3_2_ensemble.ipynb

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        "<a href=\"https://colab.research.google.com/github/minyoung347/python101/blob/main/3_2_ensemble.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
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      "source": [
        "## 4.3 앙상블 학습 개요"
      ]
    },
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      "source": [
        "### Voting Classifier"
      ]
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              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38           122.8     1001.0          0.11840   \n",
              "1        20.57         17.77           132.9     1326.0          0.08474   \n",
              "2        19.69         21.25           130.0     1203.0          0.10960   \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",
              "\n",
              "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
              "0                 0.07871  ...         25.38          17.33            184.6   \n",
              "1                 0.05667  ...         24.99          23.41            158.8   \n",
              "2                 0.05999  ...         23.57          25.53            152.5   \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",
              "\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",
              "\n",
              "[3 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>122.8</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>...</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",
              "    </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>...</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",
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              "    <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>...</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",
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              "variable_name": "data_df"
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          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "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\n",
        "\n",
        "cancer = load_breast_cancer()\n",
        "\n",
        "data_df = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
        "data_df.head(3)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "LjT1Y_pFeYRG",
        "outputId": "d8915bf5-054c-4187-cfe5-83c7c02495f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Voting 분류기 정확도: 0.9561\n",
            "LogisticRegression 정확도: 0.9474\n",
            "KNeighborsClassifier 정확도: 0.9386\n"
          ]
        }
      ],
      "source": [
        "# 개별 모델은 로지스틱 회귀와 KNN 임.\n",
        "lr_clf = LogisticRegression(solver='liblinear')\n",
        "knn_clf = KNeighborsClassifier(n_neighbors=8)\n",
        "\n",
        "# 개별 모델을 소프트 보팅 기반의 앙상블 모델로 구현한 분류기\n",
        "vo_clf = VotingClassifier( estimators=[('LR',lr_clf),('KNN',knn_clf)] , voting='soft' )\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target,\n",
        "                                                    test_size=0.2 , random_state= 156)\n",
        "\n",
        "# VotingClassifier 학습/예측/평가.\n",
        "vo_clf.fit(X_train , y_train)\n",
        "pred = vo_clf.predict(X_test)\n",
        "print('Voting 분류기 정확도: {0:.4f}'.format(accuracy_score(y_test , pred)))\n",
        "\n",
        "# 개별 모델의 학습/예측/평가.\n",
        "classifiers = [lr_clf, knn_clf]\n",
        "for classifier in classifiers:\n",
        "    classifier.fit(X_train , y_train)\n",
        "    pred = classifier.predict(X_test)\n",
        "    class_name= classifier.__class__.__name__\n",
        "    print('{0} 정확도: {1:.4f}'.format(class_name, accuracy_score(y_test , pred)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "shYlooNgeYRH"
      },
      "source": [
        "## 4.4 Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "3-owpiCIXdfv"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "iIIjoNiFeYRH"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "9A3cWgEbeYRH"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "def get_human_dataset( ):\n",
        "\n",
        "    # 각 데이터 파일들은 공백으로 분리되어 있으므로 read_csv에서 공백 문자를 sep으로 할당.\n",
        "    url = \"https://drive.google.com/file/d/1DmaNsCHpHyJ3gsv8zL-GkKYTPikabutU/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    feature_name_df = pd.read_csv(path,sep='\\s+',\n",
        "                        header=None,names=['column_index','column_name'])\n",
        "\n",
        "    # 중복된 피처명을 수정하는 get_new_feature_name_df()를 이용, 신규 피처명 DataFrame생성.\n",
        "    new_feature_name_df = get_new_feature_name_df(feature_name_df)\n",
        "\n",
        "    # DataFrame에 피처명을 컬럼으로 부여하기 위해 리스트 객체로 다시 변환\n",
        "    feature_name = new_feature_name_df.iloc[:, 1].values.tolist()\n",
        "\n",
        "    # 학습 피처 데이터 셋과 테스트 피처 데이터을 DataFrame으로 로딩. 컬럼명은 feature_name 적용\n",
        "    # X_train = pd.read_csv('./human_activity/train/X_train.txt',sep='\\s+', names=feature_name )\n",
        "    # X_test = pd.read_csv('./human_activity/test/X_test.txt',sep='\\s+', names=feature_name)\n",
        "\n",
        "    url = \"https://drive.google.com/file/d/1MsNy3K0kf89q4pS-cKMDBQIlBtZFQD11/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    X_train = pd.read_csv(path ,sep='\\s+', names=feature_name )\n",
        "    url = \"https://drive.google.com/file/d/1S6zfRS0sg2ip46IsSDLOomXYLfyrxGmr/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    X_test = pd.read_csv(path,sep='\\s+', names=feature_name)\n",
        "\n",
        "    # 학습 레이블과 테스트 레이블 데이터을 DataFrame으로 로딩하고 컬럼명은 action으로 부여\n",
        "    # y_train = pd.read_csv('./human_activity/train/y_train.txt',sep='\\s+',header=None,names=['action'])\n",
        "    # y_test = pd.read_csv('./human_activity/test/y_test.txt',sep='\\s+',header=None,names=['action'])\n",
        "\n",
        "    url = \"https://drive.google.com/file/d/1y2LRevjtT6lvDcvcMfVKBNRcevqzkK7X/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    y_train = pd.read_csv(path,sep='\\s+',header=None,names=['action'])\n",
        "    url = \"https://drive.google.com/file/d/1ywc2mKIJJQfebjP8d3GCh3HDD_egcy2-/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    y_test = pd.read_csv(path,sep='\\s+',header=None,names=['action'])\n",
        "\n",
        "    # 로드된 학습/테스트용 DataFrame을 모두 반환\n",
        "    return X_train, X_test, y_train, y_test\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = get_human_dataset()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "PPGva86BeYRH",
        "outputId": "579ec5cd-d929-4d27-abbc-66bd1233286c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "랜덤 포레스트 정확도: 0.9253\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "import pandas as pd\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# 결정 트리에서 사용한 get_human_dataset( )을 이용해 학습/테스트용 DataFrame 반환\n",
        "X_train, X_test, y_train, y_test = get_human_dataset()\n",
        "\n",
        "# 랜덤 포레스트 학습 및 별도의 테스트 셋으로 예측 성능 평가\n",
        "rf_clf = RandomForestClassifier(random_state=0)\n",
        "rf_clf.fit(X_train , y_train)\n",
        "pred = rf_clf.predict(X_test)\n",
        "accuracy = accuracy_score(y_test , pred)\n",
        "print('랜덤 포레스트 정확도: {0:.4f}'.format(accuracy))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "jVrUbhSLeYRH",
        "outputId": "2fdbe3f8-034e-4336-8295-db4f07b2fdcb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "최적 하이퍼 파라미터:\n",
            " {'max_depth': 10, 'min_samples_leaf': 8, 'min_samples_split': 8, 'n_estimators': 100}\n",
            "최고 예측 정확도: 0.9180\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "params = {\n",
        "    'n_estimators':[100],\n",
        "    'max_depth' : [6, 8, 10, 12],\n",
        "    'min_samples_leaf' : [8, 12, 18 ],\n",
        "    'min_samples_split' : [8, 16, 20]\n",
        "}\n",
        "# RandomForestClassifier 객체 생성 후 GridSearchCV 수행\n",
        "rf_clf = RandomForestClassifier(random_state=0, n_jobs=-1)\n",
        "grid_cv = GridSearchCV(rf_clf , param_grid=params , cv=2, n_jobs=-1 )\n",
        "grid_cv.fit(X_train , y_train)\n",
        "\n",
        "print('최적 하이퍼 파라미터:\\n', grid_cv.best_params_)\n",
        "print('최고 예측 정확도: {0:.4f}'.format(grid_cv.best_score_))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Bnh-_ArmeYRI",
        "outputId": "3dd3286c-a87e-4f3e-fa93-3fa733f91c87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "예측 정확도: 0.9165\n"
          ]
        }
      ],
      "source": [
        "rf_clf1 = RandomForestClassifier(n_estimators=300, max_depth=10, min_samples_leaf=8, \\\n",
        "                                 min_samples_split=8, random_state=0)\n",
        "rf_clf1.fit(X_train , y_train)\n",
        "pred = rf_clf1.predict(X_test)\n",
        "print('예측 정확도: {0:.4f}'.format(accuracy_score(y_test , pred)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "SUMmKly0eYRI",
        "outputId": "0addcb61-70c4-4ff3-cbf9-c2368307db8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "ftr_importances_values = rf_clf1.feature_importances_\n",
        "ftr_importances = pd.Series(ftr_importances_values,index=X_train.columns  )\n",
        "ftr_top20 = ftr_importances.sort_values(ascending=False)[:20]\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.title('Feature importances Top 20')\n",
        "sns.barplot(x=ftr_top20 , y = ftr_top20.index)\n",
        "fig1 = plt.gcf()\n",
        "plt.show()\n",
        "plt.draw()\n",
        "fig1.savefig('rf_feature_importances_top20.tif', format='tif', dpi=300, bbox_inches='tight')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MOAgZAxbeYRI"
      },
      "source": [
        "## 4.5 GBM(Gradient Boosting Machine)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gXgT32K_eYRI",
        "outputId": "aac4158f-31ba-4e28-8431-0216d5abae31",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GBM 정확도: 0.9386\n",
            "GBM 수행 시간: 910.7 초 \n"
          ]
        }
      ],
      "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",
        "# GBM 수행 시간 측정을 위함. 시작 시간 설정.\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))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UA_rhYJceYRI"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}