",
              " 188           1\n",
              " 168           1\n",
              " 135           1\n",
              " 87            1\n",
              "Name: var3, Length: 208, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "cust_df['var3'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "dvwjpLCfl27K",
        "outputId": "03c946f3-b893-4ec2-f471-9708f668fbfa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "피처 데이터 shape:(76020, 369)\n"
          ]
        }
      ],
      "source": [
        "# var3 피처 값 대체 및 ID 피처 드롭\n",
        "cust_df['var3'].replace(-999999, 2, inplace=True)\n",
        "cust_df.drop('ID', axis=1, inplace=True)\n",
        "\n",
        "# 피처 세트와 레이블 세트분리. 레이블 컬럼은 DataFrame의 맨 마지막에 위치해 컬럼 위치 -1로 분리\n",
        "X_features = cust_df.iloc[:, :-1]\n",
        "y_labels = cust_df.iloc[:, -1]\n",
        "print('피처 데이터 shape:{0}'.format(X_features.shape))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "SXvtfptxl27L",
        "outputId": "b32c4b4e-fff7-4736-8dfe-9d1095edf866",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "학습 세트 Shape:(60816, 369), 테스트 세트 Shape:(15204, 369)\n",
            " 학습 세트 레이블 값 분포 비율\n",
            "0    0.960964\n",
            "1    0.039036\n",
            "Name: TARGET, dtype: float64\n",
            "\n",
            " 테스트 세트 레이블 값 분포 비율\n",
            "0    0.9583\n",
            "1    0.0417\n",
            "Name: TARGET, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_features, y_labels,\n",
        "                                                    test_size=0.2, random_state=0)\n",
        "train_cnt = y_train.count()\n",
        "test_cnt = y_test.count()\n",
        "print('학습 세트 Shape:{0}, 테스트 세트 Shape:{1}'.format(X_train.shape , X_test.shape))\n",
        "\n",
        "print(' 학습 세트 레이블 값 분포 비율')\n",
        "print(y_train.value_counts()/train_cnt)\n",
        "print('\\n 테스트 세트 레이블 값 분포 비율')\n",
        "print(y_test.value_counts()/test_cnt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "4bFpkFgsl27L"
      },
      "outputs": [],
      "source": [
        "# X_train, y_train을 다시 학습과 검증 데이터 세트로 분리.\n",
        "X_tr, X_val, y_tr, y_val = train_test_split(X_train, y_train,\n",
        "                                                    test_size=0.3, random_state=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0opaMfkWl27L"
      },
      "source": [
        "### XGBoost 모델 학습과 하이퍼 파라미터 튜닝"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "scrolled": true,
        "id": "qGB0zCXQl27L",
        "outputId": "7986825d-a408-4cfa-845d-1b18bca89336",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-auc:0.82179\tvalidation_1-auc:0.80068\n",
            "[1]\tvalidation_0-auc:0.82347\tvalidation_1-auc:0.80523\n",
            "[2]\tvalidation_0-auc:0.83178\tvalidation_1-auc:0.81097\n",
            "[3]\tvalidation_0-auc:0.83401\tvalidation_1-auc:0.81091\n",
            "[4]\tvalidation_0-auc:0.83443\tvalidation_1-auc:0.81040\n",
            "[5]\tvalidation_0-auc:0.83570\tvalidation_1-auc:0.81089\n",
            "[6]\tvalidation_0-auc:0.83597\tvalidation_1-auc:0.81057\n",
            "[7]\tvalidation_0-auc:0.83643\tvalidation_1-auc:0.81082\n",
            "[8]\tvalidation_0-auc:0.83682\tvalidation_1-auc:0.81147\n",
            "[9]\tvalidation_0-auc:0.83769\tvalidation_1-auc:0.81188\n",
            "[10]\tvalidation_0-auc:0.83770\tvalidation_1-auc:0.81163\n",
            "[11]\tvalidation_0-auc:0.83911\tvalidation_1-auc:0.81355\n",
            "[12]\tvalidation_0-auc:0.83976\tvalidation_1-auc:0.81336\n",
            "[13]\tvalidation_0-auc:0.84038\tvalidation_1-auc:0.81365\n",
            "[14]\tvalidation_0-auc:0.84176\tvalidation_1-auc:0.81419\n",
            "[15]\tvalidation_0-auc:0.84306\tvalidation_1-auc:0.81586\n",
            "[16]\tvalidation_0-auc:0.84343\tvalidation_1-auc:0.81610\n",
            "[17]\tvalidation_0-auc:0.84373\tvalidation_1-auc:0.81584\n",
            "[18]\tvalidation_0-auc:0.84542\tvalidation_1-auc:0.81581\n",
            "[19]\tvalidation_0-auc:0.84580\tvalidation_1-auc:0.81622\n",
            "[20]\tvalidation_0-auc:0.84656\tvalidation_1-auc:0.81641\n",
            "[21]\tvalidation_0-auc:0.84732\tvalidation_1-auc:0.81740\n",
            "[22]\tvalidation_0-auc:0.84769\tvalidation_1-auc:0.81748\n",
            "[23]\tvalidation_0-auc:0.84833\tvalidation_1-auc:0.81744\n",
            "[24]\tvalidation_0-auc:0.84836\tvalidation_1-auc:0.81704\n",
            "[25]\tvalidation_0-auc:0.84892\tvalidation_1-auc:0.81650\n",
            "[26]\tvalidation_0-auc:0.85114\tvalidation_1-auc:0.81730\n",
            "[27]\tvalidation_0-auc:0.85293\tvalidation_1-auc:0.81843\n",
            "[28]\tvalidation_0-auc:0.85335\tvalidation_1-auc:0.81883\n",
            "[29]\tvalidation_0-auc:0.85441\tvalidation_1-auc:0.82072\n",
            "[30]\tvalidation_0-auc:0.85604\tvalidation_1-auc:0.82169\n",
            "[31]\tvalidation_0-auc:0.85755\tvalidation_1-auc:0.82256\n",
            "[32]\tvalidation_0-auc:0.85830\tvalidation_1-auc:0.82267\n",
            "[33]\tvalidation_0-auc:0.85895\tvalidation_1-auc:0.82321\n",
            "[34]\tvalidation_0-auc:0.85964\tvalidation_1-auc:0.82332\n",
            "[35]\tvalidation_0-auc:0.85988\tvalidation_1-auc:0.82408\n",
            "[36]\tvalidation_0-auc:0.86037\tvalidation_1-auc:0.82452\n",
            "[37]\tvalidation_0-auc:0.86075\tvalidation_1-auc:0.82466\n",
            "[38]\tvalidation_0-auc:0.86171\tvalidation_1-auc:0.82554\n",
            "[39]\tvalidation_0-auc:0.86241\tvalidation_1-auc:0.82582\n",
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            "[215]\tvalidation_0-auc:0.91225\tvalidation_1-auc:0.83326\n",
            "[216]\tvalidation_0-auc:0.91228\tvalidation_1-auc:0.83329\n",
            "[217]\tvalidation_0-auc:0.91234\tvalidation_1-auc:0.83326\n",
            "[218]\tvalidation_0-auc:0.91258\tvalidation_1-auc:0.83311\n",
            "[219]\tvalidation_0-auc:0.91269\tvalidation_1-auc:0.83311\n",
            "[220]\tvalidation_0-auc:0.91279\tvalidation_1-auc:0.83305\n",
            "[221]\tvalidation_0-auc:0.91301\tvalidation_1-auc:0.83310\n",
            "[222]\tvalidation_0-auc:0.91311\tvalidation_1-auc:0.83307\n",
            "[223]\tvalidation_0-auc:0.91318\tvalidation_1-auc:0.83310\n",
            "[224]\tvalidation_0-auc:0.91333\tvalidation_1-auc:0.83308\n",
            "[225]\tvalidation_0-auc:0.91340\tvalidation_1-auc:0.83309\n",
            "[226]\tvalidation_0-auc:0.91340\tvalidation_1-auc:0.83307\n",
            "[227]\tvalidation_0-auc:0.91357\tvalidation_1-auc:0.83310\n",
            "[228]\tvalidation_0-auc:0.91360\tvalidation_1-auc:0.83310\n",
            "[229]\tvalidation_0-auc:0.91367\tvalidation_1-auc:0.83312\n",
            "[230]\tvalidation_0-auc:0.91378\tvalidation_1-auc:0.83313\n",
            "[231]\tvalidation_0-auc:0.91382\tvalidation_1-auc:0.83313\n",
            "[232]\tvalidation_0-auc:0.91383\tvalidation_1-auc:0.83314\n",
            "[233]\tvalidation_0-auc:0.91405\tvalidation_1-auc:0.83312\n",
            "[234]\tvalidation_0-auc:0.91414\tvalidation_1-auc:0.83307\n",
            "[235]\tvalidation_0-auc:0.91415\tvalidation_1-auc:0.83305\n",
            "ROC AUC: 0.8429\n"
          ]
        }
      ],
      "source": [
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "# n_estimators는 500으로, learning_rate 0.05, random state는 예제 수행 시마다 동일 예측 결과를 위해 설정.\n",
        "xgb_clf = XGBClassifier(n_estimators=500, learning_rate=0.05, random_state=156)\n",
        "\n",
        "# 성능 평가 지표를 auc로, 조기 중단 파라미터는 100으로 설정하고 학습 수행.\n",
        "xgb_clf.fit(X_tr, y_tr, early_stopping_rounds=100, eval_metric='auc', eval_set=[(X_tr, y_tr), (X_val, y_val)])\n",
        "\n",
        "xgb_roc_score = roc_auc_score(y_test, xgb_clf.predict_proba(X_test)[:, 1])\n",
        "print('ROC AUC: {0:.4f}'.format(xgb_roc_score))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "n9uonrA_l27L"
      },
      "outputs": [],
      "source": [
        "from hyperopt import hp\n",
        "\n",
        "# max_depth는 5에서 15까지 1간격으로, min_child_weight는 1에서 6까지 1간격으로\n",
        "# colsample_bytree는 0.5에서 0.95사이, learning_rate는 0.01에서 0.2사이 정규 분포된 값으로 검색.\n",
        "\n",
        "xgb_search_space = {'max_depth': hp.quniform('max_depth', 5, 15, 1),\n",
        "                    'min_child_weight': hp.quniform('min_child_weight', 1, 6, 1),\n",
        "                    'colsample_bytree': hp.uniform('colsample_bytree', 0.5, 0.95),\n",
        "                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.2)\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "34aK6dXOl27M"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "# 목적 함수 설정.\n",
        "# 추후 fmin()에서 입력된 search_space값으로 XGBClassifier 교차 검증 학습 후 -1* roc_auc 평균 값을 반환.\n",
        "def objective_func(search_space):\n",
        "    xgb_clf = XGBClassifier(n_estimators=100, max_depth=int(search_space['max_depth']),\n",
        "                            min_child_weight=int(search_space['min_child_weight']),\n",
        "                            colsample_bytree=search_space['colsample_bytree'],\n",
        "                            learning_rate=search_space['learning_rate']\n",
        "                           )\n",
        "    # 3개 k-fold 방식으로 평가된 roc_auc 지표를 담는 list\n",
        "    roc_auc_list= []\n",
        "\n",
        "    # 3개 k-fold방식 적용\n",
        "    kf = KFold(n_splits=3)\n",
        "    # X_train을 다시 학습과 검증용 데이터로 분리\n",
        "    for tr_index, val_index in kf.split(X_train):\n",
        "        # kf.split(X_train)으로 추출된 학습과 검증 index값으로 학습과 검증 데이터 세트 분리\n",
        "        X_tr, y_tr = X_train.iloc[tr_index], y_train.iloc[tr_index]\n",
        "        X_val, y_val = X_train.iloc[val_index], y_train.iloc[val_index]\n",
        "        # early stopping은 30회로 설정하고 추출된 학습과 검증 데이터로 XGBClassifier 학습 수행.\n",
        "        xgb_clf.fit(X_tr, y_tr, early_stopping_rounds=30, eval_metric='auc',\n",
        "                   eval_set=[(X_tr, y_tr), (X_val, y_val)])\n",
        "\n",
        "        # 1로 예측한 확률값 추출후 roc auc 계산하고 평균 roc auc 계산을 위해 list에 결과값 담음.\n",
        "        score = roc_auc_score(y_val, xgb_clf.predict_proba(X_val)[:, 1])\n",
        "        roc_auc_list.append(score)\n",
        "\n",
        "    # 3개 k-fold로 계산된 roc_auc값의 평균값을 반환하되,\n",
        "    # HyperOpt는 목적함수의 최소값을 위한 입력값을 찾으므로 -1을 곱한 뒤 반환.\n",
        "    return -1 * np.mean(roc_auc_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "scrolled": true,
        "id": "tQF-IC6_l27M",
        "outputId": "3f615299-edfc-41ec-b085-3b22d73fc329",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[50]\tvalidation_0-auc:0.88686\tvalidation_1-auc:0.83582\n",
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            "[55]\tvalidation_0-auc:0.88934\tvalidation_1-auc:0.83712\n",
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            "[75]\tvalidation_0-auc:0.89688\tvalidation_1-auc:0.83800\n",
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            "[86]\tvalidation_0-auc:0.90012\tvalidation_1-auc:0.83877\n",
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            "[97]\tvalidation_0-auc:0.90230\tvalidation_1-auc:0.83902\n",
            "[98]\tvalidation_0-auc:0.90248\tvalidation_1-auc:0.83894\n",
            "[99]\tvalidation_0-auc:0.90263\tvalidation_1-auc:0.83884\n",
            "[0]\tvalidation_0-auc:0.82825\tvalidation_1-auc:0.80009\n",
            "[1]\tvalidation_0-auc:0.82290\tvalidation_1-auc:0.79276\n",
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            "[7]\tvalidation_0-auc:0.85110\tvalidation_1-auc:0.81288\n",
            "[8]\tvalidation_0-auc:0.85252\tvalidation_1-auc:0.81518\n",
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            "[10]\tvalidation_0-auc:0.85429\tvalidation_1-auc:0.81511\n",
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            "[12]\tvalidation_0-auc:0.85992\tvalidation_1-auc:0.81980\n",
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            "[14]\tvalidation_0-auc:0.86355\tvalidation_1-auc:0.82074\n",
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            "[18]\tvalidation_0-auc:0.86526\tvalidation_1-auc:0.81783\n",
            "[19]\tvalidation_0-auc:0.86938\tvalidation_1-auc:0.81966\n",
            "[20]\tvalidation_0-auc:0.87277\tvalidation_1-auc:0.82183\n",
            "[21]\tvalidation_0-auc:0.87221\tvalidation_1-auc:0.82025\n",
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            "[23]\tvalidation_0-auc:0.87555\tvalidation_1-auc:0.82284\n",
            "[24]\tvalidation_0-auc:0.87621\tvalidation_1-auc:0.82226\n",
            "[25]\tvalidation_0-auc:0.87843\tvalidation_1-auc:0.82376\n",
            "[26]\tvalidation_0-auc:0.88051\tvalidation_1-auc:0.82485\n",
            "[27]\tvalidation_0-auc:0.88142\tvalidation_1-auc:0.82452\n",
            "[28]\tvalidation_0-auc:0.88247\tvalidation_1-auc:0.82341\n",
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            "[30]\tvalidation_0-auc:0.88435\tvalidation_1-auc:0.82566\n",
            "[31]\tvalidation_0-auc:0.88484\tvalidation_1-auc:0.82432\n",
            "[32]\tvalidation_0-auc:0.88540\tvalidation_1-auc:0.82245\n",
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            "[34]\tvalidation_0-auc:0.88761\tvalidation_1-auc:0.82263\n",
            "[35]\tvalidation_0-auc:0.88776\tvalidation_1-auc:0.82136\n",
            "[36]\tvalidation_0-auc:0.88896\tvalidation_1-auc:0.82286\n",
            "[37]\tvalidation_0-auc:0.88887\tvalidation_1-auc:0.82213\n",
            "[38]\tvalidation_0-auc:0.88901\tvalidation_1-auc:0.82142\n",
            "[39]\tvalidation_0-auc:0.88881\tvalidation_1-auc:0.82080\n",
            "[40]\tvalidation_0-auc:0.89081\tvalidation_1-auc:0.82302\n",
            "[41]\tvalidation_0-auc:0.89316\tvalidation_1-auc:0.82464\n",
            "[42]\tvalidation_0-auc:0.89403\tvalidation_1-auc:0.82586\n",
            "[43]\tvalidation_0-auc:0.89601\tvalidation_1-auc:0.82714\n",
            "[44]\tvalidation_0-auc:0.89737\tvalidation_1-auc:0.82706\n",
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            "[46]\tvalidation_0-auc:0.89869\tvalidation_1-auc:0.82768\n",
            "[47]\tvalidation_0-auc:0.89964\tvalidation_1-auc:0.82845\n",
            "[48]\tvalidation_0-auc:0.90040\tvalidation_1-auc:0.82932\n",
            "[49]\tvalidation_0-auc:0.90137\tvalidation_1-auc:0.83005\n",
            "[50]\tvalidation_0-auc:0.90169\tvalidation_1-auc:0.82927\n",
            "[51]\tvalidation_0-auc:0.90282\tvalidation_1-auc:0.83004\n",
            "[52]\tvalidation_0-auc:0.90359\tvalidation_1-auc:0.83045\n",
            "[53]\tvalidation_0-auc:0.90520\tvalidation_1-auc:0.83097\n",
            "[54]\tvalidation_0-auc:0.90552\tvalidation_1-auc:0.83111\n",
            "[55]\tvalidation_0-auc:0.90634\tvalidation_1-auc:0.83159\n",
            "[56]\tvalidation_0-auc:0.90699\tvalidation_1-auc:0.83111\n",
            "[57]\tvalidation_0-auc:0.90715\tvalidation_1-auc:0.83111\n",
            "[58]\tvalidation_0-auc:0.90784\tvalidation_1-auc:0.83180\n",
            "[59]\tvalidation_0-auc:0.90835\tvalidation_1-auc:0.83202\n",
            "[60]\tvalidation_0-auc:0.90899\tvalidation_1-auc:0.83202\n",
            "[61]\tvalidation_0-auc:0.90931\tvalidation_1-auc:0.83141\n",
            "[62]\tvalidation_0-auc:0.91019\tvalidation_1-auc:0.83165\n",
            "[63]\tvalidation_0-auc:0.91112\tvalidation_1-auc:0.83111\n",
            "[64]\tvalidation_0-auc:0.91139\tvalidation_1-auc:0.83084\n",
            "[65]\tvalidation_0-auc:0.91245\tvalidation_1-auc:0.83098\n",
            "[66]\tvalidation_0-auc:0.91319\tvalidation_1-auc:0.83034\n",
            "[67]\tvalidation_0-auc:0.91346\tvalidation_1-auc:0.83064\n",
            "[68]\tvalidation_0-auc:0.91385\tvalidation_1-auc:0.83038\n",
            "[69]\tvalidation_0-auc:0.91432\tvalidation_1-auc:0.83030\n",
            "[70]\tvalidation_0-auc:0.91516\tvalidation_1-auc:0.83055\n",
            "[71]\tvalidation_0-auc:0.91552\tvalidation_1-auc:0.83092\n",
            "[72]\tvalidation_0-auc:0.91552\tvalidation_1-auc:0.83065\n",
            "[73]\tvalidation_0-auc:0.91640\tvalidation_1-auc:0.83003\n",
            "[74]\tvalidation_0-auc:0.91692\tvalidation_1-auc:0.83005\n",
            "[75]\tvalidation_0-auc:0.91726\tvalidation_1-auc:0.83011\n",
            "[76]\tvalidation_0-auc:0.91793\tvalidation_1-auc:0.82978\n",
            "[77]\tvalidation_0-auc:0.91855\tvalidation_1-auc:0.82990\n",
            "  2%|▏         | 1/50 [03:38<2:58:23, 218.44s/trial, best loss: -0.8364669798899221]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-cc746522e892>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m best = fmin(fn=objective_func,\n\u001b[0m\u001b[1;32m      7\u001b[0m             \u001b[0mspace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxgb_search_space\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m             \u001b[0malgo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtpe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mfmin\u001b[0;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[1;32m    538\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    539\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mallow_trials_fmin\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"fmin\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 540\u001b[0;31m         return trials.fmin(\n\u001b[0m\u001b[1;32m    541\u001b[0m             \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    542\u001b[0m             \u001b[0mspace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/hyperopt/base.py\u001b[0m in \u001b[0;36mfmin\u001b[0;34m(self, fn, space, algo, max_evals, timeout, loss_threshold, max_queue_len, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfmin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 671\u001b[0;31m         return fmin(\n\u001b[0m\u001b[1;32m    672\u001b[0m             \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    673\u001b[0m             \u001b[0mspace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mfmin\u001b[0;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[1;32m    584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m     \u001b[0;31m# next line is where the fmin is actually executed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m     \u001b[0mrval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexhaust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreturn_argmin\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mexhaust\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    362\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mexhaust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    363\u001b[0m         \u001b[0mn_done\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 364\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_evals\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mn_done\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock_until_done\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masynchronous\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    365\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefresh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, N, block_until_done)\u001b[0m\n\u001b[1;32m    298\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m                     \u001b[0;31m# -- loop over trials and do the jobs directly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserial_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefresh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mserial_evaluate\u001b[0;34m(self, N)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0mctrl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCtrl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurrent_trial\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctrl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m                     \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"job exception: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/hyperopt/base.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, config, ctrl, attach_attachments)\u001b[0m\n\u001b[1;32m    890\u001b[0m                 \u001b[0mprint_node_on_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrec_eval_print_node_on_error\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m             )\n\u001b[0;32m--> 892\u001b[0;31m             \u001b[0mrval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpyll_rval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-12-85fc314924ba>\u001b[0m in \u001b[0;36mobjective_func\u001b[0;34m(search_space)\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mval_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mval_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0;31m# early stopping은 30회로 설정하고 추출된 학습과 검증 데이터로 XGBClassifier 학습 수행.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         xgb_clf.fit(X_tr, y_tr, early_stopping_rounds=30, eval_metric='auc',\n\u001b[0m\u001b[1;32m     24\u001b[0m                    eval_set=[(X_tr, y_tr), (X_val, y_val)])\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    618\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks)\u001b[0m\n\u001b[1;32m   1488\u001b[0m             )\n\u001b[1;32m   1489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1490\u001b[0;31m             self._Booster = train(\n\u001b[0m\u001b[1;32m   1491\u001b[0m                 \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1492\u001b[0m                 \u001b[0mtrain_dmatrix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    618\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/training.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, custom_metric)\u001b[0m\n\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcb_container\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbefore_iteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcb_container\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_iteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[1;32m   1916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1917\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1918\u001b[0;31m             _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,\n\u001b[0m\u001b[1;32m   1919\u001b[0m                                                     \u001b[0mctypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miteration\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1920\u001b[0m                                                     dtrain.handle))\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "from hyperopt import fmin, tpe, Trials\n",
        "\n",
        "trials = Trials()\n",
        "\n",
        "# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.\n",
        "best = fmin(fn=objective_func,\n",
        "            space=xgb_search_space,\n",
        "            algo=tpe.suggest,\n",
        "            max_evals=50, # 최대 반복 횟수를 지정합니다.\n",
        "            trials=trials, rstate=np.random.default_rng(seed=30))\n",
        "\n",
        "print('best:', best)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1OsrtsOAl27M"
      },
      "outputs": [],
      "source": [
        "# n_estimators를 500증가 후 최적으로 찾은 하이퍼 파라미터를 기반으로 학습과 예측 수행.\n",
        "xgb_clf = XGBClassifier(n_estimators=500, learning_rate=round(best['learning_rate'], 5),\n",
        "                        max_depth=int(best['max_depth']), min_child_weight=int(best['min_child_weight']),\n",
        "                        colsample_bytree=round(best['colsample_bytree'], 5)\n",
        "                       )\n",
        "\n",
        "# evaluation metric을 auc로, early stopping은 100 으로 설정하고 학습 수행.\n",
        "xgb_clf.fit(X_tr, y_tr, early_stopping_rounds=100,\n",
        "            eval_metric=\"auc\",eval_set=[(X_tr, y_tr), (X_val, y_val)])\n",
        "\n",
        "xgb_roc_score = roc_auc_score(y_test, xgb_clf.predict_proba(X_test)[:,1])\n",
        "print('ROC AUC: {0:.4f}'.format(xgb_roc_score))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CYwbKtvXl27M"
      },
      "outputs": [],
      "source": [
        "from xgboost import plot_importance\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "fig, ax = plt.subplots(1,1,figsize=(10,8))\n",
        "plot_importance(xgb_clf, ax=ax , max_num_features=20,height=0.4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GaZlqQC2l27M"
      },
      "source": [
        "### LightGBM 모델 학습과 하이퍼 파라미터 튜닝"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BzsvDsuGl27O"
      },
      "outputs": [],
      "source": [
        "from lightgbm import LGBMClassifier\n",
        "\n",
        "lgbm_clf = LGBMClassifier(n_estimators=500)\n",
        "\n",
        "eval_set=[(X_tr, y_tr), (X_val, y_val)]\n",
        "lgbm_clf.fit(X_tr, y_tr, early_stopping_rounds=100, eval_metric=\"auc\", eval_set=eval_set)\n",
        "\n",
        "lgbm_roc_score = roc_auc_score(y_test, lgbm_clf.predict_proba(X_test)[:,1])\n",
        "print('ROC AUC: {0:.4f}'.format(lgbm_roc_score))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Va9HdbgUl27O"
      },
      "outputs": [],
      "source": [
        "lgbm_search_space = {'num_leaves': hp.quniform('num_leaves', 32, 64, 1),\n",
        "                     'max_depth': hp.quniform('max_depth', 100, 160, 1),\n",
        "                     'min_child_samples': hp.quniform('min_child_samples', 60, 100, 1),\n",
        "                     'subsample': hp.uniform('subsample', 0.7, 1),\n",
        "                     'learning_rate': hp.uniform('learning_rate', 0.01, 0.2)\n",
        "                    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rCcNki4Gl27O"
      },
      "outputs": [],
      "source": [
        "def objective_func(search_space):\n",
        "    lgbm_clf =  LGBMClassifier(n_estimators=100, num_leaves=int(search_space['num_leaves']),\n",
        "                               max_depth=int(search_space['max_depth']),\n",
        "                               min_child_samples=int(search_space['min_child_samples']),\n",
        "                               subsample=search_space['subsample'],\n",
        "                               learning_rate=search_space['learning_rate'])\n",
        "    # 3개 k-fold 방식으로 평가된 roc_auc 지표를 담는 list\n",
        "    roc_auc_list = []\n",
        "\n",
        "    # 3개 k-fold방식 적용\n",
        "    kf = KFold(n_splits=3)\n",
        "    # X_train을 다시 학습과 검증용 데이터로 분리\n",
        "    for tr_index, val_index in kf.split(X_train):\n",
        "        # kf.split(X_train)으로 추출된 학습과 검증 index값으로 학습과 검증 데이터 세트 분리\n",
        "        X_tr, y_tr = X_train.iloc[tr_index], y_train.iloc[tr_index]\n",
        "        X_val, y_val = X_train.iloc[val_index], y_train.iloc[val_index]\n",
        "\n",
        "        # early stopping은 30회로 설정하고 추출된 학습과 검증 데이터로 XGBClassifier 학습 수행.\n",
        "        lgbm_clf.fit(X_tr, y_tr, early_stopping_rounds=30, eval_metric=\"auc\",\n",
        "           eval_set=[(X_tr, y_tr), (X_val, y_val)])\n",
        "\n",
        "        # 1로 예측한 확률값 추출후 roc auc 계산하고 평균 roc auc 계산을 위해 list에 결과값 담음.\n",
        "        score = roc_auc_score(y_val, lgbm_clf.predict_proba(X_val)[:, 1])\n",
        "        roc_auc_list.append(score)\n",
        "\n",
        "    # 3개 k-fold로 계산된 roc_auc값의 평균값을 반환하되,\n",
        "    # HyperOpt는 목적함수의 최소값을 위한 입력값을 찾으므로 -1을 곱한 뒤 반환.\n",
        "    return -1*np.mean(roc_auc_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "44WSNnkel27O"
      },
      "outputs": [],
      "source": [
        "from hyperopt import fmin, tpe, Trials\n",
        "\n",
        "trials = Trials()\n",
        "\n",
        "# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.\n",
        "best = fmin(fn=objective_func, space=lgbm_search_space, algo=tpe.suggest,\n",
        "            max_evals=50, # 최대 반복 횟수를 지정합니다.\n",
        "            trials=trials, rstate=np.random.default_rng(seed=30))\n",
        "\n",
        "print('best:', best)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WvKLvo3Ml27O"
      },
      "outputs": [],
      "source": [
        "lgbm_clf =  LGBMClassifier(n_estimators=500, num_leaves=int(best['num_leaves']),\n",
        "                           max_depth=int(best['max_depth']),\n",
        "                           min_child_samples=int(best['min_child_samples']),\n",
        "                           subsample=round(best['subsample'], 5),\n",
        "                           learning_rate=round(best['learning_rate'], 5)\n",
        "                          )\n",
        "\n",
        "# evaluation metric을 auc로, early stopping은 100 으로 설정하고 학습 수행.\n",
        "lgbm_clf.fit(X_tr, y_tr, early_stopping_rounds=100,\n",
        "            eval_metric=\"auc\",eval_set=[(X_tr, y_tr), (X_val, y_val)])\n",
        "\n",
        "lgbm_roc_score = roc_auc_score(y_test, lgbm_clf.predict_proba(X_test)[:,1])\n",
        "print('ROC AUC: {0:.4f}'.format(lgbm_roc_score))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AkK49fWfl27O"
      },
      "outputs": [],
      "source": []
    }
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