분류_과제_2.ipynb

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  "metadata": {
    "colab": {
      "name": "분류 과제 2.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPTQJcP4aUbRfkpjcNcArW+",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jaesunkkk/esaa_assignment/blob/main/%EB%B6%84%EB%A5%98_%EA%B3%BC%EC%A0%9C_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y4maEw-_E-zF"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w53z6H6oFJBj"
      },
      "source": [
        "def get_new_feature_name_df(old_feature_name_df):\n",
        "    feature_dup_df = pd.DataFrame(data=old_feature_name_df.groupby('column_name').cumcount(),\n",
        "                                  columns=['dup_cnt'])\n",
        "    feature_dup_df = feature_dup_df.reset_index()\n",
        "    new_feature_name_df = pd.merge(old_feature_name_df.reset_index(), feature_dup_df, how='outer')\n",
        "    new_feature_name_df['column_name'] = new_feature_name_df[['column_name', 'dup_cnt']].apply(lambda x : x[0]+'_'+str(x[1]) \n",
        "                                                                                         if x[1] >0 else x[0] ,  axis=1)\n",
        "    new_feature_name_df = new_feature_name_df.drop(['index'], axis=1)\n",
        "    return new_feature_name_df"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EdmGidwrFP9P"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "def get_human_dataset( ):\n",
        "    \n",
        "    feature_name_df = pd.read_csv('/content/features.txt',sep='\\s+',\n",
        "                        header=None,names=['column_index','column_name'])\n",
        "    \n",
        "    new_feature_name_df = get_new_feature_name_df(feature_name_df)\n",
        "    \n",
        "    feature_name = new_feature_name_df.iloc[:, 1].values.tolist()\n",
        "    \n",
        "\n",
        "    X_train = pd.read_csv('/content/X_train.txt',sep='\\s+', names=feature_name)\n",
        "    X_test = pd.read_csv('/content/X_test.txt',sep='\\s+', names=feature_name)\n",
        "    \n",
        "\n",
        "    y_train = pd.read_csv('/content/y_train.txt',sep='\\s+',header=None,names=['action'])\n",
        "    y_test = pd.read_csv('/content/y_test.txt',sep='\\s+',header=None,names=['action'])\n",
        "    \n",
        "    \n",
        "    \n",
        "    return X_train, X_test, y_train, y_test\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = get_human_dataset()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1MqemBLOZYd",
        "outputId": "49d206a6-2ee4-4d07-c678-cfcfa2978ba2"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "import time\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "X_train, X_test, y_train, y_test = get_human_dataset()\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "gb_clf = GradientBoostingClassifier(random_state=0)\n",
        "gb_clf.fit(X_train , y_train)\n",
        "gb_pred = gb_clf.predict(X_test)\n",
        "gb_accuracy = accuracy_score(y_test, gb_pred)\n",
        "\n",
        "print('GBM 정확도: {0:.4f}'.format(gb_accuracy))\n",
        "print(\"GBM 수행 시간: {0:.1f} 초 \".format(time.time() - start_time))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GBM 정확도: 0.9386\n",
            "GBM 수행 시간: 794.8 초 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDFCw-XjRXs5",
        "outputId": "362c5799-3f45-4c0f-9ff5-73ca0b4271eb"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "params = {\n",
        "    'n_estimators':[100, 500],\n",
        "    'learning_rate' : [ 0.05, 0.1]\n",
        "}\n",
        "grid_cv = GridSearchCV(gb_clf , param_grid=params , cv=2 ,verbose=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_))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 2 folds for each of 4 candidates, totalling 8 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "hU7CSEP8RgTQ",
        "outputId": "4dbf09a5-92b5-445b-dec4-a60991d5f0b6"
      },
      "source": [
        "gb_pred = grid_cv.best_estimator_.predict(X_test)\n",
        "gb_accuracy = accuracy_score(y_test, gb_pred)\n",
        "print('GBM 정확도: {0:.4f}'.format(gb_accuracy))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-05b6be0287a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgb_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrid_cv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\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      2\u001b[0m \u001b[0mgb_accuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgb_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'GBM 정확도: {0:.4f}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgb_accuracy\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;31mNameError\u001b[0m: name 'grid_cv' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9UKkGqumoJAV",
        "outputId": "eb73f31b-8bf1-4fd0-ac26-d1f6e7372692"
      },
      "source": [
        "import xgboost\n",
        "\n",
        "print(xgboost.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.90\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "e_WQ-vCNoPbV",
        "outputId": "e49256f9-f97b-4b56-900a-888219b02636"
      },
      "source": [
        "import xgboost as xgb\n",
        "from xgboost import plot_importance\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "dataset = load_breast_cancer()\n",
        "X_features= dataset.data\n",
        "y_label = dataset.target\n",
        "\n",
        "cancer_df = pd.DataFrame(data=X_features, columns=dataset.feature_names)\n",
        "cancer_df['target']= y_label\n",
        "cancer_df.head(3)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean 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>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</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",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.8</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>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.6</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.9</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>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.8</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.0</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>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.5</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  worst fractal dimension  target\n",
              "0        17.99         10.38  ...                  0.11890       0\n",
              "1        20.57         17.77  ...                  0.08902       0\n",
              "2        19.69         21.25  ...                  0.08758       0\n",
              "\n",
              "[3 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_Pw12YroWSz",
        "outputId": "17f707ca-5733-4c20-8ed2-32deb14ab0f0"
      },
      "source": [
        "print(dataset.target_names)\n",
        "print(cancer_df['target'].value_counts())"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['malignant' 'benign']\n",
            "1    357\n",
            "0    212\n",
            "Name: target, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7uw6jBuToaN6"
      },
      "source": [
        "X_train, X_test, y_train, y_test=train_test_split(X_features, y_label, test_size=0.2, random_state=156 )\n",
        "print(X_train.shape , X_test.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hF5F2lwtoa0N"
      },
      "source": [
        "dtrain = xgb.DMatrix(data=X_train , label=y_train)\n",
        "dtest = xgb.DMatrix(data=X_test , label=y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SqHkG_FBodRv"
      },
      "source": [
        "params = { 'max_depth':3,\n",
        "           'eta': 0.1,\n",
        "           'objective':'binary:logistic',\n",
        "           'eval_metric':'logloss'\n",
        "        }\n",
        "num_rounds = 400"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZf96nAYoghN"
      },
      "source": [
        "wlist = [(dtrain,'train'),(dtest,'eval') ]\n",
        "\n",
        "xgb_model = xgb.train(params = params , dtrain=dtrain , num_boost_round=num_rounds , early_stopping_rounds=100, evals=wlist )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BzNuhEc5okvx"
      },
      "source": [
        "pred_probs = xgb_model.predict(dtest)\n",
        "print('predict( ) 수행 결과값을 10개만 표시, 예측 확률 값으로 표시됨')\n",
        "print(np.round(pred_probs[:10],3))\n",
        "\n",
        "preds = [ 1 if x > 0.5 else 0 for x in pred_probs ]\n",
        "print('예측값 10개만 표시:',preds[:10])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNWrgEr9op7m"
      },
      "source": [
        "\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "from sklearn.metrics import precision_score, recall_score\n",
        "from sklearn.metrics import f1_score, roc_auc_score\n",
        "\n",
        "def get_clf_eval(y_test, pred=None, pred_proba=None):\n",
        "    confusion = confusion_matrix( y_test, pred)\n",
        "    accuracy = accuracy_score(y_test , pred)\n",
        "    precision = precision_score(y_test , pred)\n",
        "    recall = recall_score(y_test , pred)\n",
        "    f1 = f1_score(y_test,pred)\n",
        "\n",
        "    roc_auc = roc_auc_score(y_test, pred_proba)\n",
        "    print('오차 행렬')\n",
        "    print(confusion)\n",
        "\n",
        "    print('정확도: {0:.4f}, 정밀도: {1:.4f}, 재현율: {2:.4f},\\\n",
        "    F1: {3:.4f}, AUC:{4:.4f}'.format(accuracy, precision, recall, f1, roc_auc))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_C1p5w4hosPw"
      },
      "source": [
        "get_clf_eval(y_test , preds, pred_probs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpfDtqh-ot5l"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 12))\n",
        "plot_importance(xgb_model, ax=ax)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXvLqI3Yox0a"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgb_wrapper = XGBClassifier(n_estimators=400, learning_rate=0.1, max_depth=3)\n",
        "xgb_wrapper.fit(X_train, y_train)\n",
        "w_preds = xgb_wrapper.predict(X_test)\n",
        "w_pred_proba = xgb_wrapper.predict_proba(X_test)[:, 1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RlyhNtgozKY"
      },
      "source": [
        "get_clf_eval(y_test , w_preds, w_pred_proba)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZOZF2FUo00Y"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgb_wrapper = XGBClassifier(n_estimators=400, learning_rate=0.1, max_depth=3)\n",
        "evals = [(X_test, y_test)]\n",
        "xgb_wrapper.fit(X_train, y_train, early_stopping_rounds=100, eval_metric=\"logloss\", \n",
        "                eval_set=evals, verbose=True)\n",
        "\n",
        "ws100_preds = xgb_wrapper.predict(X_test)\n",
        "ws100_pred_proba = xgb_wrapper.predict_proba(X_test)[:, 1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNIRlquso6Rs"
      },
      "source": [
        "get_clf_eval(y_test , ws100_preds, ws100_pred_proba)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svYgoATqo8RF"
      },
      "source": [
        "\n",
        "xgb_wrapper.fit(X_train, y_train, early_stopping_rounds=10, \n",
        "                eval_metric=\"logloss\", eval_set=evals,verbose=True)\n",
        "\n",
        "ws10_preds = xgb_wrapper.predict(X_test)\n",
        "ws10_pred_proba = xgb_wrapper.predict_proba(X_test)[:, 1]\n",
        "get_clf_eval(y_test , ws10_preds, ws10_pred_proba)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5YzeNxCo_cT"
      },
      "source": [
        "from xgboost import plot_importance\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 12))\n",
        "\n",
        "plot_importance(xgb_wrapper, ax=ax)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skbauZdTpBTq"
      },
      "source": [
        "import lightgbm\n",
        "\n",
        "print(lightgbm.__version__)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0p0sD5ELpDWQ"
      },
      "source": [
        "from lightgbm import LGBMClassifier\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "dataset = load_breast_cancer()\n",
        "ftr = dataset.data\n",
        "target = dataset.target\n",
        "\n",
        "X_train, X_test, y_train, y_test=train_test_split(ftr, target, test_size=0.2, random_state=156 )\n",
        "\n",
        "lgbm_wrapper = LGBMClassifier(n_estimators=400)\n",
        "\n",
        "evals = [(X_test, y_test)]\n",
        "lgbm_wrapper.fit(X_train, y_train, early_stopping_rounds=100, eval_metric=\"logloss\", \n",
        "                 eval_set=evals, verbose=True)\n",
        "preds = lgbm_wrapper.predict(X_test)\n",
        "pred_proba = lgbm_wrapper.predict_proba(X_test)[:, 1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfA7X6EfpHm5"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "from sklearn.metrics import precision_score, recall_score\n",
        "from sklearn.metrics import f1_score, roc_auc_score\n",
        "\n",
        "def get_clf_eval(y_test, pred=None, pred_proba=None):\n",
        "    confusion = confusion_matrix( y_test, pred)\n",
        "    accuracy = accuracy_score(y_test , pred)\n",
        "    precision = precision_score(y_test , pred)\n",
        "    recall = recall_score(y_test , pred)\n",
        "    f1 = f1_score(y_test,pred)\n",
        "    \n",
        "    roc_auc = roc_auc_score(y_test, pred_proba)\n",
        "    print('오차 행렬')\n",
        "    print(confusion)\n",
        "\n",
        "    print('정확도: {0:.4f}, 정밀도: {1:.4f}, 재현율: {2:.4f},\\\n",
        "    F1: {3:.4f}, AUC:{4:.4f}'.format(accuracy, precision, recall, f1, roc_auc))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQKitB9YpJPD"
      },
      "source": [
        "get_clf_eval(y_test, preds, pred_proba)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uwvz-oxbpL9s"
      },
      "source": [
        "from lightgbm import plot_importance\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 12))\n",
        "plot_importance(lgbm_wrapper, ax=ax)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}