▶smartFactoryFINAL_teamShare.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 팀 스마일C 코드 제출\n",
        "* 팀원: 정우섭, 김유민, 김유진, 장동언, 황정묵\n",
        "* PRIVATE SCORE: 0.65887 \n",
        "* PRIVATE RANKING: 36"
      ],
      "metadata": {
        "id": "ZJBm2amd3Lb5"
      },
      "id": "ZJBm2amd3Lb5"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 0.Data Load"
      ],
      "metadata": {
        "id": "QIfodgpc3HP6"
      },
      "id": "QIfodgpc3HP6"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6ab709fe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ab709fe",
        "outputId": "c66c8b0f-97bf-4544-8835-eff5e8995a9d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly->catboost) (8.2.1)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-1.1.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting optuna\n",
            "  Downloading optuna-3.1.0-py3-none-any.whl (365 kB)\n",
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            "Installing collected packages: Mako, colorlog, cmaes, alembic, optuna\n",
            "Successfully installed Mako-1.2.4 alembic-1.9.4 cmaes-0.9.1 colorlog-6.7.0 optuna-3.1.0\n"
          ]
        }
      ],
      "source": [
        "!pip install catboost\n",
        "!pip install lightgbm\n",
        "!pip install optuna "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "94682a80",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94682a80",
        "outputId": "f94d142b-771a-42df-d745-1dd16b924160"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt   \n",
        "import seaborn as sns \n",
        "\n",
        "import sklearn\n",
        "from sklearn import preprocessing, metrics\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score, classification_report, f1_score, roc_auc_score, log_loss\n",
        "from sklearn.model_selection import KFold, cross_validate\n",
        "\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import regularizers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout, Activation\n",
        "\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from catboost import CatBoostClassifier\n",
        "\n",
        "import optuna\n",
        "from optuna.samplers import TPESampler\n",
        "from optuna import Trial\n",
        "\n",
        "from sklearn.metrics import f1_score\n",
        "from lightgbm import LGBMClassifier\n",
        "from catboost import CatBoostClassifier\n",
        "from sklearn.ensemble import BaggingClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import RidgeClassifierCV\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# sequential model modeling \n",
        "import tensorflow as tf\n",
        "from tensorflow import keras \n",
        "from keras import backend as K \n",
        "from keras import regularizers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout, Activation, BatchNormalization, Conv2D, Flatten  \n",
        "from keras import optimizers, metrics, callbacks\n",
        "from tensorflow.keras.backend import clear_session\n",
        "\n",
        "import optuna\n",
        "from optuna.visualization import plot_optimization_history\n",
        "from optuna.visualization import plot_param_importances\n",
        "from optuna.visualization import plot_parallel_coordinate\n",
        "from optuna.visualization import plot_contour\n",
        "\n",
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "seed_everything(37) # Seed 고정\n",
        "seed_num=37\n",
        "\n",
        "from google.colab import drive # GoogleDrive mount\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "wqkW_3EbQxj1",
      "metadata": {
        "id": "wqkW_3EbQxj1"
      },
      "source": [
        "# 1.preprocessing\n",
        "1. Label Encoding: categorical values `LINE`, `PRODUCT_CODE`\n",
        "2. Missing values: fillna(0) \n",
        "3. scaling: StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.read_csv('/content/drive/MyDrive/train.csv')\n",
        "test_df = pd.read_csv('/content/drive/MyDrive/test.csv')\n",
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "\n",
        "train_x = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP', 'Y_Class','Y_Quality'])\n",
        "train_y = train_df['Y_Class']\n",
        "test_x = test_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP'])   "
      ],
      "metadata": {
        "id": "kZ1PM_Zrtzph"
      },
      "id": "kZ1PM_Zrtzph",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1) qualitative to quantitative\n",
        "qual_col = ['LINE', 'PRODUCT_CODE']\n",
        "for i in qual_col:\n",
        "    le = LabelEncoder()    # one-hot encoding (X): get_dummies(test_df) is not allowed (data leakage)   \n",
        "    le = le.fit(train_x[i])\n",
        "    train_x[i] = le.transform(train_x[i])\n",
        "    for label in np.unique(test_x[i]): \n",
        "        if label not in le.classes_: \n",
        "            le.classes_ = np.append(le.classes_, label)\n",
        "    test_x[i] = le.transform(test_x[i]) \n"
      ],
      "metadata": {
        "id": "3AbGp2rBuxuW"
      },
      "id": "3AbGp2rBuxuW",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2) Missing Values \n",
        "train_x = train_x.fillna(0)\n",
        "test_x = test_x.fillna(0)\n"
      ],
      "metadata": {
        "id": "GZ72F04guy3o"
      },
      "id": "GZ72F04guy3o",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c66d3bf4"
      },
      "outputs": [],
      "source": [
        "# 3) MinMaxscaling: only `X_???` values (continuous)\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "Xs = train_x.select_dtypes(include=float).iloc[:,1:].columns.tolist()\n",
        "scaler = MinMaxScaler().fit(train_x.loc[:, Xs])\n",
        "train_x.loc[:, Xs] = scaler.transform(train_x.loc[:, Xs])\n",
        "test_x.loc[:, Xs] = scaler.transform(test_x.loc[:, Xs])\n",
        "\n",
        "X_train, X_test, y_train, y_test=train_test_split(train_x,train_y,\n",
        "                                                  test_size=0.3,random_state=seed_num)"
      ],
      "id": "c66d3bf4"
    },
    {
      "cell_type": "code",
      "source": [
        "# histogram imbalance distribution --> MinMaxscaling \n"
      ],
      "metadata": {
        "id": "iA9G_a4s4yFG"
      },
      "id": "iA9G_a4s4yFG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1 EDA"
      ],
      "metadata": {
        "id": "ZK3O2Kd24yP_"
      },
      "id": "ZK3O2Kd24yP_"
    },
    {
      "cell_type": "code",
      "source": [
        "# random 20 features list (모두 NaN인 컬럼 제외하고, 랜덤 20개 feature의 상관관계 히트맵)\n",
        "Xs = train_df.iloc[:,4:].columns\n",
        "null_count = train_df.isnull().sum().to_dict()\n",
        "cols = pd.DataFrame({i for i in null_count if null_count[i]<250 and i in Xs})\n",
        "cols_name = list(cols[0])  # not_all_NAN features name in list \n",
        "\n",
        "import random\n",
        "a = ['Y_Quality']\n",
        "for i in range(20):\n",
        "    num = random.randint(1,2875)\n",
        "    a.append(cols_name[i])\n",
        "\n",
        "# heatmap: `random X features` and `Y_Quality` correlation \n",
        "corr = train_df[a].corr()  # correlation matrix\n",
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "sns.heatmap(corr, annot=True, fmt='.2f', cmap='Blues', linewidths=2);"
      ],
      "metadata": {
        "id": "E1vfXTKcK9Dt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "outputId": "d99bd6fa-de72-4da8-fffd-07bb2c85a44b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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ut0eeYPjE+QyfmM/x553j+PPO4/hrle34+20uHw4u+PHX55m+/LpsOb8uW06nLl1ZueIPtNYcPXIYFxdXfH3NT85JiYmZzx2lp6ezdetmKleuAsCObVuZNXM6P42fhKOj5ZOflDRdH36CYePnM2y86bff8e9vf8r02+e8qPH4t+45Zfrtd2xYTeOWMlxR3B1r1f3WPP4f/L+n+HHGIn6csYgWbTuyee0qtNacPn4UZ2eXPJ8lute6P/IkIycvYOTkBTRt3ZFt6/9Ea83ZW9X9zs6czaj7t63/kyatO1ChcjWmLFnHz3NX8PPcFXj5+jF84rzbNoog47cPM//tG7YwP+81zHbe278967x3L5T0645c5BkjAJRpfoUCJFRqMKYeo9H5rFfAPmCc1nqOUsoWmAxc0lp/q5Rqi2kYXlugLHAceASIwdSwaQT4Yuop+lRrPUsptRn4WGu9Xyl1CWgKaOCg1rpittj+wF7gmta6RQGKox0b5e4xmj3iRdo1qY6Phwvh0XEMnbwaeztbAKb/th2AsZ89SffWtUi6mcbrg+dx8EQwAM/3bsmAl03POnw3Yy1zV+zO9f0AyYdMj1ntu5B7jKppysxRHN2/i1IODvT74OvMaT+/eLsvwyfMB0wPDE7NmK67QbPWPP/mxyil+PDlx0hPS828m1EtqC4vv/O5WYxmVYrHnY5d52JzLTuybwcLpv47XfXDPNLnJZbNnULl6rVo1LI9qakpTB09mOALZ0zTdg4Yhl+ZsgB89NKj3ExKJD09DSdnFz4eNo6yFaqYfX+rah5FULLb23YmJs/lR/fvZHHGdN1tuvai11Mv8ce8qVSqHkTDFu1JS01h+g9DTOV3ceP1AUPxDSjLqsUzWb1kDv6B5TO/64NvfsLNw8vs+9vVMPV27ruY9/horTWzJ4zi6IFdlCrtQL8Psx1/b/Vl+MRsx9+YjClrm7bm+bcyjr+X8jj+3s06/ppVNi2/mas/Nyv+iGHfsGPHNhwcHPlm2HDq1K0HwJOP9ebXZcuJiozknbdeJzUtFaNR06x5Cz759HPs7Ozo9UA3UtNS8ciYJrZegwZ8PeibzO93kJcTALDnfN51z5yJoziW8du/mq3u+ap/X4aNz/rtp439hrSUFOo3bc1zGXXP/p2bmDtpDPE3YnBycaVCleoMGPazWYwWVYtH3SOsJ696H4qu7s/r2IfCP/7/PfZPXk3MM/bUn0ZycO8uSpd24N1PB1MtyBT7/Vf68OOMRQDMmvwj2/5eQ3RUBF7evnR96FGefukNzp46zsivPiIhIY5SpUrj4eXNz7N+M4tRq4wzAAcvx+Vb/l/Gf8+R/aY8vP7xQKpmlP+zN55h5OQFAJw/c4LJo/6drrs1L779Sa5GwjvPPcK34+eYTdfduKLptQk7zuY+9x3dtzPzNRVtu/Xi4ade4veM816jjPPetDHZznufDsUvwPTbf/Lyo9xMSsr87T8cOo6yFSrnitGmuuncZ43rDsg8/op9a8qx5acFaxDcI8m7vyuW++SeNYwy0pQHJgC1MDVyFmutX89Yp4B5mHqVTgKewGCt9Wal1CxMzwZdAW4AK/JrGGmtI5VSCzA9j/RXxnNGZEwh/ofWenIBipNnw6go3KphVBSKc8OosBX3hlFhu13DqLDdrmFU2KRhZJLfxWFhk4aRsEa9D7dvGBW2WzWMisLtGkaF7VYNo6Jwq4ZRUbhvGkatPivahtGukcVynxT4UkFrPbgAaa5g6gVCKdUaWKiUaqy1Ppgx9XfffLZ7MZ/lHbP9f6Vs//9M9nRKKSdM04MvvF0ehRBCCCGEECKnQhvkp7XeqbWuqLU+WFgxAJRSXTH1QP2stZY5FIUQQgghhBAWs3hwiVLKG9iQx6ouWuuou8+SZbTWfwPyZkEhhBBCCCHuRDGeEKEoWdwwymj8NLz3WRFCCCGEEEII65DHkYUQQgghhCjJ/qvTkFtI+s2EEEIIIYQQJZ70GAkhhBBCCFGSyTNGgAXvMfqPKZGFFkIIIYQQRa7Yj1NzbPt10b7HaPvQYrlPpMdICCGEEEKIkkyeMQJKcMNo3wXrvPKoWRXTG7AdG/W3SvzkQ+OtEjenr9ecLfKYQx+oXuQx8/LyomNWiTuzTz0AToRZ5+3rtQNNb18/cz3JKvFr+DtZJW5x8+y8I1aJO+/ZBlaJK4oPa9T7kFX3PzX7kFXiL36hEQB9rBR/UUb8hYdCrRL/6UZlAeg8bpdV4m98txUACw6GWCX+M43LWSWuuDMltmEkhBBCCCGEQJ4xyiB7QQghhBBCCFHiSY+REEIIIYQQJZn0GAHSYySEEEIIIYQQ0mMkhBBCCCFEiWYjs9KB9BgJIYQQQgghhPQYCSGEEEIIUaLJM0ZAITeMlFLlga1AE611tFLKEzgIdNJaX8qRthMwNtuiIKCP1voPpdR8oCmQBuwFXtdapyml3IF5QIWMsozWWv9yN3nWWjN38hgO79tJ6dIO9PtoIJWrBeVKd/HsSab88A2pKSk0bNaa5974CKUUC6aP49CebdjZ2eNXpiz9PhyIs4trgWJPHtSXnu3rEhEdT9MnhueZZsyAx+nRpg5JN1PpN2guh0+Z5uXv+3ALPnu1BwAjp69l/so9d7gHrOfqyQMcXjYVbTRSuWV3anV7wmz96U2/c3HXOpSNLaVd3Gj2zPs4e/kBcGT5TK6e2I/WRvxrNqLRY/1Q99nLyuoGuPBM40CUgm0XYlh9MsJsfceqXnSu7o1Ra1LSjczeF0pYXAq2Cl5sXo6Kno7YKNh5KTbXtgWhtWbGz6M4sGc7pR0ceOfTIVStUStXunnTx7N53Z8kxsex8K8dmcvTUlP5acTXnD9zElc3Dz4eNBK/gECL4k8d9z0Hdu+gdGkH3vt8CNVq5o4/Z9p4Nq1ZRUJCHEvW7sxc/tfyJfy57FdsbG1wcHSi/ydfUaFSVQv3QslUv4wrzzULxEYpNp+LZuXx8DzTNSvvznsdKvH16jNcjE4G4OE6fnSs5oVRa+bsC+PY1fiizLr4D7B23d8g0JUXm5fDRik2no1i+T/X80zXvII7H3WqwuerTnEhKpl6ZVx5pkkgdjaKdKNm3v5Qjl9LsLj8DQJdeSFb/BW3iP9hpyp8kS3+09niz7+D+GcP72XN7PEYjUYad36Qdr2fMVt/6eQR1syewPXgCzz+7tfUadnBbP3NpEQmfPwSQU3b8NDL71lWcKBZRQ/6t6+EjVKsPn6dhQfCzNb3qOXL620rEpmQCsAfR6+xOqN+6h7ky7PNTe9Imrc3lHWnLD/vnTu8lzVzJpjK3+lB2vZ+2mz95ZNHWTPn3/J/Re0W5uVPSUpkwicvE9S0DQ++9K7F8UXxU6jNQ631FWASMDJj0Uhgas5GUUbaTVrrhlrrhkBnIAlYl7F6PqaGUj3AEXg1Y/nbwAmtdQOgIzBGKVXqbvJ8ZN9OroVdYcyMpbzy7ufMGv9dnul+Gf8dr777BWNmLOVa2BWO7je9uKxeo+aMnLyQEZMWUKZsBVYunlXg2HNX7qb32xPyXd+jbW2qVvClbu8h9B+2kHFf9AHA082JL/v1pP1zo2n37Ci+7NcTD1fHghe6GDAaDRxcMol2rw+hx+cTCT64hRvXgs3SeJarStePx9Ljs/GUa9iWoytMbeDIiyeJvHiS7p/+TI/PJhATfIaIc9Z5ieqdUgqebRrI2C0X+eqvs7So4E6gW2mzNLsvxzJwzVkGrz3HXycjeKpRGQCaVnDHzkYxcM1Zvll3jo7VvPB2trc4Dwf37CAsNJiJ85bz5kdfMWXsiDzTNWvdnu8nzcm1/O/Vf+Ds6sak+St4+Im+zJnyk0XxD+zeTlhIMFMWLOftT75i0g953xxo3ro9Y6bMzbW8Q9eejJ+9hHEzF/O/p19gxvgfLIpfUikFLzQvy/cbLzJg5WlaVvIg0L10rnQOdjb0CPLhXETWC4ID3UvTspIHn648zfcbLvJi87Ly8nRhEWvX/UrByy3LM+Lv83y4/CRtKntS1t0hVzoHOxserO3H2WzHf3xKOt9vOM8nK04xcftl+reraHH5/40/8u/zfHSb+D3ziD9qw3kGZMR/28L4RqOB1TN/ou9nI3l7zC/8s2Mj4SGXzNK4e/vz6JufUq9Nlzy/Y9Ovv1AxqL5Fcf9lo+C9jpX5bPlJXpp3mM41fKjolfvaZfOZKPotPEq/hUczG0Wupe14vkU53l78D28tPsbzLcrhUtrWovhGo4HVv4yj76cjeHv0TP7ZuZGInOX38ePRNwbkW/6NS+68/KJ4Kop+s7FAS6XU+0BbYHQBtnkc+EtrnQSgtV6tM2DqMfr3NcIacFWm20MuQDSQfjeZPbB7K227PIhSimq16pGYEE9MdKRZmpjoSJKTEqlWqx5KKdp2eZD9u7YAUK9JS2xtTR1xVYPqEh2Z953XvOw4eJ7oG0n5ru/VoT4LVu0FYO+xS7i7OhLg40a31rXYsPsUMXFJxMYns2H3Kbq3qW1p0a0q+vIZXHzL4OITgK2dPRUatyfs2G6zNH7V62NXynTC8K5Uk6TYrN/FmJaKMT0dY3oaRoMBB1fPIs3/3ari5UR4fCoRiWkYjJo9wTdoWNbNLM3NdGPm/5e2szEd/QDa9NlGgb2tDekGzc00I5bau2Mznbr3QilFzdr1SUyMJzoq9x24mrXr4+Xtm/f2PXoB0LpDF44e3Ifpn2zB7N6+hc49TPGD6tQnMSGe6Mjc8YPq1MfLJ3d8J2eXzP+/eTMZ5AK9QKp6O3E9PpWIhFQMRs3uS7E0KeeeK93jDQJYdSKcNGPWb9qknDu7L8WSbtREJKZyPT6Vqt5ORZl9cZ+zdt1fzceJ63EphGcc/zsvxtCsfO7j/6lGZVh+7Dqphqy69VJ0MjHJpkuOK7E3KWVrg52FD7BX83HiWo74TfOI/2SjMqw4dp20fOKH3EH80HOn8Aooi5d/IHZ29tRt3ZnT+3eapfH0CyCgYlVUHsOswi6cIeFGDFXrNy1wzOyC/F0Ijb3J1bgU0o2ajWcjaV2lYL9fs4ruHAi+QXxKOgkpBg4E36B5RQ+L4v9bfk//QGzt7KnTqhOncpTfwzcA/4pV8+yFDLtwhsQbMVSt38SiuMWWUkX7V0wVesNIa50GfIKpgfR+xufb6QMszLlQKWUPPAesyVg0HqgFhAHHgPe01pZfEWYTExWOt49/5mcvHz9icjRuYiLD8fLxM08TlbsBtHXdSuo3a3032TET6OdByLWYzM+h12MJ9PMg0NeDkOvZlofHEujrcc/iFoXkG1E4eWRd7Dp6+JB8Iyrf9Bd3r6NMLVNl5FO5Fr7V67Ny4POs/Pp5AoIa4xZQvtDzfC95ONoRnZT1TyMmOQ1Px9y9Pp2reTGyVw2eaBjA/IOmIQf7r9wgJd3I2N61GP1IEGtPR5CYarA4D1GR4Xj7ZR373j5+eTZM8t8+Ah+/AABsbe1wcnEhPi7Wovj/bg/g7etPlAU3FgD+XLaY1/o8zKxJP/H6uwMs2rak8nSyJzopNfNzdFIank7mx14lL0e8nO05HBp/m21Tc20rxK1Yu+73cipFVGLWMRyVlIpnjh73yl6OeDuX4lBoXL7f06KiBxejkkk3FvxmUF7xo5NS8XLO/e+vMOLHRUfi5p11LePm5UNcdMHqfKPRyNq5k+j+7BsFjpeTj0spwhNSMj9HJqTi65y7t7pdNS+mPVOfQQ/WwNfFNCjIx7kUEdm2jUhIwcfZsgFD8TGRuGW7yefm7Ut8TOQttsiijUbWzZtM9753Xn5RPBXVk1Y9gatA3dslVEqVwTRkbm0eqycCW7XW2zI+9wAOA4FAQ2C8Usotj+1QSvVTSu1XSu2fOnWqxQWw1PKFM7GxtaVNpwcKPVZJc3nfJqKDz1Gzy/8AiI8II/76FXoNmUWvb2YTfvYIEef/sXIuC8fGc9F8tuoMS45c4+E6phNaZW8njBo+XH6SAStP0aOmL753MJTuv+Chx55i2qKVvPDGeyyeM93a2flPUEDfJoEsyDH2X4iiZo26XwHPNSvL3H2h+aYp5+HAM00CmbY7ON80dxP/+WZlmVeA+NMLIX5+9q1bTvVGLXDPY/TAvbTrYgzPzDrIawuOciA4ls+6VSvUeAW1b/0Kqjdsbtawuu8pm6L9K6YKfVY6pVRDoBvQEtiulFqktb56i02eBH7P2bOklBoE+AKvZ1v8EjAyY4jdOaXURUzPIu3N+aVa66nAvy0ive/Cjcx161cuYdOaPwCoUqM2UZFZDz5GR4bjma13CMDTx89siFx0ZDie2e66bF2/ikN7t/P5iIn3dAKAsPBYygVkdTOX9fcgLDyWsIhY2jWpnrXcz4NtB87es7hFwdHdm6TYrDtVybGROLp750p3/fRhTqxfTKd3RmJrZ7r4Dz26C69KNbEvbRqbHFCrKVGXTuFb9bbt8GIjNjkdr2x32j0d7YlJzr9zde/lGzzXxPTQacuKHvxzLR6DhvgUA2cjE6nk5URE4o18t//X6t8Xs/7P3wGoFlSHqPCsYz8qMjzPIWv58fbxJTL8Gj6+/hgM6SQlJODq5nHLbf5ctpi1q5YBUD2oDpHh17LiR1zHO8e/vYJq36VHvs8oCXMxSWl4OWXdafVysicmW++lg70N5dwd+DLjgsTd0Y4PO1bmh80X89i2lNm2QtyOtev+6KRUvLP1NHg7lSIm0fz4L+/hyMAHTMe/h6M9n3SuyqiN57kQlYyXkz0fdazMxG2XuR6fmuv7LY3v5VSK6Bzxy2WL7+5oz8edqzI6R/wJdxDfzcuHuGyjXeKiI3HzKlidH3L2BJdPHWPfuuWkpiRjSE+nlIMj3Z7pV+D4kQmp+Llk9RD5uJQiIjHFLE3czaynI1YfD6dfG9NzVJGJqTQomzXk0NelNEdCb3/Oy87V04e4bMPF46IicPX0KdC2meVfv4LUm8kYDKbyd336NYvyIIqfQm2yZTz7MwnTELpgYBS3f8boaXIMo1NKvYqpd+jpHEPlgoEuGWn8gZrABUvz2e3hJxg+YT7DJ8ynSasObN+wGq01504ew8nZBU8v838onl4+ODo5c+7kMbTWbN+wmiYt2wNwZP8uVi2Zy4eDxlDaIfcDlHfjzy3HeKZXcwCa16tEXEIy1yLjWL/zJF1bBeHh6oiHqyNdWwWxfufJexq7sHlVqEFCRBgJUdcwpKcRfHArgXVbmKWJCTnP/sXjafvq1zi4emQud/L0JeLcPxgNBoyGdCLOHcPN//4aSncxOgl/19L4ONtja6NoUcGdwzmGTfi5ZJ086we6Zg5BiEpMpZafMwClbBVVvZ24Gmd+csnPg//3FGOnL2Ls9EW0aNORTetWobXm9ImjODm75PksUX6ate7AprWrANi5ZQP1GjW77Y2Bhx57inEzFzNu5mJatuvExrWm+KeOZ8S3oGEWduVy5v/v37WNwHL31zFgLReikghwLYWvcylsbRQtK3lwMCTrAiM5zcibvx3ngz9O8sEfJzkfmcQPmy9yMTqZgyE3aFnJAzsbha9zKQJcS3E+Kv/nJIXIydp1//nIJALcSuPrYjr+W1f2ZH+O4/+1xcd4Z+kJ3ll6grMRiZmNIid7Wz7rUpWFB8M4nW1ShLuNfyBH/H7Z4p+LSMxsFDnZ2/Jpl6osOBjGmTuIH1g1iKhrocSEXyU9PY1/dm6kZpNWBdr2f+98yYcTFvHB+IV07/sGDdp1s6hRBHDqegJlPRwIcCuNnY2ic3Ufdl2IMUuT/YZh68peBMeYZsPcd/kGTSu441LaFpfStjSt4M6+y5Y1jMpmK78hPY3juzZRs0nBHn94rP8XfDB+Ie//vIDuz75Og3bd7v9GkTxjBBR+j9FrQLDWen3G54nAS0qpDlrrLTkTK6UqAeWBnOsmA5eBXRkXWsu01t8AQ4FZSqljmHqcP9VaF2yAaD4aNmvDkX07+ejlxyjl4EC/D77OXPfF230ZPmE+AC++PYCpGdN1N2jWmgYZzxLNnjiK9LRURn7ZH4BqQXV5+Z3PCxR79ogXadekOj4eLpxbM5Shk1djb2eaZWX6b9tZs/04PdrW4fiKQSTdTOP1wfMAiIlLYsS0NWyfZ3qmYvjUNcTE3V8XJza2tjT+3xtsnTQwY8rWbriXqcg/q+fhWb46Zeu14MjymaSn3GTXLNMkh06evrR9bSDlGrYh/OxR1n73NgpFQK3GuU6sxZ1Rw7wDYXzYoTI2NrD9QgxhcSk8WtePS9HJHA6Lp0t1b2oHuGAwahJTDUzfbZqqfeO5aF5uXo6hPaujgO0XYwi5cdPiPDRp2ZYDe7bz5rO9KV3agXc+HZy57oNX+zB2+iIAZk/+kW0b1pCScpNXn3iArg89Sp8X36DrQ4/y4/CvebPvI7i4ufPR13nPapefpi3bsn/Xdvo9/UjGdN1Z8d992dSAAvhl0o9s+fsvUm7e5MX/9aD7Q//HMy+/waplizl8YA92dna4uLrx/hdDLd4HJZFRw+x9oQzoUgUbBVvORxN6I4X/1ffPaPzk/1xD6I0U9lyO5buHa2LUmln7QrFgvg0hrF73GzXM3BPCF12rYmOj2Hw2ipDYmzzRMIALUUkcuJL/8f9ALR/8XUvxvwYB/K+B6fnIb9efN+vlKEj8X7LF32RB/B55xB9uQXxbW1sefOkd5g7/FG000KhTT/zKV2bjr78QWKUGQU3bEHr+FIvGDORmYgJnDu5i82+zeHv0Xb0VxazsP2++yHe9a2Fro/jreDiXopN5sUV5zoQnsPNiDI81LEPryp4YjJq4lHS+W38OMM3IN3dfCJOeMs0IN3dvCPEpls29ZWNry4MvvsO8EZ+ijUYaduyJX/lKbFryC4GVa1KzaWtCz59i8Q+Dssq/ZDZvjZ55T8oviidlyaxR/yFmQ+mKUrMqpq5fx0b9rRI/+dB4q8TN6es1RT/Ub+gD1W+fqAi8vMg6U4nP7FMPgBNhd3Zn827VDjT1ap25bp1Gew1/mS0N4Nl5R6wSd96zDawSVxQf1qj3Iavuf2r2IavEX/xCIwD6WCn+ooz4Cw/l/5xSYXq6kWnYd+dxu6wSf+O7pl6wBQdDrBL/mcbl4D6YJ9Wx+6gibRAkr/ukWO6T4vv0kxBCCCGEEEIUkUKffCEnpVQ9IOfbGVO01vfX2CchhBBCCCH+C4rxcz9FqcgbRlrrY5im1hZCCCGEEEKIYqHIG0ZCCCGEEEKIYqQYv1uoKMleEEIIIYQQQpR40mMkhBBCCCFESSbPGAHSYySEEEIIIYQQJfc9RtbOgBBCCCGEKBGKfXeM44M/Fe17jFa/Vyz3ifQYCSGEEEIIIUo8aRgJIYQQQgghSjyZfEEIIYQQQoiSTCZfAKTHSAghhBBCCCGkx0gIIYQQQogSTV7wCkiPkRBCCCGEEEJIj5EQQgghhBAlmvQYAdJjJIQQQgghhBDSYySEEEIIIUSJJrPSAYXcY6SUKq+UuqiU8sr47JnxuVI+6dcopWKVUqtyLO+vlDqnlNJKKZ9sy/sqpY4qpY4ppXYqpRoUZnmEEEIIIYQQ/02F2jDSWl8BJgEjMxaNBKZqrS/ls8ko4Lk8lu8AugKXcyy/CHTQWtcDhgJT7zbPQgghhBBClCjKpmj/iqmiGEo3FjiglHofaAv0zy+h1nqDUqpjHssPAagc3Xxa653ZPu4Gyt11boUQQgghhBAlTqE3jLTWaUqpT4A1QHetdVohhXoF+KuQvlsIIYQQQoj/JnnGCCi6Wel6AleBuoXx5UqpTpgaRp/eIk0/pdR+pdT+qVNlxJ0QQgghhBAiS6H3GCmlGgLdgJbAdqXUIq311Xv4/fWB6UBPrXVUfum01lPJegZJ36v4QgghhBBC3NeK8XM/RamwZ6VTmCZfeF9rHYxpcoXR9/D7KwDLgOe01mfu1fcKIYQQQgghSpbCbh6+BgRrrddnfJ4I1FJKdcgrsVJqG7AE6KKUClFK9chY/q5SKgTT5ApHlVLTMzYZCHgDE5VSh5VS+wuzMEIIIYQQQvznKFW0f8WU0rpEjiorkYUWQgghhBBFrvi2BDI4PjajSK+Nk5e9Uiz3iQwoFEIIIYQQQpR4RfEeIzNKqXrA3ByLU7TWLYo6L0IIIYQQQpR0Od8VWlIVecNIa30MaFjUcYUQQgghhBAiP0XeMBJCCCGEEEIUH9JjZCLPGAkhhBBCCCFKPOkxEkIIIYQQoiSTDiNAeoyEEEIIIYQQQnqMhBBCCCGEKMnkGSMT6TESQgghhBBClHgltsdo17lYq8RtVc0DgK/XnLVK/KEPVLdK3JwcG/Uv8pjJh8YXecy8TNtz2SpxX2tREYDd52OtEr9lVQ8AjocmWiV+nbLOVolb3DT+ZqNV4h4c2NkqcUXxYY16H7Lq/jeXnrBK/En/qw1AvyXHrRJ/6hN1ANh+NsYq8dtW9wRg5MbzVon/WeeqAMzcF2yV+C83q2CVuJYqbj1GSqkHgJ8AW2C61npkjvUVgNmAR0aaz7TWq+82rvQYCSGEEEIIIYoFpZQtMAHoCdQGnlZK1c6R7CvgV611I6APMPFexC6xPUZCCCGEEEKIYtdj1Bw4p7W+AKCUWgT0BrJ3+2rALeP/3YGwexFYeoyEEEIIIYQQRUYp1U8ptT/bX79sq8sCV7J9DslYlt1g4FmlVAiwGnjnXuRLeoyEEEIIIYQowYq6x0hrPRWYehdf8TQwS2s9RinVCpirlKqrtTbeTb6kx0gIIYQQQghRXIQC5bN9LpexLLtXgF8BtNa7AAfA524DS8NICCGEEEKIkkwV8d+t7QOqK6UqK6VKYZpcYUWONMFAFwClVC1MDaOIOyi5GWkYCSGEEEIIIYoFrXU60B9YC5zENPvccaXUN0qpRzKSfQS8ppQ6AiwEXtRa67uNXajPGCmlygNbgSZa62illCdwEOiktb6UI20nYGy2RUFAH631H0qp+UBTIA3YC7yutU5TSgUBvwCNgS+11qPvJr9H9+9iwdQfMBqNtO/+CL2efMFsfVpaKtPGDOHSuVO4uLrz5mfD8PUPJCHuBuOHf8bFsydp2/UhnnvzkzuKf/XkAQ4vm4o2Gqncsju1uj1htv70pt+5uGsdysaW0i5uNHvmfZy9/AA4snwmV0/sR2sj/jUb0eixfsVthpHbmjyoLz3b1yUiOp6mTwzPM82YAY/To00dkm6m0m/QXA6fCgGg78Mt+OzVHgCMnL6W+Sv3FFm+75WLR/excd4ktNFIvQ4P0OLhPmbrr5w6yqb5k4m4coFeb31BzebtM9dtWTSNC0f2oo1GKtZtTOdn37L49z+6fxfzp5iO/w498j7+p47OOv7f+tx0/AOsXDyLretWYmNjw7NvfES9Ji0tLr/WmhnjR3Fwz3ZKOzjQf8AQqtaolSvd+TMn+Pm7waSm3KRxi7a80v8TlFJcPH+GKWO/5WZyMn7+ZXj/y29xcnaxOB8lUeuqXnzcozq2NorfD11l1g7zd2093CCA97tWIzw+BYDF+0L449BVAN7tUpW21b0BmL71EutOhBdt5sV9rTjU+7X9nXmyQQBKKXZcjGHdmSiz9e0qe9KhqidGDSnpRuYfDONafCpeTvYM6l6V6/GpAFyMTmLhoWsWx6/j78JTjQKwUbD9QixrTkearW9fxZNO1bwwak1KupG5+69yNePfYln30jzbJBBHOxs08O3fF0g3Fvza8NiBXSycOhZtNNKu+yM8+MTzZuvT0lKZ8cMQLp87jbOrG298Ogwf/0COH9rD0lkTSU9Px87OjidefodaDZpaXPaQ4/vZ8+sUtDZSo00P6vd40mz9P38v48yOtdjY2uLg4k7b597HxdsfgFlv9cKzbCUAnD196frWIIvjXziyjw1zJ2I0GmnQsSctH8l93t0wdxLhVy7wSP8vCco4714+cZiN8yZlpou6eoVH3v6SGk3bWJwHkbeMdxKtzrFsYLb/PwHc8x1eqA0jrfUVpdQkYCTQL+O/U3M2ijLSbgIaAiilvIBzwLqM1fOBZzP+fwHwKjAJiAbeBR6927waDQbmThrFJ8N+xsvHjyEfvEijlu0oW6FKZpqta1fg5OLK99OXsnvLOpb8MoG3PvsW+1KleOy51wm5fIHQy3f2AjOj0cDBJZPo8NYwHD28+XvMBwTWa4F7QNaLwTzLVaXqx2OxK+XAue2rObriF1q9+CmRF08SefEk3T/9GYBNPw0g4twx/KrXv7udUsTmrtzN5MVbmD70+TzX92hbm6oVfKnbewjN61Vi3Bd9aP/8aDzdnPiyX0/a9P0erTU7F3zKn5uPEhufXMQluHNGo4G/54zniQEjcfXyYd6gd6jauBU+ZStmpnHz9qPnax+z76/fzLYNPXuc0LPHeeHbyQAsHPohV04dpUKtBgWPbzAwZ+IoBnxrOv4Hv5/38e/s4sqoGabj/9eZE3j7828JDb7Anq3rGT55IbFRkXz3RX++n7YEG1tbi/bBwT07uBoazIS5yzlz8hhTfxzBdxPn5Eo3ZewI3vzoK2rUqsewz9/h0N6dNG7Rhomjv+HFNz6gToMmbPjrD/5YPIdnXn7LojyURDYKPu1Zk7fmHeJ6XArzXm3KltMRXIxMMku37ng43605Y7asbXVvgsq48vSUfdjbKaY935gd56JITDUUZRHEfcza9b4C+jQsw7jtl4lJSuOzzlU4ejWeaxmNHYB9V26w7aLp5aj1y7jweP0Axu8wvSw0MiGV4Rsu3FnhM+I/07gMY7deIiYpnS+6VuFIWHxmwwdgb/ANtl4wxW9QxpUnGvozblswNgpeaV6OmXtDCLmRgnMpWwwWNIqMBgPzJ43mo2Hj8PT2Y+gHL9GwRTsCK1TOTLNt3QqcnN0YMe039mxZz2+zJvDGp9/i4ubBOwNH4+ntS8il84wd+D5j5qy0qOxGo4HdiybS491vcfL0YeXI96lQvyUeZbKue7zLV+WRz3/CrpQDp7b8yb7fZ9Lp1c8BsC1Vit5f3vmL241GA+tn/8xTn32Hq5cPswf2p1qT3OfdB1//hL2rl5htW7F2Q14aPgWA5IQ4pn70IpXrNbnjvBQH99vN9MJSFEPpxgItlVLvA22BgvTqPA78pbVOAlOrUWfA1GNULmN5uNZ6H6aepLty4cwJ/APL4VemLHb29rRo341Du7eapTm0ZyttuzwEQLO2nTlxZB9aa0o7OFKjTkPs7Uvdcfzoy2dw8S2Di08Atnb2VGjcnrBju83S+FWvj10pBwC8K9UkKTbrrpIxLRVjejrG9DSMBgMOrp53nBdr2XHwPNE3kvJd36tDfRas2gvA3mOXcHd1JMDHjW6ta7Fh9yli4pKIjU9mw+5TdG+T8z1gxdu186fx9AvEw68Mtnb2BLXswPmDO83SuPsG4FuhSq7KS6EwpKViSE/HkJaG0ZCOs5tlv39ex//BXebH/8HdW2nbNffxf3DXVlq074a9fSl8AwLxDyzHhTOWv2F+787NdOzWC6UUNWvXJzEhnugo8+HC0VERJCclUrN2fZRSdOzWiz07NgFwNSSY2vUbA9CgSUt2b9tgcR5Korpl3QiJSSI09ibpRs3a4+F0rOlboG2r+Dhz8HIsBq25mWbkbHgCrat5F3KOxX+Jtev9Sl6ORCSmEpmYhkHD/pAbNAh0NUtzMz1rkqtStvf2sqmylyPhCf/G1+y7coMGZW8R306Z3t4C1PZ3IeTGTUJumBpRiakGLBlHdOHMCfzKlMM3wFTvN8/juufw7m207vIgAE3bduLkkf1oralYtSae3qZ6omzFKqSmppCWlporxq1EXjqDq28grr6m816Vpu0JPrLLLE2Zmg0yr3t8qwSRFBOZ11fdkavnT+Phn3XerdWyI2cP5D7v+uVx3s3u9N5tVGnQDPvSDvcsb8J6Cn267owhb58Aa4DuWuuCNGL6AD/kXKiUsgeeA967t7mEmKhwvHz8Mz97+vhx4fTxHGki8PI1DV2ztbXD0cmFhLgbuLp73HX85BtROHlkXYw4evgQffl0vukv7l5HmVqmuxM+lWvhW70+Kwc+D1pTrV0v3ALK57vt/SrQz4OQazGZn0OvxxLo50Ggrwch17MtD48l0NfDCjm8c/Exkbh6Z/3+Ll6+XD1/qkDbBlavTflaDZn8bh+01jTq2hvvshVuv2E2OY9/Lx8/zhfw+I+JiqBqUF2zbWOiLB9OFR0Zjo9fVh68ff2IjozAK9t+iY6MwDsjD1lpTLHKV6zC3h2badG2Ezu3/E1k+HWL81AS+bqW5tqNrLvT4XEp1C3rlitd51q+NK7oweWoJMasO8v1uBTOXE+gX4dKzNsVjIO9LU0reXIhIrEosy/+4wq73vdwtCMmKeuyJCY5ncpejrnSdajiSZfq3tjaKH7cljXU1Nu5FF90qUxympGVxyM4F5V/Iy/v+PZEZ4sfm5RGZe/c8TtW9aJbDVP8H7ZcAsDf1XQz9r12FXEtbcu+KzdYezoq17b5ic1Wp4PpuudinvW+qV7O77rnwI5NVKxaw+Kbw0mxUTh7Zk0i5uTpQ8TF/K97zuxYS9k6WcP1DGmprBjxLjY2ttTr8QQVG7a2KH58TCRuXlnnF1cvnwKfd7M7uXszzXr+z+LtihvpMTIpqskXegJXgbq3S6iUKgPUw/TAVU4Tga1a622WZiD7i6SmTr2badOt7/K+TUQHn6NmF9M/xPiIMOKvX6HXkFn0+mY24WePEHH+HyvnUhSVmOuhRIUF8/qPC3jjp4UEnzhMyOlj1s5WkXt7wCDWLF/Cx68/Q3JSInb29tbO0n/G1jOR9Bq3k6em7GXPhWi+6W26M7/7QjQ7zkbxy8tNGP6/OhwNuYHx7p99FaLY2XIhhoFrz/HHP9d5MMh0MR93M50v/zrL8A0XWXr0Oi81L4uDXeFcVm0+H82Xf51l2dHrPFjLdDFvoxTVfJyYsSeE7zddpGFZN4L8nAslfn5CL1/gt1kTeL7/Z4Ua5/yejURdPku9bo9nLnvi21k88vk4Orw8gL1LphIXcbVQ85CXhJgoIq5cpHI9y5+vEsVToTeMlFINgW5AS+CDjIbPrTwJ/J6zZ0kpNQjwBT68k3xoradqrZtqrZv269cv13pPbz+iI7PuMMdEhmd2E2el8SU6wnR32mBIJzkpARc39zvJTi6O7t4kxWYNG0qOjcTRPfeQlOunD3Ni/WLavvY1tnamC7/Qo7vwqlQT+9KO2Jd2JKBWU6IuWX7Xo7gLC4+lXEDWELGy/h6EhccSFhFLOf9sy/08CIuItUIO75yrpw/x2YaNJURH4OpZsCFJZ/fvILBqEKUcHCnl4EjlBs0IO3fSovg5j/9oC45/0/Kc2/pREH/9sZgPX+vDh6/1wdPL16yXJyoiHC8f8zx4+fgSFRGeI40pVrkKlRk0aiKjpyygXecHCChTroClL9ki4lMIcC+d+dnPrXTmJAv/upGcTprB1OD5/VAYQWWyhvrM2H6Zp6fu4615h1HA5aj759k+UfwVdr0fm5yOp1PWTRRPRztik/Mf2LL/SlzmULt0o858ni449iaRian4uVjWaxKbnIZXtvgeTvbEJKfnm37flRs0yhhqF5ucxpmIJBJSDaQaNP9cTaCCR8GHc3lkq9PBdN3jkWe9b6qXc173REeGM+HbT3nlw4H43UF96+ThTWK2oXFJMZE4e+Q+74WdPMSRNYvp8uYgbLPd8HL2MDVQXX3LEFCjPtFXLHvG29XTh7jorPNufHQkLp6WvQbn1J4t1GjaBlu7Qh+AVeiUUkX6V1wVasNImUo+CXhfax0MjOL2zxg9jWnavezf8yrQA3j6bt9om5/KNWpxPfQKEdfCSE9LY8/W9TRq0d4sTcMW7di+4U8A9m3fSK36Te/Zj+tVoQYJEWEkRF3DkJ5G8MGtBNZtYZYmJuQ8+xePp+2rX+Pg6pG53MnTl4hz/2A0GDAa0ok4dww3///eULo/txzjmV7NAWherxJxCclci4xj/c6TdG0VhIerIx6ujnRtFcT6nZY1DKwtoEpNYq6HEhtxFUN6Gqd2b6Fqo1YF2tbN248rp45hNBgwpKcTcuoo3oGW/f6Va9TieliO47+l+fHfqEU7tv+d+/hv1LI9e7auJy0tlYhrYVwPu0KVGgUb69/z0af4Ydoifpi2iOZtO7J5/Sq01pw+cRQnZxezYXQAXt6+ODo5c/rEUbTWbF6/iuatOwIQGxMNgNFoZMm86fR45P4f2lAUjofGU97LiUAPB+xsFD3q+LHljPk4fp9sF3sdavhwKdI0XM5Ggbuj6YKgup8z1f1d2H0+uugyL/7zCrvevxyTjJ9LKbyd7LFV0LScO0fDEszS+GY7/uuWcSE8wfQsjUsp28zXsfg42+PnUorIRMues7lkFl/RrLw7R8LizdJkb2zVK+OSOQve8WsJlHMvTSlbhY2CGr5OXI0zv6lxKznr/b1b19OwRTuzNA1btGPnBtPEYPu3byIoo95PSojnp8Ef8r8X36J67YJP9JOdT8UaxIWHER9puu65sH8r5eubz2gadeU8Oxf8TJc3B+Lo5pG5PCUxHkOaqQF7M+EG18+fMJu0oSDKVKlJzLVQYsNN592TuzdTrXHBzrv/OrFrE7VadbJoG1G8FXYT9zUgWGu9PuPzROAlpVQHrfWWnImVUpUwvek257rJwGVgV0ZDZJnW+hulVACwH3ADjBkTPNTWWsdZmlFbWzueffNjRn/9LkajkXbdHqZsxSosmzuFytVr0ahle9p3f4Spowcz4NX/4ezqxpsDhmVu/9FLj3IzKZH09DQO7trCx8PGmc3odTs2trY0/t8bbJ00MGO67m64l6nIP6vn4Vm+OmXrteDI8pmkp9xk16yRgKlB1Pa1gZRr2Ibws0dZ+93bKBQBtRrnalTdD2aPeJF2Tarj4+HCuTVDGTp5NfZ2ppnNpv+2nTXbj9OjbR2OrxhE0s00Xh88D4CYuCRGTFvD9nkDABg+dQ0xcZaN87Y2G1tbujzfn6Xff4FRG6nXvgc+5SqxfelsAirXoFrjVly9cJrlPw3hZmI85w/tZufvc3lpxDRqNG9H8MnDzPqiHyhF5XpNC9yo+petrR3Pvfkxo756N2O6+ocpl3H8V6pei8Yt29O+h+n4/+QV0/H/1qem479cxSo0b9eVz1/vg62tLc+9+YnFM9IBNGnRloN7tvPWs70zpusenLnuw9f68MO0RQD0e/9zfv5uEKkpKTRu3prGLUyzdW7fuIa/lv8KQMu2nen8QG+L81ASGbTmu7/OMKFvQ2yUYsXhMC5EJPJGx8qcCItn65lI+jQvR4caPhiMmhs30xm03HQBamdjw4wXTc86Jqak89XvJzDIUDphAWvX+0YNiw5f4522FbBRip2XYrkan0Kv2r4ExyRz9GoCHat6EuTnjMEISakGZu8LA6C6jxO96vhiMJpeN7Dg0FWS0iy7d2vUsPDQVd5vXxGbjOnCr8al8EgdXy5H3+TI1Xg6VfOilp8zBq1JSjXyy75QAJLSjKw/E8UXXaqggX+uJnDsWsKtA2Zja2tH3zc+ZuzA9zAajbTt1ouyFavwx7ypVKoeRMMW7WnX/WGmjRnC5689jrOLG69/OhSADauWEH41hJULZ7Jy4UwAPhz6E24eXgWOb2NrS8s+b7Lu56/QRiPVW3fHM7AiB1fOxadCdSo0aMm+pTNIS7nJ5mkjgKxpuWOvXWHngp9RygatjdTv8YTFDSMbW1u6vdCfX7//POM1GT3wLVeJbb/NIqByDao3ac3V86dZ9uNgUpISOHdoN9uXzuHV76YDcCPiGvHREVQIur9mAM5Pce7FKUrqHrwL6X6kd52LtUrgVtU8APh6zVmrxB/6QHWrxM3JsVH/Io+ZfOjOp/W8l6btuXz7RIXgtRamKUh3n4+1SvyWVT0AOB5qnYfz65Qt2rH3xVXjbzZaJe7BgZ2tElcUH9ao9yGr7n9zqeWzZd4Lk/5n6kHvt+T4bVIWjqlP1AFg+9mY26QsHG2rm4Y8jtx4Z68zuVufda4KwMx9wVaJ/3KzCgDFvtXh/fzCIm0QRM15uljuk/t/UKQQQgghhBDizhXLZkrRK/KGkVKqHjA3x+IUrfX9N/ZLCCGEEEII8Z9Q5A0jrfUxoGFRxxVCCCGEEELkJs8YmRTVe4yEEEIIIYQQotiSZ4yEEEIIIYQowaTHyER6jIQQQgghhBAlnvQYCSGEEEIIUYJJj5FJiX2PkbUzIIQQQgghSoRi3+rwe/nXIr02Dp/5ZLHcJ9JjJIQQQgghRElWLJspRU+eMRJCCCGEEEKUeNIwEkIIIYQQQpR4MpROCCGEEEKIEkwmXzCRHiMhhBBCCCFEiSc9RkIIIYQQQpRg0mNkIj1GQgghhBBCiBJPeoyEEEIIIYQowaTHyER6jIQQQgghhBAlntUaRkqp8kqpi0opr4zPnhmfK+WT/nul1HGl1Eml1DiV0bRVSjVRSh1TSp3LvlwIIYQQQghxe0qpIv0rrqzWMNJaXwEmASMzFo0EpmqtL+VMq5RqDbQB6gN1gWZAh4zVk4DXgOoZfw8UasaFEEIIIYQQ/znWHko3FmiplHofaAuMziedBhyAUkBpwB64rpQqA7hprXdrrTUwB3i0sDMthBBCCCHEf4Yq4r9iyqqTL2it05RSnwBrgO5a67R80u1SSm0CrmLaneO11ieVUk2BkGxJQ4CyhZ1vIYQQQgghxH+LtXuMAHpiavDUzS+BUqoaUAsoh6nh01kp1c6SIEqpfkqp/Uqp/VOnTr2b/AohhBBCCPGfIc8YmVi1x0gp1RDoBrQEtiulFmmtr+aR9P+A3VrrhIzt/gJaAXMxNZb+VQ4IzSuW1noq8G+LSN+TAgghhBBCCCH+E6w5K53CNHHC+1rrYGAU+T9jFAx0UErZKaXsMU28cDKjERWnlGqZ8X3PA8uLIPtCCCGEEEL8J0iPkYk1h9K9BgRrrddnfJ4I1FJKdcgj7W/AeeAYcAQ4orVembHuLWA6cC4jzV+FmmshhBBCCCHEf44yTeZW4pTIQgshhBBCiCJXfLtIMpR/e3mRXhtfmdC7WO6T4jD5ghBCCCGEEEJYlVUnX8hJKVUP04QK2aVorVtYIz9CCCGEEEKIkqFYNYy01seAhtbOhxBCCCGEECVGsRzYVvRkKJ0QQgghhBCixCtWPUZCCCGEEEKIolWcp9AuStJjJIQQQgghhCjxpMdICCGEEEKIEkx6jEykx0gIIYQQQghR4kmPkRBCCCGEECWY9BiZlNiG0bYzMVaJ266GJwAvLzpmlfgz+9SzStycpu25XOQxX2tRschj5sWxUX+rxE0+NB6A1cfDrRL/wTp+AJwIS7RK/NqBzlaJW9x8v+m8VeIO6FTVKnFF8WGNeh+y6v5+S45bJf7UJ+oA8P7yU1aJ/2PvIFM+dltn//dradr/ju0HWyV+8lZT3D//sc6576G6flaJK+5MiW0YCSGEEEIIIaTH6F/yjJEQQgghhBCixJMeIyGEEEIIIUoy6TACpMdICCGEEEIIIaTHSAghhBBCiJJMnjEykR4jIYQQQgghRIknPUZCCCGEEEKUYNJjZGK1hpFSqjywFWiitY5WSnkCB4FOWutLeaQ3AP++/CdYa/1IxvL+wPtAVcBXax15p3n658AuFk4bi9FopF23R3jwiefN1qelpTLjhyFcPn8aF1c3Xh8wDB//QI4f2sPS2RMxpKdja2fHEy+9Q60GTS2OXzfAhWcaB6IUbLsQw+qTEWbrO1b1onN1b4xak5JuZPa+UMLiUrBV8GLzclT0dMRGwc5Lsbm2vR9cPLqPjfMmoY1G6nV4gBYP9zFbf+XUUTbNn0zElQv0eusLajZvn7luy6JpXDiyF200UrFuYzo/+9Z99Y988qC+9Gxfl4joeJo+MTzPNGMGPE6PNnVIuplKv0FzOXwqBIC+D7fgs1d7ADBy+lrmr9xzR3k4eXAPv8/8CW000qJrL7o+9qzZ+vS0VOb/9C0hF07j5OrGCx8NwcuvDIb0dBZN/I7QC2cwGAw069iDrv97zuL4Wmtm/DyKA3u2U9rBgXc+HULVGrVypZs3fTyb1/1JYnwcC//akbk8LTWVn0Z8zfkzJ3F18+DjQSPxCwi0fEeUQCHH97P71ykYjUZqtulBgweeNFt/7O9lnNm+FmVri4OLO+2efx9Xb//M9anJSSwd8joVG7Si9dNvFXX2xX3O2nV/HX8XnmoUgI2C7RdiWXPa/DKifRVPOlXzyjz3zt1/lavxKQCUdS/Ns00CcbSzQQPf/n2BdKO2KH6QnzOP1fNDodgdHMuGs9Fm61tX8qBtZQ+0hpR0I4uPXON6fCo1fJ14uLYftjZgMMKK4+GcjUyyKPbFo/vYNN+07+t2eIAWvcz3fcipo2xakLXvazTL2vdbF0/nwhHT+aZl774EtehoUWyAbs2rMfrdB7C1sWHWnwcZPX+72foK/u5M/qw3Ph7OxMQl8/KwZYRGxFHB351F3/bBRins7WyYtHQv01fstzj+yUN7+GPmTxiNRlp26UWXPM57C8Z9y5ULp3F2deP5D7POe4snfUfIhTMYDQaaduxB18csP++J4sdqQ+m01leAScDIjEUjgal5NYoyJGutG2b8PZJt+Q6gK3BXby4zGgzMnzya9wePZeiEhezduo6w4ItmabavW4Gzixsjpv5Gt95P89usCQC4unnw7tejGTJ+Pq98MJAZPwyxOL5S8GzTQMZuuchXf52lRQV3At1Km6XZfTmWgWvOMnjtOf46GcFTjcoA0LSCO3Y2ioFrzvLNunN0rOaFt7P9He4J6zAaDfw9Zzz/+/hbXho5jVO7NxMZav6Tunn70fO1j6nVqrPZ8tCzxwk9e5wXvp3MiyOmcu3CGa6cOlqU2b9rc1fupvfbE/Jd36NtbapW8KVu7yH0H7aQcV+YTl6ebk582a8n7Z8bTbtnR/Flv554uDpaHN9oMLB02g/0+2o0n/40l0Pb/ubaFfPjf/fff+Lo4sqXExfR4eEnWTlnMgCHd27CkJbKgB9n89Ho6exct4Lo8KsW5+Hgnh2EhQYzcd5y3vzoK6aMHZFnumat2/P9pDm5lv+9+g+cXd2YNH8FDz/RlzlTfrI4DyWR0Whg58KJdO//Df8bNJkL+7YQExZslsa7fFV6f/ETj309kcqN27Jv2Uyz9QdWzCGget2izLb4j7B23a+AZxqXYdy2ywxac55mFdwp42p+7t0bfIMh684zdP0F1p6K4omGppsCNgpeaV6O+QfCGLzuPKM3X8JgYaNIAY/X92fKrhBGbrxA47Ju+LuWMktzICSO7zddYtTmS2w4F82jGS/LTkw1MG1PCN9vusT8g2H0bVzGothGo4ENc8bz2Eff8uKIaZzevZmoHPve1duPB179mFotzff9hcN7uH75LM8PnUzfQePY/9dvpCRb9vJuGxvFjx88SO9P5tPo+Qk80aUuQRV9zdKMeKs789ceoflLkxg+ewvf9OsCwNWoBDq+OZ2Wr0ym/RvT+bhvW8p4u1pWfoOBZdN+oN+Xo/n0x7kc3J77vLdnQ8Z5b8IiOvR6klVzM857uzaRnpbKgLGz+XDUdHbd4XlPFD/WfsZoLNBSKfU+0BYYbekXaK0P3aIxVWAXz57Ar0w5fAPKYmdvT/P23Ti8Z6tZmsN7ttG6y4MANGnTiVNH9qO1pkLVmnh4m/4xB1aoQmpqCmlpqRbFr+LlRHh8KhGJaRiMmj3BN2hY1s0szc10Y+b/l7azgX/rX236bKPA3taGdIPmZpqR+8m186fx9AvEw68Mtnb2BLXswPmDO83SuPsG4FuhSq67gQqFIS0VQ3o6hrQ0jIZ0nN08izL7d23HwfNE38j/Tl+vDvVZsGovAHuPXcLd1ZEAHze6ta7Fht2niIlLIjY+mQ27T9G9TW2L4wefO4lPmbL4BARiZ29Po7Zd+Gev+Z27f/Zto3mnBwBo0KojZ48dQGuNUoqUlJsYDOmkpaZgZ2dHaUdni/Owd8dmOnXvhVKKmrXrk5gYT3RU7p7PmrXr4+Xtm/f2PXoB0LpDF44e3IfWll2klEQRl87g5heIm6/p316VZu0JPrrLLE1gzQbYlXIAwLdyEIkxWXfUIy+fJTk+lrK1GhdpvsV/g7Xr/spejoQnpBKZmIZBa/ZduUGDsuYX2NnPvaXsVOa5t7a/CyE3bhJyw9R7lJhqwNIap6KnA5GJqUQlpWHQcCg0jnoBLmZpUrKf+22z9kHojRTibqYDcC0+FXtbG2xtCt5bdu3CaTz8s/Z9zRYdOJffvs/xvVFhlylXsx42trbYl3bEt3xlLh21rMemWa2ynA+N5tLVGNLSDSzZ8A+92tY0SxNUyZctB02NlS0HL9KrbRAAaekGUtMMAJS2t8XGgnL/K/jcSXwCyuKd/by3L8d5b+82mnU0nffqZz/voUi9effnveJEqaL9K66s+oyR1jpNKfUJsAborrVOu0VyB6XUfiAdGKm1/uNe5iUmKgJPH7/Mz57eflw4czyPNKY7Rba2djg6u5AQdwNXd4/MNAd2bqJi1RrY25vf8bkdD0c7opOyih+TnEYVL6dc6TpX86J7kA92NorvN5oqi/1XTI2osb1rUcrOhkWHwkhMNVgU39riYyJxzXax6+Lly9Xzpwq0bWD12pSv1ZDJ7/ZBa02jrr3xLluhsLJqFYF+HoRci8n8HHo9lkA/DwJ9PQi5nm15eCyBvh4Wf39sVAQe3lnHv7u3L8FnT5qluREVmZnG1tYOBydnEuNv0KBVR/7Zu41BrzxKWkoKvV96B2dX80Z9QURFhuPtlzU8y9vHj+jIiDwbQXlvH4GPX0Bm/pxcXIiPi8XN/f5qJBe1pJgonD19Mj87efgQcfF0vunP7FhLubqmocLaaGTPb9Pp+PInhJ48VOh5Ff891q77PRztzc69sUlpVPbO3evesaoX3Wp4Y2uj+GHLJYDMnp332lXEtbQt+67cYO3pKIviuzvYE5OcnhU/OZ2Knrnjt63sQceqXtjaKCbsCM61vkEZV0Ju3LSoxyohJhJXr6x972rBvvctX4Vdf8yj6QOPk5aawpWTR/AOrFjg2ACBPm6EhMdlfg6NiKN57XJmaY6du07v9rWY8NseerevhZtzabzcHImOS6acnxvLvutL1bJefDFpHVej4i2KfyM6Ao9s130eXr5cznnei47MTJPrvLdvG4NfzTjvvXhn5z1R/Fi7xwigJ3AVuN04jIpa66bAM8CPSqmqlgRRSvVTSu1XSu2fOnXqHWb11kIvX2DprAk89/ZnhfL9ABvPRfPZqjMsOXKNhzO60yt7O2HU8OHykwxYeYoeNX3xvc+G0t2NmOuhRIUF8/qPC3jjp4UEnzhMyOljt99Q3BOXz55A2dgyZPoffDXpVzavWETktTBrZ0sUgnN7NhIZfJb63R4H4OSWPylft6lZw0qIolKUdf/m89F8+ddZlh29zoO1TI0JG6Wo5uPEjD0hfL/pIg3LuhHkVzi9BtsvxjLs7wusPB5B9xrm/94CXEvxcB1ffj18rVBi56VSvaZUbtCchcPe589JwylTrRbK5t5fUn4+cR3tGlZi1/TXadewIqHhcZmNv5DwOJq/NIm6T4/j2Qca4udZdD02wedOYGNjy+Bpf/DlpF/ZvHIRUff5eU8pVaR/xZVVG0ZKqYZAN6Al8IFSKt8Bslrr0Iz/XgA2A40siaW1nqq1bqq1btqvX79c6z29fYmJDM/8HBMVjmeOO9WmNNcBMBjSSU5MwMXNHYDoyHAmDv+Ulz8YiF8Z8zseBRGbnI6XU1ZjxtPRnpjk/DvQ9l6+QaOMoXYtK3rwz7V4DBriUwycjUykUh69TcWZq6cP8dmGTSVER+Dq6V2gbc/u30Fg1SBKOThSysGRyg2aEXbu5O03vI+EhcdSLiCr56Osvwdh4bGERcRSzj/bcj8PwiJiLf5+D29fYqOyjv8bURG4e5mffN29fTLTGAzp3ExKxNnVnYPb/iaoUXNs7exw9fCkclA9rhTwruPq3xfzwat9+ODVPnh6+xIVfj1zXVRkOF4+BestAvD28SUy/Fpm/pISEnB18yjw9iWVk6e32dC4pNhInPP4txd68hCH/1pMtzcHYWtvqqvCL5zkxOZVLP7iRfYuncG5PRvY9/svRZZ3cf+zdt0fm5xmdu71cDLvwclp35UbNMoYahebnMaZiCQSUg2kGjT/XE2ggoeDRfFv3EzD0zFr8I6Hox03buZ/7j8UGke9MllD7dwd7Hi5eTnmH7xKVNKtBt3k5uLpQ3x01r6Pj47ApYD7HqDlI8/w/NDJPDHgO9DgGWDZtU9YZBzl/LJ6Wcr6uhEaEWeW5mpUPH2+WkyrV6cwaNpGAG4k3MyV5viFcNrUt6zHyt3Ll9hs132x0RG4e+c473n5ZKbJdd5rmHHec7fsvCeKN6s1jJSpuTgJeF9rHQyMIp9njJRSnkqp0hn/7wO0AU7cy/xUql6L62FXiLgWRnpaGnu3rqdB83ZmaRq0aMfODasBOLBjE0H1m6KUIikhnnFDPuSxF96ieu0GdxT/YnQS/q6l8XG2x9ZG0aKCO4dDzSsIP5es4Xn1A10JTzCNa45KTKVWxl2qUraKqt5OXI1LuaN8WEtAlZrEXA8lNuIqhvQ0Tu3eQtVGrQq0rZu3H1dOHcNoMGBITyfk1FG8A8sXco6L1p9bjvFMr+YANK9XibiEZK5FxrF+50m6tgrCw9URD1dHurYKYv1OyxuF5asFEXE1hKjrpuP/0PYN1GnW1ixN3WZt2btpDQBHdm2mWr3GKKXw9PHn3LGDAKTcTObymeP4F3A4y4P/9xRjpy9i7PRFtGjTkU3rVqG15vSJozg5uxR4GB1As9Yd2LR2FQA7t2ygXqNmxfquVHHhW7EGceFhxEdew5CexoV9W6lQv6VZmsjg8+yY/zPd3hyIY7bGZsdXBtBnxGyeGj6L5v97hWotutDs/14q4hKI+5m16/5LMcn4uZTC28keW6VoVt6dI2HmQ7Kyn3vrlXHherzpGeLj1xIo516aUrYKGwU1fC0/9wbH3sTHuRReTvbYKmhU1o1/riWYpfHJNgKktr8LEYmm+I52NvRrWY5VJ8K5GJ1sUVyAgMo1ib0eyo2MfX96T8H3vdFoIDnBdI0SEXyBiCsXqFS3iUXx958Ko1o5byqW8cDezpYnutTlzx3mw3i93Z0y6/FP+rZl9mrTkN2yvm44lDI1KD1cHGhdvwJnrlg2KXFe5726Tc3Pe3WatWXfZtN57+iuzVSrazrvefj4c/Yf8/Oe330+hF+eMTKx5jNGr2Gadnt9xueJwEtKqQ5a6y050tYCpiiljJgacyO11icAlFLvAgOAAOCoUmq11vpVSzNja2vHM298zI+D3sNoNNKmay/KVqzCH/OmUql6EA1btKddt4eZ/sMQPu/3OM4ubrw+YCgAG/9cQvjVEFYtmsmqRabZmj745ifcPLwKHN+oYd6BMD7sUBkbG9h+IYawuBQerevHpehkDofF06W6N7UDXDAYNYmpBqbvNk3XvPFcNC83L8fQntVRwPaLMYTcuHnrgMWMja0tXZ7vz9Lvv8CojdRr3wOfcpXYvnQ2AZVrUK1xK65eOM3yn4ZwMzGe84d2s/P3ubw0Yho1mrcj+ORhZn3RD5Sicr2mBa7ci4vZI16kXZPq+Hi4cG7NUIZOXo29nS0A03/bzprtx+nRtg7HVwwi6WYarw+eB0BMXBIjpq1h+7wBAAyfuoaYOMumawXT8f+/Vz9gyjcfYTQaadHlIcpUqMxfC6dTvmoQdZu3pUWXh5j/0zC+fasPTi5uPPfhYADa9vw/Fo4fwcj3ngOtad75QQIrVbM4D01atuXAnu28+WxvSpd24J1PB2eu++DVPoydvsi0ryb/yLYNa0hJucmrTzxA14cepc+Lb9D1oUf5cfjXvNn3EVzc3Pno67xntRPmbGxtafXUm6wZ9xXaaKRG6+54BlbkwIq5+FSsTsUGLdm3bAZpKTfZOM20T128fOn21iAr51z8F1i77jdqWHjoKu+3r4iNUuy4GMPVuBQeqePL5eibHLkaT6dqXtTyc8agNUmpRn7ZFwpAUpqR9Wei+KJLFTTwz9UEjuVo1BQk/tKj13mjVXlsFOwJvsG1+FR6BvkQHHuT49cSaFfZkxq+zhi1JinVwIKDptnP2lbxxMe5FD1q+tCjpqmnY9LOKyQU8BljG1tbOj/Xn6WjvsBoNFI3Y9/vWDYb/0qmfX/twmmWj8u275fN5cUR0zCmG1j07YcAlHZ04sHXP8PG1taishsMRj74cTUrRz+HrY1i9upDnLwUwdcvd+Lg6TD+3HGa9g0r8c3rXdAath+5zPtj/wSgZkUfRr7dI3MCoB8X7eT4hfDbRDRna2vHY69+wNShpvNe884PEfDvea9aEHWbmc57C8YN49u3Tee95z8YDEDbB/6PRRNG8N17zwGaZp3u7Lwnih9VQmdt0tvOxNw+VSFoV8M07OnlRdZ5BmZmn3pWiZvTtD13Nbv6HXmthWXd7IXFsVF/q8RNPjQegNXHLTt53CsPZjwTdyLMsild75Xagff3jEH3yvebzlsl7oBOFj0WKv6DrFHvQ1bd32/J8dukLBxTn6gDwPvLrTPU6sfeppncpu62zv7v19K0/x3bD7ZK/OStprh//mOdc99Ddf3ANDN7sVbz07VF2iA4/V2PYrlPisPkC0IIIYQQQghhVVadrjsnpVQ9YG6OxSla6xbWyI8QQgghhBD/dcX5uZ+iVKwaRlrrY0BDa+dDCCGEEEIIUbIUq4aREEIIIYQQomjZ2EiXEcgzRkIIIYQQQgghPUZCCCGEEEKUZPKMkYn0GAkhhBBCCCFKvBL7HiNrZ0AIIYQQQpQIxb4/pu5X64v02vifYd2K5T6RHiMhhBBCCCFEiVdinzHad/GGVeI2q+wOwImwRKvErx3obJW4Oe0+H1vkMVtW9SjymHlZfdw6b99+sI4fAI6N+lslfvKh8QCExqZaJX5Zj1JWiVvc7LtgpbqvirtV4oriwxr1PmTV/fMOhFgl/rNNygGw/mSkVeJ3q+UDwJz9V6wS//mm5QHYeCrKKvE7B3kDsPZEhFXi96jta5W44s6U2IaREEIIIYQQQiZf+JcMpRNCCCGEEEKUeNJjJIQQQgghRAmmpMsIkB4jIYQQQgghRDGilHpAKXVaKXVOKfVZPmmeVEqdUEodV0otuBdxpcdICCGEEEKIEqw49RgppWyBCUA3IATYp5RaobU+kS1NdeBzoI3WOkYp5XcvYkuPkRBCCCGEEKK4aA6c01pf0FqnAouA3jnSvAZM0FrHAGit78mUv9JjlIPWmrmTxnB4305Kl3ag30cDqVw9KFe6i2dPMmXMN6SmpNCwWWuee/MjlFIsmDaOQ3u2YWdnj19gWfp9OBBnF9cCx57x8ygO7NlOaQcH3vl0CFVr1MqVbt708Wxe9yeJ8XEs/GtH5vK01FR+GvE158+cxNXNg48HjcQvIPDOd0YRO7p/F/On/IDRaKRDj0fo9eQLZuvT0lKZOnoIl86dwsXVnbc+H4avv6l8KxfPYuu6ldjY2PDsGx9Rr0lLaxThrpw8uIffZ/6ENhpp0bUXXR971mx9eloq83/6lpALp3FydeOFj4bg5VcGQ3o6iyZ+R+iFMxgMBpp17EHX/z1nUezJg/rSs31dIqLjafrE8DzTjBnwOD3a1CHpZir9Bs3l8CnT1Ld9H27BZ6/2AGDk9LXMX7nnDkpvOv7H/zCSPTu34eDgwICvh1EjqLZZmps3kxny+UeEhV7BxsaWVu060O/tDwBITU1l5JAvOHPqBG7uHgwcNoqAwLJ3lJeSRmvN3Mk56r1q+dR7P2Sr997IqPemZ6v3ylhW7wlh7br/3JG9rJ0zAW000qjTg7R55Gmz9ZdPHmXd3AlcD77AY+98Re0WHTLXDevbDb8KlQFw8/ajz8fDLI5/4uBufpv+I0ajkdbdHqZ7jvo7LS2VuT8OJfj8aZxd3Xn542/w9i8DQOilcyyc9D03kxJRyoYBo6djX6p0gWOfP7KXdXMnoo1GGnbsSescZQ8+eZR18yYSHnyB/+v/FbVatM9cdyPyOn9O+4G46AgU8NSA4Xj4BlhU9uMHd/PrtB/RRgNtuj1Mj8efz1X22WOHEnz+FM6u7rz6ydDMsgNER1zjm/59eajPK3T7v2csig2mfb9sxk8YjUZade1Ftzz2/byfhnHl/GmcXd148eNv8PbL2veLJ43iZrJp3388appF+764KeoOI6VUP6BftkVTtdZTM/6/LJB9fvkQoEWOr6iR8T07AFtgsNZ6zd3mq1B7jJRS5ZVSF5VSXhmfPTM+V7rFNm5KqRCl1Phsy5oopY5ljDMcpzL6+5RSo5RSp5RSR5VSvyulPO42z0f27eRa2BXGzFzKK+99zqzx3+WZ7pefv+PV975gzMylXAu7wtH9uwCo17g5I6csZMTkBZQpW4GVi2cVOPbBPTsICw1m4rzlvPnRV0wZOyLPdM1at+f7SXNyLf979R84u7oxaf4KHn6iL3Om/FTg2NZmNBiYM3EUH33zIyMmL2L3lnWEBl8wS7N17QqcXVwZNWMpPf6vD7/OnABAaPAF9mxdz/DJC/l46E/MnvA9RoPBGsW4Y0aDgaXTfqDfV6P59Ke5HNr2N9euXDRLs/vvP3F0ceXLiYvo8PCTrJwzGYDDOzdhSEtlwI+z+Wj0dHauW0F0+FWL4s9duZveb0/Id32PtrWpWsGXur2H0H/YQsZ90QcATzcnvuzXk/bPjabds6P4sl9PPFwdLSy9yZ6d2wi9cpm5v/3Jh58N4sfv877AeLLvi8z+dSVT5y7h+JHD7Nm5DYC/VizD1dWNeUtX83if55g6Yewd5aMkyqz3ZizllXdvUe+N/45X3/2CMTNy1HuNmjNy8kJGTLK83hMlm7XrfqPRwJpfxvHMgBG8OWom/+zcSETIJbM07j5+PPLGAOq27pJre7tSpeg3Yir9Rky9o0aR0WDg1yljeGvgGL76eT4Htv3N1Rx1/671q3B0cWXw5F/p9MhTLJ8zEQCDIZ3ZY7+hzxuf8NXP83lv2HhsbQt+v9toNLBm1s/0GTCc17+fwfFdm4gIuWyWxs3Hj4dfH0Dd1p1zbb9i8ne07PUkb4yayUtDJ+Ds5mFx2RdNGU3/QWMYOH4B+7b9zdVg87LvXL8SJxdXvpmyhM6PPMXvsyearf9txjjqNL6zG6FGg4ElU3/gja9H88W4eRzYnnvf7/57FU7OrgyctJiODz/FijmTANO+n/vjUJ5642O+GDePd4f+bNG+F6C1nqq1bprtb+rttzJjB1QHOgJPA9PuRTugUBtGWusrwCRgZMaikZhahJdusdlQYGuOZZMwdZlVz/h7IGP5eqCu1ro+cAbTWMO7cmDXVtp2eRClFNVq1SMxIZ6YKPOXssVERZKclEi1WvVQStG2y4Ps37kFgHpNWmb+46gaVJfoyIL37O3dsZlO3XuhlKJm7fokJsYTHZX7hWQ1a9fHyzv3C8P27thMpx69AGjdoQtHD+5Da13g+NZ04cwJ/APL4VemLHb29rRo342Du8wPg4O7t9K260MANGvbmRNHTOU7uGsrLdp3w96+FL4BgfgHluPCmRN5hSm2gs+dxKdMWXwCArGzt6dR2y78s3e7WZp/9m2jeSfTod+gVUfOHjuA1hqlFCkpNzEY0klLTcHOzo7Sjpa9yHfHwfNE30jKd32vDvVZsGovAHuPXcLd1ZEAHze6ta7Fht2niIlLIjY+mQ27T9G9Te18v+dWdm7dRLeej6CUona9BiTExxMVaX78Ozg40qhpcwDs7e2pXrMWEeHXTWXYuonuDz0CQIfO3Ti4b899c/xb24HdedR70Tnqveg86r1dd1/viZLN2nV/2LlTePqXxdM/EFs7e+q06sTpAzvN0nj4BuBfoSrK5t7fUr909iQ+ZcrhE2Aqf+O2XTi6Z5tZmqN7t9Gi04MANGrdkdNHTXX/qUN7KVupKuUqVwfAxc0dG1vbAscOO38aL/9APP1MZa/dsiNnDuwwS2MqexWUMr9cjAi5jNFgoEq9JgCUcnDEvrSDhWU/gW9AOXwzyt60XVeO7DUv+5E922jZuScAjdt04tTR/Zn1+uHdW/D2D6RMRo+dpS6fPYmv2b7vyrEc591je7fTvJMpfsPWHTnz774/vI/AilUpm7HvnS3c98WRUqpI/24jFCif7XO5jGXZhQArtNZpWuuLmNoB1e92PxTFM0ZjgZZKqfeBtsDo/BIqpZoA/sC6bMvKAG5a693a9K9hDvAogNZ6ndY6PSPpbkw77q7ERIXj7euf+dnL14+YqPBcabx8/G6ZBmDrupXUb9q6wLGjIsPx9suK7e3jR3Rkwd/UHBUZgY+fqRvb1tYOJxcX4uNiC7y9NZn2abb97uNHTI5GYUxUBF6+pv1ua2uHo5MLCXE3Mpbn3Pb+ujCLjYrAwzvrmHL39uVGjgvTG1GRmWlsbe1wcHImMf4GDVp1pHRpBwa98ijf9Hucjr2fxtnV7Z7mL9DPg5BrMZmfQ6/HEujnQaCvByHXsy0PjyXQ1+OOYkRGhOPnnzUMw9fPn8iI/H/HhPg4dm3fTONmLbK2//f4t7PD2cWFuBuxd5SXkiYmKhzvnP/+cjRuYiJz1Hv5/Dvbum4l9ZsVvN4TJZu16/64mEjcst1odPPyJT5H3Xsr6WmpTP/yTWYO7M+pfdtvv0EON6Ij8Mz278rT248b0RH5pjGV31T3h4ddARTjB3/AyA9fYv2y+RbFjo+OxDXbecfNy5f4mKgCbRt9LQQHJxd+GzuY6V+8zoYFUzAaLeuti42KwDPbb+/p7Utsjt8+Njorja2tHY7OprLfTE5i3bJ5PNTnZYti5vxuj2z73sPblxs54t+IykqT/bwbHnYFlGLikA/5/qOX+ft3y/a9uK19QHWlVGWlVCmgD7AiR5o/MPUWoZTywTS07gJ3qdD7/bTWaUqpT4A1QHetdVpe6ZTpdsQY4Fmga7ZVZTG1Cv8VkrEsp5eBxfck0/fA8oUzsbG1pU3nB26fWIi7cPnsCZSNLUOm/0FSQjw/f/U2Neo3xec+er7MUob0dIZ9PYD/e7IvgWXL334DUSQy671OUu+JkuHdcQtw8/Il5noYc7/9GL8KVfDyL5q612A0cOHkUT4ZPZ1SpR0YN/BdKlStSc0GTQs9ttFg4MrpY7wyfDLu3v4s+3koR7euo2HHnoUeG+DPRTPo8kgfHBydiiReTkZDOhdOHuXjUdMoVdqB8QPfo3zVmtSsX/j7vrAUo0np0FqnK6X6A2sxPT80U2t9XCn1DbBfa70iY113pdQJwAB8orUuWMv+FopqQGRP4CpQF9Pwt7y8BazWWodYOmWgUupLIB3It8me/SGvKVOm0KjbU5nr1q9YwqY1fwBQpUZtoiKuZ66LjgjH09t8BkBPbz+zoSI502xdt4pDe7bz+ciJt+0uXP37Ytb/+TsA1YLqEBWeFTsqMhwvn9xD5vLj7eNLZPg1fHz9MRjSSUpIwNXCMb/WYtqn2fZ7ZDieOYYLenr7Eh1hurtoMKSTnJSAi5t7xvKc296TWRuLjIe3L7HZ7nTeiIrA3cvHLI27tw+xUeF4+PhhMKRzMykRZ1d3Dm77m6BGzbG1s8PVw5PKQfW4cv7UPW0YhYXHUi7AM/NzWX8PwsJjCYuIpV2TrJ7rsn4ebDtwtsDf+8eShfy5fCkANWvXJfz6tcx1EeHX8fHN+3ccM2IIZctX5PGnsx6U9fH1Izz8Gr7+ARjS00lMSMDN3aPAeSlp1q/MUe/l/Pfnk6Pe88lR7+X4d7Z1/SoO7d3O5yNuX+8J8S9r1/1unj7EZesliIuOwDVH3XvL7b1MefX0D6Ri7QZcu3TWooaRu5evWe9sTFQ47l6+eabxzKj7kzPqfg9vP6rWaYBLxnm+TuNWXLlwusANI1cvH+KznXfioiNw9fQu0LZuXj74V6yGp5+prDWbtCH03EmwoGHk4e1LTLbfPiYqAo8cv72HlylNZtkTTWW/eOYEB3duYtnsCSQnJqCUwr5UKTo+9HjB43v5Eptt38dGReCeI767tymNZ47zroe3H9VqZ+372k1aEXL+zH3dMCputNargdU5lg3M9v8a+DDj754p9KF0SqmGmOYhbwl8kDE0Li+tgP5KqUuYhts9r5QaiWlMYfYhcmbjDJVSLwK9gL76Fg8UZH/Iq1+/fmbruj3yBMMnzmf4xPk0adWB7RtWo7Xm3MljODm74OltXkl6evvg6OTMuZPH0FqzfcNqmrQyzdRyZP8uVv02lw8Hj6G0w+3H2z74f08xdvoixk5fRIs2Hdm0bhVaa06fOIqTs0uezxLlp1nrDmxauwqAnVs2UK9Rs/vmAqVyjVpcD7tCxLUw0tPS2LN1PY1atjdL06hFO7b//ScA+7ZvpFb9piilaNSyPXu2rictLZWIa2FcD7tClRp39pyLtZSvFkTE1RCirpvKf2j7Buo0a2uWpm6ztuzdZJpw5ciuzVSr1xilFJ4+/pw7dhCAlJvJXD5zHP+yFe5p/v7ccoxnepme7WlerxJxCclci4xj/c6TdG0VhIerIx6ujnRtFcT6nScL/L2PPvE00+b9xrR5v9G2fWfW/7UCrTUnjh3B2cUF7zxuDMyYPI7EhATe/uBTs+Wt23Vk3Z+mnvYtG9fTqGnz++b4t4ZuDz/B8AnzGT4hn3ovx8Whp1ce9V7LbPXekrl8OKhg9Z4Q/7J23R9YNYjoa6HEhF/FkJ7G8V2bqNGkYENBkxPiSU9LBSAp7gYhp4/jW7aiRfErVjfV/ZEZdf/B7Ruo39y87q/XvC17NpmuDw/t3EyNek1Mz2I2ak7Y5QukZjxjeu74YQLKF/x5m8AqNYm+FkpsRtlP7N5c4LKXqVqTm0kJJGYM17904jA+Fpe9FuHZyr5/29+5yl6/eTt2b/wLgIM7NlGzvqnsH4+YxLfTlvHttGV0fvhJHnj8BYsaRQAVqgcRcfVK5nn34Pa/qdesjVmaus3asHeTKf7hnZupnnHerdWoOWHB2ff9IQLKV7IofnFTzJ4xshpVmA8nZ8wetxMYqLVer5R6B2ipte57m+1eBJpqrftnfN4LvAvswdR6/FlrvVop9QDwA9BBa13wh3FA77t4I+8VWjN7wiiOHthFqdIO9Pvw68yK9ou3+jJ8oqlT6sKZE0wd8w2pqSk0aNqa59/6GKUUH770GOlpqbi4uQNQLaguL7+bNSdEs8qm5SfCEvOMPfWnkRzat4vSpR1459PBVKtpiv3Bq30YO30RALMn/8i2DWuIjorAy9uXrg89Sp8X3yA1NYUfh3/NxbOncHFz56OvRxAQaP7YVe1Ayx7KLyy7z8fmWnZk3w7mTxmL0WikffeHeaTPSyybO4VK1WvRuGV7UlNTmDp6MJfPn8HZ1Y23Ph2GXxnTqMoVi35h67qV2Nra8ky/D2iQxzMOLat6FHKpCmb18bzHwJ84sIs/Zo7DaDTSostDdHv8ef5aOJ3yVYOo27wtaakpzP9pGKEXz+Lk4sZzHw7GJyCQlOQkFo4fwbWQS6A1zTs/SOdHc09b+mAd051Ux0b9c62bPeJF2jWpjo+HC+HRcQydvBp7O9ODpNN/M42bH/vZk3RvXYukm2m8PngeB08EA/B875YMeNk0Xfd3M9Yyd8XuPMuXfMg00WRobGqe67XWjBv1LXt378icrrtmrToAvPbs40yb9xsR16/x1CPdqFCpMvb2pQBT4+qh3v8jNSWF4YM/59yZU7i6ufP1sO/NhtmV9SiVZ9ySZt+F3HWf1prZE0dxdP8uSjk40O+DbPXe230ZPiFbvZcxXXeDZq15/s2Meu/lPOq9d8znwmlWxb2QSyaKu7zqfSi6un/egZBc6wDOHtrDurmm6bobdOxJu0f7snnJL5SpUpOaTVoTdv4Uv44dxM3EBOzs7XF29+LNUTO5cuY4f84Yi1IKrTUtHniMRhmTJGT3bBPTeXj9ybyfXTq+fye/zRyHNhho2bUXDzzxAqsWTKNCtSDqN29HWmoKc34cypULpvK/9NEQfAJM5d+7eS3rls5BKUWdxq149MW3c31/t1qmmxxz9l/Jte7c4T2snzsRo9FIgw4P0PbRvmz5bRZlKtegRkbZfxs7mJtJWWV//fsZAFw4doAN8yejtaZM5Ro8+OoH2NrZ54rxfFNTPbzxVO5RTv/s38mSGT9hNBpo3aUXPZ98kZXzTWVv0MJU9lljv+HKhTM4ubrxysff4Btg/jTFqoXTKe3glO903Z2DTL1ga0/kvkw8fmBX5nTdLbs8RI8nXuDPBdOpUC2Iehnn3bk/DiUk47z74keDM/f9vs1rWb9sLgpF7Sat6P3CW3nG71HbF6D4tgQyNBm6qUhnKzrwdadiuU8Ku2HUD+iitX4q47MtpgeqPtBab7nFdi9i3jBqCswCHIG/gHe01lopdQ4oDfz7r2231vqNAmQt34ZRYbtVw6goFOeGUWEr7g2jwnarhlFRuF3DqLBJw8gkr4ZRUZCGkbBGvQ+3bxgVtts1jArbrRpGReFWDaOicKuGUVG4XxpGTYcVbcNo/1fFs2FUqM8YZcxJPjXbZwPQuADbzcLUEPr3835MzyflTFftXuRTCCGEEEIIUbIVxXTdQgghhBBCCFGsFflrepVS9YC5ORanaK1bFHVehBBCCCGEKOmK84QIRanIG0Za62NAw6KOK4QQQgghhBD5KfKGkRBCCCGEEKL4kA4jE3nGSAghhBBCCFHiSY+REEIIIYQQJZg8Y2QiPUZCCCGEEEKIEq9QX/BajJXIQgshhBBCiCJX7LtjWo7cUqTXxrs/61As94n0GAkhhBBCCCFKvBL7jNHNdOvEdcjY42euJ1klfg1/J6vEzel4aGKRx6xT1rnIY+blRFjRlx2gdqCp/KGxqVaJX9ajFACOjfpbJX7yofFWiVvcxCQZrBLX08nWKnFF8WGNeh+y6n5rn/ePBMdbJX6DCq4ABEenWCV+Ba/SAJwLT7ZK/Gp+jgDEpxitEt+19P3RByHPGJncH7+WEEIIIYQQQhSiEttjJIQQQgghhJD3GP1LeoyEEEIIIYQQJZ70GAkhhBBCCFGCyTNGJtJjJIQQQgghhCjxpMdICCGEEEKIEkw6jEykYZSD1prvRnzL9q1bcHB0YOi3I6lVu06udG/2e4XIiAjSDQYaN2nCF18NwtbWlh9Gf8eWzZuwt7enXPkKfDNsBG5ubgWOPXXc9xzYvYPSpR147/MhVKtZK1e6OdPGs2nNKhIS4liydmfm8r+WL+HPZb9iY2uDg6MT/T/5igqVqt75zihiWmtmjB/FwT3bKe3gQP8BQ6haI3f5z585wc/fDSY15SaNW7Tllf6foJTi4vkzTBn7LTeTk/HzL8P7X36Lk7OLFUpyZ7TWzPh5FAcyyv/Op3mXf9708Wxe9yeJ8XEs/GtH5vK01FR+GvE158+cxNXNg48HjcQvINCi+ON/GMmendtwcHBgwNfDqBFU2yzNzZvJDPn8I8JCr2BjY0urdh3o9/YHAKSmpjJyyBecOXUCN3cPBg4bRUBg2QLFnjyoLz3b1yUiOp6mTwzPM82YAY/To00dkm6m0m/QXA6fCgGg78Mt+OzVHgCMnL6W+Sv3FLjMwkRrzQ/fD2fXjq2UdnDk6yHDCaqV47dPTuaLAR8QGnIFGxsb2rbvxNvvfZi5/u91fzF98gSUUlSvEcQ3I0YVdTHEfcradf/dnvfHj/uRzZs2YKNs8PT2Zui3I/Dz87co/i8TR3Nor+nc/9Yng6lSPShXugtnTjJh1GBSU1No1LwNL731cebwp7/+WMTaFUuwsbGlcYs2PPvaewWOPXHsd+zduY3SDg588vVQqtesnSvdzMnj+PuvlcTHx7FyY1YdG37tKt8P/YqE+HiMRgOvvPU+LVq3s6jsU376nv27t1O6tAMffPFNntc9s6f+zMa1q0iIj2Ppul251u/Y/DfDv/6YH6fNp3pQ7t/uVvFHfzecHdu24uDgwOChwwnK8dvfTE7m04/fJ+TKFWxtbWjXoRPvvP8RAFfDQvlm4FfExETj5u7O0OHf4x8QUOD4ovix2lA6pVR5pdRFpZRXxmfPjM+V8kn/nVLqn4y/p7Itr6yU2qOUOqeUWqyUKnU3+dq+bSvBly+x8q91DBw8lGHfDM4z3agffmLJ7ytYtnwVMdExrFu7BoCWrdqw9I9V/Pb7SipWrMSMaVMKHPvA7u2EhQQzZcFy3v7kKyb9kPcFYvPW7RkzZW6u5R269mT87CWMm7mY/z39AjPG/1Dg2MXBwT07uBoazIS5y3njw6+Y+uOIPNNNGTuCNz/6iglzl3M1NJhDe02Nw4mjv+G5197lxxm/0qJdJ/5YPKcos3/XDu7ZQVhoMBPnLefNj75iyti8y9+sdXu+n5S7bH+v/gNnVzcmzV/Bw0/0Zc6UnyyKv2fnNkKvXGbub3/y4WeD+PH7YXmme7Lvi8z+dSVT5y7h+JHD7Nm5DYC/VizD1dWNeUtX83if55g6YWyBY89duZveb0/Id32PtrWpWsGXur2H0H/YQsZ90QcATzcnvuzXk/bPjabds6P4sl9PPFwdLSi1ANi1fStXgi+zZPkaPv9qCN8PH5Jnur7Pv8Ti3/9kzqKlHD1ykJ3btwIQfPkSc2ZOY+qs+SxcupL3P/msKLMv7nPWrvvv9rz/4suv8tvvK/l12XLad+jIlEn512V5ObR3B9dCrzBu1u/0e/9Lpo/Lu/zTxo3g9Q++Ytys37kWeoXD+0zl/+fwfvbv3MqoyQv5YfqvPPz4cwWOvXfXdkKvXGbWklW8/9lAxuVT77ds24GfZyzItXz+rKl06NKdyXN+5cuh3/PzqG8LHBtgf8Z1z7SFK3hnwNdMGJP39i3adGDslHl5rktKSmT5bwuoWbueRbEBdmzfypXLl/l91Rq+HDiEEcO+yTPdcy+8zNIVq5n/6zKOHDrEjm2muu/HMaN46OHeLFq6nNdef4vx4+6v6y6Rm9UaRlrrK8AkYGTGopHAVK31pZxplVIPAY2BhkAL4GOl1L/dMN8BY7XW1YAY4JW7ydemjRt4+JFHUUpRv0FD4uPjiIgIz5XOxcV0Nyo9PZ20tLTMuzat27TFzs7UEVe/QUPCr18rcOzd27fQuUcvlFIE1alPYkI80ZERudIF1amPl49vruXZ75DdvJkM91m36N6dm+nYzVT+mrUzyh9lXv7oqAiSkxKpWbs+Sik6duvFnh2bALgaEkzt+o0BaNCkJbu3bSjyMtyNvTs206l7tvIn5i4/QM3a9fHyzv37792xmU49egHQukMXjh7ch9a6wPF3bt1Et56PoJSidr0GJMTHE5Xj+HNwcKRR0+YA2NvbU71mLSLCrwOwY+smuj/0CAAdOnfj4L49BY6/4+B5om/k/9LjXh3qs2DVXlM5j13C3dWRAB83urWuxYbdp4iJSyI2PpkNu0/RvU3uu53i1rZu2ciDvXqjlKJufdNvHxmR47d3dKRJsxYA2NuXomZQbcIzfvvlv//G/558Bjc3dwC8vLyLtgDivmbtuv9uz/v/LgdT74KlD7Hv37WF9l0fRClFjdr1SEyIJyYq0ixNTFQkyUmJ1KhdD6UU7bs+yL6dmwFYt/I3evd5AftSpvvC7p5eBY69a+smuvZ82FTv121AQkLueh+gdt0GeOdx3aFQJCaaXtybmJCQZ5pb2b19M50fuPPrHoB50yfw+DMvUqqU5ffFt2zayIMPm+q+ehm/fWSO397B0ZGmzbPqvqBatTOv7S5eOEfTFqZ1TZu3YOumjRbnobhQShXpX3Fl7ckXxgItlVLvA22B0fmkqw1s1Vqna60TgaPAA8q0ZzsDv2Wkmw08ejcZCg+/btYN6u8fQPj163mmfeO1V+jUvjXOzs50694j1/o/li2lTbv2BY4dFRmOj19WbG9ff6Iic1fOt/LnssW81udhZk36idffHWDRttYWHRmOT7bhB96+frkqyOjICLx9/XKkMe2j8hWrsHfHZgB2bvmbyPC8f7fiKioyHO/s5ffJXf5bbx+RefzY2trh5OJCfFxsgbePjAjHzz/r+PP18891gsguIT6OXds30zjjYjkyIhy/f+Pb2eHs4kLcjYLHv5VAPw9CrsVkfg69HkugnweBvh6EXM+2PDyWQF+PexKzJIkID8cvW73n5++f2eDNS3x8HNu3bqZZ85YAXLl8ieDgS7z2Yl9eeb4Pu3ZsK/Q8i/8Oa9f99+K8//NPY+nepQN/rlrJW/0LNoztX9HZ6m4Abx//zLJlpQnH2yf7PvLP3EdXQ4I5dewwX7zzAoM+7Me508cLHDtnve/je+t6P6fnXn2TDWtW8fQjXfnyo7d4+6PPC7wtQFREOL5+5vEtue45d/okEeHXad664Nda2UWEXycg528fnn/8+Lg4tm3ZRLOWrQCoXiOITX+vB2DThvUkJiYSGxuT7/ai+LNqw0hrnQZ8gqmB9H7G57wcwdQQclJK+QCdgPKANxCrtU7PSBcCFOyhhntg8rQZbNi8ndTUVPbu2W22btqUSdja2fJQr0eKKjsAPPTYU0xbtJIX3niPxXOmF2lsa3t7wCDWLF/Cx68/Q3JSInb29tbO0n+WIT2dYV8P4P+e7Etg2fLWzo4oQunp6Xz92cc8+fSzlC1n+u0NBgMhwZeZNG0WQ0eMZsTQQcTHx1k5p6KkKMq6P7/z/jvvfcC6DVt4qNfDLFqQ95CvwmI0ppMQf4Nvx83iuX7vMnbY5xaNFrgbm9b/RfeHerNwxd98O2Yi3w35AqPRWCSxjUYj08eP5tW3P7x94nsgPT2dLz/9mKeeeZZyGXXf+x8N4OCBfTzz5GMc3L8fPz9/bG1siyQ/95r0GJkUh8kXegJXgbrA+rwSaK3XKaWaATuBCGAXYLAkiFKqH9APYMqUKTz/cr/MdYsWzGfZb78CUKduPa5fyxr+dv36Nfz883+IsnTp0nTq3IVNGzfQqnUbAJb/voytWzYzdcas2/74fy5bzNpVywCoHlSHyPCs2FER1/H28ctv01tq36VHvs8oFSd//bGY9X/+DkC1mnXM7vRFRYTn6jr38vElKtvdLFMa0z4qV6Eyg0ZNBCDsymUO7N5e2Nm/a6t/z1b+oDpEZS9/ZO7y34q3jy+R4dfw8fXHYEgnKSEBVzePW27zx5KF/Ll8KQA1a9c1G/oZEX4dH9+8j78xI4ZQtnxFHn86ayy7j68f4eHX8PUPwJCeTmJCAm7ut45fUGHhsZQL8Mz8XNbfg7DwWMIiYmnXpHrWcj8Pth04e09i/tf9tngBy5ctAaBWnXqEZ6v3wq9fxzefh8dHDhtE+QoV6dP3+cxlfn7+1KlXHzt7ewLLlvt/9u47PoqiDeD4by6FdNILPQm9996LCCLYRbEXVETFXl9QsaCoWGgGVBSwYUcRQaT30KVDIIX03uvN+8cd6UEOSC6Q5+snH7O7s/fMXpbdnX1mZ2nStCmREeG0bWd5n39RN1j72H+pz/tnjb7mWh59ZCKTJj9+zvgrf/2eNSt+ASC4Vduy5/7EuOJtO8vT25ekxNLfUVzxd+Tp7UfP/kNRStG8dXsMSpGRloqbuweV+fWHb1nxm/m436ZdmeN+YkLVx/1Kt2P5z7w1ax4AbTt0Ij8/j7TUFDzO0Z3295++ZeVy03VPy9btSIgvG/98r3tysrMIP3WSFx5/AICU5CRef2EKU2d8eM4BGL7/dim//GjqaNS2XXtiy//tfSuP/+br02jctCm333l38TwfX19mzvoEMD3r9M/fq3A9zwG3RO1k1YyRUqozMALoDTyplAqoqqzW+k2tdWet9QhMT88cA5IAd6XU2QZeI+BMFeuHaK27a627T5w4scyy8bdP4PuffuX7n35lyLDhLP/tF7TW7N+3FxcXV3zKHSSys7KK+x8XFhayYcM6AgODANi8cQOLPl/IR7Pn4ej43w+BX3PDrXz8+Xd8/Pl39B4whH/++h2tNUcO7sfJ2cWiC+PoyPDi30O3bqRBo9p/J3/UdbfywYJv+WDBt/TsP5h1q03bf/SQefvLPUvj6eWDo5MzRw/tR2vNutW/07PvYABSU5IB012kZUsWMnLsjTW9ORYbff2tzFr4LbMWfkuvfoNZu+rc238uPfoOYu1fvwOwZf0aOnTp8Z8N8+tuvo0FS35gwZIf6D9wKKv//A2tNYcO7MPZxaXS/uKfzf+YrMxMHn3y+TLz+w4YzKo/fgNg/T+r6dK95yW7K/TH+gPcPsb0bFPPDs1Iz8whNjGd1VsOM7xPa9xdHXF3dWR4n9as3nL4ksS80t106+0s/u5nFn/3M4OGDGPF77+itebf/ftwcXHF26fi337+nI/IzMjkyWfLdpcZOGQYu0N3ApCakkJEeDgNJZMozsHax/5Led4PDz9dXG7t2jXF88/l6nG3MPPTr5n56df07DeYDX+vQGvNsUMHcHJ2wcPLu0x5Dy9vHJ2cOXboAFprNvy9gu59BgGmY//BvaEAREeFU1hYiOs5bkqNu2k8n361jE+/Wka/gUP5+8/lpuP+v/twdna16DkhXz9/9oSaRqkLPx1Gfn4+7v/xjNOYG8Yz+4vvmf3F96brnpUl1z3OLud/3ePs4so3v6/ji2V/8sWyP2ndtsN/NooAbhk/ga+X/czXy35m8NBhrFhuOvYd2LcXF1fXShuGcz/5kMyMDJ5+ruyxLzUlpThD9sXCBYy9/obzqnttpFTN/tRWVssYmZ8PmoepC12EUmompmeMJlRS1gZw11onKaU6Ah2BVVprrZRaC9wEfAvcDfx6MfUaMHAQmzasZ8yoETg4OPL6GyVZl1tuGMf3P/1KTk4OTzz6CPkF+RiNmh49e3HzraZRst5+czr5Bfk8/MC9AHTo1In/Tat8lJPyuvfuT+jWTUy8bax5uO5Xi5c9fp+pAQXwxbwPWf/3n+Tl5nLPjSO56prruf2+h/n9p+/Yu2s7tra2uLi6MeWl6RfzVdS4br36s3v7JibdMc48ZOurxcueenA8Hyz4FoCJU17kk3emkZ+XR9eefenay3THbtM/K/nzV9MdwN79hzL06nE1vg0Xo1vv/uzavolH7hhHvXoOPPb8q8XLnnxgPLMWmrb/y/kfsnHNSvLycnng5qsZfs11jL/nYYZfcx0fvvU/HpkwFhe3+jz9v8pHNqpKr34D2L5lA3fcOLp4uO6zHrzjJhYs+YGEuFiWfrGAJs0CeeiuWwBT4+qacTcyeuwNvPXqi9xx42hc3erzvzfePe/YX759DwO6tcDb3YUTK6czff4K7GxN3REW/rCJlZsOMrJ/Ow7+No3s3AIeetXUVSUlPZu3F6xk0xLT83RvhawkJb3qQRxE5fr2H8iWTRu4aezVODg48MqrJSND3Xnr9Sz+7mfi42JZtPBTmgYGcfdtpgvPm26dwLgbbqJ33/5s37qF8TeMwcbGhsemPEN9d3crbY243Fj72H+x5/2PPnif06dPYTAoAgIa8sq0ykd1rEqXnv3YvX0zj999Hfb1HJj0zLTiZc8+dDszPzWNBvfAYy8w971Xyc/Lo3OPvnTpadr+oVePY+77r/P0g7dga2vHo8++et43pXr2HcD2LRu5++ZrqFfPgWdeKblueOium/n0K1NWecHsD/hn1QrycnO5bexwRo29gbsemMRDjz/DB2+/xk/fLgalePaV6RbdEOvRZwCh2zbxwPhrqefgwJMvlnx3k++9hdlfmP6un8+dxTrzdc9dN1zFyDHXM+G+R847TlX6DRjE5o0buO6akTg4ODBtesnf/vabr+frZT8TFxvL5ws+pVlgEHfcajr23TL+dq678WZCd+5gzscfoJSiS9fuPP/y1Iuuk7AuVVP9UCsENnVtG6a1vtU8bQPsBJ7UWq8vV9YB2G2eTAce1lrvNS8LwtQo8gT2AHdorfP+I7zOLfyPEtXEwdwUPRZnnYu3ln5OVolb3sEzWTUes11D5xqPWZlD0TW/7QBtG5i2/0xqvlXiN3Q3jRjk2GWyVeLn7Jltlbi1TUq2Rb2QLxkPp8uz3724dKxx3IeSY7+1z/v7IjKsEr9TE1cAIpL/69KoejTxrAfAifgcq8Rv7mvqvZORVzPPPpXnWs8Al8E4wYM/3FKjDYJ1U/rWyu/EahkjrXUIEFJqugjTkNyVlc3FNDJdZcvCgJ7VUUchhBBCCCFE3VAbBl8QQgghhBBCWEltfu6nJtWqhpFSqgOwuNzsPK11L2vURwghhBBCCFE31KqGkdb6ANDZ2vUQQgghhBCirqjN7xaqSVYdrlsIIYQQQgghaoNalTESQgghhBBC1CxJGJlIxkgIIYQQQghR50nGSAghhBBCiDrMICkjwIoveLWyOrnRQgghhBCixtX6VseI2dtq9Np49eTetfI7ka50QgghhBBCiDpPutIJIYQQQghRh0lPOhPJGAkhhBBCCCHqPMkYCSGEEEIIUYfJC15NJGMkhBBCCCGEqPMkYySEEEIIIUQdZpCEESAZIyGEEEIIIYSQjJEQQgghhBB1mTxjZCIZIyGEEEIIIUSdZ7WGkVKqsVLqlFLK0zztYZ5uVknZzkqprUqpg0qp/UqpW0stG6aU2q2U2quU2qSUal6DmyGEEEIIIcRlTama/amtrNYw0lpHAvOAGeZZM4AQrfXpSopnA3dprdsBVwMfKqXczcvmARO01p2Br4FXqrHaQgghhBBCiCuQtZ8xmgXsUkpNAfoDkysrpLU+Vur3aKVUPOADpAIacDMvrg9EV2N9hRBCCCGEuKIoanEapwZZ9RkjrXUB8CymBtIU8/Q5KaV6AvbASfOsB4AVSqko4E5KMlDl15uolApVSoWGhIRckvoLIYQQQgghrgzWzhgBjAJigPbA6nMVVEoFAIuBu7XWRvPsJ4HRWuvtSqlngQ8wNZbK0FqHAGdbRPoS1V0IIYQQQojLmrzHyMSqGSOlVGdgBNAbeNLc8KmqrBvwB/Cy1nqbeZ4P0Elrvd1c7Dugb7VWWgghhBBCCHHFseaodArTwAlTtNYRwEzgvSrK2gM/A19prX8otSgFqK+UammeHgEcrr5aCyGEEEIIcWVRStXoT21lza50DwIRWuuz3efmAvcqpQZprdeXK3sLMBDwUkrdY553j9Z6r1LqQeBHpZQRU0PpvhqouxBCCCGEEOIKorSuk4/b1MmNFkIIIYQQNa72pkjMxi0IrdFr418f7F4rv5PaMPiCEEIIIYQQwkpqce+2GlWrGkZKqQ6YRp0rLU9r3csa9RFCCCGEEELUDbWqYaS1PgB0tnY9hBBCCCGEqCsMkjICrDxctxBCCCGEEELUBrUqYySEEEIIIYSoWZIwMpGMkRBCCCGEEKLOk4yREEIIIYQQdVhtfulqTZKMkRBCCCGEEKLOk4yREEIIIYQQdZgkjEwkYySEEEIIIYSo8yRjJIQQQgghRB0m7zEykYyREEIIIYQQos6TjJEQQgghhBB1mOSLTCRjJIQQQgghhKjzJGMkhBBCCCFEHSbvMTKRjJEQQgghhBCizpOGkRBCCCGEEKLOq3UNI6VUY6XUKaWUp3nawzzd7BzruCmlopRSs2usokIIIYQQQlwBDKpmf/6LUupqpdRRpdQJpdQL5yh3o1JKK6W6X5Lv4VJ8yKWktY4E5gEzzLNmACFa69PnWG06sKGaqyaEEEIIIYSoRkopG2AOMApoC9ymlGpbSTlX4Alg+6WKXesaRmazgN5KqSlAf+C9qgoqpboBfsCqmqmaEEIIIYQQVw6lVI3+/IeewAmtdZjWOh/4FhhXSbnpwDtA7qX6Hmplw0hrXQA8i6mBNMU8XYFSygC8DzzzX5+plJqolApVSoWGhIRc0voKIYQQQgghzk/p63Lzz8RSixsCkaWmo8zzSq/fFWistf7jUtarNg/XPQqIAdoDq6soMwlYobWO+q/Wp9Y6BDjbItKXqpJCCCGEEEJczmp6tO5y1+UWMSdGPgDuuZR1glraMFJKdQZGAL2BTUqpb7XWMZUU7QMMUEpNAlwAe6VUpta6yoe0hBBCCCGEELXWGaBxqelG5nlnuWJKnKwzJ0b8gd+UUmO11qEXE7jWNYyUaQvnYepCF6GUmonpGaMJ5ctqrSeUWu8eoLs0ioQQQgghhDh/tewFrzuBFkqpQEwNovHA7WcXaq3TAO+z00qpdcAzF9sogtr5jNGDQITW+mz3ublAG6XUICvWSQghhBBCCFHNtNaFwGTgL+Aw8L3W+qBS6nWl1NjqjK20rpOP29TJjRZCCCGEEDWuVqVjKnPPN/tr9Np40W0da+V3UhszRkIIIYQQQghRo2rdM0aVUUp1ABaXm52nte5ljfoIIYQQQghxpahlzxhZzWXRMNJaHwA6W7seQgghhBBCiCvTZdEwEkIIIYQQQlQPyReZyDNGQgghhBBCiDpPMkZCCCGEEELUYQZ5xgiQjJEQQgghhBBCSMNICCGEEEIIIepsV7rtJ9OsErdXcH0A7liyzyrxl9zRySpxy+v6+j81HnP31KE1HrMy7649aZW4zw0JBmBnmHX2/R5Bpn0/JbvIKvE9nGysEre2cewy2Spxc/bMtkpcUXtY47gPJcf+IR9tsUr8tU/0BWDUvO1Wif/nI6Y3m3y+M8Iq8e/r0QSAR348ZJX4825sC8Anm09ZJf5j/QKtEtdS0pPORDJGQgghhBBCiDqvzmaMhBBCCCGEEPKC17MkYySEEEIIIYSo8yRjJIQQQgghRB0mCSMTyRgJIYQQQggh6jzJGAkhhBBCCFGHyQteTSRjJIQQQgghhKjzqjVjpJRqDGwAummtk5VSHsBuYIjW+nQV67gBh4BftNaTzfPWAQFAjrnYVVrreKXUw8CjQBGQCUzUWl/UQPlaa5Z8+j77dm6hXj0HHnxqKs2at65Q7tTxwyz44HXy8/Po1KMvdzz0NEopdmz8m5+XLiA68jTTZn1BUMu25x27Y4Ard/ZogEEp1p1IZvnB+ErL9WhcnycGNeN/K45xKtn0lVzbzpfBzT0xas1XO6M5EJNxYV+AFfUN9uSZkS2wMSh+3hPDos3hZZZf28mfKcObE5+RB8B3O6P4ZU8MAI8PC6Z/Cy8AFm44zapDlX93tVnUwVC2ff8pRqORVv1G0unqW8osP/D3Txzb9BfKxgYHl/oMuGsKrl5+xcvzc7L58bWHaNqpD31vm2RxfK01i+e/z17zvj/x6akEVrHvf/rB6+Tn5dG5R1/ufNi073+98GP2bN+Ira0dvgENmfjUVJxdXC2K/8G7b7F18wbqOTjyv9feonWbsv9+cnNyeOm5JzkTFYnBYKD/wCE8+sRTxcv/XvUnC+fPQSlFi5atef3tmRZ/D3XR/GkTGDWwPQnJGXS/+a1Ky7z/3E2M7NeO7Nx8Jk5bzN4jUQBMuLYXLzwwEoAZC/9i6XLrvKtFXL6sfezv0dSdyYMCsVHwx8F4vgk9U2b5yDY+PNy/GYlZ+QD8vC+GFebz8zvj2tA2wJUD0em89NsRi2MDdGtcn4f7N8WgFCsPx7PMvG3l9Qvy4JWRLXn8h385npCFjUExZXAgwd7O2BgUa44m8v2eaItih+3byZrFczEajXQaPIreY8eXWR55ZD9rFs8jPjKMsZNfpnXPgQCEH9rLP0vmFZdLiolk7KMv07J7P4vit/Vz5pZO/iil2HwqhVXHksosHxDowaBgD4wa8gqNLN0dTWxGPp5Odky7Kpi4DNPf5FRyNt/sibUoNkD4gVA2fj0PrY20HXA13a65tczyPX/9yKENf2GwMeDo6s7Qe5/Ezdt03v3tg5eJPXmEgBbtuHbK6xbHrm0kYWRSrQ0jrXWkUmoeMAOYaP5/SFWNIrPpmBpT5U3QWoeWm/e11no+gFJqLPABcPXF1Hl/6BbizkQyc+GPnDz6L4tmv8OrH35RodyXc97hvideIrhVe96fOoX9oVvp1KMvDZsG8/gr7/LFJ29bFFcpuLtnQ2asCSM5u4DXR7VgV1Qa0Wl5Zco52BoY2dqbEwlZxfMa1K9H72buPL/8KB6OdrwwPIhnfjuC1hf2HViDQcHzo1oxacke4tLzWPJAd9YfTeBUYnaZcqsOxvPOymNl5vVv4UXrAFdu+3QndraKBXd1ZfOJJLLyrfMi0QthNBax5Zu5XP3Emzh7ePPb21No0rE3Hg2aFJfxahzMuJc+wtbegcPr/2DnT58z9MEXi5fv+u0r/Fu0v+A67Nu5hdjoSN7/7EdOHjHt+69Vsu9/MfsdHnj8JYJbt2dmqX2/Q5ee3HrvJGxsbPn2s09Y/t0ixt//2HnH37ppA5ER4Sz7dSUHD+zn3bde4/PF31UoN+Gue+nWoxcFBflMfug+tmzaQN/+A4kIP81Xny8gZNFS3Nzqk5ycVEkUUZnFy7cx/7v1LJx+V6XLR/ZvS3ATH9qPe42eHZrx8UvjGXjXe3i4OfHyxFH0m/AuWmu2fP08f6zbT2pGTqWfI0R51j72GxQ8MTiIZ38+SEJmPvPHd2RLWDLhyWX34bXHE/l4XcUXhH63O5p6tgau7eBXYdn5xn90QDNeWn6ExKx8PrqxHdtPpxKRUja+o52BcR38ORKXWTxvQLAndgYDk74/QD1bA5/e2pF1JxKJNzcW/ovRWMTqLz/h1hfewdXTmy+nTqZ5tz54N2xaXMbNy5fRDz3LjhXLyqzbtG1n7n3rUwByMtMJefoeAjt0s2jbFTC+cwAfbwonJbuAF4YGsT8mg9hS9d8ZmcbGUykAdAxw4aaO/szebHpRbWJmPm+tCbMoZmlGYxHrl8xh3NNv4eLpzfevP05g5954ltp+nybNuWXqNdjVc+DA2t/Zsuwzrn7kJQC6XH0Thfl5/LtuxQXXQdQ+NdGVbhbQWyk1BegPvFdVQaVUN8APWHU+H6y1Ti816QxcdFNg97YN9Bs2GqUUzVt3IDsrg9TkxDJlUpMTycnOonnrDiil6DdsNLu3rQegYZNAAho1reyjzynYy4m4jHwSMvMpMmq2nU6lW6P6Fcrd1Mmf3w/FU2As2dRujeqz7XQqhUZNQlY+cRn5BHs5WVwHa2rf0I2olGzOpOZSaNT8dTCewa18zmvdIG9ndoenUqQ1uQVGjsdn0re5VzXX+NJKOH0MN98GuPkEYGNrR1CPgUTs31qmTINWnbC1dwDAJ7A1WSkl+2Vi+HFyMlJp2KbrBddh17YN9D+777fpQFZmBinl9v2Us/t+G9O+33/YaEK3mvb9Dt16Y2NjutcS3Lo9yYmW3bndsP4fRo8Zh1KK9h07kZmRQWJCQpkyDo6OdOtheou7nZ09rVq3JT4+DoBff/6BG2+5HTc3078bT8/Lax+wps27T5Kcll3l8jGDOvL17zsA2HHgNPVdHfH3dmNE3zas2XaElPRsUjNyWLPtCFf1O/8suRDWPva39nMhOi2HmPQ8Co2af44l0i/I87zX3x2ZRvZF3IRr6etCdFousRmm+OtPJNO7mUeFcnf1bMSyPTHkFxqL52kNDnYGDArsbQwUGI0W1SXm5FHc/Rrg7ms677TpPZjju7aUKVPfxx/fJkHnfMfN0R0bCerUA7t6DucdG6CZpyMJWfkkZhVQpCE0Ko1ODcr2Msgttb32Npf2kjUu7Cj1fQOob97+Fr0GEba37Hm3UZtOxdvlH9SazFLn3cZtu2Dn4HhJ62RNSqka/amtqr1hpLUuAJ7F1ECaYp6uQCllAN4Hnqnio75QSu1VSv1PlfpGlVKPKqVOAu8Cj19sfZMT4/H0Kbnz4+ntW+ECLzkxHg9v33OWsZSHkx3J2SV3SZKzC/BwsitTppmnI57Oduw9k/Ef6+ZXWLe283GtR2yp7Fh8eh6+rvUqlBvaxofvHurJuze1x8/NtPxYXCZ9m3viYGvA3dGO7s08ipddLrJTknD28C6ednL3Jiul6ozHsc1/0ah9dwC00cj2HxbS68YHLqoOKUnxeHmX3fdTyu3XKYnxeJbb91OSKu77G1Ytp2OPvhbFT4iPx9ffv3ja18+PBHOjpzIZGels2rCOHj17AxAZfpqIiNM8eM8E7r9rPFs3b7QovqhaA193omJTiqfPxKXSwNedBj7uRMWVmh+fSgMfdyvUUFyurH3s93apVybDkpCZj7eLfYVyA5t7sXBCJ14d3QqfSpZfKG9nexKySuInZuXj5Vz2/B3s7YS3Sz12RqSWmb8pLJncAiNf392Vr+7szE97Y8jMO/+GUUZKIm6eJY1QV0/vMhf+5+vwtnW06TPE4vXcHW1JyS65JEzJKcTdseK1y6AgD14f2ZzrO/jx3b6S7nJezva8NCyQJwc2pfkF3AzOSk3CtdT2u3ic+7x7aONfNO3Q3eI44vJSU6PSjQJigPbA6irKTAJWaK2jKmlJTtBan1FKuQI/AncCXwForecAc5RStwOvAHdX9uFKqYmYuvPx6aef0mnYrZUVq7UUMKFbAz7dEmHtqljNhmOJrPw3joIizY1dG/D6uLY8tHgP28KSadfAlS/u60ZKdgH7o9IwXk79CC10Yvs/JEYc55qn3gXg8Po/aNy+e5mGlTX9+s3nGGxs6Dfkonq1nlNhYSH/e+EZbrntDho2agxAUVERURHhzFuwiPj4OB6+/y6WLvsFV1e3aquHEKL6WfvYv/VUCv8cS6SgSHNtez9euKoFT/908JLHqYwCJvZtyvtrT1ZY1srXGaPWTPhqDy71bHjvurbsiUonNiOv4gdVk8yUJBIiTxFYjQ2G9WEprA9LoUdjN0a39ubL0GjScwt5+c/jZOUX0cTdgYf6NGb66pNlMkyX0tGta4g/fZwbnn+3Wj6/NpDR2EyqvWGklOoMjAB6A5uUUt9qrSt7srAPMEApNQlwAeyVUpla6xe01mcAtNYZSqmvgZ6YG0alfAvMowpa6xAg5Ozk9pNpxcv+Xr6MdX/9AkBgi7YkJ5TcpU4ud4ccKt5Jr6yMpVKyC/B0KrkL5elkV+ZOioOdgUb1HXh5RHMA6jva8tTgQD5Yd6qSde3LrHs5SMjIw79+yZ0+X7d6xQ/anpWWU1j8+897onl8ePPi6c82hfPZJtMDu29e35bwpMvrGQcnD68yXeOyUxNx9qjYJeTM4T3s/fM7rnnqHWzsTHfW4sMOE3viIIfX/0FBXi7GogLsHBzpcf29/xl39fJlrF35CwBBLduSlFh23/cot197lMuOJifG4+FVUmbD6t/Zs2MTL74997xS5T989zW//mTqu96mXQfiY0vuBsbHxeHjW3m//RlvTKNxk6aMn1DyTIyvrx/tOnTE1s6OBg0b0aRpUyIjwmnbrsN/1kOcW3R8Ko38S7r3NPRzJzo+leiEVAZ0a1Ey39edjbuOW6OK4jJl7WN/YmYevq4l508fF3sSM8s+o5OeWxL/j4NxTOxveXf5KuNn5ePjXBLf29mepKyS87ejvQ1NPR15d6ypi6qHkx3TRrXktT+PMbiFN6GRaRQZNWk5hRyKyaCFr/N5N4xcPbxJTy7prpyRnIiLhTfYjmxfT8vu/bCxtfxyMjWnsEzvFg9HW1Jzqr52CY1M57YuAQAUGjWF5m6DEam5JGbl4+tiT0Rq7nnHd3b3IqPU9memVH7ejTy4m9Dfv+X652diY3fpsoWidqrWBqK5y9s8TF3oIoCZVPGMkdZ6gta6ida6GabudF9prV9QStkqpbzNn2cHjAH+NU+3KPUR1wAXdEYefu3NvDF7KW/MXkq3PoPYvGYFWmtOHDmAk7ML7p5lDxTunt44Ojlz4sgBtNZsXrOCrr0HXkjoYmFJ2fi72uPjbI+NQdG7mTu7o0oabzkFRh754SBP/nKYJ385zMnEbD5Yd4pTyTnsjkqjdzN3bA0KH2d7/F3tOZlU9fMCtdHBMxk09nSigbsDtgbFyHa+rD9WNqVfunvDoJbenE40DUBhUKaGIkALX2da+Lmw7WRyzVX+EvBp2pL0+GgyEmMpKiwgbOcGmnTsXaZMYsRJNi/9hBGPTMXRzb14/uD7n2P8219y61uL6Hnj/TTvNey8GkUAI669mbfmLOWtOaZ9f9PZff+wad/3KLfve5zd9w+b9v1Na1bQzbzv7wvdyu/LFvPUtPep53B+fc1vuvV2Fn/3M4u/+5lBQ4ax4vdf0Vrz7/59uLi44u1T8VmD+XM+IjMjkyeffbHM/IFDhrE7dCcAqSkpRISH07Bh4/Oqhzi3P9Yf4PYxPQHo2aEZ6Zk5xCams3rLYYb3aY27qyPuro4M79Oa1VsOW7m24nJi7WP/kbhMGro74u9WD1uDYmhLb7aElf0Mz1IX732DPIlIvnQ33o7FZ9LA3QE/V1P8Qc092Xa6pHtqdn4R4xft5p6le7ln6V6OxGXy2p/HOJ6QRUJGHp0amjLi9WwNtPZzJTLl/OsWENSKlNgzpMbHUFRYwOFt62jetY9F9T+0de0FdaMDCE/JwdfFHi8nO2wUdG9Un/3RmWXKlO622D7AhXhzo9XF3oazt968ne3wdbEvHjXwfPkFtiItLpr0BNN59/j29QR2LnveTQg/wdqvPuGax1/FqdR590okzxiZVHfG6EEgQmt9tvvcXOBepdQgrfX68/yMesBf5kaRDfA3sMC8bLJSajhQAKRQRTc6S3Tq0Y99O7fw7P03YF/PgQee/F/xslcmT+CN2UsBuGvScyyY9ToFeXl07N6Xjt1Nz1OEblnL4nnvk5GWwgevPkWToBY898Yn/xnXqOHLnWd4blgQBgXrTyZzJi2PGzv6mRs/6VWueyYtj+3hqbxzbSuMWrNo55nLakQ6gCKteefPY8yZ0BmDUvy2N5qwhCweHhzIoegMNhxLZHzPRgxq6W26O5ZbyLRfTRdgtgYDn91jGg0nK6+QV34+RNFl9gUYbGzoc+sjrPz4FbTRSMu+V+HRoCm7fluMd9MWNO3Um50/fUZBXi7/LDCNeOji6cOISdMuWR06m/f9p++7AXsHByaW2vdfenQCb80x7fv3PPocIebhujv16Esn87NEX86dSWFBPjNengxA89btue+xFysGqkLf/gPZsmkDN429GgcHB1559c3iZXfeej2Lv/uZ+LhYFi38lKaBQdx9240A3HTrBMbdcBO9+/Zn+9YtjL9hDDY2Njw25Rnqu7tf7NdSJ3z59j0M6NYCb3cXTqyczvT5K7CztQFg4Q+bWLnpICP7t+Pgb9PIzi3goVeXAJCSns3bC1ayaclzALwVspKU9MvrpoywLmsf+40aPl4XxrvXtcWgFH8eiuN0cg739m7M0bhMtpxK4YbOAfQL8qTIqEnPLWTG6hPF6390U3uaeDjiaG/g+/u6MfPvkxWeBfqv+PM2nuaNMa2wUYpVRxKISMnhzh4NOZaQxfbTVX/W8n/jeGpoEPNv7YBCsepoAqctaLQZbGwYcfdkvn/3RbTRSIdBI/Fp1IyNPyzCP7AlLbr1JebkUX768FXysjM5sWcbm378igfeWQhAWkIsGckJNGnd8bxjlt/2b/fG8lj/JhiUYsvpVGIy8hjT1oeIlBz2x2QyONiD1r7OFBlNjcQvd5qGI2/h7cSYdj4UGU2vevh6TwzZBZZ1ozPY2DDwjkn8+sHLaKORtv2vwqthM7b//BW+zVoQ2KUPm79fSEFeDivnms5HLl4+jHn8NQB+fPtpUmKiKMjL4Yun72DovVNo2l6eQbrcKX2ZXUBeImW60tWkXsGmEbPuWLLPKvGX3NHJKnHL6/r6PzUec/fUoTUeszLvVtJXvCY8NyQYgJ1h1tn3ewSZ9v2UbOsMo+7hZGOVuLWNY5fJVombs2e2VeKK2sMax30oOfYP+WjLf5SsHmufMN08GjXPOu/4+vMR00ien++0zjPK9/UwvXbikR8v6jWTF2zejaZuiJ9srjjcek14rF8gQO1NkZhN+fVIjTYIPhzXulZ+J/KslRBCCCGEEKLOq6lR6YoppToAi8vNztNa96rpugghhBBCCCEEWKFhpLU+AHSu6bhCCCGEEEKIigy1smNbzZOudEIIIYQQQog6r8YzRkIIIYQQQojaozYPoV2TJGMkhBBCCCGEqPMkYySEEEIIIUQdJs8YmdTZ9xhZuwJCCCGEEKJOqPXNjmd/P1qj18Yzx7Sqld+JZIyEEEIIIYSow+QRIxN5xkgIIYQQQghR50nGSAghhBBCiDrMICkjQDJGQgghhBBCCCEZIyGEEEIIIeoyyZSYyPcghBBCCCGEqPMkYySEEEIIIUQdJo8YmUjGSAghhBBCCFHnScZICCGEEEKIOkxGpTOxWsZIKdVYKXVKKeVpnvYwTzeronwTpdQqpdRhpdShs+WUUhuVUnvNP9FKqV9qbCOEEEIIIYQQVwSrNYy01pHAPGCGedYMIERrfbqKVb4CZmqt2wA9gXjz5wzQWnfWWncGtgI/VWe9hRBCCCGEEFcea3elmwXsUkpNAfoDkysrpJRqC9hqrVcDaK0zKynjBgwF7q222gohhBBCCHGFkZ50JlZtGGmtC5RSzwIrgau01gVVFG0JpCqlfgICgb+BF7TWRaXKXAes0VqnV2edhRBCCCGEEFee2jAq3SggBmh/jjK2wADgGaAHEATcU67MbcA3VX2AUmqiUipUKRUaEhJyURUWQgghhBDiSmFQNftTW1k1Y6SU6gyMAHoDm5RS32qtYyopGgXs1VqHmdf7xbzOZ+Zpb0zPHV1fVSytdQhwtkWkL9EmCCGEEEIIIa4A1hyVTmEafGGK1joCmAm8V0XxnYC7UsrHPD0UOFRq+U3A71rr3OqqrxBCCCGEEFcig1I1+lNbWbMr3YNAxNkBFYC5QBul1KDyBc3PEj0DrFFKHQAUsKBUkfGcoxudEEIIIYQQQpyL1brSlevadrbx0/Uc5VcDHatYNvhS108IIYQQQoi6oBYncWpUbRh8QQghhBBCCCGsytrvMSpDKdUBWFxudp7Wupc16iOEEEIIIcSVrjaPFFeTalXDSGt9AOhs7XoIIYQQQggh6pZa1TASQgghhBBC1CyFpIxAnjESQgghhBBCCMkYCSGEEEIIUZfJM0YmkjESQgghhBBC1HmSMRJCCCGEEKIOk4yRiWSMhBBCCCGEEHWeNIyEEEIIIYQQdZ50pRNCCCGEEKIOU0r60oFkjIQQQgghhBBCMkZCCCGEEELUZTL4golkjIQQQgghhBB1nmSMhBBCCCGEqMPkESMTyRgJIYQQQggh6jzJGAkhhBBCCFGHGSRlBNTCjJFSqrFS6pRSytM87WGeblZF+SZKqVVKqcNKqUNVlRNCCCGEEEKIqtS6jJHWOlIpNQ+YAUw0/z9Ea326ilW+At7UWq9WSrkAxpqpqRBCCCGEEJc/GZXOpNY1jMxmAbuUUlOA/sDkygoppdoCtlrr1QBa68waq6EQQgghhBDiilErG0Za6wKl1LPASuAqrXVBFUVbAqlKqZ+AQOBv4AWtdVENVVUIIYQQQojLmjxiZFLrnjEqZRQQA7Q/RxlbYADwDNADCALuqaygUmqiUipUKRUaEhJyiasqhBBCCCGEuJzVyoyRUqozMALoDWxSSn2rtY6ppGgUsFdrHWZe7xfzOp+VL6i1DgHOtoh0NVRbCCGEEEKIy46B2pUyUkpdDXwE2AALtdYzyi1/CngAKAQSgPu01uEXG7fWZYyUUgqYB0zRWkcAM4H3qii+E3BXSvmYp4cCh6q/lkIIIYQQQohLTSllA8zB1HusLXCbeVyB0vYA3bXWHYEfgHcvRexa1zACHgQizg6oAMwF2iilBpUvaH6W6BlgjVLqAKCABTVWUyGEEEIIIS5zStXsz3/oCZzQWodprfOBb4FxpQtorddqrbPNk9uARpfie6h1XenKdXk72/jpeo7yq4GONVA1IYQQQgghxEVSSk3E9Fqes0LMbQCAhkBkqWVRQK9zfNz9wJ+Xol61rmEkhBBCCCGEuHKVT4RcKKXUHUB3oELPsgtxWTSMlFIdgMXlZudprc/VehRCCCGEEEL8h1r2gtczQONS043M88pQSg0HXgYGaa3zLkXgy6JhpLU+AHS2dj2EEEIIIYQQ1Won0EIpFYipQTQeuL10AaVUF+BT4GqtdfylCnxZNIyEEEIIIYQQ1cNQi97wqrUuVEpNBv7CNFz351rrg0qp14FQrfVvmEatdgGWmQa0JkJrPfZiY0vDSAghhBBCCFFraK1XACvKzZta6vfh1RFXGkZCCCGEEELUYbUoYWRVtfE9RkIIIYQQQghRoyRjJIQQQgghRB1Wm54xsibJGAkhhBBCCCHqPMkYCSGEEEIIUYdJwshEMkZCCCGEEEKIOk8yRkIIIYQQQtRhkikxke9BCCGEEEIIUedJxkgIIYQQQog6TMlDRoBkjIQQQgghhBBCMkZCCCGEEELUZZIvMpGMkRBCCCGEEKLOq9aGkVKqsVLqlFLK0zztYZ5udo513JRSUUqp2aXm2SulQpRSx5RSR5RSN5ZadotS6pBS6qBS6uvq3B4hhBBCCCHElalau9JprSOVUvOAGcBE8/9DtNanz7HadGBDuXkvA/Fa65ZKKQNwtqHVAngR6Ke1TlFK+V7qbRBCCCGEEOJKZpDBF4CaecZoFrBLKTUF6A9MrqqgUqob4AesBLqXWnQf0BpAa20EEs3zHwTmaK1TzMviL3XlhRBCCCGEEFe+an/GSGtdADyLqYE0xTxdgTkT9D7wTLn57uZfpyuldiulliml/MzzWgItlVKblVLblFJXV8tGCCGEEEIIcYVSNfxTW9XU4AujgBig/TnKTAJWaK2jys23BRoBW7TWXYGtwHullrUABgO3AQtKNaTKUEpNVEqFKqVCQ0JCLnQ7hBBCCCGEEFegau9Kp5TqDIwAegOblFLfaq1jKinaBxiglJoEuAD2SqlMTM8QZQM/mcstA+43/x4FbDdnoU4ppY5haijtLP/hWusQ4GyLSF+KbRNCCCGEEOJyJ48YmVT3qHQKmIepC10EMJOSbE8ZWusJWusmWutmmLrTfaW1fkFrrYHlmLJCAMOAQ+bffzk7XynljalrXVh1bIsQQgghhBDiylXdGaMHgQit9Wrz9FzgXqXUIK31egs+53lgsVLqQyABuNc8/y/gKqXUIaAIeFZrnXRpqi6EEEIIIcSVT0nKCABlSsjUOXVyo4UQQgghRI2r9a2Ob/acqdFr49u6NKyV30lNDNcthBBCCCGEqKVqajS22q7GG0ZKqQ7A4nKz87TWvWq6LkIIIYQQQggBVmgYaa0PAJ1rOq4QQgghhBCiInnGyEQyZ0IIIYQQQog6T54xEkIIIYQQog6TfJGJZIyEEEIIIYQQdZ5kjIQQQgghhKjD5BkjE8kYCSGEEEIIIeo8aRgJIYQQQggh6jzpSieEEEIIIUQdJpkSE/kehBBCCCGEEHWeZIyEEEIIIYSow2TwBRPJGAkhhBBCCCHqPMkYCSGEEEIIUYdJvshEMkZCCCGEEEKIOk8yRkIIIYQQQtRh8oiRiWSMhBBCCCGEEHVetTaMlFKNlVKnlFKe5mkP83SzKsqvVEqlKqV+Lzd/qVLqqFLqX6XU50opO/P8CUqp/UqpA0qpLUqpTtW5PUIIIYQQQlxpDKga/amtqrVhpLWOBOYBM8yzZgAhWuvTVawyE7izkvlLgdZAB8AReMA8/xQwSGvdAZgOhFyamgshhBBCCCHqkpp4xmgWsEspNQXoD0yuqqDWeo1SanAl81ec/V0ptQNoZJ6/pVSxbWfnCyGEEEIIIc6PPGNkUu0NI611gVLqWWAlcJXWuuBCP8vche5O4IlKFt8P/Hmhny2EEEIIIYSou2pq8IVRQAzQ/iI/Zy6wQWu9sfRMpdQQTA2j56taUSk1USkVqpQKDQmRHndCCCGEEEIAqBr+r7aq9oyRUqozMALoDWxSSn2rtY65gM+ZBvgAD5Wb3xFYCIzSWidVtb7WOoSSZ5C0pfGFEEIIIYQQV67qHpVOYRp8YYrWOgLT4ArvXcDnPACMBG7TWhtLzW8C/ATcqbU+dmlqLYQQQgghRN2hVM3+1FbV3ZXuQSBCa73aPD0XaKOUGlRZYaXURmAZMEwpFaWUGmleNB/wA7YqpfYqpaaa508FvIC55vmh1bYlQgghhBBCiCuW0rpO9iqrkxsthBBCCCFqXC3OkZisOBhfo9fGo9v51srvpCaG6xZCCCGEEELUUrX5pas1qcYbRkqpDsDicrPztNa9arouQgghhBBCCAFWaBhprQ8AnWs6rhBCCCGEEKKi2jwgQk2qqfcYCSGEEEIIIUStJc8YCSGEEEIIUYdJxshEMkZCCCGEEEKIOk8yRkIIIYQQQtRhSkalAyRjJIQQQgghhBCSMRJCCCGEEKIuM0jCCKjDDaOtJ1KtErdPc3cA/rfyuFXiT7+6hVXilufYZXKNx8zZM7vGY1ZmwfZwq8R9sFdTALadTLVK/N7B7gAcPJNllfjtGjpbJW5t0/X1f6wSd/fUoVaJK2oPaxz3oeTY/8iPh6wSf96NbQGYuOygVeKH3NwOgE3HU6wSv38LDwBm/HPSKvFfGBoMwOc7I6wS/74eTawSV1yYOtswEkIIIYQQQsgzRmfJM0ZCCCGEEEKIOk8yRkIIIYQQQtRh8h4jE8kYCSGEEEIIIeo8yRgJIYQQQghRh8kzRiaSMRJCCCGEEELUeZIxKmV/6Fa+DvkAo9HIwKvGMuaWu8ssLyjIZ8H7r3H6xBFcXOvzyAtv4OPXgMz0NGa/9QKnjh+m//BruPORZy8ofszhXez9KQRtNBLY+yrajLi5zPKja3/m1NZVKIMN9Vzc6HH7FJw9fQHY9+vnxBwKRWsjfq260OWGiajLrMPo/GkTGDWwPQnJGXS/+a1Ky7z/3E2M7NeO7Nx8Jk5bzN4jUQBMuLYXLzwwEoAZC/9i6fLtNVbvS+XU/p38s2Qe2mikw6Cr6XXt+DLLI4/sZ+3S+SREhjFm0ku06jmweNn6bxcQtm8H2mikafuuDL1jksV///2hW1n6qWn/HzSy8v0/5L2S/X/Si6b9H2D5d4vYsGo5BoOBOx5+mg7delu8/VprPps9k93bN1HPwYHJz71GcMs2FcqdPHaIT955lfy8XLr26s/9k59FKcWpk8f4dNab5Obk4OsXwJSX38TJ2cXietRFfYM9eWZkC2wMip/3xLBoc9kh5a/t5M+U4c2Jz8gD4LudUfyyJwaAx4cF07+FFwALN5xm1aH4mq28uKzVhuN+Wz9nbunkj1KKzadSWHUsqczyAYEeDAr2wKghr9DI0t3RxGbk4+lkx7SrgonLyAfgVHI23+yJtTh+Oz8Xbu3ij0HBprBUVh5NLLN8YJAHQ5p7YtSavEIji0NjiDH/W2xYvx53dGuAo60BDbz5dxiFRn3esQ/s2so3IbPQRiMDrhrL6JvvKrO8oCCfzz54jfATR3F2dePh59/A268BB/ds58dFcyksLMTW1pab73uMNp26W7ztUQdD2f79p2htpGW/kXQceUuZ5f/+/RPHNv+FwcYGB5f69L9zCi5efgAsmjQGj4bNAHD28GH4pGkWxw/bt5M1i+diNBrpNHgUvcdWPO+uWTyP+Mgwxk5+mdbm8274ob38s2RecbmkmEjGPvoyLbv3s7gOonap9oaRUqoxsAHoprVOVkp5ALuBIVrr01Ws4wYcAn7RWk8ut+w3IEhr3d48PR0YBxiBeOAerXW0pfU0FhWxeN5Mnn3jEzy9fXntyXvo0nsADZsEFZfZ8NdvOLm48u7CH9m2fhXLvpjDpBfexM7enhvufIio8DDOhF/YOP1GYxG7l81j0KQ3cHT34u/3n6RBh17U9y8Z/96jUTDBz8zC1t6BE5tWsP+3L+hzz/MknjpM4qnDXPX8JwCs/eg5Ek4cwLdFxwuqi7UsXr6N+d+tZ+H0uypdPrJ/W4Kb+NB+3Gv07NCMj18az8C73sPDzYmXJ46i34R30Vqz5evn+WPdflIzcmp4Cy6c0VjE31/N5ubnZuDq6c2SaY8R3LUP3g2bFpdx8/Jl1IPPsPPPH8qse+b4Qc4cP8jdb84H4JvpTxF5ZD9N2nQ6//hFRXw1dybPvWna/1+dUvn+7+ziyszPTPv/95/P4dEX3+RMRBjbN6zmrfnfkJqUyDsvTebdBcsw2NhY9B3s3r6ZmDMRzFn8K8cOHyDkw7d5Z+5XFcp9OuttHnn6FVq26cAbLz7Gnh1b6NqrH3Pfe517Hn6Sdp26sebPX/jlu6+4/b5JFtWhLjIoeH5UKyYt2UNceh5LHujO+qMJnErMLlNu1cF43ll5rMy8/i28aB3gym2f7sTOVrHgrq5sPpFEVn5RTW6CuIxZ+7ivgPGdA/h4Uzgp2QW8MDSI/TEZxJobOwA7I9PYeMr0DqCOAS7c1NGf2ZtN78RJzMznrTVhF7bx5vi3dw1g1obTpGQX8tLwIPZFZxQ3fAB2RKSxIcwUv1OAKzd39uPjjREYFNzfsxGf74giKi0PZ3sbiixoFBmLilg67z2efuNjPLx8mf7kvXTuNYAGTQKLy2xc9RtOzm68veAHtq9fzQ+L5vDw82/i4ubOY1Pfw8PLh6jTJ5k1dQrvf7Xcom03GovY9u1cRj7+Jk4e3iyfMYUmHXvjHlBy3ePVOJixL36Erb0DR9b/wc6fP2fIAy8CYGNvz7iXL/z9hEZjEau//IRbX3gHV09vvpw6mebdKp53Rz/0LDtWLCuzbtO2nbn3rU8ByMlMJ+Tpewjs0O2C61IbyAteTaq9K53WOhKYB8wwz5oBhFTVKDKbjqkxVYZS6gYgs9zsmVrrjlrrzsDvwNQLqWfYsUP4NWiEb0BDbO3s6DVwBHu2la3Cnu0b6D/sGgB69B/KoX070VpTz8GRlu06Y2dnfyGhAUgOP4aLTwAu3v7Y2NrRpOtAog9sK1PGt0VHbO0dAPBq1ors1JK7SsaCfIyFhRgLCzAWFeHg6nHBdbGWzbtPkpyWXeXyMYM68vXvOwDYceA09V0d8fd2Y0TfNqzZdoSU9GxSM3JYs+0IV/VrW1PVviRiTx7Fw7cB7r4B2Nja0br3IE7u3lKmTH0ff3yaBFXIBCkURQX5FBUWUlRQgLGoEGc3y/7+le3/u7eW3f93b9tA/+EV9//dWzfQa+AI7Ozs8fFvgF+DRoQds/xFiju2rGPwiDEopWjVtiNZmRkkJyWUKZOclEBOdhat2nZEKcXgEWPYvnktADFREbTt2BWATt16s23jGovrUBe1b+hGVEo2Z1JzKTRq/joYz+BWPue1bpC3M7vDUynSmtwCI8fjM+nb3KuaayyuJNY+7jfzdCQhK5/ErAKKNIRGpdGpgWuZMrmFxuLf7W0u7WVToKcj8Zln42t2RqbRqeE54tsqMLd92vq5EJWWS1SaqRGVlV/E+TeLTMd934BG+Pibjvs9K7nu2bttI32HjQage/8hHN4XitaapsGt8PAyHScaNg0iPz+PgoL8CjHOJfH0MVx9GuDqYzrvBXUfSMS+rWXKBLTqVHzd4xPUmuyUxMo+6oLEnDyKu1/JebdN78Ec31XxvOtbyXm3tKM7NhLUqQd29RwuWd2E9dRUV7pZwC6l1BSgP1Dl66+VUt0AP2Al0L3UfBfgKWAi8P3Z+Vrr9FKrO4NFx4ViKUnxeHr7FU97ePsSdvRguTIJePqYuq7Z2Nji6ORCZnoarvXdLyRkGTlpSTi5l1yMOLp7kxx+tMryp7atIqCN6e6Ed2AbfFp0ZPnUu0Brmg8Yg5t/44uuU23TwNedqNiSN3efiUulga87DXzciYorNT8+lQY+7lao4YXLSEnE1avk7+/i6UPMySPntW6DFm1p3KYz8x8fj9aaLsPH4dXQsjdtl9//Pb19OXme+39KUgLBrduXWTclyfLuVMmJ8Xj7ltTBy8eX5MQEPEt9L8mJCXiZ61BSxhSrcdMgdmxeR6/+Q9iy/m8S4+MsrkNd5ONaj9i0krvT8el5tG/oVqHc0DY+dG3qTnhSNu+vOk5ceh7H4jKZOKgZS7ZG4GBnQ/dmHoQlZNVk9cUVrrqP++6OtqRkFxRPp+QUEujpWKHcoCAPhrXwwsag+HBjSVdTL2d7XhoWSE6BkeUHEziRVHUjr/L4diSXip+aXUCgV8X4g4M9GdHSFP+D9acB8HM13Yx9YkBTXOvZsDMyjb+OJlVYtyqppY7pYLruOVXpcd90XK7qumfX5rU0DW5p8c3h7NQknD28i6edPLxJOFX1dc+xzX/RsF1Jd72ignx+e/txDAYbOoy8maad+1oUPyMlETfPkvOLq6f3eZ93Szu8bR09Rt1o8Xq1jQy+YFIjDSOtdYFS6llMjZ2rtNYFlZVTShmA94E7gOHlFk83L6tw1FFKvQncBaQBQy5h1Wul8J1rSY44wZDHTUm4jIRoMuIiGfPaIgA2zH2FhJP/4hPc/hyfIq4UKXFnSIqO4KEPvwZg2TsvEHX0AI1adbByzWrWo89N47NPZrJs8QJ69B2ErZ2dtat0xdhwLJGV/8ZRUKS5sWsDXh/XlocW72FbWDLtGrjyxX3dSMkuYH9UGkZ9QfemhKjV1oelsD4shR6N3Rjd2psvQ6NJzy3k5T+Pk5VfRBN3Bx7q05jpq0+WyfBcKutOJrPuZDI9G9dndBsfFu08g0Epmns78dbfYeQXGXlyUDPCU3I5El9zNyfOhIfxw6I5PDX9o2qNc3L7PySFH2fUU+8Wz7v5zUU4u3uTkRDDyg9fxKNhIG4+AdVaj/IyU5JIiDxFYAfLn68StVNNjko3CogBznW1PglYobWOKj1TKdUZCNZa/1zZSlrrl7XWjYGlVJGNUkpNVEqFKqVCQ0JCKiz38PIlObHkDnNKYnxxmrikjA/JCaa700VFheRkZ+LiVv8cm3P+HOt7kZ1a0m0oJzURx/oVu6TEHd3LodXf0f/B/2Fja7rwO7N/K57NWmFXzxG7eo74t+lO0mnL73rUdtHxqTTyL+ki1tDPnej4VKITUmnkV2q+rzvRCalWqOGFc/XwJqNUt7HM5ARcPc6vS9Lx0M00CG6NvYMj9g6OBHbqQfSJwxbFL7//J1uw/5vml1/Xl/Px5y/f8dSD43nqwfF4ePqUyfIkJcTj6V22Dp7ePiQlxJcrY4rVqEkg02bO5b1Pv2bA0KvxD2h0nltftyVk5OFfv17xtK9bveJBFs5KyymkoMjU4Pl5TzStA0q6+ny2KZzbQnYyacleFBCedPk82ydqv+o+7qfmFOLhVHITxcPRltScSu/dAhAamV7c1a7QqIufp4tIzSUxKx9fF8uyJqk5BXiWiu/uZEdKTmGV5XdGptHF3NUuNaeAYwnZZOYXkV+k+Tcmkybu59+dy73UMR1M1z3ulR73Tcfl8tc9yYnxzHnzee5/aiq+F3C8dXL3IqtU17jslESc3Sue96IP72Hfyu8Y9sg0bErd8HJ2N2WbXH0C8G/ZkeRIy57xdvXwJj255LybkZyIS6kM1vk4sn09Lbv3w8b28h/LTKma/amtaqRhZG7YjAB6A08qpapq0vcBJiulTgPvAXcppWaY53c3z98EtFRKratk/aVApflMrXWI1rq71rr7xIkTKywPbNmGuDORJMRGU1hQwPYNq+nSa2CZMp17DWDTmj8A2LnpH9p07H7JRn7zbNKSzIRoMpNiKSosIGL3Bhq071WmTErUSUK/m03/B/6Hg6t78XwnDx8STvyLsagIY1EhCScO4OZ35XWl+2P9AW4f0xOAnh2akZ6ZQ2xiOqu3HGZ4n9a4uzri7urI8D6tWb3FsoaBtfkHtSIl7gypCTEUFRZwZNt6grv0Oa913bx8iTxyAGNREUWFhUQd2Y9XA8v+/oEt2xAXXW7/7112/+/SawCb/q64/3fpPZDtG1ZTUJBPQmw0cdGRBLU8v77+o667lQ8WfMsHC76lZ//BrFv9O1prjh7aj5OzS5ludACeXj44Ojlz9NB+tNasW/07PfsOBiA1JRkAo9HIsiULGTn28u/aUBMOnsmgsacTDdwdsDUoRrbzZf2xsv34vUtd7A1q6c3pRNMdaYOC+o6mC4IWvs608HNh28nkmqu8uOJV93E/PCUHXxd7vJzssFHQvVF99keXfZTZp9T+3z7AhfhM07M0LvY2xZ2PvJ3t8HWxJzHLsudsTpeJr+jRuD77ojPKlCnd2OoQ4FI8Ct7B2Ewa1a+HvY3CoKCljxMx6WVvapxL+eP+jg2r6dxrQJkynXsNYMuaFQCEblpLa/NxPzszg49efYob75lEi7bnP9BPad5NW5IeH01Goum6Jyx0A407lh3RNCnyJFu+/oRhj0zF0c29eH5eVgZFBaYGbG5mGnEnD5UZtOF8BAS1IiX2DKnxpvPu4W3raN71/M67Zx3aupY2fa74jkp1Sk2MSqcwDb4wRWsdoZSaianRM6F8Wa31hFLr3QN011q/YJ41zzy/GfC71nqwebqF1vq4ucw44IJSJTY2ttzxyDO897/HMRqNDBhxLQ2bBvHT4k8JbNGGLr0HMvCqsYS89yrPPXAjzq5uPPLcG8XrP33vdeRmZ1FYWMDuret55o2Py4zo9V8MNjZ0vfFhNsybah6uewT1A5ry74oleDRuQcMOvdj36+cU5uWydZGpC52Thw/9H5xKo879iD++n7/eeRSFwr9N1wqNqsvBl2/fw4BuLfB2d+HEyulMn78CO1vTyGYLf9jEyk0HGdm/HQd/m0Z2bgEPvboEgJT0bN5esJJNS54D4K2QlaSkW9bP29oMNjYMu2syP777EkZtpMPAkXg3asamH7/EP7Alzbv2ISbsKL9+9Bq5WRmc3LONLT8v5t63F9Cy5wAiDu9l0UsTQSkCO3Q/70bVWTY2ttz5yDPMfOVx83D119LIvP83a9GGrr0HMnCkaf9/9n7T/j/pedP+36hpED0HDOfFh8ZjY2PDnY88a/GIdADdevVn9/ZNTLpjnHm47leLlz314Hg+WPAtABOnvMgn70wjPy+Prj370rWXaXjUTf+s5M9fTY8f9u4/lKFXj7O4DnVRkda88+cx5kzojEEpftsbTVhCFg8PDuRQdAYbjiUyvmcjBrX0psioScstZNqvpgtQW4OBz+4xPeuYlVfIKz8foki60gkLWPu4b9Tw7d5YHuvfBINSbDmdSkxGHmPa+hCRksP+mEwGB3vQ2teZIiNk5xfx5U7TwLctvJ0Y086HIqPpdQNf74khu8CybnRGDd/siWHKwKYYzMOFx6TnMbadD+HJueyLyWBIc0/a+DpTpDXZ+Ua+2HkGgOwCI6uPJfHSsCA08G9MJgdiy49PVTUbG1smPPwMs6Y+gdFopP+IMTRsGsQvS0Jo1qI1nXsNZMBV17Lg/dd48cGbcHZx46HnpwOw5vdlxMdEsfybz1n+zecAPDX9I9zcPc87vsHGht7jH2HVJ6+gjUZa9L0KjwZN2b18Md5NWtCkU292/vgZBXm5rFvwNlAyLHdqbCRbvv4EpQxobaTjyJstbhgZbGwYcfdkvn/3RfNrMkbi06gZG39YhH9gS1p060vMyaP89OGr5GVncmLPNjb9+BUPvLMQgLSEWDKSE2jS+vIaAbgqtTiJU6OUruaTmFJqIjBMa32redoG2Ak8qbVef4717sHUMCo/XHczTA2js8N1/wi0wjRcdzjwsNb6zH9US289kXpB23Ox+jR3B+B/K4+fu2A1mX51C6vELc+xS5Xjb1SbnD0XPqznpbRge/h/F6oGD/YyDUG67WSqVeL3DnYH4OAZ6zyc366hs1Xi1jZdX//HKnF3Tx1qlbii9rDGcR9Kjv2P/Gj5aJmXwrwbTRn0icsO/kfJ6hFyczsANh1P+Y+S1aN/C1OXxxn/XNjrTC7WC0ODAfh8Z4RV4t/XowlcBu2OzcdTavSuVr8WHrXyO6n2jJHWOgQIKTVdBHQ9j/UWAYsqmX+aUs8paa2lv4wQQgghhBAXyFCbH/ypQTU5+IIQQgghhBBCnJNS6mql1FGl1Aml1AuVLK+nlPrOvHy7uUfZRbPKMBpKqQ7A4nKz87TWl9+DMUIIIYQQQlzGalO+yPzYzRxMA7dFATuVUr9prUv3h70fSNFaN1dKjQfeAW692NhWaRhprQ8Ana0RWwghhBBCCFFr9QROaK3DAJRS32IaYK10w2gc8Kr59x+A2UoppS9y8ATpSieEEEIIIURdpmr2p/T7Rc0/pd+l0xCILDUdZZ5HZWW01oVAGnB+L4A8h8v/jVRCCCGEEEKIy0b5wdlqC2kYCSGEEEIIUYepWvWUEWeA0m+qb2SeV1mZKKWULVAfSLrYwNKVTgghhBBCCFFb7ARaKKUClVL2wHjgt3JlfgPuNv9+E/DPxT5fBDXwgtdaqk5utBBCCCGEqHG1Kh1Tme0n02r02rhXcP1zfidKqdHAh4AN8LnW+k2l1OtAqNb6N6WUA6YRrrsAycD4s4M1XAxpGAkhhBBCCFF9an3DaEdYzTaMegadu2FkLXX2GaPtJ9OsErdXcH0Abv1yj1Xif3d3F6vELe+RHw/9d6FLbN6NbWs8ZmUmLjtolbghN7cDYMmuKKvEv6NbIwByC60SHoc6e7Qra8hHW6wSd+0Tfa0SV9Qe1jjuQ8mx37HLZKvEz9kzG4CGj/xslfhn5l0PwOurT1gl/tQRzQG44bNdVon/0/3dAHjs58NWif/J9W2sEldcGLlUEEIIIYQQog6rlekbK5DBF4QQQgghhBB1nmSMhBBCCCGEqMskZQRIxkgIIYQQQgghJGMkhBBCCCFEXVbLXvBqNZIxEkIIIYQQQtR5kjEqR2vNkk/fZ9/OLdSr58CDT02lWfPWFcqdOn6YBR+8Tn5+Hp169OWOh55GKcWOjX/z89IFREeeZtqsLwhqef5DRHdq4Mo9PRthUIp/jifx679xlZbr2aQ+Tw8J4sXfjxCWlEOHAFdu79YAW4Oi0KhZEnqGg7GZF/wdWEtbP2du6eSPUorNp1JYdSypzPIBgR4MCvbAqCGv0MjS3dHEZuTj6WTHtKuCicvIB+BUcjbf7Im1xiZclHZ+LtzaxR+Dgk1hqaw8mlhm+cAgD4Y098SoNXmFRhaHxhCTkQdAw/r1uKNbAxxtDWjgzb/DKDRa9kqCE/t28NdXc9BGI12GjKbf2NvKLA8/vJ9Vi+cQFxHGDY+9Qtteg4qXvTFhBL5NAgFw8/Jl/DNvWLz9WmveeftNNm1Yj4OjA9PfnEGbtu0qlHtk4v0kJiRQWFRE127deOmVadjY2DD74w9Zt3YNBmXAw8uL6W++ja+vn8X1qIt6NHVn8qBAbBT8cTCeb0LPlFk+so0PD/dvRmKW6d/Yz/tiWHEwHoB3xrWhbYArB6LTeem3IzVed3H5s+axf/60CYwa2J6E5Ay63/xWpWXef+4mRvZrR3ZuPhOnLWbvEdMrDyZc24sXHhgJwIyFf7F0+XZLN53BbX15/ZaOGJTim83hzFl1rMzyV2/qQN+W3gA42tvi5WpP26f/AGDJ5L50DfRg58lk7p671eLYANGHQgn9IQRtNNK871W0u+qWMssPr/mZE1v/wmCwoZ5LfXrfMQUXT19ij+1j948LisulxUXR/97nadypj0XxuzR0477ejTEY4O+jify8v/Lrnt7N3HluWDDP/nqYk4nZxfO9ne346MZ2fL87psprpnNp4+vMjR39MCjF1vBUVpfb9/o1c2dgkHnfKzLy7Z4YYjPyaerhwPjOAQAoBSsOJ7I/JsPi+LWJkoQRUM0NI6VUY2AD0E1rnayU8gB2A0O01qerWMcNOAT8orWebJ5nD8wGBgNG4GWt9Y9KqXrAV0A3IAm4tarPPV/7Q7cQdyaSmQt/5OTRf1k0+x1e/fCLCuW+nPMO9z3xEsGt2vP+1CnsD91Kpx59adg0mMdfeZcvPnnborhKwX29G/PmqhMkZRfw9jWtCI1M40xabplyDrYGRrf15XhCVvG8jLxC3l1zkpScQhq7O/DSiGAesdK7ci6UAsZ3DuDjTeGkZBfwwtAg9sdkEGs+4QHsjExj46kUADoGuHBTR39mb44AIDEzn7fWXPQLj61GAbd3DWDWhtOkZBfy0vAg9kVnFDd8AHZEpLEhzLT9nQJcubmzHx9vjMCg4P6ejfh8RxRRaXk429tQZGGjyGgsYuUXHzPhxXdx8/Jh4SuTaNm1Dz6NmhWXqe/ty9iHn2Pr78sqrG9rb8/Et0MuaNvP2rRxAxHhp1n+5yoO7N/HG6+/ytJvK8aa+cFHuLi4oLXm6SmPs+qvlYwafQ333PcAkx+fAsDSJV/x6bw5/G/a6xdVp7rAoOCJwUE8+/NBEjLzmT++I1vCkglPzilTbu3xRD5ed6rC+t/tjqaerYFrO0gjVFjO2sf+xcu3Mf+79Sycflely0f2b0twEx/aj3uNnh2a8fFL4xl413t4uDnx8sRR9JvwLlprtnz9PH+s209qRk6ln1MZg4I3x3fito83E5OSw4oXhrBqfwzHY0susF/94UDx7/cODqJ9Y/fi6fmrj+Nob8MdAwIt33BMx/2d389j6OQ3cHL3ZuXMJ2nUoTf1A5oUl/FoHMSoAR9ia+/AsY1/sOeXzxlw3wv4t+zE6BdN72fKy8rgt9ceIKCNZe9JNCh4sG8TXlt5jKSsAt4d25qdEWlEpZa77rEzcE07X47FV7zhe2+vxuyJSr+ArTftezd38mfO5ghScwp4dkggB8rte7ui0tl8OhWA9v4uXN/Bj3lbIolOz2PmulMYNbjVs+WFYYH8G5uBhadeUQtVa1c6rXUkMA+YYZ41Awj5j8bLdEyNqdJeBuK11i2BtsB68/z7gRStdXNgFvDOxdZ597YN9Bs2GqUUzVt3IDsrg9TksnfuU5MTycnOonnrDiil6DdsNLu3marUsEkgAY2aWhy3ubcTcel5xGfmU2TUbDmVQo/G9SuUu7VLAL8eiCO/yFg873RyDik5prdmRqbmYm9jwNZweTX9m3k6kpCVT2JWAUUaQqPS6NTAtUyZ3MKSbba3ubJ6gQZ6OhKfeXb7NTsj0+jU8Bzbb6vAfABu6+dCVFouUWmmRlRWfhGWHpujTxzBw68hHn4NsLG1o12fIRzdVfZFoO4+/vg1CUZV07619p81XDv2OpRSdOzUmYyMdBIS4iuUc3FxAaCwsJCCggKU+TbX2fkAuTk5xfPFubX2cyE6LYeY9DwKjZp/jiXSL8jzvNffHZlGdn5RNdZQXMmsfezfvPskyWnZVS4fM6gjX/++A4AdB05T39URf283RvRtw5ptR0hJzyY1I4c1245wVT/LXiLepZknpxOyiEjMpqBI82toFCM7BVRZ/rrujfhlZ2Tx9KajCWRexBuzk04fw9W7Aa7eAdjY2tG060Ai928rU8a/ZSds7R0A8G7WmuzUxAqfE7FnEw3adi8ud76a+zgTk55LXEY+hUbNprAUejZxr1Du9q4N+GV/LPlFZc9sPZvWJy4jj8iU82+MltbU05HErHySsk373q6odDoEVL3v1bM1FJ93C4p0cSPIzkahr4AGkarhn9qqJrrSzQJ2KaWmAP2BKl89rZTqBvgBK4HupRbdB7QG0FobgbP/MscBr5p//wGYrZRSWl/4LpqcGI+nT8mdT09vX5IT43H39C5TxsPbt0KZi+HpZE9SVsldiqTsfJr7OJcpE+jpiJezPXvOpHNte9/yHwFAr6bunErKsbgblbW5O9qSkl1QPJ2SU0igp2OFcoOCPBjWwgsbg+LDjeHF872c7XlpWCA5BUaWH0zgRFLVJ7rayN3RjuRS25+aXUCgV8XtHxzsyYiWpu3/YP1pAPxc7QF4YkBTXOvZsDMyjb+OJlVY91zSUxJx8/Ipnnbz9OHMifN/S3hhQT4LX34Eg40Nfa8dT+se/S2KDxAfH4efv3/xtJ+fP/Fxcfj4VNzXH37wfv79dz/9+w9kxFUji+d/8tEslv/2Cy4uriz84iuL61AXebvUI77UHdKEzHza+LtUKDewuRcdG7oRlZLLnA2nSMjMr1BGCEvV9mN/A193omJTiqfPxKXSwNedBj7uRMWVmh+fSgMfd4s+29/dgehSF/UxKTl0CfSotGxDT0caezuz+WiCZRtwDjlpSTh5lFzbOHl4k3T6aJXlT25dRYO23SvMD9+9gdZDrrc4vpeTHUlZJX/7pOx8WpS77gnyMl337IpMZ1yHkvODg62B6zv689qfxxl3gdlqdwfb4pvKAKk5BTTzqLjvDQg0dWO3NSg+2VSy7zX1cGBC1wZ4OtnxVWi0ZIuuENV+211rXQA8i6mBNMU8XYFSygC8DzxTbr67+dfpSqndSqllSqmz/woaApHmOIVAGuB1yTeiFlDAnT0asnjnmSrLNHJ34PZuDViwLaLmKlbD1oelMPWvE/zybxyjW5sO6Om5hbz853HeWnOKH/fHcW/PhjjYXlkZpbPWnUzm5T+P89P+OEa3MTVkDErR3NuJz7ZH8e7aU3Ru6EZrX+f/+KRL6/GPv+aBN+dx/aMvsWrxXJLjoqs13vwFn7Fm3Sby8/PZsb3kDudjTzzJqjXruWbMtXz79ZJqrUNdsvVUCrd9sYsHlu5jV0QqL1zVwtpVEnVMXT/2j+veiD92n7HaxfepHf+QFHGctsNuLDM/Jy2Z1OjTNGjb9ZLHVMA9vRqzaEdUhWW3dg1g+b/xZTI61WXjqRReX32SXw/GM7J1SUMyPCWXt9aEMXPdKa5q6XXZ9dSpQFJGQM2NSjcKiAHan6PMJGCF1rr8vwBboBGwRWvdFdgKvGdpBZRSE5VSoUqp0JCQss9C/L18Ga9MnsArkyfg7ulNckLJA3zJifF4epe9Y+3p7UtKqQxRZWUslZydj5ezffG0l5M9KaXupDjYGWjs7sjUq5vzyY1taeHjzLNDgwkyZxU8nex4enAgczeGFz+IejlJzSnEw8mueNrD0ZbUnErb0ACERqYXd7coNGqyzF15IlJzSczKx9fFvsp1a6PUnAI8S22/u5NdmTtZ5e2MTKOLuatdak4BxxKyycwvIr9I829MJk3cLevS4ObhTXpSyZ3I9OQEXEtlSf9zfU9TI83DrwFN23Yi9vTx81rv26+XcssN47jlhnH4ePsQF1vy4HRcXCy+flXfCaxXrx5Dhg5j7T9rKiwbfc21/L161XnXvy5LzMzD17Xk34uPiz2J5bJB6bmFFJi7sfxxMI6WNdzwFleu2n7sj45PpZF/SRanoZ870fGpRCek0siv1Hxfd6ITUi367NjUXBqUylAEeDgSW+75mrPGdW/Er6EVGwgXw7G+F9kpJV3jslMScaxf8d5yzJE9/PvXdwx+aCo2dnZlloXv3kjjjn0w2FjeASkpuwAv55LP83KyJ7nUdY+jnYEmHo5MH92S+be0p6WPMy8ODybY24kWPs7c1aMh829pz5h2vtzQ2Z9RbXwqC1Ol1NxCPBxL6u3uaEfqObom7o5Kp2O5rnYAcRn55BUZCXCrZ1F8UTtVe8NIKdUZGAH0Bp5USlXVgbYPMFkpdRpTw+cupdQMTIMqZAM/mcstA87emjgDNDbHsQXqm8tXoLUO0Vp311p3nzhxYpllw6+9mTdmL+WN2Uvp1mcQm9esQGvNiSMHcHJ2KdONDsDd0xtHJ2dOHDmA1prNa1bQtffA8/9SKnEyMRt/t3r4uNhjY1D0DfQgNCqteHlOgZEHvzvAYz8e4rEfD3E8IYuZ/5wkLCkHJzsbXhgWzDe7ozlaalCGy0l4Sg6+LvZ4Odlho6B7o/rsjy77oKVPqRNe+wAX4s0Xby72NsU3H7yd7fB1sS8ePetycbrM9it6NK7PvuiyI9yUPuF3CHApbgAfjM2kUf162NsoDApa+jgRk56HJRoEtyY59gwp8TEUFRZwcOtaWnbre17r5mRmUFhgqkt2ehpRRw/i0/D8nrMbf/sEvv/pV77/6VeGDBvO8t9+QWvN/n17cXFxrdCNLjsrq/i5o8LCQjZsWEdgYBAA4eGni8utXbumeL44tyNxmTR0d8TfrR62BsXQlt5sCUsuU6Z0o71vkCcRyRfWp1+I8mr7sf+P9Qe4fUxPAHp2aEZ6Zg6xiems3nKY4X1a4+7qiLurI8P7tGb1lvPvfgywNzyFQF8XGns5YWejGNe9Eav2x1QoF+znQn0nO0LL/bu8WF5NW5KRcIbMxFiKCgsI372BRh17lSmTHHmSHd/OZtBDU3Fwda/wGad3radp90EV5p+PEwlZBLg54Otij61B0T/Ig50RqcXLswuM3LN0Hw9//y8Pf/8vxxKyePvvk5xMzOaVP44Vz//9YDw/7Y3lz8OWdTOMSMnBp9S+162RGwfKjSznU6rh1s7fpbgLsZeTHWcTRB6Otvi52JfpDn85UjX8X21V3aPSKUyDL0zRWkcopWZiavRMKF9Waz2h1Hr3AN211i+Yp5djGpHuH2AYplHrAH4D7saURboJ+Odini8C6NSjH/t2buHZ+2/Avp4DDzz5v+Jlr0yewBuzlwJw16TnWDDrdQry8ujYvS8du5suIkO3rGXxvPfJSEvhg1efoklQC55745P/jGvU8Pn2KF4aHozBoFh3PImo1Fxu7uxPWFI2uyKrHnXl6jbe+Lnac2Mnf27sZOqD++bqk6RfxEOZNc2o4du9sTzWvwkGpdhyOpWYjDzGtPUhIiWH/TGZDA72oLWvM0VGyM4v4sudpu5aLbydGNPOhyKjacjnr/fEkF1Q/en1S8mo4Zs9MUwZ2BSDecjamPQ8xrbzITw5l30xGQxp7kkbX2eKtCY738gX5m6V2QVGVh9L4qVhQWjg35hMDlg4XLvBxoar73mMr2c8jzYa6TR4FL6NmrFu2RcEBLWiVbe+RJ88wvezppGblcnx3VtZ/8OXPDLzcxKjI/jjs1kopdBa03fs+DKj2Z2vAQMHsWnDesaMGoGDgyOvv1EydO4tN4zj+59+JScnhycefYT8gnyMRk2Pnr24+dbxAHz0wfucPn0Kg0ERENCQV6a9ZnEd6iKjho/XhfHudW0xKMWfh+I4nZzDvb0bczQuky2nUrihcwD9gjwpMmrScwuZsfpE8fof3dSeJh6OONob+P6+bsz8+2SZixshzsXax/4v376HAd1a4O3uwomV05k+fwV2tjYALPxhEys3HWRk/3Yc/G0a2bkFPPSqqYtuSno2by9YyaYlzwHwVshKUtIte76pyKh55dt9fP1YPwwG+G5LOMdiMnhmTBv2RaSwer8pg27KFlXsRv/T0wNo7ueKUz1bQt+6mqcX72b94fN/3tlgY0P3Wx7hnzn/Q2sjwb1H4B7QlH2/L8arSQsadezNnl8+ozAvl02fmUbadfLwYfDD0wDITIojOyURv+YdLNrus4waFm6NYOrVLTAoxZpjiUSm5jK+awAnE7PZGZH23x9yEYwalu2LZVK/xigU28JTic3IZ3QbbyJScvk3NpOBQZ608nWmyKjJLihi8S7Tvhfk5ciIlo0pMmo08P2+2OLspbi8qYtsR5z7w5WaCAzTWt9qnrYBdgJPaq3Xn2O9ezA1jM4O190UWAy4AwnAveaGloN5fhcgGRivtT6fcTv19pPV+w+uKr2CTSPN3frlHqvE/+5uy4bTrC6P/HjovwtdYvNutGzEoOoy0UpDqYfcbHon0JJdl7Y7xvm6o1sjAKzVXneQt7YBMOSjLf9dqBqsfeL8MpDiymWN4z6UHPsdu1Q59lO1ytljGta64SM/WyX+mXmmgRFeL3VDoyZNHdEcgBs+22WV+D/d3w2Ax362LKN3qXxyfRuo1U/VmOwJz6jRJ9i6NHWtld9JtV4qaK1DgJBS00WUdIM713qLgEWlpsOBCn3VtNa5wM2XoKpCCCGEEELUSfKGC5Mrc/gWIYQQQgghhLBAjXcuUUp1wNT9rbQ8rXWvysoLIYQQQgghqo8kjExqvGGktT4AdK7puEIIIYQQQghRFXkcWQghhBBCiLpMUkaAPGMkhBBCCCGEEJIxEkIIIYQQoi6rzS9drUmSMRJCCCGEEELUedX6gtdarE5utBBCCCGEqHG1Ph1zICqzRq+NOzRyqZXfiWSMhBBCCCGEEHVenX3G6HBMllXitglwBmD8l3usEv/bu7tYJW55E5cdrPGYITe3q/GYlZny6xGrxP1wXGsAVh9OtEr8EW28AdgXkWGV+J2auFolbm0zat52q8T98xF5VV1dZ43jPpQc+xs+8rNV4p+Zdz0Ajl0mWyV+zp7ZAHy354xV4t/apSEAdy7dZ5X4iyd0AuDznRFWiX9fjyZWiWupWpm+sQLJGAkhhBBCCCHqvDqbMRJCCCGEEEIgKSMzyRgJIYQQQggh6jzJGAkhhBBCCFGHyXuMTCRjJIQQQgghhKjzJGMkhBBCCCFEHaYkYQRIw6gMrTULP5nJrm2bqOfgwOMvvEZwyzYVyi1ZOJu1f/1BVkY6367cXDz/4L5dfDb7fU6fPM4zU9+m7+DhFsXv1MCVu3s2wqAU/xxP4rd/4yot17NJfZ4aEsRLvx8hLCmHDgGu3NatAbYGRaFRszT0DAdjMy3b+FqgnZ8Lt3bxx6BgU1gqK4+WHVZ6YJAHQ5p7YtSavEIji0NjiMnIA6Bh/Xrc0a0BjrYGNPDm32EUGi+v9/i29nXmhg6+KBTbIlJZczy5zPK+zdzpH+iO1pBXaOS7fbHEZeTT0seJa9v6YmOAIiP8djCe44nZFsc/tHsbPyz8EKPRSN8R13LVjXeWWV5QkM/iD6cTcfIozq71ue+Z1/HyCwDgzOkTfDPvXXKzs1DKwHPvLcTOvp5F8bXWfDH3Pfbs2Ey9eg5MevZVglq0rlAu7Nhh5sx8lfz8PLr07Me9k55BmY/of/7yLX/9tgyDwYauvfpxx4NPWPw91EXdGtfn4f5NMSjFysPxLNsTU2m5fkEevDKyJY//8C/HE7KwMSimDA4k2NsZG4NizdFEvt8TXcO1F5c7ax/7B7f15fVbOmJQim82hzNn1bEyy1+9qQN9W5peN+Bob4uXqz1tn/4DgCWT+9I10IOdJ5O5e+5Wi7d9/rQJjBrYnoTkDLrf/FalZd5/7iZG9mtHdm4+E6ctZu+RKAAmXNuLFx4YCcCMhX+xdLnlQ/Ef37uDFV/ORhuNdB06moHjbi+z/PThffz55RziIsK4+fH/0a73IABSE2L55v2paK0pKiqk98jr6TFirMXxOwS4cmf3BhiUYt2JZH4/FF9pue6N6/PEwGZM/fMYp5JzALi2nS+Dgk37xeLQaA7EWP4qiLB9O1mzeC5Go5FOg0fRe+z4Mssjj+xnzeJ5xEeGMXbyy7TuORCA8EN7+WfJvOJySTGRjH30ZVp272dxHUTtUq0NI6VUY2AD0E1rnayU8gB2A0O01qfLlR0CzCo1qzUwXmv9i1JqKdAdKAB2AA9prQuUUoOBX4FT5nV+0lq/fqH13bV9MzFREcxb+ivHDh1g/qy3mTnvqwrlevQZyOjrb2XShOvKzPf2DeDxF17ll+8WWxxbKbivd2PeXHWCpOwC3rqmFbsi0ziTllumnIOtgVFtfTmeUPIepoy8QmauOUlKTiGN3B14aUQwk6z0vogLpYDbuwYwa8NpUrILeWl4EPuiM4pPfgA7ItLYEJYCQKcAV27u7MfHGyMwKLi/ZyM+3xFFVFoezvY2FF1mjSIF3NTRj3lbIknNKeCpQc34NzaTuIz84jK7otLZcjoVgHb+LlzXzpdPt0WRlV/Egu1RpOcW4u9qz8N9GvPqqpMWxTcWFfH9p+8z+bUPcffyZeazD9ChZ38CGgcWl9m6+nccXVx5df73hG78m1+/mst9z06nqKiQL2e9zl1T/kejwBZkpqdhY2P5oWXPjs3Enonk40U/c/zwvyz8+G3e+uTLCuUWfPw2Dz35Ci3atOftl59g784tdOnZj3/3hhK6ZQMz53+Dnb09aSnJlUQR5RkUPDqgGS8tP0JiVj4f3diO7adTiUjJKVPO0c7AuA7+HIkruekyINgTO4OBSd8foJ6tgU9v7ci6E4nEl9pvhTgXax/7DQreHN+J2z7eTExKDiteGMKq/TEcjy25yH71hwPFv987OIj2jd2Lp+evPo6jvQ13DAjkQixevo35361n4fS7Kl0+sn9bgpv40H7ca/Ts0IyPXxrPwLvew8PNiZcnjqLfhHfRWrPl6+f5Y91+UjNyKv2cyhiNRfz++Ufc/fJM3Lx8+PSlR2jdrS++jZoVl6nv5cf1jzzP5t+/L7Oui4cXD06fja2dPXm5Ocx55j5adeuLm6f3ecdXCu7u0ZB3/gkjObuA169uwe6oNKLT88qUc7A1MLK1NycSS657GrjVo3dTd174/SgejnY8PyyIZ5cfQVvw5zcai1j95Sfc+sI7uHp68+XUyTTv1gfvhk2Ly7h5+TL6oWfZsWJZmXWbtu3MvW99CkBOZjohT99DYIdu5x9c1FrV+oyR1joSmAfMMM+aAYSUbxSZy67VWnfWWncGhgLZwCrz4qWYGkodAEfggVKrbjy73sU0igB2bF7H4JFjUErRql1HsjIzSE5KqFCuVbuOeHr5VJjvF9CAZsEtUcryr7W5txOx6XnEZ+ZTZNRsOZVC98b1K5S7pUsAvx2Io6DIWDzvdHIOKTmFAESl5mJvY8DWcHnlRAM9HYnPzCcxq4AirdkZmUanhmVfyJlbWLLN9rYKzAfAtn4uRKXlEpVmOphm5RdxeTWLoKmHA4lZ+SRlF1CkYc+ZdDr4u5Qpk1dq++vZlPx9z6TlkZ5r+vvHZuRjZ2PAxsK//+njh/EOaIS3f0Ns7ezo2n8Y+7dvLFNm/46N9BoyGoAufQdzdP8utNYc2bODhs2CaRTYAgAXt/oYbGwsig8QunU9A4ePRilFy7YdyMrMICWp7J3jlKREcrKzaNm2A0opBg4fzc4t6wBYtfwHxo2/Gzt7ewDqe3haXIe6qKWvC9FpucRm5FFo1Kw/kUzvZh4Vyt3VsxHL9sSQX2o/1Boc7AwYFNjbGCgwGsnOL6rJ6ovLnLWP/V2aeXI6IYuIxGwKijS/hkYxslNAleWv696IX3ZGFk9vOppApvn4eyE27z5JclrVGf4xgzry9e87ANhx4DT1XR3x93ZjRN82rNl2hJT0bFIzcliz7QhX9WtrUeyoE0fw9G+Ip18DbG3t6NB3KEdCt5Qp4+Hrj3/T4ArXNba2dtjamY61RQX5aEtaJGbBXk7EZeSTYL7u2RaeSrdKrntu7OTP7wfjKSgqidGtcX22hadSaNQkZOUTl5FPsJeTRfFjTh7F3a8B7r4B2Nja0ab3YI7vKrv99X388W0SVNwroTJHd2wkqFMP7Oo5WBS/tlE1/FNb1URXulnALqXUFKA/cD6vfr4J+FNrnQ2gtV5xdoFSagfQqBrqSXJCPN4+fsXTXj6+JCckVNoIutQ8nexJyiq5y5qcnU9zH+cyZZp5OuLlbM+eM+lc29630s/p1dSdU0k5l103MndHO5KzC4qnU7MLCPRyrFBucLAnI1p6YWNQfLD+NAB+rqaD8xMDmuJaz4adkWn8dTSpRup9qdR3sCtu3AKk5hTS1KPi9vcPdGdwsCc2BsWczRXf4t0pwJWotFyL75qmJSfg4V2yT3l4+XL6+MEqy9jY2OLo5ExWRhrx0ZGAYvarT5KZnkq3/sMZccMEi+IDJCcm4O3rXzzt5e1HcmI8Hl7epcrE4+Vd+t+oH8mJppsXMVERHDmwl2+/mIudXT3ufOgJmrdqZ3E96hpvZ3sSSh17ErPyaeVb9tgT7O2Et0s9dkakclPnkovGTWHJ9Gnmwdd3d6WerYGQzeFk5knDSJw/ax/7/d0diC6VHY1JyaFLYMUbAwANPR1p7O3M5qMVb5hWlwa+7kTFphRPn4lLpYGvOw183ImKKzU/PpUGPu4WfXZGciL1vUqO+26e3kSdOHze66clxrPk3ZdIjj3DVRMesihbBODhaEdydunrnoIKjZumHo54OdmxLzqDa9r6llm3dAYpJTsfD0c7i+JnpCTi5llyfefq6U3MySMWfQbA4W3r6DHqRovXE7VTtY9Kp7UuAJ7F1ECaYp7+L+OBb8rPVErZAXcCK0vN7qOU2qeU+lMpVeVVkFJqolIqVCkVGhISYtlG1AIKuKtHQ5bsPFNlmUbuDtzerQELt1W8YL5SrDuZzMt/Huen/XGMbmM6oBmUorm3E59tj+Ldtafo3NCN1uUu7K4Um06l8sbfYSw/mMBVLcuehPxd7bm2nQ/f742t0ToVGYsIO7yfe56axlNvz2Pf9vUc3Rdao3UAMBoLycxI482PF3HnxMeZ9caLF3QXU5SlgIl9m7JgS3iFZa18nTFqzYSv9nDP0r3c0DkAf1fLni0T4nzUhmP/uO6N+GP3GS6z+47Vpr63L4++u5AnPlzM3g1/kZl6absvK2BCtwZ8vbv2PreYmZJEQuQpAjt0t3ZVLp6kjICaG657FBADtP+vgkqpAExd5v6qZPFcYIPW+mwfn91AU611J+AT4JeqPldrHaK17q617j5x4sTi+St+/o4p949nyv3j8fDyITGhZMCDpIR4PH2qP1sEpgyRl7N98bSnkz3JWSVtSAc7A43cHZl6dXM+ubEtzX2ceWZoMEHmO2ueTnY8PTiQORvDyzyXcrlIzSnA06nkbo+7U9kMSnk7I9PoYu5ukZpTwLGEbDLzi8gv0vwbk0kT98srpZ2WW4CHY0kC193RlrTcqu8h7DmTToeAkq529R1sua9nI5bujiEp+3zuPZRV39OHlMSSh15TkuKp7+lTZZmiokJysrNwdq2Pu5cvwe064eLmjn09B9p17UNk2NHzirvy1+959qHbefah23H39CYxvqRRl5QYh6d32cyop7cvSYml/43G4entY17mR8/+Q1FK0bx1ewxKkZGWatH3UBclZuXjU+rY4+1sT1KpY4+jvQ1NPR15d2xbFk3oTGs/F6aNakkLH2cGt/AmNDKNIqMmLaeQQzEZtLhCb0qI6mHtY39sai4NSmXnAzwciU3NrbTsuO6N+DU0yqLPv1jR8ak08i/JYDX0cyc6PpXohFQa+ZWa7+tOdEKqRZ/t6ulNWlLJcT89uWwG5Xy5eXrj2ziQ8CMH/rtwKSk5BXg6lb7usSMlp9x1T30HXhrenA/GtSHY24knBwUS6OlISk5BmWsmDyf7MuueD1cPb9KTS7J/GcmJuHhYlvU6sn09Lbv3w8ZWxjK7UlR7w0gp1RkYAfQGnjQ3fM7lFuDn8pklpdQ0wAd46uw8rXW61jrT/PsKwE4pZdFePfr6W/nws2/58LNv6dV/MOv++h2tNUcP7sfZ2aVGutEBnEzMxt+tHj4u9tgYFH0DPdgVlVa8PKfAyMTvDvDYj4d47MdDnEjI4r1/ThKWlIOTnQ3PDwvm693RHCs1KMPl5HRKDr4u9ng52WGjFD0a12dfdNkRZnxdSg6CHQJcihuAB2MzaVS/HvY2CoOClj5OxJR7eLO2i0jNxdvZHk8nO2wUdGnoxr/lRhb0di65eGjr51Lc/cnR1sDE3o34/VB88Wg9lmraojUJMVEkxkVTWFDA7k1r6Nizf5kyHXr2Z/taU6/WPVvW0bJDN5RStO3Sk+jwMPLzcikqKuTEwb34lxq04VyuHncLMz/9mpmffk3PfoPZ8PcKtNYcO3QAJ2eXMt3oADy8vHF0cubYoQNordnw9wq69zGNktSj7yAO7jVlqqKjwiksLMS1vvsFfR91ybH4TBq4O+DnWg9bg2JQc0+2nS7popOdX8T4Rbu5Z+le7lm6lyNxmbz25zGOJ2SRkJFHp4ZuANSzNdDaz5XIlAvbB0XdZO1j/97wFAJ9XWjs5YSdjWJc90as2l9xVMZgPxfqO9kRGlazg7r8sf4At4/pCUDPDs1Iz8whNjGd1VsOM7xPa9xdHXF3dWR4n9as3nL+3eAAGga3Jjn2DCnxMRQWFnBgyz+07tbnvNZNS0qgIN/0XedkZhBx5F+8GzS2KH5YUjb+rvb4OJuue3o3dWd3ueueST8e5KlfD/PUr4c5mZjNrPWnOJWcw+6oNHo3dcfWoPBxtsff1Z6TSZaNxhoQ1IqU2DOkxsdQVFjA4W3raN71/Lb/rENb19KmzxCL1qmtVA3/V1tV96h0CtPgC1O01hFKqZnAe8C5HkC4DXix3Oc8AIwEhmmtjaXm+wNxWmutlOqJqaF3wQ+XdOvdn13bN/HwhHHUq+fA48+/Wrxsyv3j+fCzbwFYNP9DNv69kry8XO6/6WqGX3Mdt937MMePHGTGK0+TmZlO6NYNfLNoPp8s+uG8Yhs1fLE9ipeGB2MwKNYeTyIqNZebO/sTlpTNrsj0Ktcd2cYbP1d7buzkz42dTM9ovLX6ZPED+ZcDo4Zv9sQwZaBpyODNp1KISc9jbDsfwpNz2ReTwZDmnrTxdaZIa7LzjXxh7laYXWBk9bEkXhoWhAb+jcnkwGU2XLlRw4/743i4T2MMCrZHpBGbkc+o1t5EpOZyMDaTAYEetPQxdV3Kzi/i692mk3f/IA+8ne0Z2cqbka1MDYl5WyLJtOAheBsbW2558EnmvPYUuqiI3sPHENAkiN+/XkCT5q3p2HMAfYeP4asPp/Pqw7fg7OrGvU+/BoCTixtDx47n3WfuRylFu659aN+9r8XfQZee/di9fTOP330d9vUcmPTMtOJlzz50OzM//RqABx57gbnvvUp+Xh6de/SlS0/T8KhDrx7H3Pdf5+kHb8HW1o5Hn331nA/MChOjhnkbT/PGmFbYKMWqIwlEpORwZ4+GHEvIYrt5JMTKLP83jqeGBjH/1g4oFKuOJnD6Ahvnom6y9rG/yKh55dt9fP1YPwwG+G5LOMdiMnhmTBv2RaSwer8pi23KFlXsyv7T0wNo7ueKUz1bQt+6mqcX72b94cqHnK7Ml2/fw4BuLfB2d+HEyulMn78CO1vT4DULf9jEyk0HGdm/HQd/m0Z2bgEPvboEgJT0bN5esJJNS54D4K2QlaSkW9YwsLGx4Zp7H+Ort57HaCyi65BR+DYOZM33X9AwqCWtu/fjzMkjfPP+VHKyMjm6eyv//LCIx977goQz4fy1ZH7xZ/Ubcwt+TYIsim/U8FXoGZ4dGoRBwYaTyZxJy+OGjn6cSsphz5mqr3vOpOWxPTyVGWNaYdSaL0PPWDQiHYDBxoYRd0/m+3dfRBuNdBg0Ep9Gzdj4wyL8A1vSoltfYk4e5acPXyUvO5MTe7ax6ceveOCdhQCkJcSSkZxAk9YdLQssajVVnX3wlVITMTVmbjVP2wA7gSe11usrKd8M2Aw0LtcAKgTCgbO3kX7SWr+ulJoMPAIUAjnAU1rrskOKVE4fjrFOZqVNgKmbyfgv91gl/rd3d7FK3PImWmE48ZCba8eD+FN+tfzhzkvhw3GmdwKtPpz4HyWrx4g2pkbbvgjL3zVxKXRq4vrfheqAUfMsf9fJpfDnI72sElfUHtY47kPJsb/hIz9bJf6ZedcD4NjlfMaeuvRy9swG4Ls9VT+jXJ1u7dIQgDuX7rNK/MUTOgHw+U7rPH99X48mUKufqjE5Gptdo0/PtfJ3qpXfSbVmjLTWIUBIqekioOs5yp8GGlYyv9J6aq1nA7MvuqJCCCGEEEKIOk2eFhNCCCGEEKIOq5XpGyuo8YaRUqoDsLjc7DyttfSzEEIIIYQQQlhFjTeMtNYHgM41HVcIIYQQQghRCUkZATX3HiMhhBBCCCGEqLXkGSMhhBBCCCHqsNr8bqGaJBkjIYQQQgghRJ1Xre8xqsXq5EYLIYQQQogaV+vTMSfic2r02ri5r2Ot/E4kYySEEEIIIYSo8+rsM0a7w9OtErdrUzcAvrHSG6hv61Lh/blWsel4So3H7N/Co8ZjViZkW7hV4k7s3RSAr0IjrRL/ru6NAYhIzrNK/Cae9awSt7ax8tvfRR1mjeM+lBz7X199wirxp45oDsB3Vjrv32o+7zt2mWyV+Dl7ZgPWP/Ys2xttlfg3d25glbjiwtTZhpEQQgghhBDiMujrV0OkK50QQgghhBCizpOMkRBCCCGEEHWZpIwAyRgJIYQQQgghhGSMhBBCCCGEqMvkBa8mkjESQgghhBBC1HmSMRJCCCGEEKIOU5IwAqRhVIHWmi/nvs/enZuxr+fAI89MI7BF6wrlwo4dZv57r5Gfn0fnHv24e9LTqFJ71e8/LGFpyEd8umw1bvXdzyv28b07WPnlbIxGI12HjmbAuNvLLD99eB8rv5xDXEQYNz3+P9r1HlRmeW52FnOeuZfW3ftxzX1PWL7xVnZg11a+CZmFNhoZcNVYRt98V5nlBQX5fPbBa4SfOIqzqxsPP/8G3n4NOLhnOz8umkthYSG2trbcfN9jtOnU3UpbceFO7d/J2qXz0EYj7QddTa8x48ssjzqyn7VfzychMowxk16iZY+Bxcs2fLeQsH3bAeg9bgKtew22OP7JfTtYtXgu2mik8+BR9B17W5nlEYf3s2rJXOIjwrh+8iu06VUSPy0xjj8WfEB6cgIKuPW5t3D38bcovtaaubPeYceWjdRzcODZ/02nRau2Fcp9Pv9j/v5zORkZ6Sz/Z3vx/PjYGN6d/gqZGRkYjUXcP2kKvfoOsOxLqKPC9u1kzeK5GI1GOg0eRe+xZfe9yCP7WbN4HvGRYYyd/DKte5r+9uGH9vLPknnF5ZJiIhn76Mu07N6vRusvLm/WPvZHHwol9IcQtNFI875X0e6qW8osP7zmZ05s/QuDwYZ6LvXpfccUXDx9iT22j90/LigulxYXRf97n6dxpz4WxT++dwcrvpyNNp/7B1Zy7v/TfO6/udS5PzUhlm/en4rWmqKiQnqPvJ4eI8ZaFHv+tAmMGtiehOQMut/8VqVl3n/uJkb2a0d2bj4Tpy1m75EoACZc24sXHhgJwIyFf7F0+fZK1z+XCz32AKz9ZgEn925HayOB7bsx7M5JZa7DzsexvTtYsWg2RmMR3YZew6Dryn73pw7tY8WXc4iLOMktT0ylvfm7jzl9gt8WziIvJwtlsGHw9RPo0Heoxdsvap9q7UqnlGqslDqllPI0T3uYp5tVUnaIUmpvqZ9cpdR15mWfKaX2KaX2K6V+UEq5lFv3RqWUVkpd9NXw3p1biD0TwawvfuLBKS/x2cczKi33+SczePDJl5n1xU/Enolg384txcuS4mM5sGs73r7nf2FoNBax4vOPmPDCDB59/wv+3fwP8VGny5Sp7+XHdY88T4d+wyr9jLXff0HT1h3PO2ZtYiwqYum893jytVlMn/sN29evIjriVJkyG1f9hpOzG28v+IER427jh0VzAHBxc+exqe/x+pyl3PfkVBa+/5o1NuGiGI1FrPlqNjc8/Sb3vL2Ao9vWkXSm7ItgXb18ufqBZ2jTu+zBN2zvduLCj3PX9PlMmPYxoX/+QF5OlsXxVy76hPHPvcVD737Gwa1rSYgqG9/N25drH3qO9pUc/H+b/w69x9zCwzM/597pc3B2c7coPsCOrZs4ExnOomW/M+WFqXz87huVluvdfxCffPZ1hflLF4UwaNhVzP/qe16e/i6fzHzT4jrURUZjEau//ISbn3uLB95dyKFta0kst++5efky+qFnaVvub9+0bWfufetT7n3rU8a/NBM7ewcCO3SryeqLy5y1j/1GYxE7v5/HkEmvMeaVeZzetYG0mLIvIvVoHMSo5z7kmpfm0KRLP/b88jkA/i07MfrF2Yx+cTbDHn8bW/t6BLTpYnH83z//iDtfmMHk97/gQBXn/usrOfe7eHjx4PTZTHpnARPfmMvGX78hPTnRoviLl29j3KNzqlw+sn9bgpv40H7ca0x+4xs+fsnUcPFwc+LliaMYeOd7DLhjJi9PHIW7q6NFsS/m2BN17CBnjv3LfW9/yv0zFhATdpTIw/stjr/884+468UZPP7BIg5sXlPhu3f39uPGSc/Tsdx3b2dfjxsffZHH31/E3S++wx9fziEnK9Oi+LWNquGf2qpaG0Za60hgHnC2dTEDCNFan66k7FqtdWetdWdgKJANrDIvflJr3Ulr3RGIAIpf36yUcgWeACy/VVGJXVvWM2DENSilaNGmA9lZGaQklT3QpCQlkpOVRYs2HVBKMWDENYRuWV+8/Kv5s7j9gccsykueOXEET/+GePo1wNbWjvZ9h3I0dEuZMh6+/vg3DUapin+26LBjZKalENzx8suUAIQdO4RvQCN8/Btia2dHz4Ej2LNtQ5kye7dtpO+w0QB07z+Ew/tC0VrTNLgVHl4+ADRsGkR+fh4FBfk1vg0XIzbsKO5+DXD3DcDG1o5WvQZxYnfZv399H398mgShDGX3q6TocBq16oDBxga7eo74NA7k9P5Qi+JHnzyKp18DPHwbYGNrR9vegzm2a3OZMu4+/vg1Caqw/yVEhWMsKiLIfEFs7+CIXT0Hi+IDbN2wluGjrkUpxf/bu+/4KKq1geO/JwUIpFcIvYfeexFBxILYlWvXq2IXO17va0OxlysoiiggiAp2BVEE6SC9E3qHkE4qpOx5/5gl2YSWILsT2OfLZz/szpzd58zJ7pk5c86cad6yDVlZmaQkJx2XrnnLNkRERh23XBCys60GYXZW1gnTqOMd3F7yu9esax+2rjj+uxddp8Epz8ZuXjqfBm06ndHfXnkvu+v+lF1bCIqMJSjS+v7Xbd+bvWuXlEhTvUkb/CpZ3+vIenHkpB/f+NizagGxzTsWpSurfaX2/a269yW+jPt+Pz9//PwrAVCYn4cxplyxARau3E7q4ZyTrh94QWsm/7oUgKXrdhESFED1yGD6d2/GrCXxpGXkkJ6Zy6wl8Vzc4/ge/lP5J3WPh9OeRwAATSRJREFUiFCQn09hQQGF+fk4CguoWsbROcfs2xZPRExsibLftKzkfq+o7H1Kln1kbG0ia9QCIDg8ksDgULIz0ssVX1VMnph84T2gq4gMBXoCb5fhPdcBvxljcgCMMRkAYv0yAgDXX/9w4A3gyNnIbGpKEhFRMUWvwyOjSU1JLJUmkfCo6KLXEZHRpKZYB3DLF80lPDKKug2blCtuRmoywRHFnxkcHklG6vEHhSficDj4feJoLr7lvnLFrEjSU5JKlGlYZDTpKSW3Py0liXDn38bX14+AqoFkZRwukWbFwr+o27AJ/s6dxbkiKy2ZoPDiA/mg8Ciy0lLK9N6o2g3YtXY5+UePkJN5mL2b1pBZxu/OMZmpyQSV+P5FkVnG+KkJ+6hSNZBv33uRsf8ZwqzJn+BwFJYrPkByUiLRMcW9rJFRMSQnJZ7iHSXdevf9zJrxK/8adBHPPfEADz7xbLnz4I0y05IJLvHdiyQrrXxnnQE2LZlDs24Xns2sKS9gd92feziFqmGRRa+rhkWSe/jkdd/2xX8Q2/z4E5C7V86jbocLTvCOU8tMTSbkDPf9AIeTE/nw6bt558HB9Bw0mODwyNO/qRxio0PZl5BW9Hr/oXRio0OJjQpl3yGX5YnpxEaFluuz/0ndU7Nxc+o0b8OHD93IqIdupH6rjkTWrFuu+Bmlyz4iiowzqPv2bdtEYUEB4TGx5X5vhaJdRoAHGkbGmHzgKawG0lDn69MZDHzlukBExgEJQBww0rmsPVDbGDPtdB8oIveKyHIRWT5mzJhybkXZHD1yhB+/Gsf1t3u2gbLsj59o3K4LIRHefYZ8/+4dfDv+Q257aJjdWfGoeq06Ur9NZ756ZSjTRo+gRqNmx53dcidHYSF7N6+j3833ctfwj0hLPMjaeX+c/o1n2V8zf+Piy6/kq5//5NV3PuKNl/6Dw+HweD68UVZaCkl7d1K/1bnZY63ObZ6q+3cunU3Knq0073dtieW5h1NJP7CL2Obt3Rr/REIio3nwzbE8+v5EVs/7naz0VI/nwQ5pCftJ2b+HBz74igdHfs3ujavZG7/O4/nITEvh21Gvcc39z+Djwf2uch9PTb5wKXAQaAnMPFVCEakBtAJ+d11ujLlTRHyxGkU3isgE4F3gjrJkwBgzBjjWIjIrd2cUrfvj5ynMnv4jAA2aNicl6VDRutTkRMJdzigAhEdEk+pyJjslOZHwiCgOHdxHUsIBnrnPungvNSmR/zxwC6+MHE/oac7iBIdHkuHSM5WRWvJMyqns27qR3fHrWPbHT+QdzaWwoIBKVQLof9O9ZXp/RRAaEVWiTNOSEwkt1dALi4giNekQ4ZHRFBYWkJuTRWBwCGD9nT589Rn+/fjzRDu7t88lgWGRJXp5MlOTCAyLKPP7uw66ia6DrO/dtNGvEVa9fGUQFB5JZonvXxJBZYwfHB5JTN1GhEVbZ8uadujB/m2boM+lp33vT99+zfSfv7Pe16wFiYcSitYlJx0iMir6ZG89zoxffmDEe9ZEAM1btSEv7yiH09MICy97OXqjoLCSZ6gzU5MJDCvfWef4v+fSpGMPfP10Ph9VPnbX/QEhEeS49BLkpCUTEHJ8nXEwfhXrf/+G/kPfwNffv8S63SvnU7t1N3x8y//9DwqP5PAZ7vtdBYdHEl27Prvj1x03MdM/cSAxnVrVw4pe14wJ5UBiOgeS0unVoXHx8uhQ5q/YWq7P/id1z5blC4lt1IxKVazrmhq06cSBbRupHdeqzPGDS5d9ShLB5aj7juRk88Xrz3LR4H9Tu0n5hhFWRHofI4vbm7ci0hboD3QFHnM2fE7lBuCHE/UsGWMKga+Ba4EgrIbWHBHZ5fz8n89kAoaLB93A6x9P5vWPJ9Oxex/mz5yGMYatm9ZRtVogYRElfyhhEZEEVKvG1k3rMMYwf+Y0OnS/gDr1G/HJ1D8YOfFnRk78mfCoaEZ8NOm0jSKA2IZxpCTsJy3xIAUF+axfNJumHco2s821Dz/H4x9+zWOjvuLim++jTa/+51SjCKB+k2YcOrCXpIQDFOTns3TeTNp2KTmjWNsuvVg0azoAyxf8RVzrjogIOVmZ/O/Fx7n2jgdo3LyNHdn/x6rXb0r6of0cTjpIYUE+m/+eS8N2Zfv7OxyF5GZZDf2kPTtI2ruDei3LdwF8bIOmpCbsJz3Rir9xyRyadOhepvfWaNiUIzlZReOrd21cXeYhDVdeN5hPvpjKJ19MpUfvvvz52y8YY9i4fg3VqgWV6zqh6JjqrFpuXWq4e9cO8vLyCA0LL/P7vVWNBk1Jc/nbb1oyh0btyzer1sbFf+kwOnVG7K77I+o2ITNpP1nJCRQW5LN75Txqte5SIk3q3u0s/XoUFwx5nipBocd9xq4Vc6nb8cwaIzUbxpHqsu9ft2g2cWXc9x9OSSI/7ygAuVmZ7IlfT2Rs7TPKx8lMm7uOmwZ2BqBzq3pkZOWSkJzBzEWbuKhbHKFBAYQGBXBRtzhmLtpUrs/+J3VPcGQ0e+PX4igspLCggL2b1hIRW6dc8Ws6j7tSXcu+Y9n2ewUF+Ux+5/9o1/viopnq1PnBraf3nNcEjcYaQrdHRN7Cusbo5lO87V9A0cUBzs9oaIzZ5nw+CIg3xhwGIl3SzQGeNMaU76rzUtp17sHqpQsZesfVVK5chSFPPl+0bth9N/H6x9ZsWHc+/Awfv3Vsuu7utO1Uth/Tyfj6+nLZnQ8zccQzGEch7S68lOja9Zk9ZRyxDZoQ17EH+7fH8/U7z3MkO4stKxcz59vxPPj2uH8Ut6Lw9fXj5vue5L3nH8XhcNCz/0Bq1m3Aj5PGUK9xHG279KbXxVfw6Tsv8ew911EtMJghzwwHYNavU0k8uI9fvvqcX76yZgt6fPj/CA49dw6KfXx96XvrQ3z3ljX8q2XvAUTWqsfC7ycQU68Jjdp3I2HHZn764CWOZGeyfdUSFn0/kTte+xRHQSFfv/o4AJUDqnLZkGH4+PqWO/6AOx7mqzeGWdOmXnAJUbXqMffb8dSo34QmHbpzYHs83773Ikdysti6ajHzvpvAkDc/w8fHl343DWHyiKcwxlCjfhPa9b2s3GXQuXsv/l40n9uvv5zKlavw5H+HF60bctv1fPLFVAA+HfUus/+YztEjR/jXoIu4dNA13Hb3Awx55Enefe0lvv96Iojw1H+Hl3vqVm/k4+tL/9sfYsqbz2IcDlpdMICoWvWY/+14qtdvQuMO3Tm4fTPfv/8iR3Oy2LZqCQu++4K73xgLwOGkBDJTk6hzjs6Iqexld93v4+tLxxvuZ/aH/4cxDhp27U9ojbqs+XUiEXUaU6t1V1b9+BkFR4+w4LPXAKgaFkWf+14AICvlEDlpycQ0KntPRcnt9+XyOx/mixHP4HAU0t657581ZRw1Xfb9X73zPLnZWWxeuZjZ347n4bfHkbR/N79P+rjos3oMvIGYOg3KFX/Ca3fQq0NjIkMD2TZjOMM/no6/n7X/GPvtAmYs2MCAni3Y8PML5BzJZ8iLkwBIy8jhtU9nsGDS0wCMGDODtIyTT+JwIv+k7mnauRe7N6zms2fvQRDqt+5U7hM6vr6+DLzrESaMeBqHw0GHPpcSU7s+f075nJoNmtKsYw/2bYtn8jv/R252FvErFjN76jgeeWc86xfPYdemteRkZrBy7gwArn1gGDXqNSpXHioS3V1a5ExmMSnzh4vcC/QzxtzofO0LLMOaZW7uCdLXAxZiXTfkcC7zAeYDwViXa60B7j82IYPLe+dQ9oZRiaF0ntS+bjAAX63ab0v8f7WraUvc0hZsTTt9orOsZ+Ow0yfygDFLdp8+kRvc29Xqxfli+V5b4t/W0TqTuSf1qC3x64RXtiVuRfP5sj2nT+QGd3Uq39lcdf6xo96H4rr/5ZnbbIn/fH/rYPkbm/b7Nzr3+wHtHjpNSvfIXTUKsL/umbr6gC3xr28bCxV6ugHLntSj7msQnECd8MoVskzc2mNU6rqeY0PhTnp1onMa75qlljmA094t0BjT50zzqZRSSimllPJueqWsUkoppZRSXqxCdt/YwOMNIxFpBUwstfioMabLidIrpZRSSimllLt5vGFkjFkHtPV0XKWUUkoppdTxdPIFi96NSimllFJKKeX1tGGklFJKKaWUVxMPP/5BTkXCRWSmiGx1/n/ctMMi0lZEFovIBhFZKyI3luWztWGklFJKKaWUOlcMA2YZYxoDs5yvS8sBbjPGtAAuAd4XkdDTfbBb72NUgXnlRiullFJKKY+r8Ffw7E/P8+ixcc3QSmdcJiKyGehjjDkoIjWAOcaYpqd5zxrgOmPM1lOl0x4jpZRSSimllMeIyL0istzlcW853h5jjDnofJ4AxJwmVmegErD9dB/stfcxWmjTHbh7OO/A3feDxbbEn/1IN1vilvb67NN+N8+6YX0bejzmiQT0ftGWuLnzrLiz41Nsid83LgKAbYm5tsRvFB1gS9yK5v7vNtoSd/S1zW2JqyoOO+p9KK77r/lshS3xv/93BwBu/XKNLfEn3twGgM+X7bEl/l2d6gAQ0O4hW+LnrhoFwJTVB2yJf0PbWFvilpenu7SMMWOAMSdbLyJ/AtVPsOq5Up9jROSkvV3OHqWJwO3GGMfp8uW1DSOllFJKKaVUxWOMuehk60TkkIjUcBlKl3iSdMHANOA5Y8ySssTVoXRKKaWUUkp5MRHPPv6hn4Hbnc9vB346fnukEvAD8IUx5tuyfrA2jJRSSimllFLniteB/iKyFbjI+RoR6SgiY51pbgB6A3eIyGrno+3pPliH0imllFJKKeXFpOJPnFfEGJMC9DvB8uXA3c7nk4BJ5f1s7TFSSimllFJKeT3tMVJKKaWUUsqbnTsdRm6lPUZKKaWUUkopr2dbj5GI1AbmAR2MMakiEgasBC40xuw6QfpCYJ3z5R5jzCDn8n7AW1iNvCzgDmPMtjPJ07oVi5k85j2Mw0Gviwdx+fW3lVifn5/H2HdfYve2zVQLCub+Z14hMiaWrIzDfPTas+zcuoke/S7nlvufPJPwdKobykO96+EjwvQNh/hqRck59wc0i2JIz7okZ+UB8OPaBKZvsGYovDguils61wRg0tL9/BGfdEZ5sNO+Dcv5e8onGOOgSY8BtB5wQ4n16//8ni0Lf8fH15cqgSH0vHUogRHWPb3GPzCQsJr1AKgWFsVFD7zg6ez/Y/07N+LtRy7B18eH8dNW8vaXC0qsrxMTwsfDriQytBppGbnc9cr37E/KoE5MCF+/OhgfEfz9fBj93VLG/ry83PE3rFzClE/fxzgK6dH/CgZcd/z3f8J7w9mzPZ5qQSHc/dRwImJqFK1PTUrg5Ydu5vLB/6b/1TeVO74xhk/+9ybLlyygcuUqPPafl2nUtNlx6SaMGcns338lKzOD7/44/n5gC+f8yYj/e5L3P/2SxnEtyp0Pb9Q8pho3tKmOiLBwZxp/bCl5r6te9cO4oGEYDgNHCxx8ufIACZl5hFf154WLG3Io06qTdqbm8NWqBDs2QZ3D7K7729UM5q6utfHxgT83J/PD2kMnTNe1XihP92vIUz9tYntyTtHyyGr+/O/aFkxZeZCf1p/4vafSqkYQt3aMxUeEOdtS+XXjCWcepmPtEB7tXY/nf9vCzlTrfnBXtIjmgobhOIxh4vIDrDuYWa7YO9YsY9bEj3A4HLTpcyldBw0usX5v/FpmTRxN4t4dDHroOeI69y5a99dXn7J99d8Y46B+yw70u/UBpJzTjX38ws1c2rslSamZdLx+xAnTvPP0dQzo0YKcI3nc+8JEVsfvA+DmK7ow7O4BALw+9ne+/OXvcsUG2Lp6KdPGj8I4CunQ93J6X1Vy37Vr4xqmT/iQQ3u2c/2jz9Oy6wUApCclMPnt5zHGQWFhAV0vuYbO/QeVO76qeGxrGBlj9orIaKyZJO51/j/mRI0ip1xjTNsTLB8NXGmM2SQiDwD/Be4ob34chYVMGv02T7zyAeER0bz82J207dKLmnXqF6WZ/8fPVKsWzOuffsvfc2cydfyH3P/Mq/hXqsRVt9zL/t072L97R3lDA+Aj8Gif+jz1w0aSsvIYfWMrFu1MY3dqyZthztmSwgdzd5ZYFlTZj9u61OL+r9dhMHw8uDWLdqaSdbTwjPJiB4ejkCVff8SAR16lalgkv7w+lDqtuxJao05RmojaDRn07P/wq1SF+LnTWPbD51x497MA+FaqxJXPjbIr+/+Yj4/w/mOXcfnjE9mflMGCMffw64LNxO8ubuC+9sDFfPn7Gr6csYYL2tfn5Xv78e9Xf+BgShZ97h9LXn4h1QIqsWL8A0xbuJmDKWXfQToKC/n6k7d55KX/ERYRzetP/pvWnXtRw+X7v2jmL1QNDOLlT6aybN5MfpjwEXc/Pbxo/beffUCL9l3PuAyWL1nAgX17+PSrn9m8cR0fvvMq7405/rrJLj0u4IprBnPPTcfvhHJysvnp28k0bd7qjPPhbQQY3LYGHyzYTVpOPsP6NmDtwUwSnI0dgGV7DzN/p3VT7NY1ArmudXVGLbRuFpmclceIWWdW7ylld93vI3BP9zq8NGMLKdn5vDkojmV7DrMv/UiJdFX8fbi8RTRbErOO+4w7u9Rm1b6MM4ovArd3qskbs3eQmpPPy5c0ZuW+wxzIOFoyvp8PA+Ii2ZacXbQsNrgyXeuGMuzXzYQF+PNMvwY89Us85qS3uizJ4Shk5oSR3DjsDYLCI5nw/EM06tCNyJp1i9IER0Rz2ZCnWDp9aon37tuygf1b1nPXa58A8OXLj7F301rqNG9Tru2f+MsSPv5mLmOH33bC9QN6NqdhnShaXvkSnVvV44P/DKb3bW8TFlyV5+69lB43v4kxhkWTn2HanLWkZ5b9BuIORyG/fP4/7njuLYIjovj42fuI69id6Fr1itKERMZwzQPPsOCXb0q8NzAsgntfGYWffyWOHsll1JN3EtehO8HhkeXa/opER9JZ7B5K9x7QVUSGAj2Bt8/gMwwQ7HweApzRrY13bNlIdI1aRFeviZ+/P11692f1knkl0qxaMp/u/S4DoGPPC9m0ZjnGGCpXCaBJi7b4V6p0JqEBiIsJZH/6EQ5mHKXAYZi9NZnuDcLK9N5OdUNYsecwmUcLyDpayIo9h+lcN/SM82KH5F1bCIqKJSiqBr5+/jTo2Js9a0r2BtRo2ga/SlUAiGoQR05ash1ZdYtOzWqyfX8quw6mkV9QyNRZ6xnYs2mJNHH1opi70moUz125k4E94wDILygkL99qBFf298XHp/zV266tG4mqXoso5/e/Y6+LWLN0fok0a/6eT9e+lwLQvseFxK+1vv8Aq5fMJSImtkRDqryWLJhD30sGIiLEtWhNdlYmqcnH93zGtWhNeGTUCT9j0tgPue6mO6j0D36L3qZeeABJ2XkkZ+dTaGD5vsO0iQ0qkeZIQfHNwiv52r3bUOcTu+v+RlHVOJhxhEOZeRQ4DAt2pNG5Tuhx6W5qH8uPaxPIKyzZ6uhcN4RDmUfZm1b2A3JXDSOqcigzj6SsPAodhiW70+lQO+S4dNe2qc6vGxLJd4nfoXYIS3anU+AwJGXncSgzj4YRVcsc++D2zYTGxBIabZV9s6592LpiUYk0IVHVia7T4LieIBGhID+fwoICCvPzcRQWUDUktHwbDyxcuZ3UwzknXT/wgtZM/nUpAEvX7SIkKIDqkcH0796MWUviScvIIT0zl1lL4rm4R/Nyxd63LZ6ImFjCY2Lx8/OnVfe+bFq2sESasOjqVK/bEB+fkvWen58/fv7WfqYwPw/jKGNrVFV4tu7hjDH5wFNYDaShztcnU0VElovIEhG5ymX53cB0EdkH3IpzLvPySk9JIjwquuh1WGQ0aSlJJ0hjdd/7+voRUDWQrIzDZxLuOJGBlUjMKj5DlJyVR1S1ysel69UonE9vas0LlzUhKtD6UUZWq0SSy3uTso4SWe3cOjDMSU+hWljxmZaqYZFkp6ecNP2Whb9Ts0XHoteF+Xn8/Noj/PrGY+xeveik76uoYiOD2ZdYfMZxf1IGNaOCS6RZt+0QV/a2hpZd2bsZwdUqEx4cAECt6GCWjrufrd8+zjuTF5Srtwis73ZYZEzR67CIKNJLf/9Ti9P4+voRUK0a2ZmHOZKbwx/fT+LywXeVK2ZpKUmJREVXL3odGRVDSvKJh5ScyLbNm0hKPETn7r1Pn1gVCQ3wIy2nuOpNyy0gNMD/uHQXNAjj5QGNuLpVDN+sKR4uF1GtEv/pV5/HetelUTkOypQC++v+iKr+pGQXf/9TcvIIr1by+98gIoCIapVYsbdkr1AVPx+ubl2dKasOljvuMWEB/qTmFPfOpubkE1bq91c3LICIqv6sOZB53HtTsovfm5aTd9x7TyUzLZng8OKTTEHhkWSVsdFZs3Fz6jRvw4cP3cioh26kfquOJXqazpbY6FD2JaQVvd5/KJ3Y6FBio0LZd8hleWI6sVGh5frsjNRkQiKKj/tCIqLILEej+3ByIqOe+jdvP3Ajva4cfE73FsE5d4NXt6kIp/4uBQ4CLU+Trq4xpiNwE/C+iDR0Ln8MuMwYUwsYB7x7ojeLyL3OhtXyMWPGnKWse9binWncNH4l90xey4o96Qzr38juLNli+9+zSdm9lVb9rytadv2r4xn07AdccNfTLJ06hoykM99RVVTPfvQHvdrWY/HYIfRqW5f9iRkUOs9S7UvMoPOdo2n5rw+45ZK2RIdV81i+pn39Gf0GDaZKgH0HxQ6Hg7Gj3ubuBx+3LQ/nu7k70nj+9238uP4Ql8VZBwAZRwp47retjJi1k+/WHuLOzjWp4lcRdivqfGRH3S/AHV1qM37pvuPW3di+Br+sTyzRo3q2CXBzh1gmrzyjwTBuk5awn5T9e3jgg694cOTX7N64mr3x607/xvNISGQ0D731GUP/N4lVc/8gKz3V7iyps8DW6bqdd6DtD3QFFojI18aYE9Zqxpj9zv93iMgcoJ2IZABtjDHHrrj7BphxkvePAY61iMzCrWkl1odGRJGaVHx2Oi05kbCIqBOkOUR4ZDSFhQXk5mQRGHx8l/eZSM7KIzqwuIcoMrASSdklxxhnHCkoej59QyL39rDOziRn59GmZnE+ogIrs2b/2enJ8pSqoRFku5ypyUlLplpoxHHpDmxaxZoZ33DpY2/g6198ZqxaqHWgFhRVg+pNWpO6dzvBUTWOe39FdSA5g1rRxT1ENaOC2Z9U8uzkwZRMBv/XGudcLaASV/VuzuGsI8el2bAjkR6t6/LD3I1ljh8aEUVacvFFw2kpSYSW/v6HW2nCjn3/s7OpFhTCzi0bWbnoL76f8CG52VmICP6VKtHn8utKhznOr99/zYxfvgegSVwLkhKLeyKSkw4RERl9sreWkJuTze6d2xn2yN1W/lNTeHnYUJ5//X2dgOE00nMLCKta/FsKC/AjPffknffL92bwr3bWb6vAYSjIs4Zx7kk/QnJ2HtGBldhT6voMpU7G7ro/JSefCJceooiqlUh16UEK8PehTlgAwy9rAkBogD/PXtSQ1/7cTuOoanSrF8ZtnWpSrZIvDiCv0MFvm8o++VFabj7hVYtHeIRX9SfN5fdXxd+HWiFV+M9F1onQkAA/HrugPu/N3Ulabj4RLqNDwqpWKvHe0wkKiyQjtTivmanJBIaVrddjy/KFxDZqRqUq1qiFBm06cWDbRmrHnd3rOw8kplOrevFlBTVjQjmQmM6BpHR6dWhcvDw6lPkrtpbrs4PDIzmcUnzcdzgliaAybn/pz4mpXY9d8euKJmc4F51LN3h1J9tO7Yk1YHU01hC6PVgzy53wGiMRCRORys7nkUAPYCOQBoSISBNn0v7ApjPJT/0mzTh0YC9JCQcoyM/n73kzadulV4k0bbv0YtGs6QAsX/AXca07lnsGlpOJP5RFzdAqVA+ujJ+P0LdxJIt3lGy8hbscvHSvH84e55jmZbsP07FOCIGVfQms7EvHOiEs231uNYwi6zYhI/EAmckJFBbks2P5PGq3Lnkhf8re7SyaPJJ+9z9PQHBo0fKj2ZkU5ls7gyNZhzm0fWOJC3fPBcvjD9CoVgR1a4Ti7+fL9f1aMm3h5hJpIkKqFn3fnrq5JxOmrwKsRlSVStY5jtDAKnRvXYcte8s3Br9u42YkHtxH8iHr+798/p+07tyzRJrWnXuxZPZvAKxc+BdNW3dARHjytdG8+un3vPrp9/S94gYuue72MjWKAAZeM5hR46YwatwUuva6kNkzfsUYQ/yGtVQLDDzptUSlVQsM4qtf5zBu6m+Mm/obcc1baaOojHan5RIdWImIqv74CnSsFcLaAyUvMD82bBegZY1AEp0zYwZW8i3alUZW8yc6sBLJLkN7lDodu+v+bUnZ1AiuQnRgJfx8hJ4Nwli2J71ofU6+gzu+XMN9U9Zz35T1bEnK5rU/t7M9OYf/TttStPzXDYl8vzqhXI0igB0pOVQPqkRUtUr4+ghd64aycl/x/js338ED323g8Z828bhzNrz35u5kZ2ouK/cdpmvdUPx8hKhqlageVIntKSe/Xqe0Gg2akpawn/TEgxQW5LNpyRwate9WpvcGR0azN34tjsJCCgsK2LtpLRGxZ3+/O23uOm4a2BmAzq3qkZGVS0JyBjMXbeKibnGEBgUQGhTARd3imLmofId/NRvGkZKwn7TEgxQU5LNu0WziOnYv03sPpySRn2edvM7NymT35vVExtYu38apCsnOHqN7sKbdnul8/RFwp4hcYIyZWyptM+ATEXFgNeZeN8ZsBBCRe4DvnOvSgDO60MHX149b7nuSd59/FIfDQc/+A6lZtwE/TBpDvcZxtOvSm94XX8Gn77zEsHuuo1pgMEOeKZ6R66m7ruJITg4FBfmsWjKXx4d/UGJGu9NxGBg5ZydvXNkMXx/htw2J7ErN5Y4utdmSmMWinWlc07YG3euHUegwZBwt4I2Z1qzkmUcLmLhsH6NvbA3AxKX7yDxacKpwFY6Pry9dB9/PHyP/i3E4aNz9YsJi67Lyl4lE1mlMnTZdWfbdZ+QfPcKcT18DiqdmTU/Yy6LJIxHxwRgHrQdcf841jAoLHTz2/nR+eftWfH2ECdNXsWlXEv9314Ws3HyAaQs307ttPV4e0g9jYMGa3Qx9bxoATetG8vqDAzDGICK8//UiNuwo+7U5YH3/B9/7OCNffAyHo5Du/QYSW6cBv3z5KXUaxdGmSy969B/I+Pde5vkh11M1KJh/P/nyWS2DTt16sXzJAu4efAWVq1ThsWdfKlr30J03MGrcFAA+/+g95vz5G0ePHOG2ay5mwMCrufmu+89qXryJw8DXqxN4uGcdfERYtCudg5lHGdg8ij1puaw9mEWfhmHERVej0AE5eYVMWGYN62kcWZWBLaIodFjTrU9edZCcfPcNK1LnH7vrfoeBsYv38PwljfERYdaWZPamH2Fw+xpsT85h2R73nmR0GPhi+X6e6tsAH4F521PZf/go17SOYWdKLqv2n3y2u/2Hj/L37nReH9gUhzFMWL6/zDPSgVX2/W9/iClvPotxOGh1wQCiatVj/rfjqV6/CY07dOfg9s18//6LHM3JYtuqJSz47gvufmMsTTv3YveG1Xz27D0IQv3WncrcqHI14bU76NWhMZGhgWybMZzhH0/H388XgLHfLmDGgg0M6NmCDT+/QM6RfIa8aM1UmpaRw2ufzmDBpKcBGDFmBmkZZW8UAvj6+jLwrkeYMOJpHA4H7ftcSkzt+sya8jmxDZrSrGMP9m2L56t3/o/c7CziVyxm9tRxPPLOeJL272bGxNFFn9Vj4A1Ur9Og3NtfoWiHEQBiyvMrOn8cN5TOU3o0trqE+35w/P1XPGH2I+WvuNzh9dnbPR5zWN+Gp0/kAQG9X7Qlbu48K+7s+JNf2OxOfeOs4THbEs9s9qZ/qlF0gC1xK5r7vyv7EMuzafS15ZsxSp1/7Kj3objuv+azFbbE//7fHQC49cs1tsSfeLM1hfbny/bYEv+uTlZjNaDdQ7bEz11lTec+ZbU912nd0DYWzoFmR1JWgUcbBFGBfhWyTGy9xkgppZRSSillrwrZSrFBhWoYiUgrYGKpxUeNMV3syI9SSimllFLKO1SohpExZh3Q1u58KKWUUkop5S0q8r2FPElvOKGUUkoppZTyehWqx0gppZRSSinlWXofI4v2GCmllFJKKaW8nvYYKaWUUkop5cX0GiOL197HyO4MKKWUUkopr1Dhmx1pOYUePTYOq+pbIctEh9IppZRSSimlvJ7XDqVbvC3dlrjdGoUCMHnlPlvi39S+li1xS7PjDtzH7r5tt2nrE22Je3nLaAB+35hkS/wBzaMAyDzqsCV+UGU9DwQwcuFOW+I+3KO+LXFVxWFHvQ/Fdf/DP2yyJf7Iq5sB9m//1NUHbIl/fdtYAKbYFP8GZ/yAdg/ZEj931Shb4qozo0cKSimllFJKKa/ntT1GSimllFJKKZ184RjtMVJKKaWUUkp5Pe0xUkoppZRSyovpDV4t2mOklFJKKaWU8nraY6SUUkoppZQX02uMLNpjpJRSSimllPJ6bu0xEpHawDyggzEmVUTCgJXAhcaYXaXSXgi857IoDhhsjPlRRMYDFwCHnevuMMasFpE+wE/AsRtzfG+MeflM87t2+WImj3kXh8NB74sHMfCG20usz8/P49N3XmLXtngCg0K4f9grRMXEkpVxmFEjhrFz6yZ6XnQ5t97/1BnF37Z6KTO++BCHw0H7Cy+j55X/KrF+96a1zPjiQw7t2cF1j/yX5l0uKLH+aE42Hz51F3Ede3DZnY+cUR7stGPNMmZN/AiHw0GbPpfSddDgEuv3xq9l1sTRJO7dwaCHniOuc28Adm9czexJo4vSpRzcy6AHn6NJxx4ezf8/tWnV3/z4+f9wOBx07TeQftfcUmJ9QX4ekz94lb07NlMtKJjbHn+J8OgaFBYU8M3oN9i3YwuOwkI69hnARdfcWu74G1cu4fvPrPjdLhpI/2tLfkZ+fh6T/vcKe7db8e948mUiomsAsH/XNr4Z/RZHcrMR8eHJtz7Fv1LlcsU3xvD2GyNYOH8eVapU4cXhI4hr3qJEmiO5uTzz5FD27d2Lr68PvS64kIeHPgHAwQP7efn5/5KWlkpwSAjDR7xJTPXq5S4Hb7R73XLmTx6NMQ6a97qEDpffWGL9qt+/Y+O83/Hx9SEgKJS+dz5GcGQMAD+/+xwJ2+Op0bgFVww94+pXeTG76/5m0dW4tnUMPiIs3p3OzC0pJdb3qBdK7wZhOAwcLXTw9aqDJGTmUTesCoPbWnWgCEzflMzag5nn1LZvWb2U6eNH4XAU0qHv5Vxw1U0l1u/cuIbpEz7k0J7t3PDo87Tsah13HNy1jZ/HvsfR3GzEx5c+V99Mq+59yxUbYOvqpUwbPwrjjN+7VPxdLvGvd4mfnpTA5LefxxgHhYUFdL3kGjr3H1Tu+B+/cDOX9m5JUmomHa8fccI07zx9HQN6tCDnSB73vjCR1fHWfShvvqILw+4eAMDrY3/ny1/+Lnf8ikQ7jCxubRgZY/aKyGjgdeBe5/9jSjeKnGn/AtoCiEg4sA34wyXJU8aYb08QZr4xZuA/zaujsJCJo9/iqVdGEh4ZzUuP3UG7rr2oWadBUZp5v/9M1cAg3hz7HUvm/sHUcR/ywLBX8a9UiWtuHcK+3TvYv3v7mcV3FDJ93Afc+p83CY6I4tPnHqBph25E1apXlCYkMpqr7nuaRdOmnvAzZk8dR9241mcU324ORyEzJ4zkxmFvEBQeyYTnH6JRh25E1qxblCY4IprLhjzF0uklt79u87bcOeITAHKzMhjzxB3Ub9XBo/n/pxyFhXz/6bvc9/x7hERE8d4z99CiUw+q1y6+Kebfs6YREBjEcx9+zaoFf/LrxI+57YmXWL34Lwry83j6vQnkHT3CG4/eSvueFxHubLSUNf7UMe/y4IvvERoRzdtP303Lzj2p4RJ/yZ+/UrVaEM+P/oYV8//k5y9Gc+eTL1NYWMDE94dz66P/pWb9xmRnHMbXt/xVy8IF89i7ezc//DqD9WvX8NorLzNh8jfHpbv19rvo2LkL+fl53H/3XSycP48evXrz/jtvcfkVVzLwyqtY9vcSRn3wLsNHvFnufHgbh6OQuZM+5MonRhAYHsmUlx+hftuuhLv89qLqNOKG5y/Hv3IV1v31K4umfsYl9/8HgHaXXEdB3lHWz5lu1yaoc5jddb8A17epzocL95Cem89TF9Zn3cFMEjLzitKs2JfBwl3pALSsHsjVrWIYvWgvBzKO8tacnTgMBFf2Y1i/+qxPyMRhzo1tdzgK+eXz/3Hnc28RHBHFx8/eR7OO3Yl2Oe4IjYzh2geeYcEvJeti/0qVufbBZ4msUYuM1GQ+enYIjdp0JqBaYLnj3+ESP65U/JDIGK45QfzAsAjufWUUfv6VOHokl1FP3klch+4Eh0eWqwwm/rKEj7+Zy9jht51w/YCezWlYJ4qWV75E51b1+OA/g+l929uEBVfluXsvpcfNb2KMYdHkZ5g2Zy3pmbnliq8qHk8MpXsP6CoiQ4GewNtleM91wG/GmBx3ZszVji0biYmtRXSNmvj5+9Old39WLZlXIs2qv+fRs9/lAHTq2ZeNa5ZhjKFylQCatGiLv3+lM46/f1s84dVrEhYTi6+fPy26XUj88kUl0oRGVSembkPkBANBD+zYQvbhNBq2PrcaBMcc3L6Z0JhYQqNr4OvnT7Oufdi6ouT2h0RVJ7pOgxNu/zGbl86nQZtO+Feu4u4sn1V7tm0isnpNIqrH4ufvT7ue/Vi/bEGJNOuXzqdTn0sAaN2tD1vXrcAYgyDkHTlCYWEB+XlH8fPzo3JAtXLF3711E1E1ahFZ3fr+t+95EeuWloy/bukCOl94KQBtu/dhy1orfvzqZcTWbUjN+o0BqBYcgo+vb7nLYO5fs7nsiisREVq1aUtmZgbJSYkl0lQJCKBj5y4A+PtXIq5ZcxIPJQCwc8c2Onax1nXs3IV5f80udx680aEdmwmJrkGI87fXuMsF7Fi9uESaWs3aFP2mqjeIIystuWhd7ebt8K8S4NE8q/OH3XV/3fAAkrPzSMnJp9BYjaBWNYJKpDlS4Ch6XtnPB5wNn/xCU9QI8vcVTBkbRMfYve37tsUTERNLeEwsfn7+tOrel03LFpZIExZdnep1GyI+JQ8XI2NrE1mjFgDB4ZEEBoeSnZHutvg+peL7+fnj5zzmKszPw5S1NVrKwpXbST188kPNgRe0ZvKvSwFYum4XIUEBVI8Mpn/3ZsxaEk9aRg7pmbnMWhLPxT2an1EeKgzx8KOCcnvDyBiTDzyF1UAa6nx9OoOBr0ote1VE1orIeyLiOkanm4isEZHfRKQFZygtJZFw59AQgLDIaNJSkkqlSSI8KhoAX18/AqoGkpVxmLMhMy2Z4IiootfBEVFkuhx8nIpxOPhj0sdcfPN9ZyUvdshMSyY4vHj7g8IjSxx8ldWmJXNo1u3Cs5k1jzicmkRoZHTR69DwKA6nJJdKk1yUxtfXjypVq5GdeZg23fpQqUoVXrz7KoYPuY4+g/5FtaDgcsVPLx0/IorDpb7/h1OSThg/8cBeEOGjlx7nzSfu4s8fvixX7GOSEg9R3WXoW0xMdRITE0+aPjMjg/lz/6JT124ANG4Sx19/zgTgr1kzyc7OJj097Yzy4k2y01MIcvntBYZFkp2WctL0G+f/Tt1WHT2RNeUF7K77Q6v4kZZbUPQ6PTef0CrH93j3qh/G8/0bcmWLaL5dm1C0vG5YFf7TrwHP9mvAN6sTytxbBPZve0ZqMiERxfV+cEQUGWcQf9+2TRQWFBAeE/uP4oeU47gH4HByIqOe+jdvP3Ajva4cXO7eorKIjQ5lX0LxfmT/oXRio0OJjQpl3yGX5YnpxEaFnvX4yvM8NfnCpcBBoOXpEopIDaAV8LvL4mexrjnqBIQDzziXrwTqGmPaACOBH0/xufeKyHIRWT5mzJgz2YYKa9nMn2nctnOJhpU3ykpLIWnvTup72UHbnm0b8fHx5cVPf+S50VOY88vXpCQc8Fh8R2EBOzat5bbHnmfoiI9Yu2Qem9cud2vMgoICnnvmSW686RZq1aoNwNAnnmblimXcdMM1rFy+nOjoGHx9yt9zpU5u8+JZJO7aSvtLrrM7K0oV8UTdP39nGi/P3M5PGxIZEFd8AL477QgjZu3grTk7ubhJBH4+nj0Vbvd+LzMthW9HvcY19z9zXK+Ou4VERvPQW58x9H+TWDX3D7LSUz0a/3wjHv5XUbl9um4RaQv0B7oCC0Tka2PMwVO85QbgB9eeJZf0R0VkHPCkc3mGS5rpIvKRiEQaY4475WCMGQMcaxGZxdvSS6wPi4gmNflQ0eu05ETCSjU0wiKiSE2yepYKCwvIzckiMDjk1AVQRkFhkWS4nKHPSEkiKKxsZz/2bd3I7vh1LJv5M3lHciksLKBSlQAu+tc9ZyVvnhAUFklGavH2Z6YmE1jG7T8m/u+5NOnYA1+/c28W+pDwKNKTi3tH0lOTCImILJUmkvTkREIjoiksLOBITjbVgkL4ff6fxLXtjK+fH0EhYdSPa8Xe7fFEVC/72bvQ0vFTkggp9f0PibDShEWWjB8aEU2j5m0IDA4FoHmHbuzbvoWmrU+/o57y9Zf8+J116WDzFi1JSCg+E3voUALR0dEnfN+rL79A7bp1uenW4glSoqKjeeu9kQDk5GQz+88/CAouX8+ZN6oWGkGmy28vKy2ZamERx6Xbu2Ely3/9mqufeQvffzBsWClXdtf96UcKCAsofl9ogD/pRwpOmn7lvgxubFsd61xvsUOZeRwtdFAjuDJ704+UKbbd2x4cHsnhlOJ6PyMlieByxD+Sk80Xrz/LRYP/Te0m5R9GVjr+4XIc95T+nJja9dgVv65ocoaz5UBiOrWqhxW9rhkTyoHEdA4kpdOrQ+Pi5dGhzF+x9azGVvZwa/NerEGxo7GG0O0B3uL01xj9i1LD6Jy9SMc+7ypgvfN1decyRKQz1vacfAzIKdRv0oxD+/eSlHCAgvx8/p43k3ZdepdI07ZLLxbMmgbAsgWzada64ynH/ZZHzYZxpCTsJy3xIIUF+WxY/BdNO3Qv03uveeg/PDbqK4aOnMzFtwyhTa/+51SjCKBGg6akJewn3bn9m5bMoVH7buX6jI2L/zonh9EB1G4UR9LBfaQcsr5/qxbMomXHniXStOjUk2VzZgCwdvEcGrVsj4gQGhnD1vUrATh6JJfdWzYQXbNOueLXaRxH0sG9RfFXLviTVp1Kzm7UslMPlv71GwCrF82hcSsrfrN2nTmwZwd5R63rnLZtWEX12vXKFPeGwTczeeoPTJ76A3369mP6Lz9hjGHdmtUEBgURGXV8w+ijke+TlZnJE08/W2J5eloaDod1LcC4sZ8y6OprylUG3iqmflMOHzpARlIChQX5bP17LvXbdi2RJmn3Nv76YiSXP/IiVZ0NYKXOBrvr/j1puUQFViKiqj++Ah1qBbOu1MxyUdX8i563qB5IUpY1MUNEVX+OdRCFBfgRE1iJ1JyyXC1gsXvbjx13pCYepKAgn3WLZhPXsWzHHQUF+Ux+5/9o1/viM26MuB73lDf+4ZQk8vOOApCblcnuzeuJjK19Rvk4lWlz13HTwM4AdG5Vj4ysXBKSM5i5aBMXdYsjNCiA0KAALuoWx8xFm856fE8S8eyjonL3qfV7gD3GmJnO1x8Bd4rIBcaYuaUTi0g9oDZQet2XIhKFdbnWauDYxTTXAfeLSAGQizW99xldgefr68ct9z/J2//3CA6Hg179r6Bm3QZ8P/ET6jduRruuvel98SDGvP0iT999LdWCgrn/6VeK3v/EnVdxJCebgoJ8Vi6ey5OvfFBiRrvT8fH15bI7HmbSa89gHA7a9rmU6Nr1+GvqOGLrN6Vpx+7s3x7PN+++wJHsLLasXMycqRN44O3Pz2RzKxwfX1/63/4QU958FuNw0OqCAUTVqsf8b8dTvX4TGnfozsHtm/n+/Rc5mpPFtlVLWPDdF9z9xlgADiclkJmaRJ1zdFY+X18/rrn7McYMfwKHw0HnvpdTvU59fvtqLLUbxdGyU0+69LucyR+8wqsPDqZqYDC3PfYiAD0vuZqvP3yNNx69FTB0uvAyYus1Knf86+55nI9eetw5Xfjl1KjTgGmTx1KnURytOvek20UDmfj+cF6+/0aqBgZzxxNW/KqBwVx4xY28/dTdCELzDt1oUcadm6sevS5g4fx5XHX5AKpUqcILw4unTr3p+quZPPUHDiUk8Pmnn1CvfgNuufFaAG4YfBNXXXs9y5ct5cMP3kVEaNe+I88893y58+CNfHx96X3LA/z07nMYh4PmPS8momY9/v7hC6LrNaZ+u24snDKW/KO5zPjoVQACI6IY+MhLAHz32hOkHdxH/tFcxj1xC33vHErdlt41nFWdObvrfoeBqWsSeKBHbQRhye50EjLzuKxZJHvSjrA+IYveDcJpGl2NQochJ7+QiSusocoNIgLo36Q2hQ6DAaasSSA7r/Cc2XZfX18G3vUIE0Y8jcPhoEOfS4mpXZ8/p3xOzQZNadaxB/u2xTP5nf8jNzuL+BWLmT11HI+8M571i+ewa9NacjIzWDnXOmF37QPDqFGOfU/p+O2d8WdN+ZxYl/hfnSB+0v7dzJhYPF15j4E3UL0cx1zHTHjtDnp1aExkaCDbZgxn+MfT8fezhmCP/XYBMxZsYEDPFmz4+QVyjuQz5MVJAKRl5PDapzNYMOlpAEaMmUFahsfmC1NuJGfYjjjXHTeUzlO6NQoFYPLKfbbEv6l9LVvilvb5sj0ej3lXp/L1orjLtPUnn1DAnS5vafW+/L4x6TQp3WNAc2toXuZRx2lSukdQZb2fNcDIhTtPn8gNHu5R//SJ1HnNjnofiuv+h3+w54z+yKubAfZv/9TVnrv21NX1ba1h3VNsin+DM35Au4dsiZ+7ahRU6HnYLNl5nm0QVKtUMfuNzr2LMZRSSimllFJnTYVspdjA4w0jEWkFTCy1+Kgxpoun86KUUkoppZRSYEPDyBizDmjr6bhKKaWUUkqpE9AuI8Bz9zFSSimllFJKqQpLrzFSSimllFLKi1Xkm656kvYYKaWUUkoppbye9hgppZRSSinlxSrm5Nme57X3MbI7A0oppZRSyitU+GbHkQLPHhtX8auYZeKtQ+nknzxEZMg//QyNf27G9+Zt1/gaX+Nr3aPxNb7GP6NHhVfFD/Hkw+7tPRlvbRj9U/dqfK+N783brvE1vsb3ztgaX+NrfOUVtGGklFJKKaWU8nraMFJKKaWUUkp5PW0YnZkxGt9r43vztmt8ja/xvTO2xtf4Gl95BW+dlU4ppZRSSimlimiPkVJKKaWUUsrracNIKaWUUkop5fW0YaSUUkoppZTyetowKgMRuUJEtKyUUkoppZQ6T+nBftncCGwVkTdFJM7TwUXEz+V5oIh0FJFwT+ejIhGRQA/FGSAio0XkZ+djtIhc4onYp8nXeT9Djoj4icgQEZkhImudj99E5D4R8fdA/AYi8rmIvOL83X0qIutFZKqI1HN3/JPkaYQdcT1NRL4XkVs89Ts/Qfw6IlLF+VxE5E4RGSki97vWxx7MT7CIdBCRME/HtoOIVHfWtR+KSISIvCgi60RkiojU8HBewivC/tbuPIhIew/FqYj1ru1/f+U52jAqA2PMLUA7YDswXkQWi8i9IhLk7tgicgdwSES2iMilwFrgDWCNiPzLzbFbicgSEdkrImNcd8oistSdsctgo7sDiMj7wKPAXOBN52Mu8IiI/M8D8cNP8ogALvNA/GAReU1EJorITaXWfeTu+MBEoC3wItb2Xga8BLQBJnkg/nhgGZAFLAHigUuBGcDn7g4uIh+UeowEHjj22gPx45wN0Wki0lBExotIuogsFZFmbg7fBbgK2OM8GL5aRCq5Oaar6RTvH18HLgf+BjrhgWl7RWSSiEQ6nw8A1mPV+6tF5HoPxLe77h+PVcfvBf4CcrF+//OBj90d3Nkw/lpEkrD+7ktFJNG5rJ4H4v/X5XlzEdkCrBCRXSLSxQPx25d6dAB+FpF2HmggjcfeetfWslcVgDFGH2V8ABHAUGAX8BuwFXjYzTHXAZFAfSADaOhcHgOsdXPsBcAlQCjwJLDBJf4qD5T34yd5PAGkeiD+lpMsF2CrB+IXAjuAnS6PY6/zPBD/O6yDwquAn52vKzvXrbSr/E+37izGX+XyfM/J1rkx/l6sBuBtwO3OR9Kx5x6IPw+4AvgXsBsY7PzuXwHM8kTZA8HArVgNlSRgHHCxB7Z9o8vzFYCPy+s1Hoi/zuX5IqCe83mkh+LbXfevcnle+re32gPxF2ONFPF1Webr/A0s8UD8lS7PpwGXOp93BhZ5IL7D+b37y+WR6/x/to1/+1XujF0Ryl4f9j+0x6gMRORKEfkBmAP4A52NMZdinbl+ws3hC40xycaYnUCWMWY7gDHmkJvjAgQZY2YYY9KNMW8DDwEzRKQr4IkbYI0AwoCgUo9APNPbeUREOp1geSfgiAfi7wD6GGPquzwaGGPqA574+zc0xgwzxvxojBkErARmO3usPCFVRK4Xl+v7RMRHRG4E0jwQ3yEiTZzfgaoi0tGZh0ZYB0nu1hxIxjpAnWmMmQBkGmMmOJ+7W5Ax5hdjzFdAvjHma2P5Bet36U4GwBiTYYyZaIy5DIjDOns/zM2xAfaKSF/n811AbQAPfvd9RCTY+dwB7AEwxiQDnhjKZ3fd71q/f3GKde4SaYz5xhhTeGyBMabQGPM11glST4o1xvzmzMNSIMADMa8H8oE3jTEXGmMuBBKcz/ue5r3/lN31ris7yl7ZzONjpc9R1wDvGWPmuS40xuSIyL/dHHuPiLyG1SCIF5F3gO+Bi4CDbo6NiIQYYw4DGGP+EpFrsXoOPDHmdiXwozFmxQnydbcH4t8BjHYOmdznXFYbOOxc527vYx2A7jnBujc9EL+yiPgYYxwAxphXRWQ/Vk+CJ679GIw1fOgjEUnD6q0IwTprOdgD8Z8GfsE6ML0KeFZE2mD1Ytzj7uDGmExgqHMYy5ciMg3PDn92PQh5t9Q6dw9ryyq9wBiTgjWMyu1DqYC7gS9E5EWs3/tqEVmN1YPyuAfivwT8JSIfAguBqSLyM3Ah1pAit7O57v9JRAKNMVnGGNehTY2ALR6Iv8I5XHgCVs8tWHX/7cAqD8Rv4Px7C1BLRKoaY3Kc69x+faUx5jsR+R0YLiJ3YZ0A9kSDGGyud7G57JX9xBhPfdfPXSLyhjHmmdMtc1PsYOBBrEppFDAAuBNraMsrxhi3NY6c15XsMMYsKbW8DvB/xhi3VlIi0hRryFzSCdbFeKjXDBGpDtR0vtxvjEnwRFy7icibwB/GmD9LLb8EGGmMaezBvERA0cGxbZzXfaS5nkn2UFwBHgC6GeuaR0/EHAJ8aYzJKrW8EfCQMWaoJ/JhJ+e1VE2wTiLuA5YdO1HggdiNsA4EXeP/aIz53QOxba377ea8nu3fwJW41P1YQ4o/M8YcdXP8C0otWmGMyRKRGOA6Y8yH7oxfKi/tsE6MtDTGRHkqbqk8eKzerUhlr+yhDaMyEJGVxpj2pZatNca0titPyjNEpDdwyBizWUR6AN2wrj+YbkNe6mNNArLRGBPv6fh2EGtWskuwztYWYp0t/sODB6fBQNSxIawuy1sbY9Z6Ig+nIyKLjTHd7M7H2eQ8AE80xhxxNgrvANpjXZD/qTGmwM78HXM+ln1FJSKzPTCMS52A8zcYZIzJsCH2sf3eBmPMZk/HV95HrzE6BbGmZl0HxEnxdMFrRWQn1uxwnsiDr1hTFg93Hpi7rvvvyd7ngXx5YmYmW7ddrFnpXgcmishw4C2sMcaPi8hbHoj/o8vzK4HZWBe+/yTWbIVuJzZOVy4iN2Bt8yVY1zh0wroQf7WItPJQ/HjgOxHZUOp6s/Hujl8OVdzxoc6ztK6vbxFrRrx7nQdK7mTrrHDl4K6yv1qcUwSLSJSITBBruupvRKSWO2KWim933bu21GMd0OPYaw/E9/byryoiT4vIU2JNW38bMEmsW5a4dRj1KfZ7v3hivycirV2e+4vIf537vhEiUtXd8ZX9tMfoFEQkBOsaj9coecFvpjEm1UN5GAtUBZZiHRTONcY87lx3XE/WWY59srHkgjUzklt3EHZuuzPGBqAlVmNoP1DTeV2ZP9bsOC3dHH+VMaad8/ki4GZjzE7nAessY0wbN8d/H2sYzxcUX2NVC2snudUY86ib468FujrLPBJrWNcA547rY2NMdzfHX401I9FBEemMVQ7PGmN+cP3b2M1dvwXXz3UejPUCJgMDgX3GmMfOdkyX2BuNMc2dz1cAnY71EorIGnd/98vKjWXvuv3fYE1bPBXr2tKbjTH9z3bMUvHtrnt/xpqF9RWs2dAEa6rungDGmN1uju/t5T8F69qqAKApsAn4BhgEVDfG3OrG2Hbv91zrvXewJtsYh3W9U4Qx5jZ3xlf208kXTs0YY3aJyIOlV4hIuIcaR52PDdkTkVFYF6J/jzWFrrvP2iZhXcvkGsc4X0e7OTbYu+1g/f2NiBwbtnXsLIIDz/S2up618DPWzIQYY5Jd8uROlxljmpRe6DxQ2IJ1jyd3EqyDIoBsnN85Y8xaKZ6xy518j13DZ4xZKiIXAr+KSG08dyGynVx/Y9cAvYwx2SIyGWtiFHfaKyJ9jTGzKZ4Vbrd4blY4u7lOfNHIGHOj8/l4ERnqgfi21r3GmEEicjVW7+DbxpifRSTf3Q0iF15d/kATY8wNzp7hg8BFzn3hAmCNm2Pbvd9zLd9+WCdl8kVkHu7fdlUB6FC6U5vs/H8FsNz5/wqX155QNPuTMabAGHMvsBqre9ndM4PZPV20ndsOME1E5mOdqRwLTBGR57DuYTXvlO88O9qISIaIZAJtxXnHd7EuDPbEtKV2T1c+HWuK4OeAP7DO2B7ryfTEwUGmiDQ89sLZSOqDdUF2C3cHF+s6m5Ot6+X60k1ZCBDrho4dsBqJ2QDGmHys673c6W7g/5wHI5Wwhk/+BfyJB2aFqwBlP0dEXhaRAOfzq52xL8SaJc/d7K57Mcb8gHVjzz4i8hPunwnRldeXvzO2AaY7/z/22t0nheze74WINZTyWqz79uWDx7ZdVQA6lK6CE5FJwCRjzIxSy+8GRhtj3DZ9pLOnbIEx5rizJCLysDFmpLtiO2PYtu0usbph1YlLnAfJV2NNn/2tpyYAOEGeQoFmxpjFbo7THhiNNVV86enKHzQnmEbdDXm4DOt+PmuMMTOdy3wAf+P+maHaANnGmG2llvsDNxhjvnRz/B1YU1O/Y5yzMYk1M9I7QJwx5tj9PVoaY9a7If5fpRbd5BxWGAH8fiy+O4lNs8JVgLL3B54D7nIuqoXVa/oLMMwYc6Ip/M9mfNvr3lJx22DNyOiJqdq9vvydQ/mGmuNnpGwITDDG9HRn/JPkKRTP7PfGlVo0zBhzSKzZab80xvRzZ3xlP20YnYLzwPCkjDHuHk6izgFi88xU7o4vFXy68vO1/EUkDGvige5YwxZbYfWWvIl1cGRXw9wX60xqzmkTuz8v533Zi3Wtq5+xeap6u4nICGPMf2yIq+XvQkTEVIADR7vrfXX+0muMTu2dU6wzgNunDhWRQVjTE3ti6FKZiUj/Y2fw3RijQm77CbhlZqqKEt/ZEEoA6+AE8PjByWmcl+VvjEkDhojIo1hDyA5gTUax79TvdB+Xg1PbG0VO533ZG+dNVu1qGJTmobr/gxMsvk2cM6IZYx5xZ3xXdpS/3fu+U8WvCI0iJ3fNCDkIq0fcrSMSVMWlDaNTMMZcaHcesGaCyRaR34CvsH6wHr255El8Bpx0HP5ZUlG3vTS7dxRuiV+RDk5O43wt/1DgDaAL1pTllwG/icijzkkJ3OoEf38Bbq1gf38te8/zRN1/NTAX69rCY9dx/Qvr+l63qwDlb/e+z+74ZeGuev9c2HblRtowKiMRaYl1rUPRWQpjzBceCB2P1TN1HfAEME5EfgC+MsbMdWdgsaZMPeEqrCks3c22bVeAzQcnipXAR1jXcxUAf4hIW6wZqnYbY/7l5vgn+vsPxjv+/l5d9hWg7m8ODMdqlD5pjDkgIi8YYyZ4IDbY/923e99nd3w7efO2K/QaozIRkRewZqNqjjVT1qVYkxJc54HYJe5Z4Lze4wasA9RaxpjaboydBtwCZJVeBXxjjIlxV2xnfNu23RmvzskushWRXsaY+c7nq4wb7mlTAeIHYR2cRFN8cLLDGNPgbMc6SXy7t9/u+LVONnRLRO4xxnx6tmOWimHb31/L3vbfnq11v0s+OgBvA9OAh4wx9TwU1+7yt3vfZ+dxh92/fVvLXlUAxhh9nOYBrMOa2nyN83UMMNNDsVedYl1dN8f+DbjwJOvmnc/b7oyxA3gaa6riY8tigEnAcpdlLc/H+C6f3wH4C3gS2OXucq8o2293/IrysOPvr2VvX9k749pa95eKJ8CDWLO0eUv5rzrFurrnc3y7f/t2l70+7H/ofYzKJtdYsxAViHVjyUSsaYs94aR3lzduvtmdMeZSY0zpKXuPrevtzthOtm27UwegIdY9VPo6L8ReCiwGOrvk5axP11tB4h/7/BVYQwtygQXujFWK3dtvd/wKwaa/v5Y99v32KkDd7xrPGGM+NMbcUnqdiLh16mYb6z679312xrf7t2932Sub6VC6MhCRj7Bm4hqMNeY0C1htjLnT1oy5sHPqSrunzfTAdNWPAu9h08xUdsc/HS1/7+bOv7+W/amd73VvGeKvMm4YTlWO+HZv/3kbv6L/9u0ue+U+2mNUBsaYB4wx6ca6uVx/4PaK1ChysnPK4vNyumQRCRWRT4A7sS4C/hZrZiq3T9NeEeKXg5a/dzvrf38t+zI7L+vecrD7zK7d23/exT+Hfvt2l71yE52VrgxE5LihAyLS2xgzz478nISdOwi7d07uim/3zFR2xy8rLX/v5o6/v5Z92Zyvde+5wu7tPx/jnyu/fbvLXrmJNozK5imX51WwxrkeG3uszl+9S3ffG2NWA91F5B4viG83u7ff7vjeTMvei5V1ZjKKp9JW5w/97Stb6VC6MjDGXOHy6A+0BNI8EVtETnojPRHp5fryfIpdEeKfakyzcfN0vRUhvpa/vfHtZuffX8veu+teYI6IPC0ivi5xY0RkEtZ1J8fc6o7gdm+/N8e3+7dvd9kr+2nD6MzsA5p5KJadOwhbd04VIL630/L3bvr3t4/dZW93fLtnJrN7+709vp28edsV2jAqExEZKSIfOB+jgPlY42A9wc4dhN07J7vjezstf++mf3/72F32tsY3xqQZY4YAY4E/sYaz93BO2+1wR8xSvLr8K0B8O3nztit0uu4yEZHbXV4WYN3obaGH82Db1JV2xq4I8b2dlr9307+/fewue7vii0go8AbQBetmn5cB/YBHjTGzPZEHZz68svwrSnw7efO2ezvtMSqbqcAq5+NbTzaKxMapK+2MXRHiezstf++mf3/72F32dsfHGpGxFehojPnDGDMUa+jSKyLylbuD27393h7fTt687crJGKOPkzwAf+B9IAVrFrqVwH5gmHN9Ww/kYQfwJODnsqwtsAj46nyNXRHie/tDy9+7H/r3996yrwDxa51i3T1esP1eHd/Ohzdvuz6shw6lOwUR+QCoCjxmjMl0LgsG3gYKgUuMMfXdnIda5iRduCJyj3HjLC12xq4I8b2dlr9307+/fewue7vj283u7ff2+Hby5m1XFm0YnYKIbAMam1KF5JytJBm41BizxJbMKaWUUkoppc4avcbo1BylG0UAxphCIEkbRUoppZRSSp0ftGF0ahtF5LbSC0XkFmCTDflRSimllFJKuYEOpTsFEakJfA/kYk2+ANARCACuNsbstytvSimllFJKqbNHG0Zl4JymsYXz5UZjzKxS68OMMWmez5lSSimllFLqbNCG0VkgIiuNMe3tzodSSimllFLqzOg1RmeH2J0BpZRSSiml1JnThtHZod1uSimllFJKncO0YaSUUkoppZTyetowOgURmS4i9cqS1N15UUoppZRSSrmPNoxObRzwh4g8JyL+p0jXz1MZUkoppZRSSp19OivdaYhIIPB/wCXARMBxbJ0x5l278qWUUkoppZQ6e/zszsA5IA/IBioDQbg0jJRSSimllFLnB20YnYKIXAK8C/wMtDfG5NicJaWUUkoppZQb6FC6UxCR+cB9xpgNdudFKaWUUkop5T7aMFJKKaWUUkp5PZ2VTimllFJKKeX1tGGklFJKKaWU8nraMFJKKaWUUkp5PW0YKaWUUkoppbyeNoyUUkoppZRSXu//AcdhZ3FyjrZ3AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "E1vfXTKcK9Dt"
    },
    {
      "cell_type": "code",
      "source": [
        "# feature 중요도 \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "%matplotlib inline\n",
        "rf_run = RandomForestRegressor(random_state=0, max_depth=5, min_samples_leaf=8, min_samples_split=8,n_estimators=200)\n",
        "rf_run.fit(X_train, y_train)  \n",
        "ftr_importances_values = rf_run.feature_importances_\n",
        "ftr_importances = pd.Series(ftr_importances_values, index=X_train.columns)\n",
        "ftr_top = ftr_importances.sort_values(ascending=False)[:20]\n",
        " \n",
        "plt.figure(figsize=(10, 10))\n",
        "sns.barplot(x=ftr_top, y=ftr_top.index)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "lLsOtCgTSvRA",
        "outputId": "3b53834f-4af7-49cf-da2c-f5003f63c8b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "lLsOtCgTSvRA"
    },
    {
      "cell_type": "code",
      "source": [
        "x=train_x.loc[:,['X_1834','X_1945','X_256','X_248','X_258','X_373','X_131','X_2841']]\n",
        "y=train_y\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import sklearn.model_selection as ms\n",
        "\n",
        "model = GradientBoostingClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')\n",
        "y_pred = model.predict(X_test)\n",
        "# test_x = test_x.drop(columns=['LINE', 'PRODUCT_CODE'])\n",
        "\n",
        "test_x=test_x.loc[:,['X_1834','X_1945','X_256','X_248','X_258','X_373','X_131','X_2841']]\n",
        "preds = model.predict(test_x)\n",
        "sns.countplot(x=preds); \n",
        "# preds.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "cAqWZJ7NSva4",
        "outputId": "7d690f08-13de-4aae-839b-83ef7a8a413c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "cAqWZJ7NSva4"
    },
    {
      "cell_type": "code",
      "source": [
        "train_x.describe()"
      ],
      "metadata": {
        "id": "GdugZkQrSvJS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "80ae912f-9494-47ef-bcbf-e4914cbcc95a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             LINE  PRODUCT_CODE         X_1         X_2    X_3         X_4  \\\n",
              "count  598.000000    598.000000  598.000000  598.000000  598.0  598.000000   \n",
              "mean     3.214047      1.157191    1.406355    0.544265    0.0    0.583612   \n",
              "std      1.703579      0.983298    4.655332    0.461135    0.0    0.493372   \n",
              "min      0.000000      0.000000    0.000000    0.000000    0.0    0.000000   \n",
              "25%      2.000000      0.000000    0.000000    0.000000    0.0    0.000000   \n",
              "50%      4.000000      2.000000    2.000000    0.892157    0.0    1.000000   \n",
              "75%      5.000000      2.000000    2.000000    0.931373    0.0    1.000000   \n",
              "max      5.000000      2.000000  103.000000    1.000000    0.0    1.000000   \n",
              "\n",
              "              X_5    X_6         X_7         X_8  ...      X_2866      X_2867  \\\n",
              "count  598.000000  598.0  598.000000  598.000000  ...  598.000000  598.000000   \n",
              "mean     0.551383    0.0    0.459381    0.533141  ...    0.141039    0.130564   \n",
              "std      0.467361    0.0    0.392067    0.450953  ...    0.318552    0.295577   \n",
              "min      0.000000    0.0    0.000000    0.000000  ...    0.000000    0.000000   \n",
              "25%      0.000000    0.0    0.000000    0.000000  ...    0.000000    0.000000   \n",
              "50%      0.909091    0.0    0.725806    0.909091  ...    0.000000    0.000000   \n",
              "75%      0.909091    0.0    0.806452    0.909091  ...    0.000000    0.000000   \n",
              "max      1.000000    0.0    1.000000    1.000000  ...    1.000000    1.000000   \n",
              "\n",
              "           X_2868      X_2869      X_2870      X_2871  X_2872  X_2873  X_2874  \\\n",
              "count  598.000000  598.000000  598.000000  598.000000   598.0   598.0   598.0   \n",
              "mean     0.137318    0.144148    0.139755    0.165552     0.0     0.0     0.0   \n",
              "std      0.310467    0.327595    0.312991    0.371989     0.0     0.0     0.0   \n",
              "min      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "25%      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "50%      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "75%      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "max      1.000000    1.000000    1.000000    1.000000     0.0     0.0     0.0   \n",
              "\n",
              "       X_2875  \n",
              "count   598.0  \n",
              "mean      0.0  \n",
              "std       0.0  \n",
              "min       0.0  \n",
              "25%       0.0  \n",
              "50%       0.0  \n",
              "75%       0.0  \n",
              "max       0.0  \n",
              "\n",
              "[8 rows x 2877 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c6b46a05-9205-489c-86bc-021409db7a8b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>LINE</th>\n",
              "      <th>PRODUCT_CODE</th>\n",
              "      <th>X_1</th>\n",
              "      <th>X_2</th>\n",
              "      <th>X_3</th>\n",
              "      <th>X_4</th>\n",
              "      <th>X_5</th>\n",
              "      <th>X_6</th>\n",
              "      <th>X_7</th>\n",
              "      <th>X_8</th>\n",
              "      <th>...</th>\n",
              "      <th>X_2866</th>\n",
              "      <th>X_2867</th>\n",
              "      <th>X_2868</th>\n",
              "      <th>X_2869</th>\n",
              "      <th>X_2870</th>\n",
              "      <th>X_2871</th>\n",
              "      <th>X_2872</th>\n",
              "      <th>X_2873</th>\n",
              "      <th>X_2874</th>\n",
              "      <th>X_2875</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.214047</td>\n",
              "      <td>1.157191</td>\n",
              "      <td>1.406355</td>\n",
              "      <td>0.544265</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.583612</td>\n",
              "      <td>0.551383</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.459381</td>\n",
              "      <td>0.533141</td>\n",
              "      <td>...</td>\n",
              "      <td>0.141039</td>\n",
              "      <td>0.130564</td>\n",
              "      <td>0.137318</td>\n",
              "      <td>0.144148</td>\n",
              "      <td>0.139755</td>\n",
              "      <td>0.165552</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.703579</td>\n",
              "      <td>0.983298</td>\n",
              "      <td>4.655332</td>\n",
              "      <td>0.461135</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.493372</td>\n",
              "      <td>0.467361</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.392067</td>\n",
              "      <td>0.450953</td>\n",
              "      <td>...</td>\n",
              "      <td>0.318552</td>\n",
              "      <td>0.295577</td>\n",
              "      <td>0.310467</td>\n",
              "      <td>0.327595</td>\n",
              "      <td>0.312991</td>\n",
              "      <td>0.371989</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.892157</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.725806</td>\n",
              "      <td>0.909091</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.931373</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.806452</td>\n",
              "      <td>0.909091</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6b46a05-9205-489c-86bc-021409db7a8b')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c6b46a05-9205-489c-86bc-021409db7a8b 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-c6b46a05-9205-489c-86bc-021409db7a8b');\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": 44
        }
      ],
      "id": "GdugZkQrSvJS"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xskVkfufSvDg"
      },
      "execution_count": null,
      "outputs": [],
      "id": "xskVkfufSvDg"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xuPOUxQ7Su9m"
      },
      "execution_count": null,
      "outputs": [],
      "id": "xuPOUxQ7Su9m"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.Modeling"
      ],
      "metadata": {
        "id": "hIoaatyGu9Zq"
      },
      "id": "hIoaatyGu9Zq"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 ML models"
      ],
      "metadata": {
        "id": "GQGMvepMJy87"
      },
      "id": "GQGMvepMJy87"
    },
    {
      "cell_type": "code",
      "source": [
        "from catboost import CatBoostClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "models = [\n",
        "    RandomForestClassifier(random_state=seed_num), \n",
        "    GradientBoostingClassifier(random_state=seed_num),   # GBC: no tuning is the best \n",
        "    XGBClassifier(random_state=seed_num),\n",
        "    LGBMClassifier(objective='multiclass', random_state=seed_num),\n",
        "    CatBoostClassifier(objective='MultiClass',\n",
        "                                   task_type='GPU',\n",
        "                                   one_hot_max_size=2, random_seed=seed_num,\n",
        "                                   iterations=4000, verbose=False,\n",
        "                                   learning_rate=0.05\n",
        "                                   ),\n",
        "    RidgeClassifierCV(),  # RidgeClassifier: no seed setting argument  \n",
        "    BaggingClassifier(random_state=seed_num)]\n",
        "\n",
        "models"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m61QOAHA8lvG",
        "outputId": "063acd63-405d-4512-e524-3a36fc2a594e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[RandomForestClassifier(random_state=37),\n",
              " GradientBoostingClassifier(random_state=37),\n",
              " XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "               colsample_bylevel=None, colsample_bynode=None,\n",
              "               colsample_bytree=None, early_stopping_rounds=None,\n",
              "               enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "               gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "               interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "               max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "               max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "               min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "               n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "               predictor=None, random_state=37, ...),\n",
              " LGBMClassifier(objective='multiclass', random_state=37),\n",
              " <catboost.core.CatBoostClassifier at 0x7f2cce12d3d0>,\n",
              " RidgeClassifierCV(alphas=array([ 0.1,  1. , 10. ])),\n",
              " BaggingClassifier(random_state=37)]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "id": "m61QOAHA8lvG"
    },
    {
      "cell_type": "code",
      "source": [
        "# find weights for each model \n",
        "model_list = ['RF', 'GBC', 'XGB', 'LGBM', 'Catboost', 'Ridge', 'Bagging']\n",
        "i=0\n",
        "\n",
        "for model in models: \n",
        "        \n",
        "    if i == 4: \n",
        "        preds = model.fit(X_train, y_train).predict(X_test)\n",
        "        preds = preds.reshape(1,180)\n",
        "        preds = list(preds[0])\n",
        "    else: \n",
        "        preds = model.fit(X_train, y_train).predict(X_test)\n",
        "\n",
        "    globals()[f'preds_{model_list[i]}'] = preds   # set variable name for each model prediction results  \n",
        "  \n",
        "    score = sklearn.metrics.accuracy_score(y_test, preds)\n",
        "    i += 1\n",
        "    print(f'{model} accuracy: \\n', score)\n",
        "#   print(model,  globals()[f'preds_{model_list[i]}'])   shell script???????? .sh     \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88ebd909-28fd-466d-ac3a-4ded53fe2207",
        "id": "qfV3aJaS8lvI"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForestClassifier(random_state=37) accuracy: \n",
            " 0.7888888888888889\n",
            "GradientBoostingClassifier(random_state=37) accuracy: \n",
            " 0.8\n",
            "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
            "              colsample_bylevel=None, colsample_bynode=None,\n",
            "              colsample_bytree=None, early_stopping_rounds=None,\n",
            "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
            "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
            "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
            "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
            "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
            "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
            "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
            "              objective='multi:softprob', predictor=None, ...) accuracy: \n",
            " 0.7722222222222223\n",
            "LGBMClassifier(objective='multiclass', random_state=37) accuracy: \n",
            " 0.7888888888888889\n",
            "<catboost.core.CatBoostClassifier object at 0x7f2cce12d3d0> accuracy: \n",
            " 0.8166666666666667\n",
            "RidgeClassifierCV(alphas=array([ 0.1,  1. , 10. ])) accuracy: \n",
            " 0.7611111111111111\n",
            "BaggingClassifier(random_state=37) accuracy: \n",
            " 0.75\n"
          ]
        }
      ],
      "id": "qfV3aJaS8lvI"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 sequential MLP \n",
        "* torch code can make the same result --> TRY!!! "
      ],
      "metadata": {
        "id": "_KIk0bpq4IHj"
      },
      "id": "_KIk0bpq4IHj"
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "    # Input layer: ReLU (decide input size according to dimension)\n",
        "    Dense(2056, \n",
        "          activation='relu', input_shape=(train_x.shape[-1],)),    \n",
        "    # BatchNormalization(), \n",
        "    Dropout(0.3), \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # Output layer: softmax \n",
        "    Dense(3, activation='softmax')  \n",
        "    ])\n",
        "# model.save('sequential')\n",
        "# CUDA   torch code ㅠㅠㅠㅠ \n",
        "model.summary()\n",
        "model.compile(optimizer = optimizers.Adam(1e-3),  # default learning rate=1e-3 \n",
        "            loss='sparse_categorical_crossentropy',  # tow or more label classes (`Y_Class`: 0,1,2)\n",
        "            metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_x, train_y, \n",
        "                    epochs=5,\n",
        "                    batch_size=256,  # batch size: 2^x && half of data size(598/2) \n",
        "                    validation_split=0.2,\n",
        "                    )   \n",
        "\n",
        "preds = model.predict(test_x) \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0n3ShShET8kG",
        "outputId": "9229ef77-044f-445c-8498-4dd81509cdc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_2 (Dense)             (None, 512)               1473536   \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 3)                 1539      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,475,075\n",
            "Trainable params: 1,475,075\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "6/6 [==============================] - 1s 45ms/step - loss: 1.8346 - accuracy: 0.5858 - val_loss: 1.9028 - val_accuracy: 0.6250\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 1.1745 - accuracy: 0.6632 - val_loss: 1.3163 - val_accuracy: 0.6167\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.8698 - accuracy: 0.6904 - val_loss: 0.8561 - val_accuracy: 0.6833\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.7587 - accuracy: 0.6925 - val_loss: 1.0302 - val_accuracy: 0.6167\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.7504 - accuracy: 0.7113 - val_loss: 0.9521 - val_accuracy: 0.6250\n",
            "10/10 [==============================] - 0s 2ms/step\n"
          ]
        }
      ],
      "id": "0n3ShShET8kG"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "df1ce02b-47aa-469a-b602-759afdd9b1bd",
        "id": "FLHA36Z1iEhW"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "a=[]\n",
        "\n",
        "for i in preds: \n",
        "    i = list(i)\n",
        "    a.append(i.index(max(i)))\n",
        "\n",
        "sns.countplot(x=a);    "
      ],
      "id": "FLHA36Z1iEhW"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k1KBJb0mIYUI"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "submit['Y_Class'] = a\n",
        "\n",
        "submit.to_csv('LReluDO3b80ep5LR1e3-ANN-smartFactorySubmission.csv', index=False)"
      ],
      "id": "k1KBJb0mIYUI"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "f5a8c473",
        "outputId": "fcd7e3ac-a62a-4577-db75-1845d8d40c27"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "epochs = range(1, len(history.history[\"loss\"]) + 1)\n",
        "\n",
        "fig, axes = plt.subplots(1,2,figsize=(14,4))\n",
        "ax= axes.flatten()\n",
        "a, b = ax[0], ax[1]\n",
        "\n",
        "a.plot(epochs, history.history[\"loss\"], \"bo\", label=\"Training loss\")\n",
        "a.plot(epochs, history.history[\"val_loss\"], \"b\", label=\"Validation loss\")\n",
        "a.set_title(\"Training and validation loss\")\n",
        "a.set_xlabel(\"Epochs\")\n",
        "a.set_ylabel(\"Loss\")\n",
        "a.legend()   \n",
        "\n",
        "b.plot(epochs, history.history[\"accuracy\"], \"bo\", label=\"Training accuracy\")\n",
        "b.plot(epochs, history.history[\"val_accuracy\"], \"b\", label=\"Validation accuracy\")\n",
        "b.set_title(\"Training and validation accuracy\")\n",
        "b.set_xlabel(\"Epochs\")\n",
        "b.set_ylabel(\"Accuracy\")\n",
        "b.legend();"
      ],
      "id": "f5a8c473"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1Rro4p8r4ITC"
      },
      "id": "1Rro4p8r4ITC",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3.Params Optimization\n",
        "1. GridSearch \n",
        "2. Optuna\n",
        "3. RandomizedSearchCV"
      ],
      "metadata": {
        "id": "wE74J0d04SNc"
      },
      "id": "wE74J0d04SNc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 GBC"
      ],
      "metadata": {
        "id": "Fn79mJ8rn4Jn"
      },
      "id": "Fn79mJ8rn4Jn"
    },
    {
      "cell_type": "code",
      "source": [
        "gb_class = GradientBoostingClassifier(random_state = 37) \n",
        "\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=37)\n",
        "# kf = sklearn.model_selection.StratifiedKFoldKFold(n_splits=5, shuffle=True, random_state=37)\n",
        "\n",
        "# stratified --> too many Class 0, Class 2 over-predicted \n",
        "# non-stratified --> training score 77.0 \n",
        "\n",
        "scores = cross_validate(gb_class, X_train, y_train, \n",
        "                        cv = kf, scoring = \"accuracy\", \n",
        "                        return_train_score = True, n_jobs = -1) "
      ],
      "metadata": {
        "id": "zHCvGdc9H067"
      },
      "execution_count": null,
      "outputs": [],
      "id": "zHCvGdc9H067"
    },
    {
      "cell_type": "code",
      "source": [
        "print(scores) \n",
        "scoring = scores['test_score'].mean()\n",
        "print(f'Best Accuracy: {round(scoring, 2)*100}')  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vsgw8TQrtpmQ",
        "outputId": "03b83fa8-64e3-462c-a62d-2beac0fc54e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'fit_time': array([41.62663841, 41.74060178, 37.6939764 , 37.52241993, 24.01014924]), 'score_time': array([0.03269506, 0.03415394, 0.03555298, 0.0340457 , 0.0192306 ]), 'test_score': array([0.78333333, 0.79166667, 0.775     , 0.78991597, 0.73109244]), 'train_score': array([1.        , 0.9958159 , 1.        , 1.        , 0.99791232])}\n",
            "Best Accuracy: 77.0\n"
          ]
        }
      ],
      "id": "Vsgw8TQrtpmQ"
    },
    {
      "cell_type": "code",
      "source": [
        "def objective(trial, X, y, cv, scoring):\n",
        "  \"\"\"\n",
        "  An objective function to tune hyperparameters of Gradient Boosting Classifier.\n",
        "  Args:\n",
        "    trial: an Optuna trial\n",
        "    X: DataFrame object, features\n",
        "    y: Series object, Labels\n",
        "    cv: k folds to cross-validate\n",
        "    scoring: String, evaluation metric\n",
        "  Return:\n",
        "    Mean test accuracy\n",
        "  \"\"\"\n",
        "\n",
        "  params = {\n",
        "    \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 5000, step = 100),\n",
        "    \"learning_rate\": trial.suggest_float(\"learning_rate\", 1e-4, 0.3, log = True),\n",
        "    \"max_depth\": trial.suggest_int(\"max_depth\", 3, 9),\n",
        "    \"subsample\": trial.suggest_float(\"subsample\", 0.5, 0.9, step = 0.1),\n",
        "    \"max_features\": trial.suggest_categorical(\"max_features\", [\"auto\", \"sqrt\", \"log2\"]),\n",
        "    \"random_state\": 42,\n",
        "    }\n",
        "  # Perform cross validation\n",
        "  gb_class = GradientBoostingClassifier(**params)\n",
        "\n",
        "  # Compute scores\n",
        "  scores = cross_validate(gb_class, X, y, cv = cv, scoring = scoring, n_jobs = -1)\n",
        "  accuracy = scores[\"test_score\"].mean()\n",
        "\n",
        "  return accuracy"
      ],
      "metadata": {
        "id": "p3-9ibAcp96Q"
      },
      "execution_count": null,
      "outputs": [],
      "id": "p3-9ibAcp96Q"
    },
    {
      "cell_type": "code",
      "source": [
        "study = optuna.create_study(direction = \"maximize\")\n",
        "\n",
        "kf = sklearn.model_selection.StratifiedKFold(n_splits=5, shuffle=True, random_state=37)\n",
        "\n",
        "func = lambda trial: objective(trial, X_train, y_train, \n",
        "                               cv = kf, scoring = \"accuracy\")\n",
        "\n",
        "# %%time\n",
        "# Start optimizing with 100 trials\n",
        "study.optimize(func, n_trials = 10)\n",
        "\n",
        "print(f\"The highest accuracy reached by this study: {(study.best_value) * 100}%.\")\n",
        "print(\"Best params:\")\n",
        "for key, value in study.best_params.items():\n",
        "    print(f\"\\t{key}: {value}\")    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 869
        },
        "id": "gerDxJOEt9hE",
        "outputId": "d8e2b20e-5fc2-4552-ff67-d760f8ec790b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m[I 2023-02-23 23:40:00,843]\u001b[0m A new study created in memory with name: no-name-ed8b6a39-0289-437f-844d-22d3bdfaf955\u001b[0m\n",
            "\u001b[32m[I 2023-02-23 23:43:04,778]\u001b[0m Trial 0 finished with value: 0.7608263305322129 and parameters: {'n_estimators': 4700, 'learning_rate': 0.0005668195633865373, 'max_depth': 6, 'subsample': 0.9, 'max_features': 'log2'}. Best is trial 0 with value: 0.7608263305322129.\u001b[0m\n",
            "\u001b[32m[I 2023-02-23 23:48:01,267]\u001b[0m Trial 1 finished with value: 0.8026050420168067 and parameters: {'n_estimators': 200, 'learning_rate': 0.21851112508358236, 'max_depth': 6, 'subsample': 0.7, 'max_features': 'auto'}. Best is trial 1 with value: 0.8026050420168067.\u001b[0m\n",
            "\u001b[32m[I 2023-02-23 23:49:11,431]\u001b[0m Trial 2 finished with value: 0.79593837535014 and parameters: {'n_estimators': 3400, 'learning_rate': 0.09944316159185719, 'max_depth': 9, 'subsample': 0.9, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.8026050420168067.\u001b[0m\n",
            "\u001b[32m[I 2023-02-23 23:50:10,982]\u001b[0m Trial 3 finished with value: 0.7925630252100839 and parameters: {'n_estimators': 1700, 'learning_rate': 0.021821242865568646, 'max_depth': 4, 'subsample': 0.6, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.8026050420168067.\u001b[0m\n",
            "\u001b[33m[W 2023-02-24 00:44:01,212]\u001b[0m Trial 4 failed with parameters: {'n_estimators': 3900, 'learning_rate': 0.00015343398793601584, 'max_depth': 6, 'subsample': 0.8, 'max_features': 'auto'} because of the following error: KeyboardInterrupt().\u001b[0m\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/optuna/study/_optimize.py\", line 200, in _run_trial\n",
            "    value_or_values = func(trial)\n",
            "  File \"<ipython-input-89-03e1837b5db3>\", line 5, in <lambda>\n",
            "    func = lambda trial: objective(trial, X_train, y_train,\n",
            "  File \"<ipython-input-88-8a203aa46cbb>\", line 26, in objective\n",
            "    scores = cross_validate(gb_class, X, y, cv = cv, scoring = scoring, n_jobs = -1)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\", line 267, in cross_validate\n",
            "    results = parallel(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 1098, in __call__\n",
            "    self.retrieve()\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 975, in retrieve\n",
            "    self._output.extend(job.get(timeout=self.timeout))\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py\", line 567, in wrap_future_result\n",
            "    return future.result(timeout=timeout)\n",
            "  File \"/usr/lib/python3.8/concurrent/futures/_base.py\", line 439, in result\n",
            "    self._condition.wait(timeout)\n",
            "  File \"/usr/lib/python3.8/threading.py\", line 302, in wait\n",
            "    waiter.acquire()\n",
            "KeyboardInterrupt\n",
            "\u001b[33m[W 2023-02-24 00:44:01,217]\u001b[0m Trial 4 failed with value None.\u001b[0m\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-89-03e1837b5db3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# %%time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Start optimizing with 100 trials\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\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     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"The highest accuracy reached by this study: {(study.best_value) * 100}%.\"\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/python3.8/dist-packages/optuna/study/study.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m    423\u001b[0m         \"\"\"\n\u001b[1;32m    424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 425\u001b[0;31m         _optimize(\n\u001b[0m\u001b[1;32m    426\u001b[0m             \u001b[0mstudy\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[0m\n\u001b[1;32m    427\u001b[0m             \u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\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/python3.8/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_optimize\u001b[0;34m(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m     64\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[1;32m     65\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             _optimize_sequential(\n\u001b[0m\u001b[1;32m     67\u001b[0m                 \u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m                 \u001b[0mfunc\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/python3.8/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_optimize_sequential\u001b[0;34m(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\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--> 163\u001b[0;31m             \u001b[0mfrozen_trial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_run_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\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    164\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;31m# The following line mitigates memory problems that can be occurred in some\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/python3.8/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m     ):\n\u001b[0;32m--> 251\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mfunc_err\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfrozen_trial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    198\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mget_heartbeat_thread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trial_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\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    199\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--> 200\u001b[0;31m             \u001b[0mvalue_or_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\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[0m\u001b[1;32m    201\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrialPruned\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    202\u001b[0m             \u001b[0;31m# TODO(mamu): Handle multi-objective cases.\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-89-03e1837b5db3>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mkf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStratifiedKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m37\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m func = lambda trial: objective(trial, X_train, y_train, \n\u001b[0m\u001b[1;32m      6\u001b[0m                                cv = kf, scoring = \"accuracy\")\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-88-8a203aa46cbb>\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial, X, y, cv, scoring)\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m   \u001b[0;31m# Compute scores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m   \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgb_class\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\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     27\u001b[0m   \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"test_score\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\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     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0;31m# independent, and that it is pickle-able.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[0mparallel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m     results = parallel(\n\u001b[0m\u001b[1;32m    268\u001b[0m         delayed(_fit_and_score)(\n\u001b[1;32m    269\u001b[0m             \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\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/python3.8/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1096\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\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-> 1098\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\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   1099\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    973\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[1;32m    974\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\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--> 975\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\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    976\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    977\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\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/usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    565\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    566\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--> 567\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\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    568\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\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    569\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    437\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\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    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\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    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\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/lib/python3.8/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\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--> 302\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\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    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\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[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "id": "gerDxJOEt9hE"
    },
    {
      "cell_type": "code",
      "source": [
        "import imblearn\n",
        "\n",
        "# Trial 8 finished with value: 0.7976750700280112 and \n",
        "# param = {'n_estimators': 2500, 'learning_rate': 0.24437231062646464, 'max_depth': 7, 'subsample': 0.9, 'max_features': 'sqrt'}\n",
        "\n",
        "# value: 0.7926050420168067 \n",
        "param= {'n_estimators': 1400, 'learning_rate': 0.007793231258605556, 'max_depth': 6, 'subsample': 0.8, 'max_features': 'sqrt'}\n",
        "\n",
        "GBC =  GradientBoostingClassifier(random_state=37, **param,                              \n",
        "                                  ).fit(train_x, train_y)\n",
        "preds = GBC.predict(test_x)\n",
        "sns.countplot(x=preds);\n",
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "S4zxDMJ4YGGd",
        "outputId": "996ee95b-34b7-4777-9d4e-f809f1c57065"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "S4zxDMJ4YGGd"
    },
    {
      "cell_type": "code",
      "source": [
        "# Best is trial 0 with value: 0.8026750700280111.\n",
        "params= {'n_estimators': 4700, 'learning_rate': 0.007080906403563565, 'max_depth': 4, 'subsample': 0.8, 'max_features': 'sqrt'}\n",
        "\n",
        "GBC =  GradientBoostingClassifier(random_state=37,\n",
        "                                  **params,                              \n",
        "                                  ).fit(train_x, train_y)\n",
        "preds = GBC.predict(test_x)\n",
        "sns.countplot(x=preds);\n",
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "DbxqiKTwSsO2",
        "outputId": "4aeb79a1-1ad9-4b6b-b28e-6d87c09418ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "DbxqiKTwSsO2"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HLeS0rZUO_q_"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "submit['Y_Class'] = preds\n",
        "\n",
        "submit.to_csv('GBCoptuna-smartFactorySubmission.csv', index=False)"
      ],
      "id": "HLeS0rZUO_q_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e7f4ff7b"
      },
      "outputs": [],
      "source": [
        "def objective(trial):\n",
        "    model = CatBoostClassifier(\n",
        "        iterations=trial.suggest_int(\"iterations\", 100, 1000),\n",
        "        learning_rate=trial.suggest_float(\"learning_rate\", 1e-3, 1e-1, log=True),\n",
        "        depth=trial.suggest_int(\"depth\", 4, 10),\n",
        "        l2_leaf_reg=trial.suggest_float(\"l2_leaf_reg\", 1e-8, 100.0, log=True),\n",
        "        bootstrap_type=trial.suggest_categorical(\"bootstrap_type\", [\"Bayesian\"]),\n",
        "        random_strength=trial.suggest_float(\"random_strength\", 1e-8, 10.0, log=True),\n",
        "        bagging_temperature=trial.suggest_float(\"bagging_temperature\", 0.0, 10.0),\n",
        "        od_type=trial.suggest_categorical(\"od_type\", [\"IncToDec\", \"Iter\"]),\n",
        "        od_wait=trial.suggest_int(\"od_wait\", 10, 50),\n",
        "        verbose=False\n",
        "    )\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    return f1_score(y_test, y_pred, average='macro')\n",
        "     \n",
        "# hyper-parameter tuning with OPTUNA  \n",
        "optuna.logging.set_verbosity(optuna.logging.WARNING)\n",
        "\n",
        "sampler = TPESampler(seed=37)\n",
        "study = optuna.create_study(study_name=\"catboost\", direction=\"maximize\", sampler=sampler)\n",
        "study.optimize(objective, n_trials=10)  "
      ],
      "id": "e7f4ff7b"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4ee90ada"
      },
      "outputs": [],
      "source": [
        "print(\"Number of trials: \", len(study.trials))\n",
        "print(\"Best trial:\")\n",
        "trial = study.best_trial\n",
        "print(\"  Value: \", trial.value)\n",
        "print(\"  Params: \",trial.params)\n",
        "\n",
        "model = CatBoostClassifier(**trial.params, verbose=False, random_state = 37)\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n"
      ],
      "id": "4ee90ada"
    },
    {
      "cell_type": "code",
      "source": [
        "params=  {'iterations': 857, 'learning_rate': 0.07097207730593516, 'depth': 8, 'l2_leaf_reg': 0.00012090525126196811, 'bootstrap_type': 'Bayesian', 'random_strength': 5.359110894800412, 'bagging_temperature': 0.7137022569955509, 'od_type': 'IncToDec', 'od_wait': 42}\n",
        "a = CatBoostClassifier(**params, verbose=False, random_state=37)\n",
        "a.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYWFMNT9ycd7",
        "outputId": "80c11b86-e20b-4ea5-c191-98d09c28f7d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7fda25fe50d0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "id": "OYWFMNT9ycd7"
    },
    {
      "cell_type": "code",
      "source": [
        "# [I 2023-02-23 17:18:12,087] A new study created in memory with name: no-name-1c47b3dc-1271-4545-be27-7bdc29c83e1d\n",
        "# [I 2023-02-23 17:19:24,357] Trial 0 finished with value: 0.9402040816326529 and parameters: {'n_estimators': 1900, 'learning_rate': 0.00848473740560785, 'max_depth': 6, 'subsample': 0.5, 'max_features': 'log2'}. Best is trial 0 with value: 0.9402040816326529.\n",
        "# [I 2023-02-23 17:20:55,754] Trial 1 finished with value: 0.9483907661425226 and parameters: {'n_estimators': 1000, 'learning_rate': 0.04356159311638129, 'max_depth': 6, 'subsample': 0.8, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.9483907661425226.\n",
        "# [I 2023-02-23 17:22:07,237] Trial 2 finished with value: 0.9246403479424556 and parameters: {'n_estimators': 700, 'learning_rate': 0.0028581111146054757, 'max_depth': 8, 'subsample': 0.5, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.9483907661425226.\n",
        "# The highest accuracy reached by this study: 94.83907661425226%.\n",
        "# Best params:\n",
        "# \tn_estimators: 1000\n",
        "# \tlearning_rate: 0.04356159311638129\n",
        "# \tmax_depth: 6\n",
        "# \tsubsample: 0.8\n",
        "# \tmax_features: sqrt\n",
        "\n",
        "\n",
        "# 'n_estimators': 500, 'learning_rate': 0.23063625169737476, 'max_depth': 9, \n",
        "# 'subsample': 0.6, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.9508464369354298"
      ],
      "metadata": {
        "id": "vqcJKGkzTlC1"
      },
      "execution_count": null,
      "outputs": [],
      "id": "vqcJKGkzTlC1"
    },
    {
      "cell_type": "code",
      "source": [
        "from optuna.visualization import plot_optimization_history\n",
        "from optuna.visualization import plot_param_importances\n",
        "from optuna.visualization import plot_parallel_coordinate\n",
        "from optuna.visualization import plot_contour\n",
        "\n",
        "plot_optimization_history(study)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "eACiKQAXwMSW",
        "outputId": "fd4ed02e-fc57-4d19-c884-814c7a1906c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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              "                            \n",
              "var gd = document.getElementById('eec493e0-6b36-4513-b0b7-4bb8905d91dc');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pickle\n",
        "pickle.dump(model, open(\"catboost_model.pkl\", \"wb\"))\n",
        "     \n",
        "\n",
        "optuna.visualization.plot_optimization_history(study)\n",
        "     \n",
        "\n",
        "optuna.visualization.plot_parallel_coordinate(study)\n",
        "     \n",
        "\n",
        "# 각 파라미터들의 상관관계\n",
        "optuna.visualization.plot_contour(\n",
        "    study,\n",
        "    params=[\n",
        "        \"iterations\",\n",
        "        \"learning_rate\",\n",
        "        \"depth\",\n",
        "        \"l2_leaf_reg\",\n",
        "        \"bootstrap_type\",\n",
        "        \"random_strength\",\n",
        "        \"bagging_temperature\",\n",
        "        \"od_type\",\n",
        "        \"od_wait\"\n",
        "        ],\n",
        ")"
      ],
      "id": "91bbd3b7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fab2a838",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "e41cddf6-41b7-40bb-efd4-6860389a697a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 하이퍼파라미터 중요도\n",
        "optuna.visualization.plot_param_importances(study)"
      ],
      "id": "fab2a838"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "GBC_model = GradientBoostingClassifier(n_estimators= 200, \n",
        "                                       learning_rate=0.21851112508358236, \n",
        "\t\t\t\t\t\t\t\t\tmax_depth= 6,\n",
        "\t\t\t\t\t\t\t\t\tsubsample= 0.7, \n",
        "\t\t\t\t\t\t\t\t\tmax_features= 'auto').fit(X_train, y_train) "
      ],
      "metadata": {
        "id": "wi_Ll5YCzLbr"
      },
      "execution_count": null,
      "outputs": [],
      "id": "wi_Ll5YCzLbr"
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "# GBC_model = GradientBoostingClassifier(random_state=37,\n",
        "#                                     n_estimators= 3700,\n",
        "# \tlearning_rate= 0.03084747392010545,\n",
        "# \tmax_depth= 6,\n",
        "# \tsubsample= 0.8,\n",
        "# \tmax_features= 'auto').fit(X_train, y_train) "
      ],
      "metadata": {
        "id": "obuhe7YIKO1N"
      },
      "execution_count": null,
      "outputs": [],
      "id": "obuhe7YIKO1N"
    },
    {
      "cell_type": "code",
      "source": [
        "# preds = GBC_model.predict(X_test)\n",
        "# print(f1_score(y_test, preds, avg='macro'))\n",
        "\n",
        "preds = GBC_model.predict(test_x)\n",
        "print('Done.')\n",
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "kvIWDIg5UiQ_",
        "outputId": "f417ce72-6b3a-4145-d098-7b471ee59fc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "kvIWDIg5UiQ_"
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "fEh0ziZfra0c",
        "outputId": "1e3ba841-e3c4-4396-99e0-8b2f2d932457"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "fEh0ziZfra0c"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "n8myqtPr4SSl"
      },
      "id": "n8myqtPr4SSl",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T5Lapd4Z4SYo"
      },
      "id": "T5Lapd4Z4SYo",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.3 MLP"
      ],
      "metadata": {
        "id": "gNzAotw2Jlr-"
      },
      "id": "gNzAotw2Jlr-"
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "class Dataset(torch.utils.data.Dataset):\n",
        "\n",
        "    def __init__(self, df):\n",
        "        self.labels = [0 if label == 0 else 1 for label in df['HeartDisease']]\n",
        "        self.features = df.drop(columns=['HeartDisease'], axis=1).values.tolist()\n",
        "\n",
        "    def classes(self):\n",
        "        return self.labels\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def get_batch_labels(self, idx):\n",
        "        return np.array(self.labels[idx])\n",
        "\n",
        "    def get_batch_features(self, idx):\n",
        "        return np.array(self.features[idx])\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        batch_features = self.get_batch_features(idx)\n",
        "        batch_y = self.get_batch_labels(idx)\n",
        "\n",
        "        return batch_features, batch_y"
      ],
      "metadata": {
        "id": "pHd-qrBQ3REB"
      },
      "execution_count": null,
      "outputs": [],
      "id": "pHd-qrBQ3REB"
    },
    {
      "cell_type": "code",
      "source": [
        "def objective(trial):\n",
        "\n",
        "    params = {\n",
        "              'learning_rate': trial.suggest_loguniform('learning_rate', 1e-5, 1e-1),\n",
        "              'optimizer': trial.suggest_categorical(\"optimizer\", [\"Adam\", \"RMSprop\", \"SGD\"]),\n",
        "              'n_unit': trial.suggest_int(\"n_unit\", 4, 18)\n",
        "              }\n",
        "    \n",
        "    model = build_model(params)\n",
        "    \n",
        "    accuracy = train_and_evaluate(params, model)\n",
        "\n",
        "    return accuracy"
      ],
      "metadata": {
        "id": "ieivpz673RO-"
      },
      "execution_count": null,
      "outputs": [],
      "id": "ieivpz673RO-"
    },
    {
      "cell_type": "code",
      "source": [
        "study = optuna.create_study(direction=\"maximize\", sampler=optuna.samplers.TPESampler())\n",
        "study.optimize(objective, n_trials=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tsZzHRcx3RKB",
        "outputId": "9f54ed97-4193-4613-dde0-69cb8826b981"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m[I 2023-02-26 13:16:04,739]\u001b[0m A new study created in memory with name: no-name-18123d27-0eb4-4209-9d15-b9575511aa52\u001b[0m\n"
          ]
        }
      ],
      "id": "tsZzHRcx3RKB"
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(params):\n",
        "    \n",
        "    in_features = train_x.shape[-1]\n",
        "    \n",
        "    return nn.Sequential(\n",
        "    \n",
        "        nn.Linear(in_features, params['n_unit']),\n",
        "        nn.LeakyReLU(),\n",
        "\n",
        "        nn.Linear(params['n_unit'], 2),\n",
        "        nn.LeakyReLU()\n",
        "        \n",
        "    )\n",
        " "
      ],
      "metadata": {
        "id": "ybk-xTzF3nT_"
      },
      "execution_count": null,
      "outputs": [],
      "id": "ybk-xTzF3nT_"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Train and evaluate the accuarcy of neural network model\n",
        "def train_and_evaluate(param, model):\n",
        "    \n",
        "    df = pd.read_csv('heart.csv')\n",
        "    df = pd.get_dummies(df)\n",
        "    \n",
        "    train_data, val_data = train_test_split(df, test_size = 0.2, random_state = 42)\n",
        "    train, val = Dataset(train_data), Dataset(val_data)\n",
        "\n",
        "    train_dataloader = torch.utils.data.DataLoader(train, batch_size=2, shuffle=True)\n",
        "    val_dataloader = torch.utils.data.DataLoader(val, batch_size=2)\n",
        "\n",
        "    use_cuda = torch.cuda.is_available()\n",
        "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = getattr(optim, param['optimizer'])(model.parameters(), lr= param['learning_rate'])\n",
        "\n",
        "    if use_cuda:\n",
        "\n",
        "            model = model.cuda()\n",
        "            criterion = criterion.cuda()\n",
        "\n",
        "    for epoch_num in range(EPOCHS):\n",
        "\n",
        "            total_acc_train = 0\n",
        "            total_loss_train = 0\n",
        "\n",
        "            for train_input, train_label in train_dataloader:\n",
        "\n",
        "                train_label = train_label.to(device)\n",
        "                train_input = train_input.to(device)\n",
        "\n",
        "                output = model(train_input.float())\n",
        "                \n",
        "                batch_loss = criterion(output, train_label.long())\n",
        "                total_loss_train += batch_loss.item()\n",
        "                \n",
        "                acc = (output.argmax(dim=1) == train_label).sum().item()\n",
        "                total_acc_train += acc\n",
        "\n",
        "                model.zero_grad()\n",
        "                batch_loss.backward()\n",
        "                optimizer.step()\n",
        "            \n",
        "            total_acc_val = 0\n",
        "            total_loss_val = 0\n",
        "\n",
        "            with torch.no_grad():\n",
        "\n",
        "                for val_input, val_label in val_dataloader:\n",
        "\n",
        "                    val_label = val_label.to(device)\n",
        "                    val_input = val_input.to(device)\n",
        "\n",
        "                    output = model(val_input.float())\n",
        "\n",
        "                    batch_loss = criterion(output, val_label.long())\n",
        "                    total_loss_val += batch_loss.item()\n",
        "                    \n",
        "                    acc = (output.argmax(dim=1) == val_label).sum().item()\n",
        "                    total_acc_val += acc\n",
        "            \n",
        "            accuracy = total_acc_val/len(val_data)\n",
        "\n",
        "    return accuracy\n",
        "  \n",
        " # Define a set of hyperparameter values, build the model, train the model, and evaluate the accuracy \n",
        "def objective(trial):\n",
        "\n",
        "     params = {\n",
        "              'learning_rate': trial.suggest_loguniform('learning_rate', 1e-5, 1e-1),\n",
        "              'optimizer': trial.suggest_categorical(\"optimizer\", [\"Adam\", \"RMSprop\", \"SGD\"]),\n",
        "              'n_unit': trial.suggest_int(\"n_unit\", 4, 18)\n",
        "              }\n",
        "    \n",
        "     model = build_model(params)\n",
        "    \n",
        "     accuracy = train_and_evaluate(params, model)\n",
        "\n",
        "     return accuracy"
      ],
      "metadata": {
        "id": "ITsy_mGB3RUf"
      },
      "execution_count": null,
      "outputs": [],
      "id": "ITsy_mGB3RUf"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YI_2hGHRJlx8"
      },
      "id": "YI_2hGHRJlx8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wfjjW10s4SfR"
      },
      "id": "wfjjW10s4SfR",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4.Ensemble"
      ],
      "metadata": {
        "id": "o8N-QHuIvNN3"
      },
      "id": "o8N-QHuIvNN3"
    },
    {
      "cell_type": "markdown",
      "id": "2fe15f1a",
      "metadata": {
        "id": "2fe15f1a"
      },
      "source": [
        "## 3.1 VotingClassifier\n",
        "1. Cross-Validation 구축 후 성능 비교"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#GBC_param= {'n_estimators': 1400, 'learning_rate': 0.007793231258605556, 'max_depth': 6, 'subsample': 0.8, 'max_features': 'sqrt'}\n",
        "CB_param= {'iterations': 857, 'learning_rate': 0.07097207730593516, 'depth': 8, 'l2_leaf_reg': 0.00012090525126196811, 'bootstrap_type': 'Bayesian', 'random_strength': 5.359110894800412, 'bagging_temperature': 0.7137022569955509, 'od_type': 'IncToDec', 'od_wait': 42}\n",
        "\n",
        "models = [\n",
        "    CatBoostClassifier(\n",
        "        # objective='MultiClass', task_type='GPU', one_hot_max_size=2, iterations=4000, learning_rate=0.05, \n",
        "        verbose=False, random_seed=seed_num, \n",
        "        **CB_param\n",
        "                                   ),\n",
        "    # LGBMClassifier(objective='multiclass', random_state=seed_num),\n",
        "    XGBClassifier(random_state=seed_num),\n",
        "    GradientBoostingClassifier(random_state=seed_num),    \n",
        "    # RidgeClassifierCV(),\n",
        "]\n",
        "[x.fit(train_x, train_y) for x in models]\n"
      ],
      "metadata": {
        "id": "Vm8q0p4Hyzg2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c57b7290-a361-4395-945a-3187ac10e332"
      },
      "id": "Vm8q0p4Hyzg2",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<catboost.core.CatBoostClassifier at 0x7f77ed586760>,\n",
              " XGBClassifier(objective='multi:softprob', random_state=37),\n",
              " GradientBoostingClassifier(random_state=37)]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0bcafdb3",
      "metadata": {
        "id": "0bcafdb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "89a0bd59-9018-4336-a67f-d5f680a6658a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "def predict(models, test, mode=None, weights=None):\n",
        "    if mode == \"hard\":\n",
        "        preds = np.asarray([x.predict(test).reshape(-1) for x in models]).T\n",
        "        res = np.apply_along_axis(\n",
        "            lambda x: np.argmax(np.bincount(x, weights=weights)),\n",
        "            axis=1,\n",
        "            arr=preds\n",
        "        )\n",
        "    elif mode == \"soft\":  \n",
        "        preds = np.asarray([x.predict_proba(test) for x in models])\n",
        "        res = np.zeros(preds[0].shape)\n",
        "        for pred, weight in zip(preds, weights):\n",
        "            res = res + pred*weight\n",
        "        res = np.argmax(preds, axis=0) \n",
        "    else:\n",
        "        res = models[0].predict(test)\n",
        "    return res\n",
        "\n",
        "preds = predict(models, test_x, 'hard',  \n",
        "                [2,1,2] )\n",
        "\n",
        "sns.countplot(x=preds);    \n",
        "preds\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "fd0e84e4-35d7-4b2e-9681-f5470968d15c",
        "id": "zfIcqXBqymkN"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    263\n",
              "0     40\n",
              "2      7\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "\n",
        "submit['Y_Class'] = preds\n",
        "submit.to_csv('CparamXG212-smartFactorySubmission.csv', index=False)  \n",
        "sns.countplot(x=preds);\n",
        "submit.Y_Class.value_counts()"
      ],
      "id": "zfIcqXBqymkN"
    },
    {
      "cell_type": "code",
      "source": [
        "aaa = pd.read_csv('CXG212(1)-smartFactorySubmission.csv')\n",
        "sns.countplot(x=aaa.Y_Class);\n",
        "aaa.Y_Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "rXw6yXcgEzO0",
        "outputId": "0f92d4b5-d47e-47be-bd1a-8f22876fe67a"
      },
      "id": "rXw6yXcgEzO0",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    265\n",
              "0     37\n",
              "2      8\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 148  1 2 \n",
        "# 163  2\n",
        "# 166  2 \n",
        "# 295  1 0 \n",
        "36/495 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nWXbLTICgP5C",
        "outputId": "9db1fcf9-2e2b-40ea-a500-efe50f7f9cb5"
      },
      "id": "nWXbLTICgP5C",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07272727272727272"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SO2TqYzHUda_"
      },
      "id": "SO2TqYzHUda_",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "aaa = pd.read_csv('/content/drive/MyDrive/GBCminmax-smartFactorySubmission.csv')\n",
        "sns.countplot(x=aaa.Y_Class);\n",
        "aaa.Y_Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "fj9KCdluffIp",
        "outputId": "8218e2a2-1e66-420e-c292-446528229e24"
      },
      "id": "fj9KCdluffIp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    253\n",
              "0     42\n",
              "2     15\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aaa = pd.read_csv('/content/drive/MyDrive/GBCoptuna-smartFactorySubmission.csv')\n",
        "sns.countplot(x=aaa.Y_Class);\n",
        "aaa.Y_Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "21AetCxgST0D",
        "outputId": "b7e56c38-312c-469a-d37c-f6f812433401"
      },
      "id": "21AetCxgST0D",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    260\n",
              "0     43\n",
              "2      7\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEHCAYAAABBW1qbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPaklEQVR4nO3df6xfdX3H8ecLii5TnLB2WEu3oqkm6KTgDSNjMyhxAslWNEpgEZhjqVtgkcUZ0SzTmZCYzB+bOlnqQIpxIAZ/oCMq69yMv71lDKGMWRVGm0KvYAQ1osX3/vie+/G7cgvfFs733PY+H8nJ95zP+Zzvfd986X1xPud8PydVhSRJAIcMXYAkafEwFCRJjaEgSWoMBUlSYyhIkpplQxfweCxfvrzWrFkzdBmSdEDZsmXL96pqxUL7DuhQWLNmDbOzs0OXIUkHlCR37W2fw0eSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKk5oD+RrOWjv99228OXcJB79f/+ptDl6BFwDMFSVJjKEiSGkNBktQYCpKkprdQSLI6yeeTbE1yW5LXde1vTbIjyc3dcsbYMW9Ksi3JHUle1ldtkqSF9Xn30W7g9VV1U5LDgS1Jbuz2vbuq3jHeOcmxwNnA84BnAv+a5DlV9XCPNUqSxvR2plBVO6vqpm79QeB2YNWjHLIeuKaqHqqq7wLbgBP7qk+S9EhTuaaQZA1wPPC1rumiJLckuSLJEV3bKuDuscO2s0CIJNmQZDbJ7NzcXJ9lS9KS03soJHkqcB1wcVU9AFwGPBtYB+wE3rkv71dVG6tqpqpmVqxY8BGjkqT91GsoJDmMUSB8uKo+BlBV91bVw1X1c+AD/GKIaAeweuzwo7s2SdKU9Hn3UYDLgdur6l1j7SvHur0cuLVbvx44O8mTkxwDrAW+3ld9kqRH6vPuo5OBc4FvJrm5a3szcE6SdUABdwKvBaiq25JcC2xldOfShd55JEnT1VsoVNUXgSyw64ZHOeZS4NK+apIkPTq/0SxJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDW9hUKS1Uk+n2RrktuSvK5rPzLJjUm+1b0e0bUnyXuSbEtyS5IT+qpNkrSwPs8UdgOvr6pjgZOAC5McC1wCbK6qtcDmbhvgdGBtt2wALuuxNknSAnoLharaWVU3desPArcDq4D1wKau2ybgzG59PXBVjXwVeHqSlX3VJ0l6pKlcU0iyBjge+BpwVFXt7HbdAxzVra8C7h47bHvXJkmakt5DIclTgeuAi6vqgfF9VVVA7eP7bUgym2R2bm7uCaxUktRrKCQ5jFEgfLiqPtY13zs/LNS97uradwCrxw4/umv7f6pqY1XNVNXMihUr+itekpagPu8+CnA5cHtVvWts1/XA+d36+cAnx9rP6+5COgn4wdgwkyRpCpb1+N4nA+cC30xyc9f2ZuDtwLVJLgDuAs7q9t0AnAFsA34MvKbH2iRJC+gtFKrqi0D2svvUBfoXcGFf9UiSHpvfaJYkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLU9BYKSa5IsivJrWNtb02yI8nN3XLG2L43JdmW5I4kL+urLknS3vV5pnAlcNoC7e+uqnXdcgNAkmOBs4Hndce8P8mhPdYmSVpAb6FQVV8A7p+w+3rgmqp6qKq+C2wDTuyrNknSwiYKhSSbJ2mb0EVJbumGl47o2lYBd4/12d61LVTLhiSzSWbn5ub2swRJ0kIeNRSS/FKSI4HlSY5IcmS3rGEvf7Qfw2XAs4F1wE7gnfv6BlW1sapmqmpmxYoV+1GCJGlvlj3G/tcCFwPPBLYA6dofAN63rz+squ6dX0/yAeDT3eYOYPVY16O7NknSFD3qmUJV/X1VHQP8ZVU9q6qO6ZbjqmqfQyHJyrHNlwPzdyZdD5yd5MlJjgHWAl/f1/eXJD0+j3WmAEBVvTfJbwNrxo+pqqv2dkySq4FTGA09bQfeApySZB1QwJ2MzkSoqtuSXAtsBXYDF1bVw/v+60iSHo+JQiHJhxhdC7gZmP9jXcBeQ6Gqzlmg+fJH6X8pcOkk9UiS+jFRKAAzwLFVVX0WI0ka1qTfU7gVeEafhUiShjfpmcJyYGuSrwMPzTdW1R/0UpUkaRCThsJb+yxCkrQ4THr30X/0XYgkaXiT3n30IKO7jQCeBBwG/KiqntZXYZKk6Zv0TOHw+fUkYTSB3Ul9FSVJGsY+z5JaI58AfOaBJB1kJh0+esXY5iGMvrfwk14qkiQNZtK7j35/bH03oykq1j/h1UiSBjXpNYXX9F2IJGl4kz5k5+gkH++eubwryXVJju67OEnSdE16ofmDjKa3fma3fKprkyQdRCYNhRVV9cGq2t0tVwI+9kySDjKThsJ9SV6d5NBueTVwX5+FSZKmb9JQ+GPgLOAeRs9WfiXwRz3VJEkayKS3pL4NOL+qvg+Q5EjgHYzCQpJ0kJj0TOEF84EAUFX3A8f3U5IkaSiThsIhSY6Y3+jOFCY9y5AkHSAm/cP+TuArST7abb8Kn6csSQedSb/RfFWSWeAlXdMrqmprf2VJkoYw8RBQFwIGgSQdxPZ56mxJ0sHLUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpKa3UEhyRfc851vH2o5McmOSb3WvR3TtSfKeJNuS3JLkhL7qkiTtXZ9nClcCp+3RdgmwuarWApu7bYDTgbXdsgG4rMe6JEl70VsoVNUXgPv3aF4PbOrWNwFnjrVfVSNfBZ6eZGVftUmSFjbtawpHVdXObv0e4KhufRVw91i/7V3bIyTZkGQ2yezc3Fx/lUrSEjTYheaqKqD247iNVTVTVTMrVqzooTJJWrqmHQr3zg8Lda+7uvYdwOqxfkd3bZKkKZp2KFwPnN+tnw98cqz9vO4upJOAH4wNM0mSpqS35ywnuRo4BVieZDvwFuDtwLVJLgDuAs7qut8AnAFsA34MvKavuiRJe9dbKFTVOXvZdeoCfQu4sK9aJEmT8RvNkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkpplQ/zQJHcCDwIPA7uraibJkcBHgDXAncBZVfX9IeqTpKVqyDOFF1fVuqqa6bYvATZX1Vpgc7ctSZqixTR8tB7Y1K1vAs4crhRJWpqGCoUCPpdkS5INXdtRVbWzW78HOGqhA5NsSDKbZHZubm4atUrSkjHINQXgd6pqR5JfA25M8t/jO6uqktRCB1bVRmAjwMzMzIJ9JEn7Z5Azhara0b3uAj4OnAjcm2QlQPe6a4jaJGkpm3ooJHlKksPn14HfA24FrgfO77qdD3xy2rVJ0lI3xPDRUcDHk8z//H+uqs8k+QZwbZILgLuAswaoTZKWtKmHQlV9Bzhugfb7gFOnXY8k6ReGutA8dS98w1VDl7AkbPnb84YuQdLjsJi+pyBJGpihIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUrNs6AIkHfxOfu/JQ5dw0PvSn3/pCXkfzxQkSY2hIElqFl0oJDktyR1JtiW5ZOh6JGkpWVShkORQ4B+A04FjgXOSHDtsVZK0dCyqUABOBLZV1Xeq6qfANcD6gWuSpCUjVTV0DU2SVwKnVdWfdNvnAr9VVReN9dkAbOg2nwvcMfVCp2c58L2hi9B+8/M7cB3sn91vVNWKhXYccLekVtVGYOPQdUxDktmqmhm6Du0fP78D11L+7Bbb8NEOYPXY9tFdmyRpChZbKHwDWJvkmCRPAs4Grh+4JklaMhbV8FFV7U5yEfBZ4FDgiqq6beCyhrQkhskOYn5+B64l+9ktqgvNkqRhLbbhI0nSgAwFSVJjKCxCTvVxYEtyRZJdSW4duhbtmySrk3w+ydYktyV53dA1TZvXFBaZbqqP/wFeCmxndEfWOVW1ddDCNLEkLwJ+CFxVVc8fuh5NLslKYGVV3ZTkcGALcOZS+vfnmcLi41QfB7iq+gJw/9B1aN9V1c6quqlbfxC4HVg1bFXTZSgsPquAu8e2t7PE/qOUFoMka4Djga8NXMpUGQqStIckTwWuAy6uqgeGrmeaDIXFx6k+pAElOYxRIHy4qj42dD3TZigsPk71IQ0kSYDLgdur6l1D1zMEQ2GRqardwPxUH7cD1y7xqT4OOEmuBr4CPDfJ9iQXDF2TJnYycC7wkiQ3d8sZQxc1Td6SKklqPFOQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0Eak5EvJjl9rO1VST6zl/7PSHJNkm8n2ZLkhiTPSbLGqbN1IPJ7CtIekjwf+CijydCWAf8JnFZV396jX4AvA5uq6h+7tuOApzGa1PDTTp2tA82yoQuQFpuqujXJp4A3Ak9h9FyEby/Q9cXAz+YDoTv2v6DNsMnY+oe69wK4qKq+3M3d/xFGIbIM+DNGIXM5MAMUcEVVvfsJ/QWlR2EoSAv7G+Am4KeM/kAv5PmMHsLyWHYBL62qnyRZC1zdvecfAp+tqku7hyv9MrAOWDV/hpHk6Y/nl5D2laEgLaCqfpTkI8APq+qhx/l2hwHvS7IOeBh4Ttf+DeCKblbOT1TVzUm+AzwryXuBfwE+9zh/trRPvNAs7d3Pu2VvbgNeOMH7/AVwL3AcozOEJ0F7QtuLGE2NfmWS86rq+12/fwf+FPin/S1e2h+GgrT//g14cpIN8w1JXpDkd/fo9yvAzqr6OaMZOA/t+v4GcG9VfYDRH/8TkiwHDqmq64C/Ak6Ywu8hNQ4fSfupqirJy4G/S/JG4CfAncDFe3R9P3BdkvOAzwA/6tpPAd6Q5GfAD4HzGD169YNJ5v+H7U19/g7SnrwlVZLUOHwkSWocPpIeQ5JfBTYvsOvUqrpv2vVIfXL4SJLUOHwkSWoMBUlSYyhIkhpDQZLU/B9zDOWfSP8V2AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aaa = pd.read_csv('/content/drive/MyDrive/voting.catB_GBC-smartFactorySubmission.csv')\n",
        "sns.countplot(x=aaa.Y_Class);\n",
        "aaa.Y_Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "Mt733EeeHECW",
        "outputId": "9939c6f1-b6ec-4e95-a462-f2e01766acfb"
      },
      "id": "Mt733EeeHECW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    267\n",
              "0     37\n",
              "2      6\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sdf = pd.read_csv('/content/drive/MyDrive/smartFactory_submission18.csv')\n",
        "sns.countplot(x=sdf.Y_Class);\n",
        "sdf.Y_Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "Be0Yji5AEiu0",
        "outputId": "6cc41f99-ec6d-4f04-8f91-2ed1da1f69ab"
      },
      "id": "Be0Yji5AEiu0",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    274\n",
              "0     29\n",
              "2      7\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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GP8LyWHYDL6mqnyVZDdzQ7fOPgc9U1VXdjys9CTgFWD47wkjytMfzR0gHylCQ5lBVP0nyYeDHVfXw49zdUcB7kpwCPAKc2LV/A9jQPZXz41V1R5LvAs9K8m7gX4HPPs73lg6IJ5qlfftlN+3LncALRtjPXwG7gJMZjBCeAO0X2l7I4NHo1yW5pKp+2PX7d+DPgfcdbPHSwTAUpIP3b8ATk6ybbUjy/CS/v1e/Xwd2VtUvGTyB88iu7zOBXVX1XgZf/qclWQocUVU3A38DnDaBv0NqPHwkHaSqqiQvA/4hyRuAnwH3AFfs1fWfgZuTXAJ8GvhJ134W8PokvwB+DFzC4KdX359k9n/Y3jjOv0Ham5ekSpIaDx9JkhoPH0mPIclvAJvmWHV2VT0w6XqkcfLwkSSp8fCRJKkxFCRJjaEgSWoMBUlS839Nh+UDzdNKigAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2 StackingClassifier"
      ],
      "metadata": {
        "id": "F4CYiugG2423"
      },
      "id": "F4CYiugG2423"
    },
    {
      "cell_type": "code",
      "source": [
        "from catboost import CatBoostClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "models = [\n",
        "    CatBoostClassifier(objective='MultiClass',\n",
        "                                   task_type='GPU',\n",
        "                                   one_hot_max_size=2, random_seed=seed_num,\n",
        "                                   iterations=4000, verbose=False,\n",
        "                                   learning_rate=0.05\n",
        "                                   ),\n",
        "    LGBMClassifier(objective='multiclass', random_state=seed_num),\n",
        "    XGBClassifier(random_state=seed_num),\n",
        "    GradientBoostingClassifier(random_state=seed_num),   # GBC: no tuning is the best \n",
        "    RidgeClassifierCV(),\n",
        "]\n",
        "\n",
        "# [x.fit(train_x, train_y) for x in models]   "
      ],
      "metadata": {
        "id": "Dsbc_zz0KxDp"
      },
      "execution_count": null,
      "outputs": [],
      "id": "Dsbc_zz0KxDp"
    },
    {
      "cell_type": "code",
      "source": [
        "# find weights for each model \n",
        "model_list = ['catboost', 'lgbm', 'xgb', 'gradient', 'ridge']\n",
        "num=0\n",
        "\n",
        "for model in models: \n",
        "  preds = model.fit(train_x, train_y).predict(test_x)\n",
        "  if num == 0:  # catboostclassifier \n",
        "    preds= preds.reshape(1,test_x.shape[0])  # test_x.shape[0] = number of columns \n",
        "    preds = list(preds[0])\n",
        "    num+=1\n",
        "  else: \n",
        "    preds.reshape(1,test_x.shape[0])\n",
        "    preds=list(preds)\n",
        "  \n",
        "  globals()[f'preds_{model_list[num]}'] = preds   # set variable name for each model prediction results  \n",
        "  \n",
        "  print(model,  globals()[f'preds_{model_list[num]}'])   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jS9YIWtJIMfn",
        "outputId": "624b944a-20ae-44cd-b71e-cd950dbe7656"
      },
      "id": "jS9YIWtJIMfn",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<catboost.core.CatBoostClassifier object at 0x7f17b43af610> [1, 2, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 2, 2, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "LGBMClassifier(objective='multiclass', random_state=37) [1, 1, 1, 1, 1, 1, 1, 0, 0, 2, 1, 1, 2, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "XGBClassifier(objective='multi:softprob', random_state=37) [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "BaggingClassifier(random_state=37) [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1]\n",
            "GradientBoostingClassifier(random_state=37) [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 2, 1, 2, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1]\n",
            "RidgeClassifierCV(alphas=array([ 0.1,  1. , 10. ])) [1, 1, 1, 1, 2, 1, 1, 0, 2, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_catb = [1, 2, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 2, 2, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
        "preds_lgbm = [1, 1, 1, 1, 1, 1, 1, 0, 0, 2, 1, 1, 2, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
        "preds_xgb = [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
        "preds_bagging = [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1]\n",
        "preds_gradient = [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 2, 1, 2, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1]\n",
        "preds_ridge = [1, 1, 1, 1, 2, 1, 1, 0, 2, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
      ],
      "metadata": {
        "id": "Q-71TFSqg2aH"
      },
      "id": "Q-71TFSqg2aH",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e1e8e853",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "e1e8e853",
        "outputId": "9dde57df-5ca2-459f-e458-53e0519767c7"
      },
      "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-10-dc92d8057380>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msubmit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/sample_submission.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msubmit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Y_Class'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0msubmit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'CXG212-smartFactorySubmission.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpreds\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 'preds' is not defined"
          ]
        }
      ],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "\n",
        "submit['Y_Class'] = preds\n",
        "submit.to_csv('CXG212-smartFactorySubmission.csv', index=False)  \n",
        "sns.countplot(x=preds);"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stack = StackingClassifier(\n",
        "    estimators=models,\n",
        "    final_estimator=LogisticRegression(),  \n",
        "    cv=5,\n",
        "    n_jobs=4,\n",
        "    passthrough=True\n",
        ")\n",
        "\n",
        "# use X_test as validation set      \n",
        "stack.fit(train_x, train_y)\n",
        "\n",
        "train_stack = np.concatenate(\n",
        "    [model.predict_proba(train_x) for model in models], axis=1)\n",
        "val_stack = np.concatenate(\n",
        "    [model.predict_proba(X_test) for model in models], axis=1)\n",
        "\n",
        "# meta model training\n",
        "meta_model = LogisticRegression().fit(train_stack, train_y)\n",
        "preds = meta_model.predict(val_stack)\n",
        "\n"
      ],
      "metadata": {
        "id": "X7bzI9lZO4VA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "73e07a3c-db64-4fbc-a7d4-fe70504eda59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-0bc19587b29d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# use X_test as validation set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_y\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     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m train_stack = np.concatenate(\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_stacking.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    486\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_le\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLabelEncoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    487\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_le\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 488\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_le\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\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    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mif_delegate_has_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelegate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"final_estimator_\"\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/python3.8/dist-packages/sklearn/ensemble/_stacking.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;31m# all_estimators contains all estimators, the one to be fitted and the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0;31m# 'drop' string.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mall_estimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_estimators\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    151\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_final_estimator\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    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_base.py\u001b[0m in \u001b[0;36m_validate_estimators\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    245\u001b[0m                 \u001b[0;34m\" of (string, estimator) tuples.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m             )\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators\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    248\u001b[0m         \u001b[0;31m# defined by MetaEstimatorMixin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'CatBoostClassifier' object is not iterable"
          ]
        }
      ],
      "id": "X7bzI9lZO4VA"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "669e28a5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "669e28a5",
        "outputId": "49b3ddea-dd79-4b33-83f6-26d30d0243ef"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "seed_num=37\n",
        "\n",
        "GBC_model = GradientBoostingClassifier(random_state=37\n",
        "              ).fit(train_x, train_y)\n",
        "\n",
        "preds = GBC_model.predict(test_x)\n",
        "sns.countplot(x=preds);"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bId7aEA7wZm5"
      },
      "id": "bId7aEA7wZm5",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5.Submit"
      ],
      "metadata": {
        "id": "zbNtWowN78mX"
      },
      "id": "zbNtWowN78mX"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YQHmg0PCJetf"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "submit['Y_Class'] = preds\n",
        "\n",
        "submit.to_csv('smartFactorySubmission.csv', index=False)"
      ],
      "id": "YQHmg0PCJetf"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CYrSQNYg78q-"
      },
      "id": "CYrSQNYg78q-",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_dt_cn6Q78vw"
      },
      "id": "_dt_cn6Q78vw",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wYyMG1FW781N"
      },
      "id": "wYyMG1FW781N",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lGGGi1ld79D0"
      },
      "id": "lGGGi1ld79D0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nj6G-kfy79JN"
      },
      "id": "nj6G-kfy79JN",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# THE END"
      ],
      "metadata": {
        "id": "utf4-GCG5REy"
      },
      "id": "utf4-GCG5REy"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gvXsoboM5RL6"
      },
      "id": "gvXsoboM5RL6",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
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