3. Modelling.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/BrendaLoznik/waterpumps/blob/main/3.%20Modelling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZMVwsP0kUP2"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "piH3cRaXkcpO"
      },
      "source": [
        "# 1 Housekeeping"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVjEhtmblIS2"
      },
      "source": [
        "### 1.1 Load libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ywC4mg0WX1zn"
      },
      "outputs": [],
      "source": [
        "pip install shap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "syHL_Ac0kqi2"
      },
      "outputs": [],
      "source": [
        "pip install catboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Tf_uksxAkhWt"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
        "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "#machine learning\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from sklearn.linear_model import LogisticRegression \n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from catboost import CatBoostClassifier\n",
        "import shap\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qOHJxI8XlFPb"
      },
      "source": [
        "### 1.2 Custom functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "_bzvfBWBkv1G"
      },
      "outputs": [],
      "source": [
        "def confusion_matrix_plotter (y_test,y_pred):\n",
        "  fig, ax = plt.subplots(figsize=(8,5)) \n",
        "  data = confusion_matrix(y_test, y_pred)\n",
        "  df_cm = pd.DataFrame(data, columns=np.unique(y_test), index = np.unique(y_test))\n",
        "  ax = sns.heatmap(df_cm, cmap='Blues', fmt='g' ,annot=True,annot_kws={\"size\": 14})\n",
        "  ax.set_xlabel(\"Predicted\")\n",
        "  ax.set_ylabel (\"Actual\")\n",
        "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
        "  ax.set_yticklabels(ax.get_xticklabels(), rotation=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "cfdOy6LGlNMj"
      },
      "outputs": [],
      "source": [
        "def feature_importances (model, title):\n",
        "  features= X_train.columns\n",
        "  importances = np.round(model.feature_importances_,3) \n",
        "  indices = np.argsort(importances)\n",
        "  num_features = len(features)\n",
        "  fig, ax = plt.subplots(figsize=(10,10)) \n",
        "  plt.barh(range(num_features), importances[indices[-num_features:]],  align='center', color =  '#66c2a5')\n",
        "  plt.yticks(range(num_features), [features[i] for i in indices[-num_features:]])\n",
        "  plt.xlabel('Relative Importance')\n",
        "  plt.title('Feature Importance'+ title)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aAb_a3nslTZn"
      },
      "source": [
        "### 1.3 Load data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwUYI4kXlWdV",
        "outputId": "ca09327c-9787-4ed3-9632-539871b5572d"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "zj2o4tNslaYV"
      },
      "outputs": [],
      "source": [
        "train_labels = pd.read_csv('/content/gdrive/MyDrive/Kaggle/Waterpump-github/train_labels.csv')\n",
        "train_values = pd.read_csv('/content/gdrive/MyDrive/Kaggle/Waterpump-github/train_values.csv', parse_dates = ['date_recorded' ],  na_values = [0, '0'])\n",
        "test_values = pd.read_csv('/content/gdrive/MyDrive/Kaggle/Waterpump-github/test_values.csv', parse_dates = ['date_recorded' ] , na_values = [0, '0'])\n",
        "final_df = pd.read_csv('/content/gdrive/MyDrive/Kaggle/Waterpump-github/final_df.csv')\n",
        "final_df = final_df.drop('Unnamed: 0', axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TsZUb1ZIlqyj"
      },
      "source": [
        "# 2 Modelling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "-U6xSomTlppk"
      },
      "outputs": [],
      "source": [
        "#define train and test \n",
        "train_set = final_df [final_df [\"id\"].isin(train_values[\"id\"])]\n",
        "test_set =  final_df  [final_df  [\"id\"].isin(test_values[\"id\"])]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "-zDx3k4FlaQF"
      },
      "outputs": [],
      "source": [
        "#define X and y for training the model\n",
        "X= train_set.drop(['id', 'status_group'], axis=1)\n",
        "y = train_set['status_group']\n",
        "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.2, random_state=42 , stratify=y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R4ti5peolvs8"
      },
      "source": [
        "### 2.1 Model overview"
      ]
    },
    {
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      "outputs": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Std</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.666510</td>\n",
              "      <td>0.006001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KNN Classifier</td>\n",
              "      <td>0.701140</td>\n",
              "      <td>0.007301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.756158</td>\n",
              "      <td>0.004169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.804121</td>\n",
              "      <td>0.004219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGB Classifier</td>\n",
              "      <td>0.742740</td>\n",
              "      <td>0.005622</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Bagging Classifier</td>\n",
              "      <td>0.793599</td>\n",
              "      <td>0.003323</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AdaBoost Classifier</td>\n",
              "      <td>0.723985</td>\n",
              "      <td>0.005083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.005385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Catboost Classifier</td>\n",
              "      <td>0.796428</td>\n",
              "      <td>0.003599</td>\n",
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              "                          Model  Accuracy       Std\n",
              "0           Logistic Regression  0.666510  0.006001\n",
              "1                KNN Classifier  0.701140  0.007301\n",
              "2      Decision Tree Classifier  0.756158  0.004169\n",
              "3      Random Forest Classifier  0.804121  0.004219\n",
              "4                XGB Classifier  0.742740  0.005622\n",
              "5            Bagging Classifier  0.793599  0.003323\n",
              "6           AdaBoost Classifier  0.723985  0.005083\n",
              "7  Gradient Boosting Classifier  0.751174  0.005385\n",
              "8           Catboost Classifier  0.796428  0.003599"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#define the models\n",
        "models=[LogisticRegression(max_iter = 1000000,  random_state=42),\n",
        "        KNeighborsClassifier(),\n",
        "        DecisionTreeClassifier( random_state=42),\n",
        "       RandomForestClassifier( random_state=42),\n",
        "       XGBClassifier( random_state=42),\n",
        "        BaggingClassifier( random_state=42), \n",
        "        AdaBoostClassifier( random_state=42),\n",
        "        GradientBoostingClassifier( random_state=42),\n",
        "       CatBoostClassifier( random_state=42)]\n",
        "\n",
        "#create a list of model names\n",
        "names =['Logistic Regression', 'KNN Classifier', 'Decision Tree Classifier', 'Random Forest Classifier',  'XGB Classifier', 'Bagging Classifier', 'AdaBoost Classifier', 'Gradient Boosting Classifier', 'Catboost Classifier']\n",
        "\n",
        "#create empty lists for the accuracy and standard deviation\n",
        "accuracy=[]\n",
        "std = []\n",
        "\n",
        "#peform 10-fold cross validaton on each model and append the results to the list\n",
        "for model in models:\n",
        "  cv = KFold(n_splits=10, shuffle = True,  random_state=1)\n",
        "  n_scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')\n",
        "  mean_score = np.mean(n_scores)\n",
        "  std_score = np.std(n_scores)\n",
        "  accuracy.append(mean_score)\n",
        "  std.append(std_score)\n",
        "\n",
        "#create a dataframe of the model performances\n",
        "d = {'Model':names,'Accuracy':accuracy, 'Std':std}   \n",
        "score_df = pd.DataFrame(d)\n",
        "score_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xeybZ0YMmNNR"
      },
      "source": [
        "### 2.2 Random forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YaRVdUW0lOdr"
      },
      "outputs": [],
      "source": [
        "#(running the script on Google Colab takes too long, so I will just share the results of the best_model\n",
        "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.2, random_state=42 , stratify=y)\n",
        "\n",
        "\n",
        "param_grid = { 'n_estimators': [ 150, 175,  200, 225],\n",
        "             'max_features':['log2', ],\n",
        "             'max_depth' : [30 , 40, 50, 60, 70],      \n",
        "             'min_samples_split':[6, 7, 8, 9, 10]}\n",
        "\n",
        "\n",
        "\n",
        "#Create a based model\n",
        "rf =     RandomForestClassifier( random_state=42, warm_start = True)\n",
        "\n",
        "grid_search_rf = GridSearchCV(estimator = rf, param_grid = param_grid, cv = 5,  verbose = 2,  scoring = 'accuracy')\n",
        "\n",
        "#fitmodel\n",
        "grid_search_rf .fit(X_train, y_train)\n",
        "\n",
        "#print best parameters \n",
        "print('Best Score:', grid_search_rf .best_score_)\n",
        "print('Parameters:', grid_search_rf .best_params_)\n",
        "print('Best Model:', grid_search_rf .best_estimator_)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
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        "id": "oK5XDR1smSZd",
        "outputId": "d987a385-0798-4f19-d7cc-2d3d3c06faea"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score train: 0.9216313474610156\n",
            "Accuracy score test: 0.813047138047138\n",
            "\n",
            "                         precision    recall  f1-score   support\n",
            "\n",
            "             functional       0.80      0.90      0.85      6452\n",
            "functional needs repair       0.62      0.28      0.39       863\n",
            "         non functional       0.85      0.78      0.81      4565\n",
            "\n",
            "               accuracy                           0.81     11880\n",
            "              macro avg       0.76      0.66      0.68     11880\n",
            "           weighted avg       0.81      0.81      0.80     11880\n",
            "\n"
          ]
        },
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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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tQx+XJBkY69gkSZK6xGR2HFTV+VV1cts9FBh1MitJkiST2bWWnlOSXJ/kuiRzWvkBbZb0vCQ3JjkrSVrds1rZgiQfSHJBKz8qyQeT7As8FzglycIku/bPuCbZMcmStr1lkk8n+UGSLwBb9sX2jCTfSXJNknOTTN24V0eSJGl8mMyuvcOAWcCewMH0EtAHt7rHA6+nN8O6C7BfkinAacAzq2pvYPrgDqvqSuB84E1VNauqfjTC+K8EfldVjwH+Cdgbegkv8Dbg4KraC5gPvHHwwUmOSTI/yfyVS5eP/uwlSZImIJPZtfcU4JyqureqfglcCuzT6q6qqp9W1X3AQmAGMBP4cVXd0tqcs57j7w98CqCqFgOLW/mT6CXRVyRZCLwE2HnwwVU1t6oGqmpgyrRt1jMUSZKkicG/ADY27u7bvpf1u65/5M+/ZExZi/YBvlFVL1yPMSVJkjrJmdm1Nw+Yk2SzJNPpzZReNUL7m4Bdksxo+3OGabcc6J8qXUJbQgAc3ld+GXAEQJI9gMe18u/SW9bwiFa3dZJHrcX5SJIkdZ7J7Nr7Ar2v9hcBFwP/UFW/GK5xVf0eeBXw9SQL6CWty4Zo+mngTUmuTbIr8B7glUmuBXbsa/dhYGqSHwDvBBa0cX4NHAWck2Qx8B16SxwkSZI2eamq8Y5hk5VkalWtaE83+E/gh1V16njHNX3mLnXY6SeNdxiStMk6bfYR4x2CtMlIsqCqhn22vjOzG9bL201ZNwDb0Xu6gSRJksaIN4BtQG0WdtxnYiVJkjZVzsxKkiSps5yZnYR2nrq967kkSdImwZlZSZIkdZbJrCRJkjrLZFaSJEmd5ZrZSejWFXdw7LyzxzsMSdqkeC+CND6cmZUkSVJnmcxKkiSps0xmJUmS1Fkms5IkSeosk1lJkiR1lsnsekqyYg3105K8qm//IUnOa9uzkjxrHcY8Icnxo49WkiRp02Iyu+FNA/6UzFbVbVV1eNudBYw6mZUkSVKPyewYSTI1ybeSXJPkuiSHtKqTgV2TLExySpIZSa5Pcn/gncCcVjdn8Ixrazejbb81yc1JLgce3ddm1yRfT7IgybwkMzfaSUuSJI0z/2jC2FkJPK+q7kyyI/DdJOcDbwb2qKpZAKuS06r6Q5J3AANV9ZpWd8JQHSfZG/g7ejO5mwPXAAta9VzgFVX1wyRPBD4EHDREH8cAxwBM3WnHsThfSZKkcWcyO3YC/EuS/YH7gIcCO41R37OBL1TV7wBakkySqcC+wLlJVrV9wFAdVNVceokv02fuUmMUlyRJ0rgymR07RwLTgb2r6p4kS4Apo+zjj6y+9GNNx98PWLpq1leSJGmycc3s2NkO+FVLZA8Edm7ly4FthjlmcN0SYC+AJHsBf93KLwMOTbJlkm2A5wBU1Z3ALUle0I5Jkj3H7pQkSZImNpPZsXMWMJDkOuDFwI0AVfUb4Ip2M9cpg475NrDbqhvAgM8B2ye5AXgNcHPr4xrgM8Ai4GvA1X19HAm8LMki4AbgECRJkiYJlxmsp6qa2n7eDjx5mDZHDCrao5XfAewzqO4Zw/TxLuBdQ5TfAvzP0UUtSZK0aXBmVpIkSZ1lMitJkqTOMpmVJElSZ7lmdhLaeer2nDZ78DJeSZKk7nFmVpIkSZ1lMitJkqTOMpmVJElSZ7lmdhK6dcUdHDvv7PEOQ1KHue5e0kThzKwkSZI6y2RWkiRJnWUyK0mSpM4ymZUkSVJnmcxKkiSps0xmJUmS1Fkms5IkSeosk9kJKMkXkyxIckOSY1rZy5LcnOSqJKcn+WArn57kc0mubq/9xjd6SZKkjcc/mjAxvbSq7kiyJXB1kq8Abwf2ApYDFwOLWtv3A6dW1eVJHg5cCDxmcIctKT4GYOpOO26EU5AkSdrwTGYnpuOSPK9tPwx4EXBpVd0BkORc4FGt/mBgtySrjt02ydSqWtHfYVXNBeYCTJ+5S23g+CVJkjYKk9kJJskB9BLUJ1fV75JcAtzIELOtzf2AJ1XVyo0ToSRJ0sThmtmJZzvgty2RnQk8CdgaeGqSBybZHHh+X/uLgNeu2kkya6NGK0mSNI5MZieerwObJ/kBcDLwXeBnwL8AVwFXAEuAZa39ccBAksVJvg+8YqNHLEmSNE5cZjDBVNXdwDMHlyeZX1Vz28zsF4Avtva3A3M2bpSSJEkTgzOz3XFCkoXA9cAttGRWkiRpMnNmtiOq6vjxjkGSJGmiMZmdhHaeuj2nzT5ivMOQJElaby4zkCRJUmeZzEqSJKmzTGYlSZLUWSazkiRJ6ixvAJuEbl1xB8fOO3u8w5DUx5syJWndODMrSZKkzjKZlSRJUmeZzEqSJKmzTGYlSZLUWSazkiRJ6iyT2Q0kyQFJLhjlMe9McvAa2pyQ5PghyqcledVo45QkSeoyk9kJpKreUVXfXMfDpwEms5IkaVIxmQWSvD3JTUkuT3JOkuOTXJLk/UkWJrk+yRNa26e2soVJrk2yzQhdT01yXpIbk5yVJK2PvZNcmmRBkguTPLiVn5nk8Lb9rHbcgiQfGDTLu1uL78dJjmtlJwO7trhOGeIcj0kyP8n8lUuXj8VlkyRJGneT/o8mJNkHeD6wJ7AFcA2woFVvVVWzkuwPnAHsARwPvLqqrkgyFVg5QvePB3YHbgOuAPZL8j3gP4BDqurXSeYA7wJe2hfTFOA0YP+quiXJOYP6nQkcCGwD3JTkw8CbgT2qatZQgVTVXGAuwPSZu9RaXBpJkqQJb9Ins8B+wJeqaiWwMsmX++rOAaiqy5Jsm2QavaT0vUnOAj5fVT8doe+rVtUnWQjMAJbSS4q/0SZqNwN+Pui4mcCPq+qWvjiO6av/SlXdDdyd5FfATqM9aUmSpE2ByezIBs9gVlWdnOQrwLOAK5L8TVXdOMzxd/dt30vvege4oaqevB5xDdWvJEnSpOOa2d5M63OSTGnLBp7dVzcHIMlTgGVVtSzJrlV1XVW9G7ia3izqaNwETE/y5Nb3Fkl2H6LNLklm9MexBsvpLTuQJEmaNCb9jF5VXZ3kfGAx8EvgOmBZq16Z5Fp6a2lXrWl9fZIDgfuAG4CvjXK8P7SbvD6QZDt678H7Wl+r2vy+PWbr60nuopc0r6nf3yS5Isn1wNeq6k2jiUuSJKmLUuW9QEmmVtWKJFsBl9Fbn/pe4Piqmj/OMQX4T+CHVXXqWPQ9feYuddjpJ41FV5LGyGmzjxjvECRpQkqyoKoGhqt3mUHP3HaD1jXA56rqmvEOCHh5i+kGYDt6TzeQJElSn0m/zACgqv5iSqSqDlibY5M8FvjkoOK7q+qJ6xnTqcCYzMRKkiRtqkxm11NVXQcM+WzXiWrnqdv7laYkSdokuMxAkiRJnWUyK0mSpM4ymZUkSVJnuWZ2Erp1xR0cO+/s8Q5D2iS5Hl2SNi5nZiVJktRZJrOSJEnqLJNZSZIkdZbJrCRJkjrLZFaSJEmdZTIrSZKkztqkktkkr0+y1UYY57lJ3ryGNjOSjPiMniSzkjxrbKOTJEmaPDapZBZ4PTCqZDbJZqMdpKrOr6qT19BsBrCmB07OAkxmJUmS1tGETGaTvCnJcW371CQXt+2DkpyV5MNJ5ie5IcmJre444CHAt5N8u5U9I8l3klyT5NwkU1v5kiTvTnIN8IIklyR5f5KFSa5P8oTWbvskX0yyOMl3kzyulR+V5INt+8wkH0hyZZIfJzm8ncbJwOzW5xuGOMf7A+8E5rQ2c5L8MMn0Vn+/JP8vyfQ2xkfaOd+c5NmtzWZJTklydYvx2BGu6THt+Pkrly5fz3dIkiRpYpiQySwwD5jdtgeAqUm2aGWXAW+tqgHgccBTkzyuqj4A3AYcWFUHJtkReBtwcFXtBcwH3tg3xm+qaq+q+nTb36qqZgGvAs5oZScC11bV44C3AJ8YJt4HA08Bnk0viQV4MzCvqmZV1amDD6iqPwDvAD7T2nwG+BRwZGtyMLCoqn7d9mcATwD+FvhIkinAy4BlVbUPsA/w8iR/PVSAVTW3qgaqamDKtG2GOQ1JkqRumajJ7AJg7yTbAncD36GX1M6ml+j+rzarei2wO7DbEH08qZVfkWQh8BJg5776zwxqfw5AVV0GbJtkGr0E9ZOt/GJghxbTYF+sqvuq6vvATutwvqucAby4bb8U+Fhf3WfbGD8EfgzMBJ4BvLid3/eAHYBHrsf4kiRJnbL5eAcwlKq6J8ktwFHAlcBi4EDgEcDvgeOBfarqt0nOBKYM0U2Ab1TVC4cZ5q7Bw65hfyR3Dxp3nVTVT5L8MslB9GZhj+yvHiK+AK+tqgvXdUxJkqQum6gzs9CbgT2e3rKCecAr6M3EbksvEV2WZCfgmX3HLAdWfYf+XWC/JI8ASLJ1kkeNMN6c1u4p9L66X9bGPbKVHwDcXlV3rmX8/bGMps1H6S03OLeq7u0rf0FbR7srsAtwE3Ah8Mq2BIMkj0qy9VrGJ0mS1HkTPZl9MPCdqvolsJLeGtRF9JLaG4GzgSv6jpkLfD3Jt9ta06OAc5IsprdUYeYI461Mci3wEXprUQFOoLfcYTG9tbAvGUX8i4F7kywa6gaw5tvAbqtuAGtl5wNTWX2JAcB/A1cBXwNeUVUr6SW+3weuSXI9cBoTdLZdkiRpQ0jVaL5N3zQluQQ4vqrmT4BYBoBTq2p2X9mZwAVVdd5YjDF95i512OknjUVXkgY5bfaansgnSRqNJAvajf9DchZvAml/iOGVrL5WVpIkScMwmQWq6oAN2X+SvwHePaj4lqp63qA4TubPj/bqLz9qw0UnSZLUXS4zmIQGBgZq/vxxX1EhSZK0RmtaZjCRbwCTJEmSRmQyK0mSpM4ymZUkSVJneQPYJHTrijs4dt7Z4x2GNCQfbSVJGg1nZiVJktRZJrOSJEnqLJNZSZIkdZbJrCRJkjrLZFaSJEmdZTK7kSSZkeT6tWhzRN/+QJIPbPjoJEmSuslkdmKZAfwpma2q+VV13PiFI0mSNLGZzDZtVvTGJGcl+UGS85JsleRpSa5Ncl2SM5I8oLVfkuTfWvlVSR7Rys9McnhfvyuGGWtekmvaa99WdTIwO8nCJG9IckCSC9ox2yf5YpLFSb6b5HGt/IQW1yVJfpzE5FeSJE0aJrOrezTwoap6DHAn8EbgTGBOVT2W3h+ZeGVf+2Wt/IPA+0Yxzq+Ap1fVXsAcYNVSgjcD86pqVlWdOuiYE4Frq+pxwFuAT/TVzQT+BngC8E9Jthg8YJJjksxPMn/l0uWjCFWSJGniMpld3U+q6oq2/SngacAtVXVzK/s4sH9f+3P6fj55FONsAZye5DrgXGC3tTjmKcAnAarqYmCHJNu2uq9U1d1VdTu9RHmnwQdX1dyqGqiqgSnTthlFqJIkSROXf852dTVofymww1q2X7X9R9ovCUnuB9x/iOPeAPwS2LO1Xbkuwfa5u2/7XnxfJUnSJOHM7OoenmTVDOsRwHxgxqr1sMCLgEv72s/p+/mdtr0E2LttP5feLOxg2wE/r6r7Wp+btfLlwHDTpvOAIwGSHADcXlV3rtVZSZIkbaKcwVvdTcCrk5wBfB84DvgucG6SzYGrgY/0tX9gksX0ZkZf2MpOB76UZBHwdeCuIcb5EPC5JC8e1GYxcG879kzg2r5jTgDOaOP9DnjJ+p2qJElS96Vq8Dfrk1OSGcAFVbXHWrZfAgy0daqdMn3mLnXY6SeNdxjSkE6bfcSaG0mSJo0kC6pqYLh6lxlIkiSps1xm0FTVEmCtZmVb+xkbLBhJkiStFZPZSWjnqdv7Va4kSdokuMxAkiRJnWUyK0mSpM4ymZUkSVJnmcxKkiSps7wBbBK6dcUdHDvv7PEOQx3lzYOSpInEmVlJkiR1lsmsJEmSOstkVpIkSZ1lMitJkqTOMpndQJIcl+QHSc5az35mJLl+rOKSJEnalPg0gw3nVcDBVfXTjTloks2r6o8bc0xJkqTx4szsBpDkI8AuwNeSLEtyfF/d9W22dUabuT09yQ1JLkqyZWuzd5JFSRYBr+47drMkpyS5OsniJMe28gOSzEtyPvD9jXu2kiRJ48dkdgOoqlcAtwEHAqeO0PSRwH9W1e7AUuD5rfxjwGuras9B7V8GLKuqfYB9gJcn+etWtxfwuqp61FADJRq10Y8AACAASURBVDkmyfwk81cuXb5O5yVJkjTRmMyOr1uqamHbXgDMSDINmFZVl7XyT/a1fwbw4iQLge8BO9BLiAGuqqpbhhuoquZW1UBVDUyZts3YnoUkSdI4cc3shvdHVv+lYUrf9t192/cCW66hr9Cbsb1wtcLkAOCu9YhRkiSpk5yZ3fCW0FsCQJK9gL8eqXFVLQWWJnlKKzqyr/pC4JVJtmj9PSrJ1mMesSRJUkc4M7vhfY7e0oAb6C0NuHktjjkaOCNJARf1lX8UmAFckyTAr4FDxzZcSZKk7khVjXcM2simz9ylDjv9pPEOQx112uwjxjsESdIkkmRBVQ0MV+8yA0mSJHWWyawkSZI6y2RWkiRJneUNYJPQzlO3d92jJEnaJDgzK0mSpM4ymZUkSVJnmcxKkiSps1wzOwnduuIOjp139niHoQ5xjbUkaaJyZlaSJEmdZTIrSZKkzjKZlSRJUmeZzEqSJKmzTGYlSZLUWSazkiRJ6qxJncwmeWeSgzdg/0uS7Ni2r1xD27esof6rSaaNUH9okt3WLVJJkqRu2mSS2fSM6nyq6h1V9c0NFdOgsfZdQ5Mhk9lV51VVz6qqpSMcfyhgMitJkiaVTiezSWYkuSnJJ4DrgbcnuTrJ4iQn9rV7e2t3eZJzkhzfys9McnjbflqSa5Ncl+SMJA9o5UuSnJjkmlY3c4R4dkhyUZIbknwUSF/divbzwUkuS7IwyfVJZic5GdiylZ01xHk9bNAs74vbOS5K8skk+wLPBU5pfew6RGzHJJmfZP7KpcvX99JLkiRNCJ1OZptHAh8C3gA8FHgCMAvYO8n+SfYBng/sCTwTGBjcQZIpwJnAnKp6LL2/jPbKvia3V9VewIeB40eI5Z+Ay6tqd+ALwMOHaHMEcGFVzWoxLayqNwO/r6pZVXVk/3lV1e5VdWtfrLsDbwMOqqo9gddV1ZXA+cCbWh8/GjxoVc2tqoGqGpgybZsRTkGSJKk7NoVk9taq+i7wjPa6FrgGmEkvIdwP+FJVrayq5cCXh+jj0cAtVXVz2/84sH9f/efbzwXAjBFi2R/4FEBVfQX47RBtrgaOTnIC8NgW00jnNdhBwLlVdXsb544R4pEkSdqkbQrJ7F3tZ4B/bTOTs6rqEVX1X2M0xt3t5730Zm3XWVVdRi/p/RlwZpIXD9P0rmHKJUmS1GwKyewqFwIvTTIVIMlDkzwIuAJ4TpIpre7ZQxx7EzAjySPa/ouAS9chhsvoLSMgyTOBBw5ukGRn4JdVdTrwUWCvVnVPki3WYoyLgRck2aH1t30rXw64fkCSJE0qm0wyW1UXAWcD30lyHXAesE1VXU1vPeli4GvAdcCyQceuBI4Gzm3H3gd8ZB3COBHYP8kNwGHAfw/R5gBgUZJrgTnA+1v5XGBxkrPWcJ43AO8CLk2yCHhvq/o08KZ2E9tf3AAmSZK0KUpVjXcMG1ySqVW1IslW9GZPj6mqa8Y7rvEyfeYuddjpJ413GOqQ02YfMd4hSJImqSQLquovbuBfZb3Wf3bI3PYHBaYAH5/MiawkSdKmZFIks1U1ptNKSY4GXjeo+IqqevVYjiNJkqSRTYplBlrdwMBAzZ8/f7zDkCRJWqM1LTPYZG4AkyRJ0uRjMitJkqTOMpmVJElSZ02KG8C0ultX3MGx884e7zA0DnzEliRpU+PMrCRJkjrLZFaSJEmdZTIrSZKkzjKZlSRJUmeZzEqSJKmzTGYlSZLUWRssmU0yI8mYPQcoyaFJduvbf2eSg8ew/wOS7DtW/a1jDJckGfbPtUmSJGl1G3JmdgYwZDKbZF2eb3so8KdktqreUVXfXLfQhnQAMK7JrCRJkkZn1Mlskr9PclWShUlOS/LEJIuTTEmydZIbkuwBnAzMbu3ekOSoJOcnuRj4VpKpSb6V5Jok1yU5pG+MF7c+FyX5ZJsxfS5wSutv1yRnJjm8tX9akmtbP2ckeUArX5LkxL4xZg5zTjOAVwBvaP3PTnJLki1a/bar9tvs6ftbu+uTPKG12bqNfVWL5ZChxmptN0vynnb84iSvHaLNh5PMb9fzxL7yk5N8vx33nlb2gtbXoiSXDTPmMa2/+SuXLh/hHZYkSeqOUc2QJnkMMAfYr6ruSfIh4NHA+cBJwJbAp6rq+iRvBo6vqme3Y48C9gIeV1V3tNnZ51XVnUl2BL6b5Hx6s69vA/atqtuTbN/anw9cUFXntf5WxTQFOBN4WlXdnOQTwCuB97Wwb6+qvZK8Cjge+N+Dz6uqliT5CLCiqlYliJcAfwt8Efg74PPtnAG2qqpZSfYHzgD2AN4KXFxVL00yDbgqyTer6q4hLuUx9GauZ1XVH5NsP0Sbt7bz3oxe8v844GfA84CZVVVtHIB3AH9TVT/rKxt8jnOBuQDTZ+5SQ7WRJEnqmtHOzD4N2Bu4OsnCtr8L8E7g6cAA8G8jHP+NqrqjbQf4lySLgW8CDwV2Ag4Czq2q2wH62g/n0cAtVXVz2/84sH9f/efbzwX0Esi19VHg6LZ9NPCxvrpzWmyXAdu2BPIZwJvbdbkEmAI8fJi+DwZOq6o/tn6GOsf/leQa4Fpgd3pJ/jJgJfBfSQ4DftfaXgGcmeTlwGajOEdJkqROG+3a1QAfr6p/XK0weTAwFdiCXhI31Gwkg8qPBKYDe7cZzyXt2LF2d/t5L6M436q6ot3EdgCwWVVd3189uDm9a/P8qrppfYIFSPLX9GaR96mq3yY5E5jSZnGfQO+XiMOB1wAHVdUrkjyR3kzygiR7V9Vv1jcOSZKkiW60M7PfAg5P8iCAJNsn2Rk4DXg7cBbw7tZ2ObDNCH1tB/yqJbIHAju38ouBFyTZYdUYa+jvJmBGkke0/RcBl47yvIbr/xPA2aw+Kwu9pRYkeQqwrKqWARcCr01bh5Dk8SOM9Q3g2LbUov8cV9mWXuK/LMlOwDNbu6nAdlX1VeANwJ6tfNeq+l5VvQP4NfCwtT5rSZKkDhvVzGxVfT/J24CLktwPuAf4EnBPVZ3d1ndemeQgYB5wb5JF9Na0/nZQd2cBX05yHTAfuLGNcUOSdwGXJrmX3tfsRwGfBk5Pchy9WclVMa1McjRwbksOrwY+Mprzar4MnNdu3HptVc1rMZ5EW1bQZ2WSa+nNRL+0lf0zvXW6i9u1uQV49jBjfRR4VGt7D3A68MG+c1rU+r8R+Am9ZQTQS7a/1NYJB3hjKz8lySNb2beARetw/pIkSZ2TKu8FGk57WsIhVfWivrJL6N3YNn/cAltP02fuUoedftJ4h6FxcNrsMXv0syRJG0WSBVU17HP41+V5r5NCkv+g9/X+s8Y7FkmSJA1t0s3MtiUJrxtUfEVVvXoDjPU3/HkN8Sq3VNXzxnqs0RgYGKj58zs7sSxJkiYRZ2YHqaqP8Zc3dG2osS6kd2OYJEmSNoAN+edsJUmSpA3KZFaSJEmdZTIrSZKkzpp0a2YFt664g2PnnT3eYWgM+cgtSdJk5cysJEmSOstkVpIkSZ1lMitJkqTOMpmVJElSZ5nMrqMkV67jcYcm2W0t2p2Q5Pi2fWaSw9dlPEmSpE2Zyew6qqp91/HQQ4E1JrPrI4lPqZAkSZOCyew6SrKi/TwgySVJzktyY5KzkqTVnZzk+0kWJ3lPkn2B5wKnJFmYZNckL09ydZJFST6XZKs1jLt3kkuTLEhyYZIHt/JLkrwvyXzgdRv49CVJkiYEZ/DGxuOB3YHbgCuA/ZL8AHgeMLOqKsm0qlqa5Hzggqo6DyDJ0qo6vW2fBLwM+I+hBkmyRas7pKp+nWQO8C7gpa3J/atqYJhjjwGOAZi6045jctKSJEnjzWR2bFxVVT8FSLIQmAF8F1gJ/FeSC4ALhjl2j5bETgOmAheOMM6jgT2Ab7TJ382An/fVf2a4A6tqLjAXYPrMXWrNpyRJkjTxmcyOjbv7tu8FNq+qPyZ5AvA04HDgNcBBQxx7JnBoVS1KchRwwAjjBLihqp48TP1do4xbkiSp01wzu4EkmQpsV1VfBd4A7NmqlgPb9DXdBvh5W0Jw5Bq6vQmYnuTJbYwtkuw+tpFLkiR1h8nshrMNcEGSxcDlwBtb+aeBNyW5NsmuwNuB79Fba3vjSB1W1R/ozfK+O8kiYCGwrk9VkCRJ6rxUuXxyspk+c5c67PSTxjsMjaHTZh8x3iFIkrRBJFkw3A3u4MysJEmSOsxkVpIkSZ1lMitJkqTO8tFck9DOU7d3jaUkSdokODMrSZKkzjKZlSRJUmeZzEqSJKmzXDM7Cd264g6OnXf2eIeh9eCaZ0mSepyZlSRJUmeZzEqSJKmzTGYlSZLUWSazkiRJ6iyTWUmSJHWWyewGlOSoJA9ZQ5vXJ9lqY8UkSZK0KTGZ3bCOAkZMZoHXAyazkiRJ66DTyWySGUluTHJWkh8kOS/JVknekeTqJNcnmZueXZNc03fsI1ftJ1mS5F+TLEwyP8leSS5M8qMkr+g75k2t38VJTuyL4QdJTk9yQ5KLkmyZ5HBgADir9bvlEPEfRy/Z/XaSbyd5aZL39dW/PMmpw51na7N3kkuTLGgxP3hDXW9JkqSJptPJbPNo4ENV9RjgTuBVwAerap+q2gPYEnh2Vf0IWJZkVjvuaOBjff38d1XNAuYBZwKHA08CViWtzwAeCTwBmAXsnWT/duwjgf+sqt2BpcDzq+o8YD5wZFXNqqrfDw68qj4A3AYcWFUHAp8FnpNki74YzxjuPFu7/wAOr6q9W9t3DXWRkhzTEvX5K5cuX9M1lSRJ6oRNIZn9SVVd0bY/BTwFODDJ95JcBxwE7N7qPwocnWQzYA7Q/2ewzm8/rwO+V1XLq+rXwN1JpgHPaK9rgWuAmfSSWIBbqmph214AzFiXE6mqFcDFwLOTzAS2qKrrRjjPRwN7AN9IshB4G/BXw/Q9t6oGqmpgyrRt1iU8SZKkCWdT+HO2NcT+h4CBqvpJkhOAKa3uc8A/0UsYF1TVb/qOu7v9vK9ve9X+5kCAf62q0/oHSzJjUPt76c0Gr6uPAm8BbmT1meOhzjPADVX15PUYT5IkqbM2hZnZhydZlcwdAVzetm9PMpXecgEAqmolcCHwYVZPFNfGhcBLW58keWiSB63hmOXAmqZBV2tTVd8DHkbvXM7pazfUed4ETF9VnmSLJLsjSZI0SWwKyexNwKuT/AB4IL1E9XTgenoJ6NWD2p9Fb7b1otEMUlUX0VuW8J22fOE81pyongl8ZLgbwJq5wNeTfLuv7LPAFVX1276yvzjPqvoDvWT93UkWAQuBfUdzXpIkSV2WqsHfXndH+4r/gnaj19oeczywXVW9fUPFtb6SXACcWlXfavszGOV5jmT6zF3qsNNPGouuNE5Om33EeIcgSdJGkWRBVQ0MV78prJlda0m+AOxK76awCafdaHYVsGhVIitJkqThdTqZraol9O7mX9v2z9tw0YysJdJ/Paj4/1bVhat2qmop8KjBx472PCVJkiaLTiezXTKeibQkSdKmymR2Etp56vauuZQkSZuETeFpBpIkSZqkTGYlSZLUWSazkiRJ6izXzE5Ct664g2PnnT3eYQifFytJ0vpyZlaSJEmdZTIrSZKkzjKZlSRJUmeZzEqSJKmzTGYlSZLUWSazkiRJ6qxNLplNMiPJmD3vKMmhSXbr239nkoPHsP8Dkuw7Vv1JkiRNJptcMgvMAIZMZpOsy3N1DwX+lMxW1Tuq6pvrFtqQDgBMZiVJktZBZ5LZJH+f5KokC5OcluSJSRYnmZJk6yQ3JNkDOBmY3dq9IclRSc5PcjHwrSRTk3wryTVJrktySN8YL259LkryyTZj+lzglNbfrknOTHJ4a/+0JNe2fs5I8oBWviTJiX1jzBzmnGYArwDe0PqfneSWJFu0+m1X7Se5JMn7W7vrkzyhtdm6jX1Vi+WQYcY6Jsn8JPNXLl0+Ru+KJEnS+OpEMpvkMcAcYL+qmgXcCzwaOB84Cfg34FNVdT3wZmBeVc2qqlNbF3sBh1fVU4GVwPOqai/gQODf07M78DbgoKraE3hdVV3ZxnhT6+9HfTFNAc4E5lTVY+n9NbVX9oV9exvjw8DxQ51XVS0BPgKc2vqfB1wC/G1r8nfA56vqnra/VTv/VwFntLK3AhdX1RPa+ZySZOshxppbVQNVNTBl2jbDXWpJkqRO6UQyCzwN2Bu4OsnCtr8L8E7g6cAAvYR2ON+oqjvadoB/SbIY+CbwUGAn4CDg3Kq6HaCv/XAeDdxSVTe3/Y8D+/fVf779XEBv6cPa+ihwdNs+GvhYX905LbbLgG2TTAOeAby5XZdLgCnAw0cxniRJUmetyxrS8RDg41X1j6sVJg8GpgJb0Evi7hrm+P7yI4HpwN5VdU+SJe3YsXZ3+3kvo7jOVXVFu4ntAGCzNtv8p+rBzeldm+dX1U3rE6wkSVIXdWVm9lvA4UkeBJBk+yQ7A6cBbwfOAt7d2i4HRvoefTvgVy2RPRDYuZVfDLwgyQ6rxlhDfzcBM5I8ou2/CLh0Hc5tqP4/AZzN6rOy0FtqQZKnAMuqahlwIfDaJGl1j1+HGCRJkjqpE8lsVX2f3nrWi9rygG8ALwHuqaqz6d30tU+Sg4DFwL3tJq43DNHdWcBAkuuAFwM3tjFuAN4FXJpkEfDe1v7TwJvazVW79sW0kt4ygHNbX/fRW/86Wl8GnrfqBrC+GB9IW1bQZ2WSa9s4L2tl/0xvZnpxkhvaviRJ0qSQqsHfXGu8taclHFJVL+oruwQ4vqrmr2//02fuUoedftL6dqMxcNrsMXsksiRJm6QkC6pqYLj6rqyZnTSS/AfwTOBZ4x2LJEnSROfM7EaS5GjgdYOKr6iqV2/sWAYGBmr+/PWe4JUkSdrgnJmdIKrqY/zlDV2SJElaD524AUySJEkaismsJEmSOstkVpIkSZ3lmtlJ6NYVd3DsvLPHO4xJxUdwSZK0YTgzK0mSpM4ymZUkSVJnmcxKkiSps0xmJUmS1Fkms5IkSeosk9kOSOJTJyRJkoZgMrsBJNk6yVeSLEpyfZI5SZ6W5Nok1yU5I8kDWtslSXZs2wNJLmnbJyT5ZJIrgE8m2SnJF1qfi5Ls29r9fZKrkixMclqSzcbrvCVJkjY2k9kN438Ct1XVnlW1B/B14ExgTlU9lt7zfV+5Fv3sBhxcVS8EPgBcWlV7AnsBNyR5DDAH2K+qZgH3AkcO1VGSY5LMTzJ/5dLl63l6kiRJE4PJ7IZxHfD0JO9OMhuYAdxSVTe3+o8D+69FP+dX1e/b9kHAhwGq6t6qWgY8DdgbuDrJwra/y1AdVdXcqhqoqoEp07ZZ1/OSJEmaUFyLuQFU1c1J9gKeBZwEXDxC8z/y518qpgyqu2sNQwX4eFX94zoFKkmS1HHOzG4ASR4C/K6qPgWcAjwZmJHkEa3Ji4BL2/YSerOrAM8fodtv0ZYmJNksyXat7PAkD2rl2yfZeSzPRZIkaSIzmf3/7d17mJ1Vfff/9wdQIwQ5KPUCC0QQmiKHCAFPRMEirdZHKWIp2EPUR6Ci1PrDpzz1UFFaUdqf1XoCWolWES8QkWIfEAU0jSBJgCQcPQFa9ddHRWiiEgW+vz/2im4nk5lJ5rDnnnm/rmuuufe611r3d63MDF/WXve+J8cBwI3trf+/Ad4MvAK4OMka4BHgw63umcB7k6ygt+d1U/4COLK1XwnsV1W3t74/n2Q1cDWw62QMSJIkaTpym8EkqKqrgKuGOfW0YeouBfYdpvxtQ17/F/CSYep9CvjUlsYqSZLUZa7MSpIkqbNMZiVJktRZJrOSJEnqLPfMzkJ7zt2ZcxedOOgwJEmSxs2VWUmSJHWWyawkSZI6y2RWkiRJneWe2Vno3nX3cfLSCwcdxozmnmRJkqaGK7OSJEnqLJNZSZIkdZbJrCRJkjrLZFaSJEmdZTIrSZKkzjKZlSRJUmfNmGQ2yTFJ9hulzuIku41SZ0mS4yY4tlOS/OlmttktySUTGYckSdJMM2OSWeAYYMRkFlgMjJjMToaq+nBVfWwz23yvqiY0qZYkSZppJiSZTXJZkpVJbktyUitbl+ScVvaFJIcluS7Jt5K8uNWZk+SCJGuS3JzkyFa+OMn7+/q/IskRff3+bZJVSW5I8sQkzwJeDJyT5JYkew8T43HAQuATrc5jk5yd5PYkq5P8fV/15yT5Sot1kwllkiOSfCnJZ1vds5O8PMmNbUx7t3pvS3J6Oz6t75oXtbLntphuafOwfZJ5SW7tm49Lk1yZ5OtJ3t0Xw6uSfK1d8/z+eRsS60lJViRZ8eD9a0f/R5UkSeqAiVqZfWVVHUIvWTwtyeOB7YBrquqpwFrgLOD5wB8Ab2/tTgWqqg4ATgA+mmTOKNfaDrihqg4Cvgy8uqq+AlwOvLGqFlTVN4c2qqpLgBXAy6tqAbBti+WpVXVgi2+DXYHDgRcBZ48Sz0HAKcBvA38C7FtVhwH/DLxumPpnAE9r1zyllZ0OnNriWgT8bJh2C4DjgQOA45Ps3rZMvAV4BvBsYP6mgqyq86pqYVUtnLPj9qMMSZIkqRsmKpk9Lckq4AZgd2Af4OfAle38GuBLVfWLdjyvlR8OfBygqu4E7gX2HeVaPweuaMcr+/raXA8ADwL/kuRY4Kd95y6rqkeq6nbgiaP0s7yqvl9V64FvAp9v5f3j7Lea3urwHwMPtbJlwP+b5DRgx6p6aJh2X6yqB6rqQeB2YE/gMHrzel+b24tHiVWSJGlGGXcy297+Pwp4ZlstvRmYA/yiqqpVewRYD1BVjwDbjNLtQ0Ni61+t7e/34TH0NayWMB4GXEJvBfbKvtPr+44zSlf9dR/pe72pcf4+8AHgYGB5km2q6mzgfwKPBZYlGW6Ftf86WzxuSZKkmWQiVmZ3AH5cVT9tSdgzNqPtUuDlAEn2BfYA7gLuARYk2SrJ7vSSztGsBUZ7//yXdZLMBXaoqn8H/pLedoFJlWQrYPequhb4K3pzNzfJ3lW1pqreBSxnhO0CQywHnptkpyTbAC+dlMAlSZKmqYlY3bsSOCXJHfQS0Rs2o+0HgQ8lWUNvNXZxVa1Psgy4m97b6XcAN42hr4uA89tb9ccNt28WWAJ8OMnPgBcAn217dAO8YTPi3lJbAx9PskO75vuq6v4k72g3vz0C3Ab8H3r7dkdUVd9N8nfAjcB9wJ30tk9IkiTNCvnVO/bqoiRzq2pdW5n9DPCRqvrMSG12mb9XHXv+WSNV0Tidu+jEQYcgSdKMkGRlVS3c1PmZ9Dmzs9XbktwC3EpvNfuyAccjSZI0ZWbkTURJPkDvo6r6vbeqLtjC/g4A/nVI8fqqevqW9DeRqur0QccgSZI0KG4zmIUWLlxYK1asGHQYkiRJo3KbgSRJkmYsk1lJkiR1lsmsJEmSOmtG3gCmkd277j5OXnrhoMOYcfw4LkmSpp4rs5IkSeosk1lJkiR1lsmsJEmSOstkVpIkSZ1lMitJkqTOMpmVJElSZ5nMjlGStyc5atBxACRZnOT9g45DkiRp0Gbl58wmCZCqemSsbarqrZMYkiRJkrbArFmZTTIvyV1JPgbcCrwlyfIkq5Oc2VfvLa3efyT5ZJLTW/mSJMe1499JcnOSNUk+kuQxrfyeJGcmuamdmz9CPHOTXNDqrU7y0lZ+Qiu7Ncm7+uq/IsnXktwIPLuvfJckn25jWZ7k2cNcjiQnJVmRZMWD968d11xKkiRNF7MmmW32AT4I/CXwJOAwYAFwSJLnJDkUeClwEPACYOHQDpLMAZYAx1fVAfRWt/+8r8oPq+pg4EPA6SPE8hbggao6oKoOBK5JshvwLuB5La5DkxyTZFfgTHpJ7OHAfn39vBd4T1VtiP2fh7tYVZ1XVQurauGcHbcfISxJkqTumG3bDO6tqhuS/D1wNHBzK59LL9HdHvhsVT0IPJjk34bp47eAu6vqa+31R4FTgX9sry9t31cCx44Qy1HAH214UVU/TvIc4Lqq+gFAkk8Az2lV+ss/Bezb189+vZ0TADwuydyqWjfCtSVJkmaE2ZbM/qR9D/DOqjq3/2SS10/ANda37w8zNfO7FfCMloBLkiTNKrNtm8EGVwGvTDIXIMmTkvwGsAz4H0nmtHMvGqbtXcC8JE9pr/8E+NIWxHA1vRVdWgw7ATcCz03yhCRbAye0vr/ayh+f5FHAy/r6+Tzwur5+FmxBLJIkSZ00K5PZqvo8cCFwfZI1wCXA9lW1HLgcWA38H2AN8MCQtg8CrwAubm0fAT68BWGcBezUbvRaBRxZVd8HzgCuBVYBK6vqs638bcD19BLuO/r6OQ1Y2G4iux04ZQtikSRJ6qRU1aBjmFY27DdNsi3wZeCkqrpp0HFNpF3m71XHnn/WoMOYcc5ddOKgQ5AkacZJsrKqNropf4PZtmd2LM5Lsh8wB/joTEtkJUmSZhKT2SGqakKX15K8AviLIcXLqurU4epPhT3n7uwqoiRJmhFMZidZVV0AXDDoOCRJkmaiWXkDmCRJkmYGk1lJkiR1lsmsJEmSOss9s7PQvevu4+SlFw46jCnnTW+SJM08rsxKkiSps0xmJUmS1Fkms5IkSeosk1lJkiR1lsmsJEmSOstkFkjy+iTbDjqOoZJcl2RhO/73JDu2r9cMOjZJkqTpwGS25/XAZiWzSbaepFiGVVUvrKr7gR0Bk1lJkiRmWDKb5I1JTmvH70lyTTt+XpJPJPlQkhVJbktyZjt3GrAbcG2Sa1vZ0UmuT3JTkouTzG3l9yR5V5KbgJdtIoZDkqxqX+ckubWVL07y/r56VyQ5oh1vFNcw/d6T5AnA2cDeSW5p/X8syTF99T6R5CXDtD+pXWPFg/ev3dyplSRJmpZmVDILLAUWteOFwNwkj2plXwbeVFULgQOB5yY5sKreB3wPOLKqjmwJ45uBo6rqYGAF8Ia+a/yoqg6uqos2EcMFwOuq6qDNiHujuEaoewbwzapaUFVvBP4FWAyQZAfgWcDnhjaqqvOqamFVLZyzdRpY+wAAH7VJREFU4/abEZokSdL0NdOS2ZXAIUkeB6wHrqeX1C6il+j+YVtVvRl4KrDfMH08o5UvS3IL8GfAnn3nP7WpiyfZEdixqr7civ51jHGPJa5hVdWXgH2S7AKcAHy6qh4aa3tJkqQum1GPs62qXyS5m95K5VeA1cCRwFOAnwGnA4dW1Y+TLAHmDNNNgKur6oRNXOYnWxjeQ/z6/zzMAUjy5DHGNZKPAX8M/BHwii2MT5IkqXNm2sos9FZgT6e3rWApcAq9Fc/H0UtEH0jyROAFfW3WAhvee78BeHaSpwAk2S7JvmO5cLtB6/4kh7eil/edvgdYkGSrJLsDh7XykeIaTn+sGyyhdxMbVXX7WGKVJEmaCWZqMrsrcH1V/RfwILC0qlbRS2rvBC4ElvW1OQ+4Msm1VfUDeiu7n0yymt5Whfmbcf1XAB9oWxTSV74MuBu4HXgfcBPAKHFtpKp+RG8LxK1Jzmll/wXcQW+/riRJ0qyRqhp0DDNWknnAFVW1/yRfZ1tgDXBwVT0wWv1d5u9Vx55/1mSGNC2du+jEQYcgSZI2U5KV7Ub5Yc3EldlZJclR9FZl/2ksiawkSdJMMqNuAJtKST4APHtI8Xur6pdv9VfVPcCkrspW1Rf49U9bkCRJmjXcZjALLVy4sFasWDHoMCRJkkblNgNJkiTNWCazkiRJ6iyTWUmSJHWWN4DNQveuu4+Tl1446DCmnB/NJUnSzOPKrCRJkjrLZFaSJEmdZTIrSZKkzjKZlSRJUmeZzEqSJKmzTGYlSZLUWROWzCZZnOT9E9WfNk+SeUn87ClJkjSruDI7c8wDTGYlSdKsMmoym2S7JJ9LsirJrUmOT3Jokq+0shuTbN+q75bkyiRfT/Luvj6OTnJ9kpuSXJxkbiu/J8k7k9ySZEWSg5NcleSbSU7pa//GJMuTrE5y5gixzktyZ5IlSb6W5BNJjkqyrMV0WKt3WIvn5jaO32rli5NcuokxfKjFeFt/DEle2K65Msn7klzRN28fafNzc5KX9F3jsiRXt/G/NskbWp0bkuzc6u3d4liZZGmS+a18SbvOV5J8K8lxLZSzgUVtLv9ymLk5qcW/4sH71472zy5JktQJY1mZ/T3ge1V1UFXtD1wJfAr4i6o6CDgK+FmruwA4HjgAOD7J7kmeALwZOKqqDgZWAG/o6//bVbUAWAosAY4DngGcCb1EGNgHOKz1f0iS54wQ71OAfwDmt68TgcOB04G/bnXuBBZV1dOAtwJ/19d+ozG08jdV1ULgQOC5SQ5MMgc4F3hBVR0C7NLXz5uAa6rqMOBI4Jwk27Vz+wPHAocCfwv8tMVyPfCnrc55wOtav6cDH+zre9c2phfRS2IBzgCWVtWCqnrP0EmpqvOqamFVLZyz4/ZDT0uSJHXSWB5nuwb4hyTvAq4A7ge+X1XLAarqvwGSAHyxqh5or28H9gR2BPYDlrU6j6aXtG1wed915lbVWmBtkvVJdgSObl83t3pz6SW3X95EvHdX1ZoWw20tpkqyht5b8QA7AB9Nsg9QwKP62g83hu8Af5jkpDZnu7YxbQV8q6rubm0/CZzUjo8GXpzk9PZ6DrBHO762b5wPAP/WNwcHtpXrZwEXtzkDeExfjJdV1SPA7UmeuIl5kCRJmvFGTWar6mtJDgZeCJwFXDNC9fV9xw+3/gNcXVUnjNLmkSHtH+lr/86qOne0WIeJob/PDf0BvINeQvkHSeYB1400hiRPprc6emhV/TjJEnrJ6UgCvLSq7vq1wuTpY4hxK+D+tmI9nP722UQdSZKkGW8se2Z3o/c2+MeBc4CnA7smObSd3z7JSEnxDcCzkzyl1d8uyb6bEeNVwCv79tk+KclvbEb74ewAfLcdLx5D/ccBPwEeaCuhL2jldwF7tYQYetsTNrgKeF3a0mqSp401uLbafXeSl7W2SXLQKM3WAu4fkCRJs8pY9sweANyY5Bbgb+jtMT0e+Kckq4CrGWGVsqp+QC9h/GSS1fS2GMwfa4BV9XngQuD6tlXgEsaftL0beGeSmxnb6vQqetsc7myxLGvlPwNeA1yZZCW9hPKB1uwd9LYvrG7bHd6xmTG+HHhVm+PbgJeMUn818HB6N+VtdAOYJEnSTJSqGnQMnZZkblWtayuwHwC+PtwNWNPJLvP3qmPPP2vQYUy5cxf5yWWSJHVNkpXtJvxh+Tmz4/fqtmp9G73tC2Pd2ytJkqRxGsunGUw7SR4PfHGYU79TVT+ayljaKuy0XomVJEmaqTqZzLaEdVN3+msUe87d2bfcJUnSjOA2A0mSJHWWyawkSZI6y2RWkiRJndXJPbMan3vX3cfJSy8cdBiTwr3AkiTNLq7MSpIkqbNMZiVJktRZJrOSJEnqLJNZSZIkdZbJrCRJkjrLZHaKJZmX5NZx9vHiJGdMVEySJEld5UdzdVBVXQ5cPug4JEmSBs2V2cHYJsknktyR5JIk2yZ5a5LlSW5Ncl6SACQ5LcntSVYnuaiVLU7y/na8JMn7knwlybeSHDfIgUmSJE0lk9nB+C3gg1X128B/A68B3l9Vh1bV/sBjgRe1umcAT6uqA4FTNtHfrsDhrc3Zw1VIclKSFUlWPHj/2gkciiRJ0uCYzA7Gd6pqWTv+OL1E9MgkX02yBnge8NR2fjXwiSR/DDy0if4uq6pHqup24InDVaiq86pqYVUtnLPj9hM3EkmSpAEymR2MGub1B4HjquoA4HxgTjv3+8AHgIOB5UmG2+e8vu84ExyrJEnStGUyOxh7JHlmOz4R+I92/MMkc4HjAJJsBexeVdcCfwXsAMyd6mAlSZKmKz/NYDDuAk5N8hHgduBDwE7ArcD/Byxv9bYGPp5kB3orru+rqvvbvWGSJEmznsnsFKuqe4D5w5x6c/sa6vBh+lgCLGnHi4ecc+VWkiTNGm4zkCRJUmeZzEqSJKmz3GYwC+05d2fOXXTioMOQJEkaN1dmJUmS1Fkms5IkSeosk1lJkiR1lsmsJEmSOssbwGahe9fdx8lLLxx0GJvFG9YkSdJwXJmVJElSZ5nMSpIkqbNMZiVJktRZJrOSJEnqLJNZSZIkdZbJrCRJkjrLZHYCJdkuyeeSrEpya5LjkxyS5EtJVia5Ksmure6rkyxvdT+dZNtW/rLWdlWSL7eyOUkuSLImyc1Jjmzli5NcmuTKJF9P8u7BjV6SJGnqmcxOrN8DvldVB1XV/sCVwD8Bx1XVIcBHgL9tdS+tqkOr6iDgDuBVrfytwO+28he3slOBqqoDgBOAjyaZ084tAI4HDgCOT7L7cIElOSnJiiQrHrx/7USOWZIkaWBMZifWGuD5Sd6VZBGwO7A/cHWSW4A3A7/Z6u6fZGmSNcDLgae28mXAkiSvBrZuZYcDHweoqjuBe4F927kvVtUDVfUgcDuw53CBVdV5VbWwqhbO2XH7CRyyJEnS4PgEsAlUVV9LcjDwQuAs4Brgtqp65jDVlwDHVNWqJIuBI1ofpyR5OvD7wMokh4xy2fV9xw/jv6kkSZpFXJmdQEl2A35aVR8HzgGeDuyS5Jnt/KOSbFiB3R74fpJH0VuZ3dDH3lX11ap6K/ADequ7SzfUSbIvsAdw1xQNS5IkadpyFW9iHQCck+QR4BfAnwMPAe9LsgO9+f5H4DbgLcBX6SWsX6WX3NLa7wME+CKwCrgT+FDbkvAQsLiq1ieZsoFJkiRNR6mqQcegKbbL/L3q2PPPGnQYm+XcRScOOgRJkjQASVZW1cJNnXebgSRJkjrLZFaSJEmdZTIrSZKkzvIGsFloz7k7uwdVkiTNCK7MSpIkqbNMZiVJktRZJrOSJEnqLPfMzkL3rruPk5deOOgwRuSeXkmSNBauzEqSJKmzTGYlSZLUWSazkiRJ6iyTWUmSJHWWyawkSZI6y2RWkiRJndWJZDbJMUn263t9XZKF4+hvtySXtOMFSV44EXGOcs17kjxhsq8jSZI0m3QimQWOAfYbtdYYJNmmqr5XVce1ogXApCezkiRJmngDS2aTXJZkZZLbkpzUytb1nT8uyZIkzwJeDJyT5JYke7cqL0tyY5KvJVnU2sxJckGSNUluTnJkK1+c5PIk1wBfTDIvya1JHg28HTi+9X18kq8n2aW12yrJNza8HmYML2v9rEry5Va2dZK/b+Wrk7yur8nrktzU4pvf6m+X5CNtLDcneUlfzJclubqt6r42yRtanRuS7Nzq7Z3kyjaXSzf0O0ysJyVZkWTFg/ev3aJ/M0mSpOlmkE8Ae2VV3ZfkscDyJJ8erlJVfSXJ5cAVVbVhawDANlV1WNsi8DfAUcCpvSZ1QEvqPp9k39bVwcCB7ZrzWt8/T/JWYGFVvbb1PR94OfCPrc9VVfWDTYzhrcDvVtV3k+zYyk4C5gELquqhDUln88OqOjjJa4DTgf8JvAm4pqpe2fq4MckXWv39gacBc4BvAH9VVU9L8h7gT1uM5wGnVNXXkzwd+CDwvGHm8bxWl13m71WbGI8kSVKnDHKbwWlJVgE3ALsD+2xm+0vb95X0kkeAw4GPA1TVncC9wIZk9uqqum8M/X6EXqII8ErgghHqLgOWJHk1sHUrOwo4t6oeanH0X3O4mI8GzkhyC3AdvcR1j3bu2qpa25LpB4B/a+VrgHlJ5gLPAi5u7c8Fdh3DGCVJkmaEgazMJjmCXtL3zKr6aZLr6CVx/SuGc0bpZn37/jBjG8dPxhJbVX0nyX8leR5wGL1V2k3VPaWthv4+sDLJIVsQc4CXVtVd/RVbv+v7ih7pe/1Ia78VcH9VLRh9ZJIkSTPPoFZmdwB+3BLZ+cAzWvl/JfntJFsBf9BXfy2w/Rj6XUpLPtv2gj2Au0ZsMXzf/0xvhffiqnp4Uw2T7F1VX62qtwI/oLfCfDVwcpJtWp2dN9W+uYreXtq0+k8bpf4vVdV/A3cneVlrmyQHjbW9JElS1w0qmb0S2CbJHcDZ9LYaAJwBXAF8Bfh+X/2LgDe2m5/2ZtM+CGyVZA3wKWBxVa0foT7AtcB+G24Aa2WXA3MZeYsB9G5KW5Pk1hbzKnqJ8LeB1W0bxYmj9PEO4FGt/m3t9eZ4OfCqdq3bgJdsZntJkqTOSpX3Ag2V3mfYvqeqFg06lsmwy/y96tjzzxp0GCM6d9Fo/w8gSZJmgyQrq2qTzxcY5KcZTEtJzgD+nBH2ykqSJGl6MJkdoqrOprf14ZeSvAl42ZCqF1fV305ZYJIkSdqI2wxmoYULF9aKFSsGHYYkSdKoRttm0JXH2UqSJEkbMZmVJElSZ5nMSpIkqbO8AWwWunfdfZy89MKBXNuP3JIkSRPJlVlJkiR1lsmsJEmSOstkVpIkSZ1lMitJkqTOMpmVJElSZ5nMdlySr7Tv85L4UQGSJGlWMZmdhpKM+SPTqupZ7XAeYDIrSZJmFZPZSdJWSu9M8okkdyS5JMm2SQ5J8qUkK5NclWTXVv+6JP+YZAXwF+31e5KsaO0PTXJpkq8nOavvOuva4dnAoiS3JPnLAQxZkiRpypnMTq7fAj5YVb8N/DdwKvBPwHFVdQjwEeBv++o/uqoWVtU/tNc/r6qFwIeBz7b2+wOLkzx+yLXOAJZW1YKqes/QQJKc1BLjFQ/ev3YixyhJkjQwPgFscn2nqpa1448Df00vGb06CcDWwPf76n9qSPvL2/c1wG1V9X2AJN8Cdgd+NNZAquo84DyAXebvVZs3DEmSpOnJZHZyDU0a19JLSp+5ifo/GfJ6ffv+SN/xhtf+20mSpFnPbQaTa48kGxLXE4EbgF02lCV5VJKnTtC11gLbT1BfkiRJnWAyO7nuAk5NcgewE22/LPCuJKuAW4BnjdB+c6wGHk6yyhvAJEnSbOFb1ZProar64yFltwDPGVqxqo7Y1Ouqug64bhPn5rbvvwCeN+6IJUmSOsSVWUmSJHWWK7OTpKruoffJBZIkSZokJrOz0J5zd+bcRT4sTJIkdZ/bDCRJktRZJrOSJEnqLJNZSZIkdZbJrCRJkjrLG8BmoXvX3cfJSy+ctP69uUySJE0VV2YlSZLUWSazkiRJ6iyTWUmSJHWWyawkSZI6y2RWkiRJnTVjk9kkb0ty+jDl85Lc2o4XJnnf1Ee3sSQ7JnlN3+vdklwyyJgkSZKmuxmbzI5FVa2oqtMGHUezI/DLZLaqvldVxw0wHkmSpGmvM8lsW1G9M8knktyR5JIk2ya5J8kTWp2FSa7ra3ZQkuuTfD3Jq4fp84gkV7TjuUkuSLImyeokLx0hlnVJzklyW5IvJDksyXVJvpXkxa3O1q3O8tbfyX3t39hXfmYrPhvYO8ktrV3/CvLiJJcmubKN5d19fb0qydeS3Jjk/CTv30TMJyVZkWTFg/evHeu0S5IkTWtde2jCbwGvqqplST5C30rmJhwIPAPYDrg5yedGqPsW4IGqOgAgyU4j1N0OuKaq3pjkM8BZwPOB/YCPApcDr2r9HZrkMcCyJJ8H9mlfhwEBLk/yHOAMYP+qWtCuP2/INRcATwPWA3cl+Sfg4Rb3wcBa4Bpg1XABV9V5wHkAu8zfq0YYmyRJUmd0LZn9TlUta8cfB0bbIvDZqvoZ8LMk19JLIG/ZRN2jgD/a8KKqfjxCvz8HrmzHa4D1VfWLJGuAea38aODAJBu2CuxAL4k9un3d3MrntvJvjzKWL1bVAwBJbgf2BJ4AfKmq7mvlFwP7jtKPJEnSjNG1ZHboimIBD/Gr7RJzxlB/Ivyiqjb09Qi91VKq6pEkG+Y0wOuq6qr+hkl+F3hnVZ07pHzeKNdc33f8MN37t5MkSZpwndkz2+yR5Jnt+ETgP4B7gENa2dB9ri9JMifJ44EjgOUj9H01cOqGF6NsMxiLq4A/T/Ko1t++SbZr5a9MMreVPynJb9DbJrD9Zl5jOfDcJDu1JHqT+3wlSZJmoq4ls3cBpya5A9gJ+BBwJvDeJCvorVj2Ww1cC9wAvKOqvjdC32cBOyW5Nckq4MhxxvrPwO3ATe1GrnOBbarq88CFwPVtW8IlwPZV9SN6+2pvTXLOWC5QVd8F/g64EVhGL7F/YJxxS5IkdUZ+9W759Nbehr+iqvYfcCjTSpK5VbWurcx+BvhIVX1mpDa7zN+rjj3/rEmL6dxFJ05a35IkaXZJsrKqFm7qfNdWZrWxtyW5BbgVuBu4bMDxSJIkTZnO3ERUVfcAU7oqm+SrwGOGFP9JVa2ZyjhGUlUbPeVMkiRptuhMMjsIVfX0QccwGfacu7NbASRJ0ozgNgNJkiR1lsmsJEmSOstkVpIkSZ1lMitJkqTOMpmVJElSZ5nMSpIkqbNMZiVJktRZJrOSJEnqLJNZSZIkdZbJrCRJkjrLZFaSJEmdZTIrSZKkzjKZlSRJUmeZzEqSJKmzTGYlSZLUWSazkiRJ6iyTWUmSJHWWyawkSZI6y2RWkiRJnWUyK0mSpM4ymZUkSVJnmcxKkiSps0xmJUmS1Fkms5IkSeqsVNWgY9AUS7IWuGvQcUwzTwB+OOggphHnY2POya9zPjbmnPw652NjzsnGxjIne1bVLps6uc3ExqOOuKuqFg46iOkkyQrn5Fecj405J7/O+diYc/LrnI+NOScbm4g5cZuBJEmSOstkVpIkSZ1lMjs7nTfoAKYh5+TXOR8bc05+nfOxMefk1zkfG3NONjbuOfEGMEmSJHWWK7OSJEnqLJNZSZIkdZbJ7AyT5PeS3JXkG0nOGOb8Y5J8qp3/apJ5fef+dyu/K8nvTmXck2VL5yPJ85OsTLKmfX/eVMc+WcbzM9LO75FkXZLTpyrmyTTO35kDk1yf5Lb2szJnKmOfLOP4vXlUko+2ubgjyf+e6tgnwxjm4zlJbkryUJLjhpz7syRfb19/NnVRT64tnZMkC/p+Z1YnOX5qI5884/k5aecfl+Q/k7x/aiKeXOP8vdkjyefb35Hbh/53aCNV5dcM+QK2Br4J7AU8GlgF7DekzmuAD7fjPwI+1Y73a/UfAzy59bP1oMc0wPl4GrBbO94f+O6gxzPoOek7fwlwMXD6oMcz4J+RbYDVwEHt9eO7/jszAXNyInBRO94WuAeYN+gxTcF8zAMOBD4GHNdXvjPwrfZ9p3a806DHNOA52RfYpx3vBnwf2HHQYxrknPSdfy9wIfD+QY9n0PMBXAc8vx3PBbYd6XquzM4shwHfqKpvVdXPgYuAlwyp8xLgo+34EuB3kqSVX1RV66vqbuAbrb8u2+L5qKqbq+p7rfw24LFJHjMlUU+u8fyMkOQY4G56czITjGc+jgZWV9UqgKr6UVU9PEVxT6bxzEkB2yXZBngs8HPgv6cm7Ekz6nxU1T1VtRp4ZEjb3wWurqr7qurHwNXA701F0JNsi+ekqr5WVV9vx98D/i+wySc7dch4fk5IcgjwRODzUxHsFNji+UiyH7BNVV3d6q2rqp+OdDGT2ZnlScB3+l7/Zysbtk5VPQQ8QG9FaSxtu2Y889HvpcBNVbV+kuKcSls8J0nmAn8FnDkFcU6V8fyM7AtUkqvaW2X/awrinQrjmZNLgJ/QW237NvD3VXXfZAc8ycbzt3Em/l2FCRpXksPordp9c4LiGqQtnpMkWwH/AMyIrVvNeH5G9gXuT3JpkpuTnJNk65Ea+DhbaQRJngq8i94q3Gz3NuA9VbWuLdTOdtsAhwOHAj8FvphkZVV9cbBhDdRhwMP03j7eCVia5AtV9a3BhqXpJsmuwL8Cf1ZVG61UzjKvAf69qv7Tv61A72/rInrb/b4NfApYDPzLphq4MjuzfBfYve/1b7ayYeu0twJ3AH40xrZdM575IMlvAp8B/rSqZsLKAYxvTp4OvDvJPcDrgb9O8trJDniSjWc+/hP4clX9sL0F9u/AwZMe8eQbz5ycCFxZVb+oqv8LLAO6/hz68fxtnIl/V2Gc40ryOOBzwJuq6oYJjm1QxjMnzwRe2/62/j3wp0nOntjwptx45uM/gVvaFoWHgMsY5W+ryezMshzYJ8mTkzya3o0Zlw+pczmw4Y7a44BrqrfD+nLgj9pdyk8G9gFunKK4J8sWz0eSHen9sT2jqpZNWcSTb4vnpKoWVdW8qpoH/CPwd1XV9btux/M7cxVwQJJtW0L3XOD2KYp7Mo1nTr4NPA8gyXbAM4A7pyTqyTOW+diUq4Cjk+yUZCd67/BcNUlxTqUtnpNW/zPAx6rqkkmMcapt8ZxU1curao/2t/V0enOz0d3/HTOe35vlwI5JNuylfh6j/W0d1J1ufk3aHYQvBL5Gbw/Sm1rZ24EXt+M59O5E/wa9ZHWvvrZvau3uAl4w6LEMcj6AN9Pb+3dL39dvDHo8g/4Z6evjbcyATzMY73wAf0zvZrhbgXcPeiyDnhN6dx1f3ObkduCNgx7LFM3HofRWk35Cb4X6tr62r2zz9A3gFYMey6DnpP3O/GLI39YFgx7PoH9O+vpYzAz4NIPxzgfwfHqfFrMGWAI8eqRr+ThbSZIkdZbbDCRJktRZJrOSJEnqLJNZSZIkdZbJrCRJkjrLZFaSJEmdZTIrSdNAkoeT3JLk1iT/1j7reKT6b0sy4uMvkxzTnnO+4fXbkxw1AbEuSXLcePvZzGu+Psm2U3lNSd1gMitJ08PPqmpBVe0P3AecOgF9HgP8MpmtqrdW1RcmoN8p1Z7L/nrAZFbSRkxmJWn6uR54EkCSvZNcmWRlkqVJ5g+tnOTVSZYnWZXk0+2pZM8CXgyc01Z8996woprk95Jc3Nf+iCRXtOOjk1yf5KYkFyeZO1KgSe5J8s52jRVJDk5yVZJvJjmlr/8vJ/lckruSfDjJVu3cCUnWtBXpd/X1uy7JPyRZRe+BLrsB1ya5tp3/ULvebUnOHBLPmS3+NRvmK8ncJBe0stVJXrol45U0/ZjMStI00lYhf4dfPfrxPOB1VXUIvUddfnCYZpdW1aFVdRBwB/CqqvpK6+ONbcX3m331vwA8vT1yFuB44KIkT6D39LujqupgYAXwhjGE/e2qWgAspfe0nuPoPcr2zL46hwGvo7dSvDdwbJLdgHfRe1zlAuDQJMe0+tsBX62qg6rq7cD3gCOr6sh2/k1VtRA4EHhukgP7rvXDFv+H2pwBvAV4oKoOqKoDgWvGMV5J08g2gw5AkgTAY5PcQm9F9g7g6rZK+Czg4iQb6j1mmLb7JzkL2JHeI2WvGulCVfVQkiuB/5HkEuD3gf8FPJdesrmsXe/R9FaJR7Mh8V4DzK2qtcDaJOv79v7eWFXfAkjySeBweo81va6qftDKPwE8B7gMeBj49AjX/MMkJ9H779iuLe7V7dyl7ftK4Nh2fBS958NvmIMfJ3nRFo5X0jRiMitJ08PPqmpBu8npKnp7ZpcA97dVz5EsAY6pqlVJFgNHjOF6FwGvpbc/d0VVrU0vo7u6qk7YzNjXt++P9B1veL3hvzNDn50+2rPUH6yqh4c7keTJ9FZcD21J6RJgzjDxPMzI/53b0vFKmkbcZiBJ00hV/RQ4Dfh/gJ8Cdyd5GUB6Dhqm2fbA95M8Cnh5X/nadm44XwIOBl5NL7EFuAF4dpKntOttl2TfcQ5pg8OSPLntlT0e+A/gRnpbBJ7Qtlec0OIaTv9YHgf8BHggyROBF4zh+lfTd1Ndkp2Y3PFKmiIms5I0zVTVzfTeMj+BXnL6qnYj1G3AS4Zp8hbgq8Ay4M6+8ouANya5OcneQ67xMHAFvUTwilb2A2Ax8Mkkq+m95b7RDWdbaDnwfnpbKO4GPlNV3wfOAK4FVgErq+qzm2h/HnBlkmurahVwM72xXkhv3KM5C9ip3Wi2it7+28kcr6QpkqrR3umRJGnLJTkCOL2qXjToWCTNPK7MSpIkqbNcmZUkSVJnuTIrSZKkzjKZlSRJUmeZzEqSJKmzTGYlSZLUWSazkiRJ6qz/H3fjB5hiMb7ZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#score model\n",
        "best_model = RandomForestClassifier(max_depth=30, max_features='log2', min_samples_split=7, n_estimators=225, random_state=42, warm_start=True)\n",
        "\n",
        "#fit and predict\n",
        "best_model.fit(X_train, y_train)\n",
        "y_pred = best_model.predict(X_test)\n",
        "y_pred_train = best_model.predict(X_train)\n",
        "\n",
        "#print best model scores on test data\n",
        "print(\"Accuracy score train: {}\".format(accuracy_score(y_train, y_pred_train)))\n",
        "print(\"Accuracy score test: {}\".format(accuracy_score(y_test, y_pred)))\n",
        "print()\n",
        "\n",
        "#print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "confusion_matrix_plotter(y_test, y_pred)\n",
        "\n",
        "feature_importances(best_model, \" Random forest classifier\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fHUGF_semxGH"
      },
      "source": [
        "### 2.3 Xgboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kHVXWSPVmwgq"
      },
      "outputs": [],
      "source": [
        "# Create training and test sets: test size 0.2\n",
        "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)\n",
        "\n",
        "\n",
        "param_grid = { 'n_estimators': [ 200, ],\n",
        "              'colsample_bytree' :[  0.3, ],\n",
        "              'eta': [0.2,],\n",
        "             'max_depth': [12]}\n",
        "\n",
        "\n",
        "#Create a based model\n",
        "xgb =     XGBClassifier( random_state=42)\n",
        "\n",
        "grid_search_xgb = GridSearchCV(estimator = xgb, param_grid = param_grid, cv = 5,  verbose = 2,  scoring = 'accuracy')\n",
        "\n",
        "#fitmodel\n",
        "grid_search_xgb .fit(X_train, y_train,  eval_metric = 'mlogloss',)\n",
        "\n",
        "#print best parameters \n",
        "print('Best Score:', grid_search_xgb .best_score_)\n",
        "print('Parameters:', grid_search_xgb .best_params_)\n",
        "print('Best Model:', grid_search_xgb .best_estimator_)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1u8XFWiznFcK",
        "outputId": "f779fb19-3466-4468-ebea-0c6707a45045"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score train: 0.9213367284665082\n",
            "Accuracy score test: 0.8115319865319865\n",
            "\n",
            "                         precision    recall  f1-score   support\n",
            "\n",
            "             functional       0.80      0.91      0.85      6452\n",
            "functional needs repair       0.61      0.27      0.38       863\n",
            "         non functional       0.85      0.78      0.81      4565\n",
            "\n",
            "               accuracy                           0.81     11880\n",
            "              macro avg       0.76      0.65      0.68     11880\n",
            "           weighted avg       0.81      0.81      0.80     11880\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#score model\n",
        "best_model = XGBClassifier( random_state=42, n_estimators = 200, colsample_bytree = 0.3, eta = 0.2, max_depth = 12 )\n",
        "best_model .fit(X_train, y_train,  eval_metric = 'mlogloss',)\n",
        "y_pred = best_model.predict(X_test)\n",
        "y_pred_train = best_model.predict(X_train)\n",
        "\n",
        "\n",
        "#print best model scores on test data\n",
        "print(\"Accuracy score train: {}\".format(accuracy_score(y_train, y_pred_train)))\n",
        "print(\"Accuracy score test: {}\".format(accuracy_score(y_test, y_pred)))\n",
        "print()\n",
        "\n",
        "#print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "confusion_matrix_plotter(y_test, y_pred)\n",
        "\n",
        "feature_importances(best_model, \" XGBoost classifier\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmNk68Funbcn"
      },
      "source": [
        "### 2.4 Catboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tGCAhK_mnfG5"
      },
      "outputs": [],
      "source": [
        "# Create training and test sets: test size 0.2\n",
        "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.2, random_state=42 , stratify=y)\n",
        "\n",
        "\n",
        "param_grid = { 'max_depth': [ 7,8,9],\n",
        "              'iterations': [1000, 750],\n",
        "              'rsm' :[0.3, 0.2]}\n",
        "\n",
        "#Create a based model\n",
        "cat  =     CatBoostClassifier( random_state=42)\n",
        "\n",
        "grid_search_cat = GridSearchCV(estimator = cat, param_grid = param_grid, cv = 5,  verbose = 2,  scoring = 'accuracy')\n",
        "\n",
        "#fitmodel\n",
        "grid_search_cat .fit(X_train, y_train)\n",
        "\n",
        "#print best parameters \n",
        "print('Best Score:', grid_search_cat .best_score_)\n",
        "print('Parameters:', grid_search_cat .best_params_)\n",
        "print('Best Model:', grid_search_cat .best_estimator_)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V-1WSSrAnj3x"
      },
      "outputs": [],
      "source": [
        "#score model\n",
        "best_model = CatBoostClassifier( random_state=42, rsm = 0.2, max_depth =9, iterations = 1000)\n",
        "best_model.fit(X_train, y_train)\n",
        "y_pred = best_model.predict(X_test)\n",
        "y_pred_train = best_model.predict(X_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ghD1NaPvQQ5_",
        "outputId": "eca01e46-4f92-4450-ead5-7fb7f1f2bf45"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score train: 0.8718196931753615\n",
            "Accuracy score test: 0.8038720538720538\n",
            "\n",
            "                         precision    recall  f1-score   support\n",
            "\n",
            "             functional       0.79      0.90      0.85      6452\n",
            "functional needs repair       0.60      0.28      0.39       863\n",
            "         non functional       0.84      0.76      0.80      4565\n",
            "\n",
            "               accuracy                           0.80     11880\n",
            "              macro avg       0.75      0.65      0.68     11880\n",
            "           weighted avg       0.80      0.80      0.79     11880\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "\n",
        "#print best model scores on test data\n",
        "print(\"Accuracy score train: {}\".format(accuracy_score(y_train, y_pred_train)))\n",
        "print(\"Accuracy score test: {}\".format(accuracy_score(y_test, y_pred)))\n",
        "print()\n",
        "\n",
        "#print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "confusion_matrix_plotter(y_test, y_pred)\n",
        "\n",
        "feature_importances(best_model, \" CatBoost classifier\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJn61b07oHcG"
      },
      "source": [
        "### 2.5 Bagging"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZlAJcfB3oPNO"
      },
      "outputs": [],
      "source": [
        "# Create training and test sets: test size 0.2\n",
        "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.2, random_state=42)\n",
        "\n",
        "\n",
        "param_grid = { 'n_estimators': [100, 200, 300, 400, 500, 700, 800, 900, 1000],\n",
        "              'max_features': [0.2, 0.25, 0.3, 0.35, 0.4]}\n",
        "\n",
        "    \n",
        "\n",
        "#Create a based model\n",
        "bag  =      BaggingClassifier( random_state=42, )\n",
        "\n",
        "grid_search_bag = RandomizedSearchCV(estimator = bag, param_distributions = param_grid, cv =5, n_iter = 45,\n",
        "                               verbose = 2, scoring = 'accuracy')\n",
        "\n",
        "#fitmodel\n",
        "grid_search_bag.fit(X_train, y_train )\n",
        "\n",
        "#print best parameters \n",
        "print('Best Score:', grid_search_bag.best_score_)\n",
        "print('Parameters:', grid_search_bag.best_params_)\n",
        "print('Best Model:',grid_search_bag .best_estimator_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "xJnLewHroZxW",
        "outputId": "e11c08ee-c0b8-437b-d108-21374407d9bc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score train: 0.9642879690229171\n",
            "Accuracy score test: 0.8079124579124579\n",
            "\n",
            "                         precision    recall  f1-score   support\n",
            "\n",
            "             functional       0.80      0.91      0.85      6452\n",
            "functional needs repair       0.61      0.26      0.37       863\n",
            "         non functional       0.85      0.77      0.81      4565\n",
            "\n",
            "               accuracy                           0.81     11880\n",
            "              macro avg       0.75      0.65      0.67     11880\n",
            "           weighted avg       0.80      0.81      0.80     11880\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#get scores\n",
        "best_model = BaggingClassifier( random_state=42, n_estimators = 1000, max_features = 0.4 )\n",
        "best_model.fit(X_train, y_train)\n",
        "y_pred = best_model.predict(X_test)\n",
        "y_pred_train = best_model.predict(X_train)\n",
        "\n",
        "\n",
        "#print best model scores on test data\n",
        "print(\"Accuracy score train: {}\".format(accuracy_score(y_train, y_pred_train)))\n",
        "print(\"Accuracy score test: {}\".format(accuracy_score(y_test, y_pred)))\n",
        "print()\n",
        "\n",
        "#print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "confusion_matrix_plotter(y_test, y_pred)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s7JlZb_VpQjH"
      },
      "source": [
        "### 2.6 Mixed vote"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cl0Js8PAowx7"
      },
      "outputs": [],
      "source": [
        "rf = RandomForestClassifier(max_depth=30, max_features='log2', min_samples_split=7, n_estimators=225, random_state=42, warm_start=True)\n",
        "xgb = XGBClassifier(max_depth =12, n_estimators= 200, colsample_bytree = 0.3, eta = 0.2, random_state = 42) \n",
        "cat = CatBoostClassifier(max_depth = 9, rsm = 0.2, iterations = 1000, random_state = 42)\n",
        "bag = BaggingClassifier(max_features=0.4, n_estimators=1000, random_state=42) \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "k89gDzCbpcTL",
        "outputId": "acbb53e1-07c5-430f-cb2d-d28e958d1355"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Learning rate set to 0.096348\n",
            "0:\tlearn: 1.0292113\ttotal: 31.2ms\tremaining: 31.2s\n",
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            "943:\tlearn: 0.3408706\ttotal: 31.7s\tremaining: 1.88s\n",
            "944:\tlearn: 0.3407855\ttotal: 31.7s\tremaining: 1.85s\n",
            "945:\tlearn: 0.3407081\ttotal: 31.8s\tremaining: 1.81s\n",
            "946:\tlearn: 0.3406079\ttotal: 31.8s\tremaining: 1.78s\n",
            "947:\tlearn: 0.3405420\ttotal: 31.8s\tremaining: 1.75s\n",
            "948:\tlearn: 0.3404605\ttotal: 31.9s\tremaining: 1.71s\n",
            "949:\tlearn: 0.3403591\ttotal: 31.9s\tremaining: 1.68s\n",
            "950:\tlearn: 0.3403084\ttotal: 31.9s\tremaining: 1.65s\n",
            "951:\tlearn: 0.3401808\ttotal: 32s\tremaining: 1.61s\n",
            "952:\tlearn: 0.3401005\ttotal: 32s\tremaining: 1.58s\n",
            "953:\tlearn: 0.3400010\ttotal: 32s\tremaining: 1.54s\n",
            "954:\tlearn: 0.3399393\ttotal: 32.1s\tremaining: 1.51s\n",
            "955:\tlearn: 0.3399011\ttotal: 32.1s\tremaining: 1.48s\n",
            "956:\tlearn: 0.3398517\ttotal: 32.1s\tremaining: 1.44s\n",
            "957:\tlearn: 0.3397683\ttotal: 32.2s\tremaining: 1.41s\n",
            "958:\tlearn: 0.3397224\ttotal: 32.2s\tremaining: 1.38s\n",
            "959:\tlearn: 0.3395491\ttotal: 32.3s\tremaining: 1.34s\n",
            "960:\tlearn: 0.3394575\ttotal: 32.3s\tremaining: 1.31s\n",
            "961:\tlearn: 0.3393763\ttotal: 32.3s\tremaining: 1.28s\n",
            "962:\tlearn: 0.3393135\ttotal: 32.4s\tremaining: 1.24s\n",
            "963:\tlearn: 0.3392298\ttotal: 32.4s\tremaining: 1.21s\n",
            "964:\tlearn: 0.3391679\ttotal: 32.4s\tremaining: 1.18s\n",
            "965:\tlearn: 0.3389716\ttotal: 32.5s\tremaining: 1.14s\n",
            "966:\tlearn: 0.3388419\ttotal: 32.5s\tremaining: 1.11s\n",
            "967:\tlearn: 0.3387734\ttotal: 32.5s\tremaining: 1.07s\n",
            "968:\tlearn: 0.3387283\ttotal: 32.5s\tremaining: 1.04s\n",
            "969:\tlearn: 0.3386294\ttotal: 32.6s\tremaining: 1.01s\n",
            "970:\tlearn: 0.3384492\ttotal: 32.6s\tremaining: 974ms\n",
            "971:\tlearn: 0.3383298\ttotal: 32.7s\tremaining: 941ms\n",
            "972:\tlearn: 0.3382491\ttotal: 32.7s\tremaining: 907ms\n",
            "973:\tlearn: 0.3381735\ttotal: 32.7s\tremaining: 873ms\n",
            "974:\tlearn: 0.3381523\ttotal: 32.7s\tremaining: 839ms\n",
            "975:\tlearn: 0.3380598\ttotal: 32.8s\tremaining: 806ms\n",
            "976:\tlearn: 0.3380089\ttotal: 32.8s\tremaining: 772ms\n",
            "977:\tlearn: 0.3378835\ttotal: 32.8s\tremaining: 739ms\n",
            "978:\tlearn: 0.3378322\ttotal: 32.9s\tremaining: 705ms\n",
            "979:\tlearn: 0.3377483\ttotal: 32.9s\tremaining: 672ms\n",
            "980:\tlearn: 0.3377065\ttotal: 32.9s\tremaining: 638ms\n",
            "981:\tlearn: 0.3376108\ttotal: 33s\tremaining: 604ms\n",
            "982:\tlearn: 0.3374613\ttotal: 33s\tremaining: 571ms\n",
            "983:\tlearn: 0.3373672\ttotal: 33s\tremaining: 537ms\n",
            "984:\tlearn: 0.3372678\ttotal: 33.1s\tremaining: 504ms\n",
            "985:\tlearn: 0.3371817\ttotal: 33.1s\tremaining: 470ms\n",
            "986:\tlearn: 0.3370594\ttotal: 33.2s\tremaining: 437ms\n",
            "987:\tlearn: 0.3369473\ttotal: 33.2s\tremaining: 403ms\n",
            "988:\tlearn: 0.3368735\ttotal: 33.2s\tremaining: 369ms\n",
            "989:\tlearn: 0.3367956\ttotal: 33.3s\tremaining: 336ms\n",
            "990:\tlearn: 0.3367449\ttotal: 33.3s\tremaining: 302ms\n",
            "991:\tlearn: 0.3366634\ttotal: 33.3s\tremaining: 269ms\n",
            "992:\tlearn: 0.3365960\ttotal: 33.4s\tremaining: 235ms\n",
            "993:\tlearn: 0.3365822\ttotal: 33.4s\tremaining: 201ms\n",
            "994:\tlearn: 0.3365183\ttotal: 33.4s\tremaining: 168ms\n",
            "995:\tlearn: 0.3364368\ttotal: 33.4s\tremaining: 134ms\n",
            "996:\tlearn: 0.3363302\ttotal: 33.5s\tremaining: 101ms\n",
            "997:\tlearn: 0.3362520\ttotal: 33.5s\tremaining: 67.2ms\n",
            "998:\tlearn: 0.3361416\ttotal: 33.6s\tremaining: 33.6ms\n",
            "999:\tlearn: 0.3360574\ttotal: 33.6s\tremaining: 0us\n",
            "Accuracy score train: 0.9256297481007597\n",
            "Accuracy score test: 0.8160774410774411\n",
            "                         precision    recall  f1-score   support\n",
            "\n",
            "             functional       0.80      0.91      0.85      6452\n",
            "functional needs repair       0.64      0.28      0.39       863\n",
            "         non functional       0.86      0.78      0.82      4565\n",
            "\n",
            "               accuracy                           0.82     11880\n",
            "              macro avg       0.77      0.66      0.69     11880\n",
            "           weighted avg       0.81      0.82      0.81     11880\n",
            "\n"
          ]
        },
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create training and test sets: test size 0.2\n",
        "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)\n",
        "\n",
        "\n",
        "vc = VotingClassifier([('rf', rf), ('xgb', xgb),('cat', cat), ('bag', bag)], voting = 'soft')\n",
        "                       \n",
        "\n",
        "#fit and predict\n",
        "vc.fit(X_train , y_train)\n",
        "pred= vc .predict(X_test)\n",
        "pred_train = vc .predict(X_train)\n",
        "\n",
        "\n",
        "#print best model scores on test data\n",
        "print(\"Accuracy score train: {}\".format(accuracy_score(y_train ,pred_train)))\n",
        "print(\"Accuracy score test: {}\".format(accuracy_score(y_test, pred)))\n",
        "\n",
        "#print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, pred))\n",
        "\n",
        "#heatmap\n",
        "fig, ax = plt.subplots(figsize=(8,5)) \n",
        "data = confusion_matrix(y_test, pred)\n",
        "df_cm = pd.DataFrame(data, columns=np.unique(y_test), index = np.unique(y_test))\n",
        "ax = sns.heatmap(df_cm, cmap='Blues', fmt='g' ,annot=True,annot_kws={\"size\": 14})\n",
        "ax.set_xlabel(\"Predicted\")\n",
        "ax.set_ylabel (\"Actual\")\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
        "ax.set_yticklabels(ax.get_xticklabels(), rotation=0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1pbXJPxMpq3C"
      },
      "source": [
        "###2.7  Xgboost vote"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TGYip9i8pn0e"
      },
      "outputs": [],
      "source": [
        "xgb1 = XGBClassifier(max_depth =12, n_estimators= 200, colsample_bytree = 0.3, eta = 0.2, random_state = 42)\n",
        "xgb2 = XGBClassifier(max_depth =12, n_estimators= 200, colsample_bytree = 0.3, eta = 0.2, random_state = 2) \n",
        "xgb3 = XGBClassifier(max_depth =12, n_estimators= 200, colsample_bytree = 0.3, eta = 0.2, random_state = 142) \n",
        "xgb4 = XGBClassifier(max_depth =12, n_estimators= 200, colsample_bytree = 0.3, eta = 0.2, random_state = 33 )\n",
        "xgb5 = XGBClassifier(max_depth =12, n_estimators= 200, colsample_bytree = 0.3, eta = 0.2, random_state = 678)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "peDD9dFDpnub",
        "outputId": "e9e5df7c-390d-420d-d216-f2d087522ae7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score train: 0.9217155243165892\n",
            "Accuracy score test: 0.8128787878787879\n",
            "                         precision    recall  f1-score   support\n",
            "\n",
            "             functional       0.80      0.91      0.85      6452\n",
            "functional needs repair       0.62      0.28      0.39       863\n",
            "         non functional       0.86      0.77      0.81      4565\n",
            "\n",
            "               accuracy                           0.81     11880\n",
            "              macro avg       0.76      0.66      0.68     11880\n",
            "           weighted avg       0.81      0.81      0.80     11880\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create training and test sets: test size 0.2\n",
        "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)\n",
        "\n",
        "\n",
        "vc = VotingClassifier([('xgb1', xgb1), ('xgb2', xgb2), ('xgb3', xgb3), ('xgb4', xgb4), ('xgb5', xgb5)], voting = 'soft')\n",
        "                       \n",
        "\n",
        "#fit and predict\n",
        "vc.fit(X_train , y_train)\n",
        "pred= vc .predict(X_test)\n",
        "pred_train = vc .predict(X_train)\n",
        "\n",
        "\n",
        "#print best model scores on test data\n",
        "print(\"Accuracy score train: {}\".format(accuracy_score(y_train ,pred_train)))\n",
        "print(\"Accuracy score test: {}\".format(accuracy_score(y_test, pred)))\n",
        "\n",
        "#print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, pred))\n",
        "\n",
        "#heatmap\n",
        "fig, ax = plt.subplots(figsize=(8,5)) \n",
        "data = confusion_matrix(y_test, pred)\n",
        "df_cm = pd.DataFrame(data, columns=np.unique(y_test), index = np.unique(y_test))\n",
        "ax = sns.heatmap(df_cm, cmap='Blues', fmt='g' ,annot=True,annot_kws={\"size\": 14})\n",
        "ax.set_xlabel(\"Predicted\")\n",
        "ax.set_ylabel (\"Actual\")\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
        "ax.set_yticklabels(ax.get_xticklabels(), rotation=0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K25sw2nHp00q"
      },
      "source": [
        "### 2.8 Stacking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ybROFQMpp9Oa",
        "outputId": "883a6f39-7650-4c52-b1d7-0817fdd6770a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaminguitvoer ingekort tot de laatste 5000 regels.\u001b[0m\n",
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            "986:\tlearn: 0.3243649\ttotal: 28.5s\tremaining: 375ms\n",
            "987:\tlearn: 0.3242498\ttotal: 28.5s\tremaining: 346ms\n",
            "988:\tlearn: 0.3241572\ttotal: 28.5s\tremaining: 317ms\n",
            "989:\tlearn: 0.3240241\ttotal: 28.5s\tremaining: 288ms\n",
            "990:\tlearn: 0.3239129\ttotal: 28.6s\tremaining: 260ms\n",
            "991:\tlearn: 0.3238574\ttotal: 28.6s\tremaining: 231ms\n",
            "992:\tlearn: 0.3237549\ttotal: 28.6s\tremaining: 202ms\n",
            "993:\tlearn: 0.3237447\ttotal: 28.7s\tremaining: 173ms\n",
            "994:\tlearn: 0.3236365\ttotal: 28.7s\tremaining: 144ms\n",
            "995:\tlearn: 0.3235374\ttotal: 28.7s\tremaining: 115ms\n",
            "996:\tlearn: 0.3234565\ttotal: 28.7s\tremaining: 86.5ms\n",
            "997:\tlearn: 0.3233730\ttotal: 28.8s\tremaining: 57.7ms\n",
            "998:\tlearn: 0.3232608\ttotal: 28.8s\tremaining: 28.8ms\n",
            "999:\tlearn: 0.3231909\ttotal: 28.8s\tremaining: 0us\n",
            "Accuracy score train: 0.9367621372503631\n",
            "Accuracy score test: 0.8173400673400674\n",
            "                         precision    recall  f1-score   support\n",
            "\n",
            "             functional       0.81      0.91      0.85      6452\n",
            "functional needs repair       0.65      0.27      0.39       863\n",
            "         non functional       0.85      0.79      0.82      4565\n",
            "\n",
            "               accuracy                           0.82     11880\n",
            "              macro avg       0.77      0.66      0.69     11880\n",
            "           weighted avg       0.81      0.82      0.81     11880\n",
            "\n"
          ]
        },
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create training and test sets: test size 0.2\n",
        "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)\n",
        "\n",
        "\n",
        "stack = StackingClassifier([('rf', rf), \n",
        "                              ('xgb', xgb),\n",
        "                              ('cat', cat),\n",
        "                           ('bag', bag)], final_estimator=LogisticRegression())\n",
        "                       \n",
        "\n",
        "#fit and predict\n",
        "stack.fit(X_train , y_train)\n",
        "pred= stack.predict(X_test)\n",
        "pred_train = stack.predict(X_train)\n",
        "\n",
        "\n",
        "#print best model scores on test data\n",
        "print(\"Accuracy score train: {}\".format(accuracy_score(y_train ,pred_train)))\n",
        "print(\"Accuracy score test: {}\".format(accuracy_score(y_test, pred)))\n",
        "\n",
        "\n",
        "#print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, pred))\n",
        "\n",
        "#heatmap\n",
        "fig, ax = plt.subplots(figsize=(8,5)) \n",
        "data = confusion_matrix(y_test, pred)\n",
        "df_cm = pd.DataFrame(data, columns=np.unique(y_test), index = np.unique(y_test))\n",
        "ax = sns.heatmap(df_cm, cmap='Blues', fmt='g' ,annot=True,annot_kws={\"size\": 14})\n",
        "ax.set_xlabel(\"Predicted\")\n",
        "ax.set_ylabel (\"Actual\")\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
        "ax.set_yticklabels(ax.get_xticklabels(), rotation=0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHi3HFAaqEGF"
      },
      "source": [
        "### 2.9 Weighted vote"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oftPIVaXqDqy",
        "outputId": "e56cdc7c-8c66-4a75-bcc3-965386bd13df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Learning rate set to 0.096348\n",
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            "937:\tlearn: 0.3414813\ttotal: 32.9s\tremaining: 2.17s\n",
            "938:\tlearn: 0.3413516\ttotal: 32.9s\tremaining: 2.14s\n",
            "939:\tlearn: 0.3412598\ttotal: 32.9s\tremaining: 2.1s\n",
            "940:\tlearn: 0.3411525\ttotal: 32.9s\tremaining: 2.06s\n",
            "941:\tlearn: 0.3410417\ttotal: 33s\tremaining: 2.03s\n",
            "942:\tlearn: 0.3409718\ttotal: 33s\tremaining: 2s\n",
            "943:\tlearn: 0.3408706\ttotal: 33s\tremaining: 1.96s\n",
            "944:\tlearn: 0.3407855\ttotal: 33.1s\tremaining: 1.93s\n",
            "945:\tlearn: 0.3407081\ttotal: 33.1s\tremaining: 1.89s\n",
            "946:\tlearn: 0.3406079\ttotal: 33.1s\tremaining: 1.85s\n",
            "947:\tlearn: 0.3405420\ttotal: 33.2s\tremaining: 1.82s\n",
            "948:\tlearn: 0.3404605\ttotal: 33.2s\tremaining: 1.78s\n",
            "949:\tlearn: 0.3403591\ttotal: 33.2s\tremaining: 1.75s\n",
            "950:\tlearn: 0.3403084\ttotal: 33.3s\tremaining: 1.71s\n",
            "951:\tlearn: 0.3401808\ttotal: 33.3s\tremaining: 1.68s\n",
            "952:\tlearn: 0.3401005\ttotal: 33.3s\tremaining: 1.64s\n",
            "953:\tlearn: 0.3400010\ttotal: 33.4s\tremaining: 1.61s\n",
            "954:\tlearn: 0.3399393\ttotal: 33.4s\tremaining: 1.57s\n",
            "955:\tlearn: 0.3399011\ttotal: 33.4s\tremaining: 1.54s\n",
            "956:\tlearn: 0.3398517\ttotal: 33.5s\tremaining: 1.5s\n",
            "957:\tlearn: 0.3397683\ttotal: 33.5s\tremaining: 1.47s\n",
            "958:\tlearn: 0.3397224\ttotal: 33.5s\tremaining: 1.43s\n",
            "959:\tlearn: 0.3395491\ttotal: 33.6s\tremaining: 1.4s\n",
            "960:\tlearn: 0.3394575\ttotal: 33.6s\tremaining: 1.36s\n",
            "961:\tlearn: 0.3393763\ttotal: 33.6s\tremaining: 1.33s\n",
            "962:\tlearn: 0.3393135\ttotal: 33.7s\tremaining: 1.29s\n",
            "963:\tlearn: 0.3392298\ttotal: 33.7s\tremaining: 1.26s\n",
            "964:\tlearn: 0.3391679\ttotal: 33.7s\tremaining: 1.22s\n",
            "965:\tlearn: 0.3389716\ttotal: 33.8s\tremaining: 1.19s\n",
            "966:\tlearn: 0.3388419\ttotal: 33.8s\tremaining: 1.15s\n",
            "967:\tlearn: 0.3387734\ttotal: 33.9s\tremaining: 1.12s\n",
            "968:\tlearn: 0.3387283\ttotal: 33.9s\tremaining: 1.08s\n",
            "969:\tlearn: 0.3386294\ttotal: 33.9s\tremaining: 1.05s\n",
            "970:\tlearn: 0.3384492\ttotal: 34s\tremaining: 1.01s\n",
            "971:\tlearn: 0.3383298\ttotal: 34s\tremaining: 979ms\n",
            "972:\tlearn: 0.3382491\ttotal: 34s\tremaining: 944ms\n",
            "973:\tlearn: 0.3381735\ttotal: 34.1s\tremaining: 909ms\n",
            "974:\tlearn: 0.3381523\ttotal: 34.1s\tremaining: 874ms\n",
            "975:\tlearn: 0.3380598\ttotal: 34.1s\tremaining: 839ms\n",
            "976:\tlearn: 0.3380089\ttotal: 34.1s\tremaining: 804ms\n",
            "977:\tlearn: 0.3378835\ttotal: 34.2s\tremaining: 769ms\n",
            "978:\tlearn: 0.3378322\ttotal: 34.2s\tremaining: 734ms\n",
            "979:\tlearn: 0.3377483\ttotal: 34.2s\tremaining: 699ms\n",
            "980:\tlearn: 0.3377065\ttotal: 34.3s\tremaining: 664ms\n",
            "981:\tlearn: 0.3376108\ttotal: 34.3s\tremaining: 629ms\n",
            "982:\tlearn: 0.3374613\ttotal: 34.3s\tremaining: 594ms\n",
            "983:\tlearn: 0.3373672\ttotal: 34.4s\tremaining: 559ms\n",
            "984:\tlearn: 0.3372678\ttotal: 34.4s\tremaining: 524ms\n",
            "985:\tlearn: 0.3371817\ttotal: 34.4s\tremaining: 489ms\n",
            "986:\tlearn: 0.3370594\ttotal: 34.5s\tremaining: 454ms\n",
            "987:\tlearn: 0.3369473\ttotal: 34.5s\tremaining: 419ms\n",
            "988:\tlearn: 0.3368735\ttotal: 34.5s\tremaining: 384ms\n",
            "989:\tlearn: 0.3367956\ttotal: 34.6s\tremaining: 349ms\n",
            "990:\tlearn: 0.3367449\ttotal: 34.6s\tremaining: 314ms\n",
            "991:\tlearn: 0.3366634\ttotal: 34.6s\tremaining: 279ms\n",
            "992:\tlearn: 0.3365960\ttotal: 34.7s\tremaining: 244ms\n",
            "993:\tlearn: 0.3365822\ttotal: 34.7s\tremaining: 209ms\n",
            "994:\tlearn: 0.3365183\ttotal: 34.7s\tremaining: 175ms\n",
            "995:\tlearn: 0.3364368\ttotal: 34.8s\tremaining: 140ms\n",
            "996:\tlearn: 0.3363302\ttotal: 34.8s\tremaining: 105ms\n",
            "997:\tlearn: 0.3362520\ttotal: 34.8s\tremaining: 69.8ms\n",
            "998:\tlearn: 0.3361416\ttotal: 34.9s\tremaining: 34.9ms\n",
            "999:\tlearn: 0.3360574\ttotal: 34.9s\tremaining: 0us\n",
            "Accuracy score train: 0.9242197857699026\n",
            "Accuracy score test: 0.8159090909090909\n",
            "                         precision    recall  f1-score   support\n",
            "\n",
            "             functional       0.80      0.91      0.85      6452\n",
            "functional needs repair       0.64      0.28      0.39       863\n",
            "         non functional       0.86      0.78      0.82      4565\n",
            "\n",
            "               accuracy                           0.82     11880\n",
            "              macro avg       0.77      0.66      0.69     11880\n",
            "           weighted avg       0.81      0.82      0.81     11880\n",
            "\n"
          ]
        },
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create training and test sets: test size 0.2\n",
        "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)\n",
        "\n",
        "\n",
        "vc = VotingClassifier([('rf', rf), ('xgb', xgb),('cat', cat), ('bag', bag)], voting = 'soft', weights = [0.85, 1, 0.85, 0.75])\n",
        "                       \n",
        "\n",
        "#fit and predict\n",
        "vc.fit(X_train , y_train)\n",
        "pred= vc .predict(X_test)\n",
        "pred_train = vc .predict(X_train)\n",
        "\n",
        "\n",
        "#print best model scores on test data\n",
        "print(\"Accuracy score train: {}\".format(accuracy_score(y_train ,pred_train)))\n",
        "print(\"Accuracy score test: {}\".format(accuracy_score(y_test, pred)))\n",
        "\n",
        "#print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, pred))\n",
        "\n",
        "#heatmap\n",
        "fig, ax = plt.subplots(figsize=(8,5)) \n",
        "data = confusion_matrix(y_test, pred)\n",
        "df_cm = pd.DataFrame(data, columns=np.unique(y_test), index = np.unique(y_test))\n",
        "ax = sns.heatmap(df_cm, cmap='Blues', fmt='g' ,annot=True,annot_kws={\"size\": 14})\n",
        "ax.set_xlabel(\"Predicted\")\n",
        "ax.set_ylabel (\"Actual\")\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
        "ax.set_yticklabels(ax.get_xticklabels(), rotation=0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DG3xN8DDqPPR"
      },
      "outputs": [],
      "source": [
        "#fit on foll training set\n",
        "vc.fit(X , y)\n",
        "\n",
        "#predict\n",
        "submission = test_set.drop(['id', 'status_group'], axis=1)\n",
        "submission['status_group'] = vc.predict(submission)\n",
        "\n",
        "#bring the id column back\n",
        "submission['id'] = test_set['id']\n",
        "\n",
        "#create df for submission and save\n",
        "best_submission = submission[['id', 'status_group']]\n",
        "best_submission.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "AtjUALccDsbH",
        "outputId": "6da35dd5-c794-4c99-81c6-671778b74b3e"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "          id    status_group\n",
              "59399  50785  non functional\n",
              "59400  51630      functional\n",
              "59401  17168      functional\n",
              "59402  45559  non functional\n",
              "59403  49871      functional"
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      "source": [
        "best_submission.head()"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lUnNPaPtr7EG"
      },
      "outputs": [],
      "source": [
        "#best_submission.to_csv('best_submission.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zmhdyxrUqfXr"
      },
      "source": [
        "# 3 Result overview"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mJvTaLlFsT8m",
        "outputId": "82f654f7-f6ce-47b0-d2ce-414574e3220c"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Train accuracy</th>\n",
              "      <th>Test accuracy</th>\n",
              "      <th>Competition accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.9219</td>\n",
              "      <td>0.8131</td>\n",
              "      <td>0.8133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>XgBoost Classifier</td>\n",
              "      <td>0.9501</td>\n",
              "      <td>0.8110</td>\n",
              "      <td>0.8173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CatBoost Classifier</td>\n",
              "      <td>0.8721</td>\n",
              "      <td>0.8034</td>\n",
              "      <td>0.8125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bagging Classifier</td>\n",
              "      <td>0.8741</td>\n",
              "      <td>0.8667</td>\n",
              "      <td>0.8150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mixed Voting Classifier</td>\n",
              "      <td>0.9358</td>\n",
              "      <td>0.8160</td>\n",
              "      <td>0.8234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>XGBoost Voting Classifier</td>\n",
              "      <td>0.9509</td>\n",
              "      <td>0.8131</td>\n",
              "      <td>0.8205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Stacking Classifier</td>\n",
              "      <td>0.9384</td>\n",
              "      <td>0.8160</td>\n",
              "      <td>0.8226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Weighted Voting Classifier</td>\n",
              "      <td>0.9356</td>\n",
              "      <td>0.8150</td>\n",
              "      <td>0.8235</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          const element = document.querySelector('#df-27f6b63d-ceca-48fe-be44-e6d5cb57f3c5');\n",
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              "            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",
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              "  "
            ],
            "text/plain": [
              "                        Model  ...  Competition accuracy\n",
              "0    Random Forest Classifier  ...                0.8133\n",
              "1          XgBoost Classifier  ...                0.8173\n",
              "2         CatBoost Classifier  ...                0.8125\n",
              "3          Bagging Classifier  ...                0.8150\n",
              "4     Mixed Voting Classifier  ...                0.8234\n",
              "5   XGBoost Voting Classifier  ...                0.8205\n",
              "6         Stacking Classifier  ...                0.8226\n",
              "7  Weighted Voting Classifier  ...                0.8235\n",
              "\n",
              "[8 rows x 4 columns]"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "models = ['Random Forest Classifier', 'XgBoost Classifier', 'CatBoost Classifier', 'Bagging Classifier', 'Mixed Voting Classifier','XGBoost Voting Classifier', 'Stacking Classifier', 'Weighted Voting Classifier']\n",
        "training_accuracy = [0.9219, 0.9501, 0.8721, 0.8741, 0.9358, 0.9509, 0.9384, 0.9356]\n",
        "testing_accuracy = [0.8131, 0.8110, 0.8034, 0.8667, 0.8160, 0.8131, 0.8160, 0.8150]\n",
        "competition_accuracy = [0.8133, 0.8173, 0.8125, 0.8150, 0.8234,  0.8205, 0.8226, 0.8235  ]\n",
        "\n",
        "d = {'Model':models,'Train accuracy':training_accuracy, 'Test accuracy':testing_accuracy, 'Competition accuracy': competition_accuracy}   \n",
        "score_df = pd.DataFrame(d)\n",
        "score_df "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1hYf2D7xu-8"
      },
      "source": [
        "![submission.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ELa8pwTsz07"
      },
      "source": [
        "Disclaimer: I did all my modelling in Databricks and transfered by code to Google Colab. The results may therefore vary slightly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jP5fq6bIYNRm"
      },
      "source": [
        "# 4 SHAP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ed8oMXLMEjmh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "faf7eb0c-aea8-4c5a-c131-eff7d0bbb882"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x684 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df=train_set.copy()\n",
        "\n",
        "#calcualte how many samples of each status group I need\n",
        "required_samples = len(df)*0.4  #40%\n",
        "functional_samples = round(required_samples* 0.543081)\n",
        "non_functional_samples = round(required_samples * 0.384242)\n",
        "repair_samples = round(required_samples * 0.072677)\n",
        "\n",
        "#create sepperate df for each status group\n",
        "functional = df[df['status_group']=='functional']\n",
        "non_functional = df[df['status_group']=='non functional']\n",
        "repair = df[df['status_group']=='functional needs repair']\n",
        "\n",
        "#create a random sample of each rstatus group\n",
        "sample_functional = functional.sample(n = functional_samples, random_state=1, )\n",
        "sample_non_functional = non_functional.sample(n = non_functional_samples, random_state=1, )\n",
        "sample_repair = repair.sample(n = repair_samples, random_state=1, )\n",
        "\n",
        "#combine the random samples to a single df\n",
        "sample = sample_functional.append(sample_non_functional)\n",
        "sample = sample.append(sample_repair)\n",
        "\n",
        "#define X and y for training the model\n",
        "X= sample.drop(['id', 'status_group'], axis=1)\n",
        "y = sample['status_group']\n",
        "\n",
        "#define and fit the model\n",
        "rf = RandomForestClassifier(max_depth=30, max_features='log2', min_samples_split=7, n_estimators=225, random_state=42, warm_start=True)\n",
        "rf.fit(X, y)\n",
        "\n",
        "#calculate SHAP\n",
        "explainer = shap.TreeExplainer(rf)\n",
        "shap_values_train = explainer.shap_values(X)\n",
        "\n",
        "#plot SHAP\n",
        "shap.summary_plot(shap_values_train, X_train.values, plot_type=\"bar\",  feature_names = X.columns)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "T9fm-ywPNNAe"
      },
      "execution_count": 10,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "waterpumps -modelling.ipynb",
      "provenance": [],
      "mount_file_id": "15zvYPlJaP-TrEfDXMa5cqDuurqA-2X_G",
      "authorship_tag": "ABX9TyMYC7J1eRZYwE0HcbL+C9Iz",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}