XGBoost(0304).ipynb

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        "<a href=\"https://colab.research.google.com/github/cnddlf/2022_1_Study_AI/blob/main/XGBoost(0304).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",
      "source": [
        "#1. 데이터 읽기"
      ],
      "metadata": {
        "id": "t-ZwJS2Bqpxf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oAG0IMFTj_dt"
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      "outputs": [],
      "source": [
        "# 아래 모듈을 이용하여 데이터를 판다스로 변경하여 head 정보를 출력하시오\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "\n",
        "cancer_dataset = load_breast_cancer()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cancer_dataset['data']"
      ],
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        "id": "cwbIijamryMf",
        "outputId": "6d3a42bd-39b0-4efc-e3b6-a8772137820f"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
              "        1.189e-01],\n",
              "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
              "        8.902e-02],\n",
              "       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
              "        8.758e-02],\n",
              "       ...,\n",
              "       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
              "        7.820e-02],\n",
              "       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
              "        1.240e-01],\n",
              "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
              "        7.039e-02]])"
            ]
          },
          "metadata": {},
          "execution_count": 2
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      "cell_type": "code",
      "source": [
        "cancer_dataset.feature_names"
      ],
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        "id": "rA0t3rYpr24f",
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            "text/plain": [
              "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
              "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "       'smoothness error', 'compactness error', 'concavity error',\n",
              "       'concave points error', 'symmetry error',\n",
              "       'fractal dimension error', 'worst radius', 'worst texture',\n",
              "       'worst perimeter', 'worst area', 'worst smoothness',\n",
              "       'worst compactness', 'worst concavity', 'worst concave points',\n",
              "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "cancer_dataset_df = pd.DataFrame(cancer_dataset.data, columns=cancer_dataset.feature_names)\n",
        "cancer_dataset_df.head()"
      ],
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            "text/plain": [
              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
              "0                 0.07871  ...         25.38          17.33           184.60   \n",
              "1                 0.05667  ...         24.99          23.41           158.80   \n",
              "2                 0.05999  ...         23.57          25.53           152.50   \n",
              "3                 0.09744  ...         14.91          26.50            98.87   \n",
              "4                 0.05883  ...         22.54          16.67           152.20   \n",
              "\n",
              "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "2      1709.0            0.1444             0.4245           0.4504   \n",
              "3       567.7            0.2098             0.8663           0.6869   \n",
              "4      1575.0            0.1374             0.2050           0.4000   \n",
              "\n",
              "   worst concave points  worst symmetry  worst fractal dimension  \n",
              "0                0.2654          0.4601                  0.11890  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "2                0.2430          0.3613                  0.08758  \n",
              "3                0.2575          0.6638                  0.17300  \n",
              "4                0.1625          0.2364                  0.07678  \n",
              "\n",
              "[5 rows x 30 columns]"
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              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
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              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. 훈련 및 검증 데이터"
      ],
      "metadata": {
        "id": "QMlD5XDCr-47"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "# dataset split\n",
        "X_train, X_test, y_train, y_test = train_test_split(cancer_dataset.data, cancer_dataset.target, test_size=0.2, random_state = 62)\n",
        "\n",
        "print(X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pp4BE0yBr-lQ",
        "outputId": "5d3f96c6-7215-426a-e10c-f9c660292296"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(455, 30) (114, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3. 모델"
      ],
      "metadata": {
        "id": "5jQHYl1XtQtc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3.1 모델 만들기"
      ],
      "metadata": {
        "id": "dtor1TyLtSZv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgb = XGBClassifier(n_estimators=400, learning_rate = 0.1, max_depth=3)\n",
        "xgb.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFqcqv-VtPzQ",
        "outputId": "2b9cbca3-b6d4-473f-b747-95e4694e86f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(n_estimators=400)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3.2 예측"
      ],
      "metadata": {
        "id": "A-QtC-mRtioJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "pred = xgb.predict(X_test)\n",
        "accuracy_score(y_test, pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EDxKMbzutyeY",
        "outputId": "8c8a86c4-d188-408b-b319-57a1f60c4858"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9473684210526315"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3.3 early_stopping"
      ],
      "metadata": {
        "id": "Qv4XrGcsvAb7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "evals = [(X_test, y_test)]\n",
        "xgb = XGBClassifier(n_estimators=400, learning_rate=0.1, max_depth=3)\n",
        "xgb.fit(X_train, y_train, early_stopping_rounds=100, eval_set=evals, eval_metric='logloss', verbose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8HU74UOpvGM4",
        "outputId": "2573c552-cbdb-4f69-900b-40911cac6347"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-logloss:0.619649\n",
            "Will train until validation_0-logloss hasn't improved in 100 rounds.\n",
            "[1]\tvalidation_0-logloss:0.557655\n",
            "[2]\tvalidation_0-logloss:0.5065\n",
            "[3]\tvalidation_0-logloss:0.464666\n",
            "[4]\tvalidation_0-logloss:0.426128\n",
            "[5]\tvalidation_0-logloss:0.39497\n",
            "[6]\tvalidation_0-logloss:0.366475\n",
            "[7]\tvalidation_0-logloss:0.340439\n",
            "[8]\tvalidation_0-logloss:0.318717\n",
            "[9]\tvalidation_0-logloss:0.298431\n",
            "[10]\tvalidation_0-logloss:0.282137\n",
            "[11]\tvalidation_0-logloss:0.264985\n",
            "[12]\tvalidation_0-logloss:0.252261\n",
            "[13]\tvalidation_0-logloss:0.238293\n",
            "[14]\tvalidation_0-logloss:0.228732\n",
            "[15]\tvalidation_0-logloss:0.220083\n",
            "[16]\tvalidation_0-logloss:0.210824\n",
            "[17]\tvalidation_0-logloss:0.202517\n",
            "[18]\tvalidation_0-logloss:0.194337\n",
            "[19]\tvalidation_0-logloss:0.187467\n",
            "[20]\tvalidation_0-logloss:0.18391\n",
            "[21]\tvalidation_0-logloss:0.17882\n",
            "[22]\tvalidation_0-logloss:0.173741\n",
            "[23]\tvalidation_0-logloss:0.170639\n",
            "[24]\tvalidation_0-logloss:0.165883\n",
            "[25]\tvalidation_0-logloss:0.16492\n",
            "[26]\tvalidation_0-logloss:0.160912\n",
            "[27]\tvalidation_0-logloss:0.157376\n",
            "[28]\tvalidation_0-logloss:0.153441\n",
            "[29]\tvalidation_0-logloss:0.152253\n",
            "[30]\tvalidation_0-logloss:0.149747\n",
            "[31]\tvalidation_0-logloss:0.150361\n",
            "[32]\tvalidation_0-logloss:0.148285\n",
            "[33]\tvalidation_0-logloss:0.146107\n",
            "[34]\tvalidation_0-logloss:0.143179\n",
            "[35]\tvalidation_0-logloss:0.141485\n",
            "[36]\tvalidation_0-logloss:0.139964\n",
            "[37]\tvalidation_0-logloss:0.138538\n",
            "[38]\tvalidation_0-logloss:0.138114\n",
            "[39]\tvalidation_0-logloss:0.138961\n",
            "[40]\tvalidation_0-logloss:0.139319\n",
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            "[43]\tvalidation_0-logloss:0.137759\n",
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            "[45]\tvalidation_0-logloss:0.135986\n",
            "[46]\tvalidation_0-logloss:0.135352\n",
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            "[49]\tvalidation_0-logloss:0.135795\n",
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            "[56]\tvalidation_0-logloss:0.137768\n",
            "[57]\tvalidation_0-logloss:0.137227\n",
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            "[60]\tvalidation_0-logloss:0.137739\n",
            "[61]\tvalidation_0-logloss:0.138208\n",
            "[62]\tvalidation_0-logloss:0.13706\n",
            "[63]\tvalidation_0-logloss:0.137194\n",
            "[64]\tvalidation_0-logloss:0.137354\n",
            "[65]\tvalidation_0-logloss:0.137682\n",
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            "[136]\tvalidation_0-logloss:0.135884\n",
            "[137]\tvalidation_0-logloss:0.136141\n",
            "[138]\tvalidation_0-logloss:0.136493\n",
            "[139]\tvalidation_0-logloss:0.136375\n",
            "[140]\tvalidation_0-logloss:0.136449\n",
            "[141]\tvalidation_0-logloss:0.136569\n",
            "[142]\tvalidation_0-logloss:0.136356\n",
            "[143]\tvalidation_0-logloss:0.136649\n",
            "[144]\tvalidation_0-logloss:0.13699\n",
            "[145]\tvalidation_0-logloss:0.137027\n",
            "[146]\tvalidation_0-logloss:0.136905\n",
            "[147]\tvalidation_0-logloss:0.13719\n",
            "[148]\tvalidation_0-logloss:0.137349\n",
            "Stopping. Best iteration:\n",
            "[48]\tvalidation_0-logloss:0.135155\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(n_estimators=400)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4. 하이퍼 파라미터"
      ],
      "metadata": {
        "id": "1qfLD82Typ0n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "xgb = XGBClassifier(n_estimators=48)\n",
        "\n",
        "param = {\n",
        "    'learning_rate' : [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
        "    'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \n",
        "    'subsample': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n",
        "}\n",
        "\n",
        "grid_cv = GridSearchCV(xgb, param_grid=param, scoring='accuracy',  cv=5, verbose=1, n_jobs=-1)\n",
        "grid_cv.fit(X_train, y_train)\n",
        "print('최적 하이퍼 파라미터: \\n', grid_cv.best_params_)\n",
        "print('최고 예측 정확도: {0:.4f}'.format(grid_cv.best_score_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "giBX7Zt5yraV",
        "outputId": "ef36d05c-975d-457a-acfe-bb0e320851d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 1000 candidates, totalling 5000 fits\n",
            "최적 하이퍼 파라미터: \n",
            " {'learning_rate': 0.2, 'max_depth': 4, 'subsample': 0.6}\n",
            "최고 예측 정확도: 0.9758\n"
          ]
        }
      ]
    }
  ]
}