Modelling_Log_RE.ipynb

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  "metadata": {
    "colab": {
      "name": "Modelling_Log_RE.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "mount_file_id": "1dA5PJ_LNxcYILx5Agss8Hv6ES2IrZXwu",
      "authorship_tag": "ABX9TyOPPDX1Cq5WFHt5llncLPmI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashishthomaschempolil/Santander-Customer-Satisfaction/blob/new_1/Modelling_Log_RE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVQr_7USbczr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(palette='muted',style='whitegrid')\n",
        "import sklearn\n",
        "from sklearn import metrics,ensemble,model_selection,linear_model,tree,calibration\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb\n",
        "import scipy\n",
        "import random\n",
        "from tqdm import tqdm\n",
        "import os\n",
        "np.random.seed(13154)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRB4PxAYa5xu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "4e6fb73c-9c2c-40f3-a785-529fdd3f98b3"
      },
      "source": [
        "file = [k for k in os.listdir('/content/drive/My Drive/Santander_Customer_Satisfaction') if (('train' in k)|('test' in k))&('X' not in k)] #getting all the necessary files for modelling\n",
        "file"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['train_Normal.pkl',\n",
              " 'test_Normal.pkl',\n",
              " 'train_Normal with Response encoding.pkl',\n",
              " 'test_Normal with Response encoding.pkl',\n",
              " 'train_Normal with One Hot Encoding.pkl',\n",
              " 'test_Normal with One Hot Encoding.pkl',\n",
              " 'train_Log Transformed.pkl',\n",
              " 'test_Log Transformed.pkl',\n",
              " 'train_Log Transformed with Response encoding.pkl',\n",
              " 'test_Log Transformed with Response encoding.pkl',\n",
              " 'train_Log Transformed with One Hot Encoding.pkl',\n",
              " 'test_Log Transformed with One Hot Encoding.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mV0hmG66S7ZC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f2fbda83-2ca8-458e-df39-f3ee65c2c88a"
      },
      "source": [
        "file_path = '/content/drive/My Drive/Santander_Customer_Satisfaction/'\n",
        "train_file = file_path+'train_Log Transformed with Response encoding.pkl' #log transformed\n",
        "test_file = file_path+'test_Log Transformed with Response encoding.pkl'\n",
        "dataset= \"Log Transformed RE\"\n",
        "train = pd.read_pickle(train_file)\n",
        "test = pd.read_pickle(test_file)\n",
        "X_train = train.drop(['ID','TARGET'],axis=1)\n",
        "y_train = train['TARGET'].values\n",
        "X_test = test.drop('ID',axis=1)\n",
        "del train,test\n",
        "X_train,X_val,y_train,y_val = model_selection.train_test_split(X_train,y_train,stratify=y_train,test_size=0.15)\n",
        "X_train.shape,X_val.shape,X_test.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((64617, 374), (11403, 374), (75818, 374))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4yLi9O_CtFR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "global i\n",
        "i=0\n",
        "def plot_auc(y_true,y_pred,label,dataset=dataset):\n",
        "  \"\"\"\n",
        "  Plots auc curve when y_true and y_pred are given\n",
        "  dataset is string which tells which dataset were used.\n",
        "  label: tells which model was used\n",
        "  If label is a list plots all the auc curve for all labels (here y_pred will be a list of lists )\n",
        "  \"\"\"\n",
        "  \n",
        "  \n",
        "  if (type(label)!=list) & (type(label)!=np.array):\n",
        "    print(\"\\t\\t %s on %s dataset \\t\\t \\n\"%(label,dataset))\n",
        "    fpr,tpr,threshold = metrics.roc_curve(y_true,y_pred) #getting true positive rate, false positive rate\n",
        "    auc = metrics.roc_auc_score(y_true,y_pred) #getting auc score\n",
        "    log_loss = metrics.log_loss(y_true,y_pred) #getting logloss\n",
        "    label_1 = label+' AUC=%.3f'%(auc)\n",
        "\n",
        "    #plotting tpr vs fpr\n",
        "    sns.lineplot(fpr,tpr,label=label_1)\n",
        "    x=np.arange(0,1.1,0.1) #auc=0.5 line \n",
        "    sns.lineplot(x,x,label=\"AUC=0.5\")\n",
        "    plt.title(\"ROC on %s dataset\"%(dataset))\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel(\"True Positive Rate\")\n",
        "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) #legend box outside graph\n",
        "    plt.show()\n",
        "    print(\"The log loss and auc was found to be %.3f and %.3f for %s on %s dataset.\"%(log_loss,auc,label,dataset))\n",
        "    #creating a dataframe of the result\n",
        "    result_dict = {\"Model\":label,'Dataset':dataset,'log_loss':log_loss,'auc':auc}\n",
        "    \n",
        "    return pd.DataFrame(result_dict,index=[i])\n",
        "\n",
        "  else:\n",
        "    plt.figure(figsize=(12,8))\n",
        "    for k,y in enumerate(y_pred):\n",
        "      fpr,tpr,threshold = metrics.roc_curve(y_true,y)\n",
        "      auc = metrics.roc_auc_score(y_true,y)\n",
        "      label_ = label[k]+' AUC=%.3f'%(auc)\n",
        "      #plotting tpr vs fpr\n",
        "      sns.lineplot(fpr,tpr,label=label_)\n",
        "\n",
        "    x=np.arange(0,1.1,0.1)\n",
        "    sns.lineplot(x,x,label=\"AUC=0.5\")\n",
        "    plt.title(\"Combined ROC\")\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel(\"True Positive Rate\")\n",
        "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) #legend box outside graph\n",
        "    plt.show()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spCK7X7beIiy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def find_best_params(model,params,cv=10,n_jobs=-1,X_train=X_train):\n",
        "  \"\"\"\n",
        "  Funcion which implements Random Seacrh CV and returns best model\n",
        "  \"\"\"\n",
        "  random_cv = model_selection.RandomizedSearchCV(model,param_distributions=params,scoring='roc_auc',n_jobs=n_jobs,cv=cv,verbose=2,random_state=42)\n",
        "  random_cv.fit(X_train,y_train)\n",
        "  print(\"The best auc score was %.3f\"%(random_cv.best_score_))\n",
        "  print(\"The best params were: %s\"%(random_cv.best_params_))\n",
        "  return random_cv.best_estimator_"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Mho8KPmabxm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_feature_importance(df,feat_imp,top=20,size=(6,6)):\n",
        "    \"\"\"\n",
        "    getting most important top features if df and feature_importance array is passed\n",
        "    \"\"\"\n",
        "    top_indices = np.argsort(feat_imp)[::-1][:top] #getting the indices with top feature importace\n",
        "    most_important_feat = df.columns[top_indices] #getiing the columns names\n",
        "    plt.figure(figsize=size)\n",
        "    sns.barplot(x=feat_imp[top_indices],y=most_important_feat) #plotting feature importance \n",
        "    plt.title('Feature Importance')\n",
        "    plt.xlabel('Importance')\n",
        "    plt.ylabel(\"Feature names\")\n",
        "    plt.show()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VaQtOvkvfNeO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_top_features(df,feat_imp,top=50):\n",
        "  \"\"\"\n",
        "  gets the top features.\n",
        "  if top is of integer dtype it will return top features,\n",
        "  if top is between 0 and 1, it will return features that have at least feature importance value= top\n",
        "  \"\"\"\n",
        "  if (top>0.)&(top<=1.): #getting the features that have feature importance value greater than top\n",
        "    feature_to_consider = [1 if i>=top else 0 for i in feat_imp]\n",
        "    most_important_feat = [i  for i,j in zip(df.columns,feature_to_consider) if (j==1)]#getiing the columns names\n",
        "  else:\n",
        "    top_indices = np.argsort(feat_imp)[::-1][:top] #getting the indices with top feature importace\n",
        "    most_important_feat = df.columns[top_indices] #getiing the columns names\n",
        "  return most_important_feat    "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vvLQsSsVAsUA",
        "colab_type": "text"
      },
      "source": [
        "# Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLh2j2XvAxeO",
        "colab_type": "text"
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nsbNtvxAYB2O",
        "colab": {}
      },
      "source": [
        "model = linear_model.LogisticRegression(class_weight='balanced')\n",
        "params = {'penalty':['l2','l1'], 'C':[10.**i for i in np.arange(-3,3,1)], \n",
        "          'fit_intercept':[True,False] ,\n",
        "          }\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mXTZa5oPYB2R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "68fd55a6-301a-40e6-defc-2e053a43a17f"
      },
      "source": [
        "%%time\n",
        "find_best_params(model,params)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:  2.3min\n",
            "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  2.5min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The best auc score was 0.811\n",
            "The best params were: {'penalty': 'l2', 'fit_intercept': True, 'C': 0.001}\n",
            "CPU times: user 9.58 s, sys: 1.18 s, total: 10.8 s\n",
            "Wall time: 2min 32s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.001, class_weight='balanced', dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eYp1oCU5YB2U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "732f631f-bd0a-4268-d10b-74198e9bf0f7"
      },
      "source": [
        "model = linear_model.LogisticRegression(C=0.001, class_weight='balanced', dual=False,\n",
        "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
        "                   max_iter=100, multi_class='auto', n_jobs=-1, penalty='l2',\n",
        "                    solver='lbfgs', tol=0.0001, verbose=0,\n",
        "                   warm_start=False)\n",
        "\n",
        "model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.001, class_weight='balanced', dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=-1, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DlDUHTI-YB2X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "3f7d57b1-c7cf-4060-fc80-33c5eea9e2f7"
      },
      "source": [
        "labels=[] #storing all labels\n",
        "y_preds = [] #storing all y_pred\n",
        "y_pred = model.predict_proba(X_val)[:,1]\n",
        "label=\"Logisic Regression\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "result = plot_auc(y_val,y_pred,label,dataset)\n",
        "result_df = result\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t Logisic Regression on Log Transformed RE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.524 and 0.824 for Logisic Regression on Log Transformed RE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hh6_qnWsBOVa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "fc0b1b84-df13-4f56-f368-826cfaf95327"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.524004</td>\n",
              "      <td>0.823513</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Model             Dataset  log_loss       auc\n",
              "0  Logisic Regression  Log Transformed RE  0.524004  0.823513"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0y9PvYUNX-t2",
        "colab_type": "text"
      },
      "source": [
        "# Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kVyDscneGv3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tree.DecisionTreeClassifier(class_weight='balanced')\n",
        "params = { \n",
        "          'max_depth':[10,100,500,1000,-1], \n",
        "         'min_samples_split':[2,5,10,100,500], \n",
        "          'min_samples_leaf':[1,3,5,7,10],\n",
        "          'max_leaf_nodes':[100,500,1000,5000,-1] \n",
        "          }\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWmU9ZdnjHEk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "c0af7a0c-17bc-499e-a99c-5e63bc728c3b"
      },
      "source": [
        "%%time\n",
        "find_best_params(model,params)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:  3.0min\n",
            "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  5.8min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The best auc score was 0.780\n",
            "The best params were: {'min_samples_split': 2, 'min_samples_leaf': 10, 'max_leaf_nodes': 100, 'max_depth': 100}\n",
            "CPU times: user 5.49 s, sys: 345 ms, total: 5.83 s\n",
            "Wall time: 5min 53s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
              "                       max_depth=100, max_features=None, max_leaf_nodes=100,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=10, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11n9ZcTNirnF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "da742441-62ca-4899-b984-a2882494fd74"
      },
      "source": [
        "model = tree.DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
        "                       max_depth=1000, max_features=None, max_leaf_nodes=100,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=1, min_samples_split=500,\n",
        "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
        "                      splitter='best')\n",
        "\n",
        "model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
              "                       max_depth=1000, max_features=None, max_leaf_nodes=100,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqO7PiHUhFIR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "02a0d793-b3e2-417e-c237-0ca23acbf083"
      },
      "source": [
        "cc_model = calibration.CalibratedClassifierCV(model,cv='prefit')\n",
        "cc_model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CalibratedClassifierCV(base_estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n",
              "                                                             class_weight='balanced',\n",
              "                                                             criterion='gini',\n",
              "                                                             max_depth=1000,\n",
              "                                                             max_features=None,\n",
              "                                                             max_leaf_nodes=100,\n",
              "                                                             min_impurity_decrease=0.0,\n",
              "                                                             min_impurity_split=None,\n",
              "                                                             min_samples_leaf=1,\n",
              "                                                             min_samples_split=500,\n",
              "                                                             min_weight_fraction_leaf=0.0,\n",
              "                                                             presort='deprecated',\n",
              "                                                             random_state=None,\n",
              "                                                             splitter='best'),\n",
              "                       cv='prefit', method='sigmoid')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CIxYiOfoih44",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "6e35bd48-afbc-4b70-cdd3-ddfc199415ea"
      },
      "source": [
        "i+=1\n",
        "y_pred = cc_model.predict_proba(X_val)[:,1]\n",
        "label=\"Decision Tree Classifier\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t Decision Tree Classifier on Log Transformed RE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.142 and 0.803 for Decision Tree Classifier on Log Transformed RE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qusVRO8HdpHW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "099d51b0-4d0c-4b4b-c190-ce7b82332532"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.524004</td>\n",
              "      <td>0.823513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.141823</td>\n",
              "      <td>0.803198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Model             Dataset  log_loss       auc\n",
              "0        Logisic Regression  Log Transformed RE  0.524004  0.823513\n",
              "1  Decision Tree Classifier  Log Transformed RE  0.141823  0.803198"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "msPtDUHT0sQN"
      },
      "source": [
        "# Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OwHc1zRG0sQP",
        "colab": {}
      },
      "source": [
        "model = ensemble.RandomForestClassifier(class_weight='balanced')\n",
        "params = { 'n_estimators':[1000,2000],\n",
        "          'max_depth':[500,1000], \n",
        "         'min_samples_split':[100,500],\n",
        "          'min_samples_leaf':[3,5],\n",
        "          'max_leaf_nodes':[100,250] \n",
        "          }\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zDOPEuJy0sQS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "2cc20045-9a16-416a-9c87-4fad9ba6b72a"
      },
      "source": [
        "%%time\n",
        "find_best_params(model,params,cv=3)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed: 80.8min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The best auc score was 0.820\n",
            "The best params were: {'n_estimators': 1000, 'min_samples_split': 500, 'min_samples_leaf': 5, 'max_leaf_nodes': 250, 'max_depth': 1000}\n",
            "CPU times: user 3min 20s, sys: 595 ms, total: 3min 20s\n",
            "Wall time: 1h 24min 8s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=1000, max_features='auto',\n",
              "                       max_leaf_nodes=250, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=5, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RBeRKL7R0sQW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "df9438fe-0dbb-4b9f-9803-2eae41f7dded"
      },
      "source": [
        "model = ensemble.RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
        "                       criterion='gini', max_depth=500, max_features='auto',\n",
        "                       max_leaf_nodes=250, max_samples=None,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=3, min_samples_split=500,\n",
        "                       min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
        "                       n_jobs=-1, oob_score=False,\n",
        "                       verbose=0, warm_start=False)\n",
        "\n",
        "%time model.fit(X_train,y_train)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 10min 16s, sys: 2 s, total: 10min 18s\n",
            "Wall time: 5min 17s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=500, max_features='auto',\n",
              "                       max_leaf_nodes=250, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=3, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
              "                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oJLF56Ts0sQY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "54427530-a463-430f-8aaf-c241533e50d5"
      },
      "source": [
        "cc_model = calibration.CalibratedClassifierCV(model,cv='prefit')\n",
        "cc_model.fit(X_train,y_train)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CalibratedClassifierCV(base_estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                             ccp_alpha=0.0,\n",
              "                                                             class_weight='balanced',\n",
              "                                                             criterion='gini',\n",
              "                                                             max_depth=500,\n",
              "                                                             max_features='auto',\n",
              "                                                             max_leaf_nodes=250,\n",
              "                                                             max_samples=None,\n",
              "                                                             min_impurity_decrease=0.0,\n",
              "                                                             min_impurity_split=None,\n",
              "                                                             min_samples_leaf=3,\n",
              "                                                             min_samples_split=500,\n",
              "                                                             min_weight_fraction_leaf=0.0,\n",
              "                                                             n_estimators=2000,\n",
              "                                                             n_jobs=-1,\n",
              "                                                             oob_score=False,\n",
              "                                                             random_state=None,\n",
              "                                                             verbose=0,\n",
              "                                                             warm_start=False),\n",
              "                       cv='prefit', method='sigmoid')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4Jct69qq0sQa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "bcaca040-2b00-4465-b87b-43818e43e67d"
      },
      "source": [
        "i+=1\n",
        "y_pred = cc_model.predict_proba(X_val)[:,1]\n",
        "label=\"Random Forest Classifier\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "result = plot_auc(y_val,y_pred,label,dataset)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t Random Forest Classifier on Log Transformed RE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.137 and 0.836 for Random Forest Classifier on Log Transformed RE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0igsmQs_0sQd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "f84bbae5-0632-43c3-8f4c-f18f6ac4f366"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.524004</td>\n",
              "      <td>0.823513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.141823</td>\n",
              "      <td>0.803198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.136824</td>\n",
              "      <td>0.836372</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Model             Dataset  log_loss       auc\n",
              "0        Logisic Regression  Log Transformed RE  0.524004  0.823513\n",
              "1  Decision Tree Classifier  Log Transformed RE  0.141823  0.803198\n",
              "2  Random Forest Classifier  Log Transformed RE  0.136824  0.836372"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZqVnGrga8Tu",
        "colab_type": "text"
      },
      "source": [
        "# Selecting top features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFB132azbBQS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "outputId": "1467a10b-8979-4224-a5f8-56b71b3492c1"
      },
      "source": [
        "plot_feature_importance(X_train,model.feature_importances_,top=50,size=(7,12))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XxwkRX5gnXWi",
        "colab_type": "text"
      },
      "source": [
        "We will create new datasets with less features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfArYVshrZNh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c944d694-5d78-4fd4-f906-e55f34fddc14"
      },
      "source": [
        "#getting top k features\n",
        "top=250\n",
        "features = get_top_features(X_train,model.feature_importances_,top=top)\n",
        "print(\"Taking top %i features\"%(top))\n",
        "X_train_200 = X_train[features].copy()\n",
        "X_val_200 = X_val[features].copy()\n",
        "X_test_200 = X_test[features].copy()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Taking top 250 features\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xA9bSzUARRZ_"
      },
      "source": [
        "# XGB Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3UWwclfyRRaC",
        "colab": {}
      },
      "source": [
        "model = xgb.XGBClassifier(n_jobs=-1,\n",
        "                      nthread=-1,\n",
        "                      scale_pos_weight=1,\n",
        "                      learning_rate=0.02,  \n",
        "                      colsample_bytree = 0.5,\n",
        "                      subsample = 0.9,\n",
        "                      objective='binary:logistic', \n",
        "                      n_estimators=665,\n",
        "                      reg_alpha = 0.3,\n",
        "                      max_depth=5\n",
        "                     )\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0CEyW37Dai9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b0bc9be8-6b2f-4b7c-d9ff-0a8d2aa54c4e"
      },
      "source": [
        "#https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n",
        "\"\"\"\n",
        "manually changed each parameters 1 by 1 and compared the validation auc\n",
        "and selected the best parameter value\n",
        "\"\"\"\n",
        "eval_metric = ['auc','error']\n",
        "%time model.fit(X_train,y_train,eval_set=[(X_val,y_val)],eval_metric=eval_metric,verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\tvalidation_0-auc:0.809043\tvalidation_0-error:0.039639\n",
            "[1]\tvalidation_0-auc:0.813614\tvalidation_0-error:0.039551\n",
            "[2]\tvalidation_0-auc:0.814948\tvalidation_0-error:0.039551\n",
            "[3]\tvalidation_0-auc:0.817204\tvalidation_0-error:0.039551\n",
            "[4]\tvalidation_0-auc:0.816977\tvalidation_0-error:0.039551\n",
            "[5]\tvalidation_0-auc:0.821357\tvalidation_0-error:0.039551\n",
            "[6]\tvalidation_0-auc:0.81882\tvalidation_0-error:0.039551\n",
            "[7]\tvalidation_0-auc:0.818706\tvalidation_0-error:0.039551\n",
            "[8]\tvalidation_0-auc:0.817699\tvalidation_0-error:0.039551\n",
            "[9]\tvalidation_0-auc:0.820328\tvalidation_0-error:0.039551\n",
            "[10]\tvalidation_0-auc:0.818581\tvalidation_0-error:0.039551\n",
            "[11]\tvalidation_0-auc:0.821256\tvalidation_0-error:0.039551\n",
            "[12]\tvalidation_0-auc:0.82104\tvalidation_0-error:0.039551\n",
            "[13]\tvalidation_0-auc:0.821938\tvalidation_0-error:0.039551\n",
            "[14]\tvalidation_0-auc:0.822759\tvalidation_0-error:0.039551\n",
            "[15]\tvalidation_0-auc:0.824394\tvalidation_0-error:0.039551\n",
            "[16]\tvalidation_0-auc:0.823384\tvalidation_0-error:0.039551\n",
            "[17]\tvalidation_0-auc:0.824087\tvalidation_0-error:0.039551\n",
            "[18]\tvalidation_0-auc:0.824405\tvalidation_0-error:0.039551\n",
            "[19]\tvalidation_0-auc:0.824988\tvalidation_0-error:0.039551\n",
            "[20]\tvalidation_0-auc:0.824997\tvalidation_0-error:0.039551\n",
            "[21]\tvalidation_0-auc:0.825122\tvalidation_0-error:0.039551\n",
            "[22]\tvalidation_0-auc:0.824913\tvalidation_0-error:0.039551\n",
            "[23]\tvalidation_0-auc:0.825648\tvalidation_0-error:0.039551\n",
            "[24]\tvalidation_0-auc:0.82563\tvalidation_0-error:0.039551\n",
            "[25]\tvalidation_0-auc:0.825783\tvalidation_0-error:0.039551\n",
            "[26]\tvalidation_0-auc:0.826022\tvalidation_0-error:0.039551\n",
            "[27]\tvalidation_0-auc:0.826172\tvalidation_0-error:0.039551\n",
            "[28]\tvalidation_0-auc:0.826456\tvalidation_0-error:0.039551\n",
            "[29]\tvalidation_0-auc:0.826012\tvalidation_0-error:0.039551\n",
            "[30]\tvalidation_0-auc:0.82597\tvalidation_0-error:0.039551\n",
            "[31]\tvalidation_0-auc:0.826461\tvalidation_0-error:0.039551\n",
            "[32]\tvalidation_0-auc:0.825978\tvalidation_0-error:0.039551\n",
            "[33]\tvalidation_0-auc:0.826409\tvalidation_0-error:0.039551\n",
            "[34]\tvalidation_0-auc:0.826334\tvalidation_0-error:0.039551\n",
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            "[624]\tvalidation_0-auc:0.846695\tvalidation_0-error:0.039551\n",
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            "[627]\tvalidation_0-auc:0.846702\tvalidation_0-error:0.039551\n",
            "[628]\tvalidation_0-auc:0.846691\tvalidation_0-error:0.039551\n",
            "[629]\tvalidation_0-auc:0.846642\tvalidation_0-error:0.039551\n",
            "[630]\tvalidation_0-auc:0.846613\tvalidation_0-error:0.039551\n",
            "[631]\tvalidation_0-auc:0.846576\tvalidation_0-error:0.039551\n",
            "[632]\tvalidation_0-auc:0.846566\tvalidation_0-error:0.039551\n",
            "[633]\tvalidation_0-auc:0.846543\tvalidation_0-error:0.039551\n",
            "[634]\tvalidation_0-auc:0.846546\tvalidation_0-error:0.039551\n",
            "[635]\tvalidation_0-auc:0.846528\tvalidation_0-error:0.039551\n",
            "[636]\tvalidation_0-auc:0.846553\tvalidation_0-error:0.039551\n",
            "[637]\tvalidation_0-auc:0.846592\tvalidation_0-error:0.039551\n",
            "[638]\tvalidation_0-auc:0.846586\tvalidation_0-error:0.039551\n",
            "[639]\tvalidation_0-auc:0.84657\tvalidation_0-error:0.039551\n",
            "[640]\tvalidation_0-auc:0.846576\tvalidation_0-error:0.039551\n",
            "[641]\tvalidation_0-auc:0.846567\tvalidation_0-error:0.039551\n",
            "[642]\tvalidation_0-auc:0.846558\tvalidation_0-error:0.039551\n",
            "[643]\tvalidation_0-auc:0.846548\tvalidation_0-error:0.039551\n",
            "[644]\tvalidation_0-auc:0.846623\tvalidation_0-error:0.039551\n",
            "[645]\tvalidation_0-auc:0.846622\tvalidation_0-error:0.039551\n",
            "[646]\tvalidation_0-auc:0.846643\tvalidation_0-error:0.039551\n",
            "[647]\tvalidation_0-auc:0.846604\tvalidation_0-error:0.039551\n",
            "[648]\tvalidation_0-auc:0.846574\tvalidation_0-error:0.039551\n",
            "[649]\tvalidation_0-auc:0.846519\tvalidation_0-error:0.039551\n",
            "[650]\tvalidation_0-auc:0.846502\tvalidation_0-error:0.039551\n",
            "[651]\tvalidation_0-auc:0.846477\tvalidation_0-error:0.039551\n",
            "[652]\tvalidation_0-auc:0.846537\tvalidation_0-error:0.039551\n",
            "[653]\tvalidation_0-auc:0.846535\tvalidation_0-error:0.039551\n",
            "[654]\tvalidation_0-auc:0.846568\tvalidation_0-error:0.039551\n",
            "[655]\tvalidation_0-auc:0.846701\tvalidation_0-error:0.039551\n",
            "[656]\tvalidation_0-auc:0.84672\tvalidation_0-error:0.039551\n",
            "[657]\tvalidation_0-auc:0.846764\tvalidation_0-error:0.039551\n",
            "[658]\tvalidation_0-auc:0.846734\tvalidation_0-error:0.039551\n",
            "[659]\tvalidation_0-auc:0.846811\tvalidation_0-error:0.039551\n",
            "[660]\tvalidation_0-auc:0.846832\tvalidation_0-error:0.039551\n",
            "[661]\tvalidation_0-auc:0.846836\tvalidation_0-error:0.039551\n",
            "[662]\tvalidation_0-auc:0.846809\tvalidation_0-error:0.039551\n",
            "[663]\tvalidation_0-auc:0.846798\tvalidation_0-error:0.039551\n",
            "[664]\tvalidation_0-auc:0.846845\tvalidation_0-error:0.039551\n",
            "CPU times: user 8min 50s, sys: 1.49 s, total: 8min 51s\n",
            "Wall time: 4min 34s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=0.5, gamma=0,\n",
              "              learning_rate=0.02, max_delta_step=0, max_depth=5,\n",
              "              min_child_weight=1, missing=None, n_estimators=665, n_jobs=-1,\n",
              "              nthread=-1, objective='binary:logistic', random_state=0,\n",
              "              reg_alpha=0.3, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=0.9, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "q3LRv6T8RRaN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "1162401b-bb7a-48c5-ea2e-4bdd9027906b"
      },
      "source": [
        "y_pred = model.predict_proba(X_val)[:,1]\n",
        "label=\"XGBoost Classifer\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "i+=1\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t XGBoost Classifer on Log Transformed RE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.133 and 0.847 for XGBoost Classifer on Log Transformed RE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qUwaU5nuRRaR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "63ba856e-22f2-4295-84ab-24e8713d68f1"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.524004</td>\n",
              "      <td>0.823513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.141823</td>\n",
              "      <td>0.803198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.136824</td>\n",
              "      <td>0.836372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.132661</td>\n",
              "      <td>0.846845</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Model             Dataset  log_loss       auc\n",
              "0        Logisic Regression  Log Transformed RE  0.524004  0.823513\n",
              "1  Decision Tree Classifier  Log Transformed RE  0.141823  0.803198\n",
              "2  Random Forest Classifier  Log Transformed RE  0.136824  0.836372\n",
              "3         XGBoost Classifer  Log Transformed RE  0.132661  0.846845"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LeV2q75ULzvm"
      },
      "source": [
        "# LightGBM Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "32dzyVRQLzvn",
        "colab": {}
      },
      "source": [
        "model = lgb.LGBMClassifier(n_jobs=-1,\n",
        "                      nthread=-1,\n",
        "                      scale_pos_weight=1,\n",
        "                      learning_rate=0.02,  \n",
        "                      colsample_bytree = 0.5,\n",
        "                      subsample = 0.7,\n",
        "                      objective='binary', \n",
        "                      n_estimators=250,\n",
        "                      reg_alpha = 0.3,\n",
        "                      max_depth=6\n",
        "                     )\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yJS6sV0qLzvr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "15acb312-2638-4e9a-91fc-d7958b85bae5"
      },
      "source": [
        "#https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n",
        "\"\"\"\n",
        "manually changed each parameters 1 by 1 and compared the validation auc\n",
        "and selected the best parameter value\n",
        "\"\"\"\n",
        "eval_metric = ['auc','error']\n",
        "%time model.fit(X_train,y_train,eval_set=[(X_val,y_val)],eval_metric=eval_metric,verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]\tvalid_0's auc: 0.778738\tvalid_0's binary_logloss: 0.165257\n",
            "[2]\tvalid_0's auc: 0.806028\tvalid_0's binary_logloss: 0.163929\n",
            "[3]\tvalid_0's auc: 0.821604\tvalid_0's binary_logloss: 0.162632\n",
            "[4]\tvalid_0's auc: 0.824784\tvalid_0's binary_logloss: 0.16145\n",
            "[5]\tvalid_0's auc: 0.822426\tvalid_0's binary_logloss: 0.160564\n",
            "[6]\tvalid_0's auc: 0.824437\tvalid_0's binary_logloss: 0.159482\n",
            "[7]\tvalid_0's auc: 0.82796\tvalid_0's binary_logloss: 0.158504\n",
            "[8]\tvalid_0's auc: 0.828823\tvalid_0's binary_logloss: 0.15763\n",
            "[9]\tvalid_0's auc: 0.831616\tvalid_0's binary_logloss: 0.156726\n",
            "[10]\tvalid_0's auc: 0.831478\tvalid_0's binary_logloss: 0.155958\n",
            "[11]\tvalid_0's auc: 0.831125\tvalid_0's binary_logloss: 0.155248\n",
            "[12]\tvalid_0's auc: 0.833148\tvalid_0's binary_logloss: 0.154486\n",
            "[13]\tvalid_0's auc: 0.832759\tvalid_0's binary_logloss: 0.153912\n",
            "[14]\tvalid_0's auc: 0.832232\tvalid_0's binary_logloss: 0.153273\n",
            "[15]\tvalid_0's auc: 0.833375\tvalid_0's binary_logloss: 0.152585\n",
            "[16]\tvalid_0's auc: 0.832448\tvalid_0's binary_logloss: 0.152083\n",
            "[17]\tvalid_0's auc: 0.832545\tvalid_0's binary_logloss: 0.151536\n",
            "[18]\tvalid_0's auc: 0.834012\tvalid_0's binary_logloss: 0.15093\n",
            "[19]\tvalid_0's auc: 0.834702\tvalid_0's binary_logloss: 0.150382\n",
            "[20]\tvalid_0's auc: 0.835106\tvalid_0's binary_logloss: 0.149863\n",
            "[21]\tvalid_0's auc: 0.836183\tvalid_0's binary_logloss: 0.149322\n",
            "[22]\tvalid_0's auc: 0.835986\tvalid_0's binary_logloss: 0.148854\n",
            "[23]\tvalid_0's auc: 0.836144\tvalid_0's binary_logloss: 0.148393\n",
            "[24]\tvalid_0's auc: 0.836548\tvalid_0's binary_logloss: 0.147932\n",
            "[25]\tvalid_0's auc: 0.837093\tvalid_0's binary_logloss: 0.147489\n",
            "[26]\tvalid_0's auc: 0.837325\tvalid_0's binary_logloss: 0.147068\n",
            "[27]\tvalid_0's auc: 0.837659\tvalid_0's binary_logloss: 0.146646\n",
            "[28]\tvalid_0's auc: 0.837846\tvalid_0's binary_logloss: 0.14631\n",
            "[29]\tvalid_0's auc: 0.838337\tvalid_0's binary_logloss: 0.145927\n",
            "[30]\tvalid_0's auc: 0.838507\tvalid_0's binary_logloss: 0.145554\n",
            "[31]\tvalid_0's auc: 0.838351\tvalid_0's binary_logloss: 0.145214\n",
            "[32]\tvalid_0's auc: 0.83816\tvalid_0's binary_logloss: 0.144987\n",
            "[33]\tvalid_0's auc: 0.837957\tvalid_0's binary_logloss: 0.144695\n",
            "[34]\tvalid_0's auc: 0.838154\tvalid_0's binary_logloss: 0.14445\n",
            "[35]\tvalid_0's auc: 0.838332\tvalid_0's binary_logloss: 0.144173\n",
            "[36]\tvalid_0's auc: 0.838331\tvalid_0's binary_logloss: 0.143947\n",
            "[37]\tvalid_0's auc: 0.838414\tvalid_0's binary_logloss: 0.143644\n",
            "[38]\tvalid_0's auc: 0.838474\tvalid_0's binary_logloss: 0.143339\n",
            "[39]\tvalid_0's auc: 0.838911\tvalid_0's binary_logloss: 0.143034\n",
            "[40]\tvalid_0's auc: 0.839168\tvalid_0's binary_logloss: 0.14277\n",
            "[41]\tvalid_0's auc: 0.839458\tvalid_0's binary_logloss: 0.142484\n",
            "[42]\tvalid_0's auc: 0.839406\tvalid_0's binary_logloss: 0.142299\n",
            "[43]\tvalid_0's auc: 0.83955\tvalid_0's binary_logloss: 0.142043\n",
            "[44]\tvalid_0's auc: 0.839501\tvalid_0's binary_logloss: 0.141863\n",
            "[45]\tvalid_0's auc: 0.839626\tvalid_0's binary_logloss: 0.141652\n",
            "[46]\tvalid_0's auc: 0.839553\tvalid_0's binary_logloss: 0.141417\n",
            "[47]\tvalid_0's auc: 0.839517\tvalid_0's binary_logloss: 0.141265\n",
            "[48]\tvalid_0's auc: 0.839331\tvalid_0's binary_logloss: 0.141113\n",
            "[49]\tvalid_0's auc: 0.839175\tvalid_0's binary_logloss: 0.140895\n",
            "[50]\tvalid_0's auc: 0.839359\tvalid_0's binary_logloss: 0.14069\n",
            "[51]\tvalid_0's auc: 0.839389\tvalid_0's binary_logloss: 0.140516\n",
            "[52]\tvalid_0's auc: 0.839798\tvalid_0's binary_logloss: 0.140333\n",
            "[53]\tvalid_0's auc: 0.839949\tvalid_0's binary_logloss: 0.140121\n",
            "[54]\tvalid_0's auc: 0.839897\tvalid_0's binary_logloss: 0.139984\n",
            "[55]\tvalid_0's auc: 0.840051\tvalid_0's binary_logloss: 0.139804\n",
            "[56]\tvalid_0's auc: 0.84012\tvalid_0's binary_logloss: 0.139652\n",
            "[57]\tvalid_0's auc: 0.840354\tvalid_0's binary_logloss: 0.139477\n",
            "[58]\tvalid_0's auc: 0.8406\tvalid_0's binary_logloss: 0.139303\n",
            "[59]\tvalid_0's auc: 0.840775\tvalid_0's binary_logloss: 0.139134\n",
            "[60]\tvalid_0's auc: 0.840727\tvalid_0's binary_logloss: 0.139017\n",
            "[61]\tvalid_0's auc: 0.840868\tvalid_0's binary_logloss: 0.138858\n",
            "[62]\tvalid_0's auc: 0.840928\tvalid_0's binary_logloss: 0.138701\n",
            "[63]\tvalid_0's auc: 0.841147\tvalid_0's binary_logloss: 0.138544\n",
            "[64]\tvalid_0's auc: 0.84127\tvalid_0's binary_logloss: 0.138425\n",
            "[65]\tvalid_0's auc: 0.841337\tvalid_0's binary_logloss: 0.138328\n",
            "[66]\tvalid_0's auc: 0.841403\tvalid_0's binary_logloss: 0.138192\n",
            "[67]\tvalid_0's auc: 0.84145\tvalid_0's binary_logloss: 0.138088\n",
            "[68]\tvalid_0's auc: 0.841402\tvalid_0's binary_logloss: 0.13798\n",
            "[69]\tvalid_0's auc: 0.841499\tvalid_0's binary_logloss: 0.137869\n",
            "[70]\tvalid_0's auc: 0.841472\tvalid_0's binary_logloss: 0.137764\n",
            "[71]\tvalid_0's auc: 0.841478\tvalid_0's binary_logloss: 0.137674\n",
            "[72]\tvalid_0's auc: 0.841575\tvalid_0's binary_logloss: 0.137575\n",
            "[73]\tvalid_0's auc: 0.841492\tvalid_0's binary_logloss: 0.13748\n",
            "[74]\tvalid_0's auc: 0.841651\tvalid_0's binary_logloss: 0.137367\n",
            "[75]\tvalid_0's auc: 0.841554\tvalid_0's binary_logloss: 0.137292\n",
            "[76]\tvalid_0's auc: 0.841481\tvalid_0's binary_logloss: 0.1372\n",
            "[77]\tvalid_0's auc: 0.841662\tvalid_0's binary_logloss: 0.1371\n",
            "[78]\tvalid_0's auc: 0.841447\tvalid_0's binary_logloss: 0.137031\n",
            "[79]\tvalid_0's auc: 0.841533\tvalid_0's binary_logloss: 0.136954\n",
            "[80]\tvalid_0's auc: 0.841559\tvalid_0's binary_logloss: 0.136877\n",
            "[81]\tvalid_0's auc: 0.841738\tvalid_0's binary_logloss: 0.136789\n",
            "[82]\tvalid_0's auc: 0.841818\tvalid_0's binary_logloss: 0.136712\n",
            "[83]\tvalid_0's auc: 0.841932\tvalid_0's binary_logloss: 0.136639\n",
            "[84]\tvalid_0's auc: 0.841941\tvalid_0's binary_logloss: 0.13655\n",
            "[85]\tvalid_0's auc: 0.842077\tvalid_0's binary_logloss: 0.136486\n",
            "[86]\tvalid_0's auc: 0.842019\tvalid_0's binary_logloss: 0.136427\n",
            "[87]\tvalid_0's auc: 0.842131\tvalid_0's binary_logloss: 0.136347\n",
            "[88]\tvalid_0's auc: 0.842217\tvalid_0's binary_logloss: 0.136275\n",
            "[89]\tvalid_0's auc: 0.842312\tvalid_0's binary_logloss: 0.13619\n",
            "[90]\tvalid_0's auc: 0.842477\tvalid_0's binary_logloss: 0.136134\n",
            "[91]\tvalid_0's auc: 0.84243\tvalid_0's binary_logloss: 0.136084\n",
            "[92]\tvalid_0's auc: 0.842461\tvalid_0's binary_logloss: 0.136002\n",
            "[93]\tvalid_0's auc: 0.842636\tvalid_0's binary_logloss: 0.13592\n",
            "[94]\tvalid_0's auc: 0.842864\tvalid_0's binary_logloss: 0.135836\n",
            "[95]\tvalid_0's auc: 0.842838\tvalid_0's binary_logloss: 0.135801\n",
            "[96]\tvalid_0's auc: 0.842878\tvalid_0's binary_logloss: 0.135739\n",
            "[97]\tvalid_0's auc: 0.842934\tvalid_0's binary_logloss: 0.135677\n",
            "[98]\tvalid_0's auc: 0.843088\tvalid_0's binary_logloss: 0.135629\n",
            "[99]\tvalid_0's auc: 0.843157\tvalid_0's binary_logloss: 0.135554\n",
            "[100]\tvalid_0's auc: 0.843232\tvalid_0's binary_logloss: 0.135506\n",
            "[101]\tvalid_0's auc: 0.843381\tvalid_0's binary_logloss: 0.135449\n",
            "[102]\tvalid_0's auc: 0.843503\tvalid_0's binary_logloss: 0.135377\n",
            "[103]\tvalid_0's auc: 0.843522\tvalid_0's binary_logloss: 0.135322\n",
            "[104]\tvalid_0's auc: 0.843532\tvalid_0's binary_logloss: 0.135261\n",
            "[105]\tvalid_0's auc: 0.843487\tvalid_0's binary_logloss: 0.135214\n",
            "[106]\tvalid_0's auc: 0.843682\tvalid_0's binary_logloss: 0.135151\n",
            "[107]\tvalid_0's auc: 0.843663\tvalid_0's binary_logloss: 0.135126\n",
            "[108]\tvalid_0's auc: 0.84366\tvalid_0's binary_logloss: 0.135081\n",
            "[109]\tvalid_0's auc: 0.843827\tvalid_0's binary_logloss: 0.135028\n",
            "[110]\tvalid_0's auc: 0.843863\tvalid_0's binary_logloss: 0.134984\n",
            "[111]\tvalid_0's auc: 0.843965\tvalid_0's binary_logloss: 0.134941\n",
            "[112]\tvalid_0's auc: 0.844065\tvalid_0's binary_logloss: 0.134891\n",
            "[113]\tvalid_0's auc: 0.843991\tvalid_0's binary_logloss: 0.134873\n",
            "[114]\tvalid_0's auc: 0.844039\tvalid_0's binary_logloss: 0.134832\n",
            "[115]\tvalid_0's auc: 0.844047\tvalid_0's binary_logloss: 0.1348\n",
            "[116]\tvalid_0's auc: 0.844082\tvalid_0's binary_logloss: 0.13476\n",
            "[117]\tvalid_0's auc: 0.844161\tvalid_0's binary_logloss: 0.134734\n",
            "[118]\tvalid_0's auc: 0.8441\tvalid_0's binary_logloss: 0.134715\n",
            "[119]\tvalid_0's auc: 0.844088\tvalid_0's binary_logloss: 0.134672\n",
            "[120]\tvalid_0's auc: 0.844104\tvalid_0's binary_logloss: 0.134645\n",
            "[121]\tvalid_0's auc: 0.844191\tvalid_0's binary_logloss: 0.134612\n",
            "[122]\tvalid_0's auc: 0.84421\tvalid_0's binary_logloss: 0.134585\n",
            "[123]\tvalid_0's auc: 0.844171\tvalid_0's binary_logloss: 0.134558\n",
            "[124]\tvalid_0's auc: 0.844155\tvalid_0's binary_logloss: 0.134536\n",
            "[125]\tvalid_0's auc: 0.844079\tvalid_0's binary_logloss: 0.134512\n",
            "[126]\tvalid_0's auc: 0.84411\tvalid_0's binary_logloss: 0.134504\n",
            "[127]\tvalid_0's auc: 0.844199\tvalid_0's binary_logloss: 0.134464\n",
            "[128]\tvalid_0's auc: 0.844185\tvalid_0's binary_logloss: 0.134442\n",
            "[129]\tvalid_0's auc: 0.844203\tvalid_0's binary_logloss: 0.134411\n",
            "[130]\tvalid_0's auc: 0.844286\tvalid_0's binary_logloss: 0.134395\n",
            "[131]\tvalid_0's auc: 0.844235\tvalid_0's binary_logloss: 0.134374\n",
            "[132]\tvalid_0's auc: 0.844197\tvalid_0's binary_logloss: 0.134356\n",
            "[133]\tvalid_0's auc: 0.844245\tvalid_0's binary_logloss: 0.134325\n",
            "[134]\tvalid_0's auc: 0.844252\tvalid_0's binary_logloss: 0.134302\n",
            "[135]\tvalid_0's auc: 0.844302\tvalid_0's binary_logloss: 0.13428\n",
            "[136]\tvalid_0's auc: 0.844424\tvalid_0's binary_logloss: 0.13423\n",
            "[137]\tvalid_0's auc: 0.84441\tvalid_0's binary_logloss: 0.134218\n",
            "[138]\tvalid_0's auc: 0.844401\tvalid_0's binary_logloss: 0.1342\n",
            "[139]\tvalid_0's auc: 0.844447\tvalid_0's binary_logloss: 0.134166\n",
            "[140]\tvalid_0's auc: 0.844614\tvalid_0's binary_logloss: 0.134138\n",
            "[141]\tvalid_0's auc: 0.844683\tvalid_0's binary_logloss: 0.134104\n",
            "[142]\tvalid_0's auc: 0.844733\tvalid_0's binary_logloss: 0.134083\n",
            "[143]\tvalid_0's auc: 0.844777\tvalid_0's binary_logloss: 0.134052\n",
            "[144]\tvalid_0's auc: 0.844744\tvalid_0's binary_logloss: 0.134041\n",
            "[145]\tvalid_0's auc: 0.844908\tvalid_0's binary_logloss: 0.13401\n",
            "[146]\tvalid_0's auc: 0.845105\tvalid_0's binary_logloss: 0.133974\n",
            "[147]\tvalid_0's auc: 0.845144\tvalid_0's binary_logloss: 0.133955\n",
            "[148]\tvalid_0's auc: 0.845195\tvalid_0's binary_logloss: 0.133922\n",
            "[149]\tvalid_0's auc: 0.845208\tvalid_0's binary_logloss: 0.133904\n",
            "[150]\tvalid_0's auc: 0.845273\tvalid_0's binary_logloss: 0.133886\n",
            "[151]\tvalid_0's auc: 0.845271\tvalid_0's binary_logloss: 0.133868\n",
            "[152]\tvalid_0's auc: 0.845252\tvalid_0's binary_logloss: 0.133851\n",
            "[153]\tvalid_0's auc: 0.845292\tvalid_0's binary_logloss: 0.133835\n",
            "[154]\tvalid_0's auc: 0.845337\tvalid_0's binary_logloss: 0.133808\n",
            "[155]\tvalid_0's auc: 0.84528\tvalid_0's binary_logloss: 0.13379\n",
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            "[159]\tvalid_0's auc: 0.845256\tvalid_0's binary_logloss: 0.133746\n",
            "[160]\tvalid_0's auc: 0.845312\tvalid_0's binary_logloss: 0.133718\n",
            "[161]\tvalid_0's auc: 0.845463\tvalid_0's binary_logloss: 0.133695\n",
            "[162]\tvalid_0's auc: 0.845501\tvalid_0's binary_logloss: 0.13367\n",
            "[163]\tvalid_0's auc: 0.845516\tvalid_0's binary_logloss: 0.133654\n",
            "[164]\tvalid_0's auc: 0.845453\tvalid_0's binary_logloss: 0.133643\n",
            "[165]\tvalid_0's auc: 0.845464\tvalid_0's binary_logloss: 0.133627\n",
            "[166]\tvalid_0's auc: 0.845471\tvalid_0's binary_logloss: 0.1336\n",
            "[167]\tvalid_0's auc: 0.84545\tvalid_0's binary_logloss: 0.133593\n",
            "[168]\tvalid_0's auc: 0.845488\tvalid_0's binary_logloss: 0.133571\n",
            "[169]\tvalid_0's auc: 0.845567\tvalid_0's binary_logloss: 0.13355\n",
            "[170]\tvalid_0's auc: 0.845588\tvalid_0's binary_logloss: 0.133545\n",
            "[171]\tvalid_0's auc: 0.845704\tvalid_0's binary_logloss: 0.133526\n",
            "[172]\tvalid_0's auc: 0.845766\tvalid_0's binary_logloss: 0.133501\n",
            "[173]\tvalid_0's auc: 0.845706\tvalid_0's binary_logloss: 0.133503\n",
            "[174]\tvalid_0's auc: 0.845739\tvalid_0's binary_logloss: 0.13349\n",
            "[175]\tvalid_0's auc: 0.845755\tvalid_0's binary_logloss: 0.13348\n",
            "[176]\tvalid_0's auc: 0.84581\tvalid_0's binary_logloss: 0.133468\n",
            "[177]\tvalid_0's auc: 0.845931\tvalid_0's binary_logloss: 0.13344\n",
            "[178]\tvalid_0's auc: 0.845899\tvalid_0's binary_logloss: 0.133418\n",
            "[179]\tvalid_0's auc: 0.845916\tvalid_0's binary_logloss: 0.133415\n",
            "[180]\tvalid_0's auc: 0.845911\tvalid_0's binary_logloss: 0.133418\n",
            "[181]\tvalid_0's auc: 0.845874\tvalid_0's binary_logloss: 0.133408\n",
            "[182]\tvalid_0's auc: 0.845832\tvalid_0's binary_logloss: 0.133406\n",
            "[183]\tvalid_0's auc: 0.84583\tvalid_0's binary_logloss: 0.133403\n",
            "[184]\tvalid_0's auc: 0.845926\tvalid_0's binary_logloss: 0.133388\n",
            "[185]\tvalid_0's auc: 0.845947\tvalid_0's binary_logloss: 0.133378\n",
            "[186]\tvalid_0's auc: 0.845973\tvalid_0's binary_logloss: 0.13337\n",
            "[187]\tvalid_0's auc: 0.845951\tvalid_0's binary_logloss: 0.133365\n",
            "[188]\tvalid_0's auc: 0.845921\tvalid_0's binary_logloss: 0.133365\n",
            "[189]\tvalid_0's auc: 0.845951\tvalid_0's binary_logloss: 0.133352\n",
            "[190]\tvalid_0's auc: 0.845911\tvalid_0's binary_logloss: 0.133342\n",
            "[191]\tvalid_0's auc: 0.845966\tvalid_0's binary_logloss: 0.133331\n",
            "[192]\tvalid_0's auc: 0.845978\tvalid_0's binary_logloss: 0.133321\n",
            "[193]\tvalid_0's auc: 0.845904\tvalid_0's binary_logloss: 0.133325\n",
            "[194]\tvalid_0's auc: 0.845901\tvalid_0's binary_logloss: 0.133325\n",
            "[195]\tvalid_0's auc: 0.845997\tvalid_0's binary_logloss: 0.133308\n",
            "[196]\tvalid_0's auc: 0.846036\tvalid_0's binary_logloss: 0.133294\n",
            "[197]\tvalid_0's auc: 0.845986\tvalid_0's binary_logloss: 0.133295\n",
            "[198]\tvalid_0's auc: 0.845981\tvalid_0's binary_logloss: 0.133293\n",
            "[199]\tvalid_0's auc: 0.846162\tvalid_0's binary_logloss: 0.133267\n",
            "[200]\tvalid_0's auc: 0.846236\tvalid_0's binary_logloss: 0.133249\n",
            "[201]\tvalid_0's auc: 0.846254\tvalid_0's binary_logloss: 0.133239\n",
            "[202]\tvalid_0's auc: 0.846272\tvalid_0's binary_logloss: 0.133232\n",
            "[203]\tvalid_0's auc: 0.846301\tvalid_0's binary_logloss: 0.133223\n",
            "[204]\tvalid_0's auc: 0.846323\tvalid_0's binary_logloss: 0.133206\n",
            "[205]\tvalid_0's auc: 0.846283\tvalid_0's binary_logloss: 0.133209\n",
            "[206]\tvalid_0's auc: 0.846356\tvalid_0's binary_logloss: 0.133199\n",
            "[207]\tvalid_0's auc: 0.846381\tvalid_0's binary_logloss: 0.133198\n",
            "[208]\tvalid_0's auc: 0.846368\tvalid_0's binary_logloss: 0.133198\n",
            "[209]\tvalid_0's auc: 0.846338\tvalid_0's binary_logloss: 0.133196\n",
            "[210]\tvalid_0's auc: 0.846301\tvalid_0's binary_logloss: 0.133191\n",
            "[211]\tvalid_0's auc: 0.84633\tvalid_0's binary_logloss: 0.133174\n",
            "[212]\tvalid_0's auc: 0.84634\tvalid_0's binary_logloss: 0.133171\n",
            "[213]\tvalid_0's auc: 0.84629\tvalid_0's binary_logloss: 0.13317\n",
            "[214]\tvalid_0's auc: 0.846301\tvalid_0's binary_logloss: 0.133163\n",
            "[215]\tvalid_0's auc: 0.846272\tvalid_0's binary_logloss: 0.133168\n",
            "[216]\tvalid_0's auc: 0.846197\tvalid_0's binary_logloss: 0.13318\n",
            "[217]\tvalid_0's auc: 0.846242\tvalid_0's binary_logloss: 0.133172\n",
            "[218]\tvalid_0's auc: 0.846245\tvalid_0's binary_logloss: 0.133172\n",
            "[219]\tvalid_0's auc: 0.84619\tvalid_0's binary_logloss: 0.133187\n",
            "[220]\tvalid_0's auc: 0.846151\tvalid_0's binary_logloss: 0.133189\n",
            "[221]\tvalid_0's auc: 0.846102\tvalid_0's binary_logloss: 0.133198\n",
            "[222]\tvalid_0's auc: 0.846019\tvalid_0's binary_logloss: 0.133204\n",
            "[223]\tvalid_0's auc: 0.846068\tvalid_0's binary_logloss: 0.133194\n",
            "[224]\tvalid_0's auc: 0.846035\tvalid_0's binary_logloss: 0.133194\n",
            "[225]\tvalid_0's auc: 0.846061\tvalid_0's binary_logloss: 0.133184\n",
            "[226]\tvalid_0's auc: 0.846042\tvalid_0's binary_logloss: 0.133185\n",
            "[227]\tvalid_0's auc: 0.845993\tvalid_0's binary_logloss: 0.13319\n",
            "[228]\tvalid_0's auc: 0.845931\tvalid_0's binary_logloss: 0.133196\n",
            "[229]\tvalid_0's auc: 0.845941\tvalid_0's binary_logloss: 0.133193\n",
            "[230]\tvalid_0's auc: 0.845917\tvalid_0's binary_logloss: 0.133196\n",
            "[231]\tvalid_0's auc: 0.84586\tvalid_0's binary_logloss: 0.133203\n",
            "[232]\tvalid_0's auc: 0.845852\tvalid_0's binary_logloss: 0.133201\n",
            "[233]\tvalid_0's auc: 0.845827\tvalid_0's binary_logloss: 0.133197\n",
            "[234]\tvalid_0's auc: 0.845826\tvalid_0's binary_logloss: 0.133192\n",
            "[235]\tvalid_0's auc: 0.845801\tvalid_0's binary_logloss: 0.133197\n",
            "[236]\tvalid_0's auc: 0.845795\tvalid_0's binary_logloss: 0.133199\n",
            "[237]\tvalid_0's auc: 0.8458\tvalid_0's binary_logloss: 0.1332\n",
            "[238]\tvalid_0's auc: 0.845798\tvalid_0's binary_logloss: 0.133185\n",
            "[239]\tvalid_0's auc: 0.845852\tvalid_0's binary_logloss: 0.13318\n",
            "[240]\tvalid_0's auc: 0.845836\tvalid_0's binary_logloss: 0.133177\n",
            "[241]\tvalid_0's auc: 0.845772\tvalid_0's binary_logloss: 0.133178\n",
            "[242]\tvalid_0's auc: 0.845866\tvalid_0's binary_logloss: 0.133171\n",
            "[243]\tvalid_0's auc: 0.845885\tvalid_0's binary_logloss: 0.133164\n",
            "[244]\tvalid_0's auc: 0.845931\tvalid_0's binary_logloss: 0.133148\n",
            "[245]\tvalid_0's auc: 0.845968\tvalid_0's binary_logloss: 0.133143\n",
            "[246]\tvalid_0's auc: 0.846023\tvalid_0's binary_logloss: 0.133129\n",
            "[247]\tvalid_0's auc: 0.846003\tvalid_0's binary_logloss: 0.13313\n",
            "[248]\tvalid_0's auc: 0.846024\tvalid_0's binary_logloss: 0.133122\n",
            "[249]\tvalid_0's auc: 0.846003\tvalid_0's binary_logloss: 0.133115\n",
            "[250]\tvalid_0's auc: 0.845982\tvalid_0's binary_logloss: 0.133117\n",
            "CPU times: user 36.5 s, sys: 378 ms, total: 36.8 s\n",
            "Wall time: 19.8 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.5,\n",
              "               importance_type='split', learning_rate=0.02, max_depth=6,\n",
              "               min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
              "               n_estimators=250, n_jobs=-1, nthread=-1, num_leaves=31,\n",
              "               objective='binary', random_state=None, reg_alpha=0.3,\n",
              "               reg_lambda=0.0, scale_pos_weight=1, silent=True, subsample=0.7,\n",
              "               subsample_for_bin=200000, subsample_freq=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsYBoxOSobEh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "ede11c6f-ad69-4636-bc60-7b133adf534d"
      },
      "source": [
        "y_pred = model.predict_proba(X_val)[:,1]\n",
        "label=\"LGBM Classifer\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "i+=1\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t LGBM Classifer on Log Transformed RE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.133 and 0.846 for LGBM Classifer on Log Transformed RE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BF22prvpLzv1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d4ea08d1-3a17-43e8-d4e3-ff2974a78644"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.524004</td>\n",
              "      <td>0.823513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.141823</td>\n",
              "      <td>0.803198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.136824</td>\n",
              "      <td>0.836372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.132661</td>\n",
              "      <td>0.846845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LGBM Classifer</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.133117</td>\n",
              "      <td>0.845982</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Model             Dataset  log_loss       auc\n",
              "0        Logisic Regression  Log Transformed RE  0.524004  0.823513\n",
              "1  Decision Tree Classifier  Log Transformed RE  0.141823  0.803198\n",
              "2  Random Forest Classifier  Log Transformed RE  0.136824  0.836372\n",
              "3         XGBoost Classifer  Log Transformed RE  0.132661  0.846845\n",
              "4            LGBM Classifer  Log Transformed RE  0.133117  0.845982"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LECmbx42-V2m"
      },
      "source": [
        "# Random Forest (top 250)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CUgv74lV-V2n",
        "colab": {}
      },
      "source": [
        "model = ensemble.RandomForestClassifier(class_weight='balanced')\n",
        "params = { 'n_estimators':[1000,2000],\n",
        "          'max_depth':[500,1000], \n",
        "         'min_samples_split':[100,500],\n",
        "          'min_samples_leaf':[3,5],\n",
        "          'max_leaf_nodes':[100,250] \n",
        "          }\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eCt8L0AV-V2q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "76e7fe14-f11f-4532-b25d-d3c3aeccd767"
      },
      "source": [
        "%%time\n",
        "find_best_params(model,params,cv=3,X_train=X_train_200)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed: 67.3min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The best auc score was 0.821\n",
            "The best params were: {'n_estimators': 1000, 'min_samples_split': 500, 'min_samples_leaf': 5, 'max_leaf_nodes': 250, 'max_depth': 1000}\n",
            "CPU times: user 3min 18s, sys: 657 ms, total: 3min 19s\n",
            "Wall time: 1h 10min 40s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=1000, max_features='auto',\n",
              "                       max_leaf_nodes=250, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=5, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DVdFANOy-V2u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "f91f1e17-0921-49c9-c8d5-3965b48a85e1"
      },
      "source": [
        "model = ensemble.RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
        "                       criterion='gini', max_depth=1000, max_features='auto',\n",
        "                       max_leaf_nodes=250, max_samples=None,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=3, min_samples_split=500,\n",
        "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
        "                       n_jobs=-1, oob_score=False,\n",
        "                       verbose=0, warm_start=False)\n",
        "\n",
        "model.fit(X_train_200,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=1000, max_features='auto',\n",
              "                       max_leaf_nodes=250, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=3, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
              "                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "okg4Xksh-V2y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "9c4cc40e-9351-4c4e-c267-df9c4666fdfd"
      },
      "source": [
        "cc_model = calibration.CalibratedClassifierCV(model,cv='prefit')\n",
        "cc_model.fit(X_train_200,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CalibratedClassifierCV(base_estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                             ccp_alpha=0.0,\n",
              "                                                             class_weight='balanced',\n",
              "                                                             criterion='gini',\n",
              "                                                             max_depth=1000,\n",
              "                                                             max_features='auto',\n",
              "                                                             max_leaf_nodes=250,\n",
              "                                                             max_samples=None,\n",
              "                                                             min_impurity_decrease=0.0,\n",
              "                                                             min_impurity_split=None,\n",
              "                                                             min_samples_leaf=3,\n",
              "                                                             min_samples_split=500,\n",
              "                                                             min_weight_fraction_leaf=0.0,\n",
              "                                                             n_estimators=1000,\n",
              "                                                             n_jobs=-1,\n",
              "                                                             oob_score=False,\n",
              "                                                             random_state=None,\n",
              "                                                             verbose=0,\n",
              "                                                             warm_start=False),\n",
              "                       cv='prefit', method='sigmoid')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5eSsmef9-V21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "0731b11c-4587-4f21-e210-26aba1cef785"
      },
      "source": [
        "i+=1\n",
        "y_pred = cc_model.predict_proba(X_val_200)[:,1]\n",
        "label=\"Random Forest Classifier (top 250)\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "result = plot_auc(y_val,y_pred,label,dataset)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t Random Forest Classifier (top 250) on Log Transformed RE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.136 and 0.837 for Random Forest Classifier (top 250) on Log Transformed RE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8Mo702Rt-V23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "0a0a2ed4-1829-4959-b36a-6fe5b30b8b46"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.524004</td>\n",
              "      <td>0.823513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.141823</td>\n",
              "      <td>0.803198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.136824</td>\n",
              "      <td>0.836372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.132661</td>\n",
              "      <td>0.846845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LGBM Classifer</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.133117</td>\n",
              "      <td>0.845982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest Classifier (top 250)</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.136456</td>\n",
              "      <td>0.837322</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Model             Dataset  log_loss       auc\n",
              "0                  Logisic Regression  Log Transformed RE  0.524004  0.823513\n",
              "1            Decision Tree Classifier  Log Transformed RE  0.141823  0.803198\n",
              "2            Random Forest Classifier  Log Transformed RE  0.136824  0.836372\n",
              "3                   XGBoost Classifer  Log Transformed RE  0.132661  0.846845\n",
              "4                      LGBM Classifer  Log Transformed RE  0.133117  0.845982\n",
              "5  Random Forest Classifier (top 250)  Log Transformed RE  0.136456  0.837322"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "i-y4IONn-V26"
      },
      "source": [
        "# XGB Classifier (top 250)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Gjg5rhGa-V27",
        "colab": {}
      },
      "source": [
        "model = xgb.XGBClassifier(n_jobs=-1,\n",
        "                      nthread=-1,\n",
        "                      scale_pos_weight=1,\n",
        "                      learning_rate=0.01,  \n",
        "                      colsample_bytree = 0.6,\n",
        "                      subsample = 0.7,\n",
        "                      objective='binary:logistic', \n",
        "                      n_estimators=945,\n",
        "                      reg_alpha = 0.1,\n",
        "                      max_depth=6,\n",
        "                      gamma=0,\n",
        "                     )\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8Q0dR9Kt-V2-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d1d2e71b-92ec-434b-dc75-e8324900c172"
      },
      "source": [
        "#https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n",
        "\"\"\"\n",
        "manually changed each parameters 1 by 1 and compared the validation auc\n",
        "and selected the best parameter value\n",
        "\"\"\"\n",
        "eval_metric = ['auc','error']\n",
        "%time model.fit(X_train_200,y_train,eval_set=[(X_val_200,y_val)],eval_metric=eval_metric,verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\tvalidation_0-auc:0.785138\tvalidation_0-error:0.040516\n",
            "[1]\tvalidation_0-auc:0.80341\tvalidation_0-error:0.039639\n",
            "[2]\tvalidation_0-auc:0.814649\tvalidation_0-error:0.039551\n",
            "[3]\tvalidation_0-auc:0.821339\tvalidation_0-error:0.039551\n",
            "[4]\tvalidation_0-auc:0.822482\tvalidation_0-error:0.039551\n",
            "[5]\tvalidation_0-auc:0.818476\tvalidation_0-error:0.039551\n",
            "[6]\tvalidation_0-auc:0.820929\tvalidation_0-error:0.039551\n",
            "[7]\tvalidation_0-auc:0.823344\tvalidation_0-error:0.039551\n",
            "[8]\tvalidation_0-auc:0.826651\tvalidation_0-error:0.039551\n",
            "[9]\tvalidation_0-auc:0.827312\tvalidation_0-error:0.039551\n",
            "[10]\tvalidation_0-auc:0.828098\tvalidation_0-error:0.039551\n",
            "[11]\tvalidation_0-auc:0.827693\tvalidation_0-error:0.039551\n",
            "[12]\tvalidation_0-auc:0.829186\tvalidation_0-error:0.039551\n",
            "[13]\tvalidation_0-auc:0.829404\tvalidation_0-error:0.039551\n",
            "[14]\tvalidation_0-auc:0.828553\tvalidation_0-error:0.039551\n",
            "[15]\tvalidation_0-auc:0.827986\tvalidation_0-error:0.039551\n",
            "[16]\tvalidation_0-auc:0.82935\tvalidation_0-error:0.039551\n",
            "[17]\tvalidation_0-auc:0.829653\tvalidation_0-error:0.039551\n",
            "[18]\tvalidation_0-auc:0.829738\tvalidation_0-error:0.039551\n",
            "[19]\tvalidation_0-auc:0.829844\tvalidation_0-error:0.039551\n",
            "[20]\tvalidation_0-auc:0.830231\tvalidation_0-error:0.039551\n",
            "[21]\tvalidation_0-auc:0.829883\tvalidation_0-error:0.039551\n",
            "[22]\tvalidation_0-auc:0.830011\tvalidation_0-error:0.039551\n",
            "[23]\tvalidation_0-auc:0.829795\tvalidation_0-error:0.039551\n",
            "[24]\tvalidation_0-auc:0.829453\tvalidation_0-error:0.039551\n",
            "[25]\tvalidation_0-auc:0.82975\tvalidation_0-error:0.039551\n",
            "[26]\tvalidation_0-auc:0.83005\tvalidation_0-error:0.039551\n",
            "[27]\tvalidation_0-auc:0.830217\tvalidation_0-error:0.039551\n",
            "[28]\tvalidation_0-auc:0.830085\tvalidation_0-error:0.039551\n",
            "[29]\tvalidation_0-auc:0.829952\tvalidation_0-error:0.039551\n",
            "[30]\tvalidation_0-auc:0.830165\tvalidation_0-error:0.039551\n",
            "[31]\tvalidation_0-auc:0.830113\tvalidation_0-error:0.039551\n",
            "[32]\tvalidation_0-auc:0.830214\tvalidation_0-error:0.039551\n",
            "[33]\tvalidation_0-auc:0.83032\tvalidation_0-error:0.039551\n",
            "[34]\tvalidation_0-auc:0.830567\tvalidation_0-error:0.039551\n",
            "[35]\tvalidation_0-auc:0.830725\tvalidation_0-error:0.039551\n",
            "[36]\tvalidation_0-auc:0.830372\tvalidation_0-error:0.039551\n",
            "[37]\tvalidation_0-auc:0.830214\tvalidation_0-error:0.039551\n",
            "[38]\tvalidation_0-auc:0.830269\tvalidation_0-error:0.039551\n",
            "[39]\tvalidation_0-auc:0.830571\tvalidation_0-error:0.039551\n",
            "[40]\tvalidation_0-auc:0.830568\tvalidation_0-error:0.039551\n",
            "[41]\tvalidation_0-auc:0.830715\tvalidation_0-error:0.039551\n",
            "[42]\tvalidation_0-auc:0.83074\tvalidation_0-error:0.039551\n",
            "[43]\tvalidation_0-auc:0.830839\tvalidation_0-error:0.039551\n",
            "[44]\tvalidation_0-auc:0.830673\tvalidation_0-error:0.039551\n",
            "[45]\tvalidation_0-auc:0.830527\tvalidation_0-error:0.039551\n",
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            "[47]\tvalidation_0-auc:0.830491\tvalidation_0-error:0.039551\n",
            "[48]\tvalidation_0-auc:0.830793\tvalidation_0-error:0.039551\n",
            "[49]\tvalidation_0-auc:0.830707\tvalidation_0-error:0.039551\n",
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            "[51]\tvalidation_0-auc:0.831019\tvalidation_0-error:0.039551\n",
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            "[55]\tvalidation_0-auc:0.831517\tvalidation_0-error:0.039551\n",
            "[56]\tvalidation_0-auc:0.831304\tvalidation_0-error:0.039551\n",
            "[57]\tvalidation_0-auc:0.831618\tvalidation_0-error:0.039551\n",
            "[58]\tvalidation_0-auc:0.831812\tvalidation_0-error:0.039551\n",
            "[59]\tvalidation_0-auc:0.831879\tvalidation_0-error:0.039551\n",
            "[60]\tvalidation_0-auc:0.831737\tvalidation_0-error:0.039551\n",
            "[61]\tvalidation_0-auc:0.831753\tvalidation_0-error:0.039551\n",
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            "[78]\tvalidation_0-auc:0.833108\tvalidation_0-error:0.039551\n",
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            "[82]\tvalidation_0-auc:0.833389\tvalidation_0-error:0.039551\n",
            "[83]\tvalidation_0-auc:0.833267\tvalidation_0-error:0.039551\n",
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            "[88]\tvalidation_0-auc:0.833551\tvalidation_0-error:0.039551\n",
            "[89]\tvalidation_0-auc:0.833332\tvalidation_0-error:0.039551\n",
            "[90]\tvalidation_0-auc:0.833405\tvalidation_0-error:0.039551\n",
            "[91]\tvalidation_0-auc:0.833469\tvalidation_0-error:0.039551\n",
            "[92]\tvalidation_0-auc:0.833515\tvalidation_0-error:0.039551\n",
            "[93]\tvalidation_0-auc:0.833526\tvalidation_0-error:0.039551\n",
            "[94]\tvalidation_0-auc:0.833734\tvalidation_0-error:0.039551\n",
            "[95]\tvalidation_0-auc:0.833715\tvalidation_0-error:0.039551\n",
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            "[100]\tvalidation_0-auc:0.833561\tvalidation_0-error:0.039551\n",
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            "[109]\tvalidation_0-auc:0.833604\tvalidation_0-error:0.039551\n",
            "[110]\tvalidation_0-auc:0.833829\tvalidation_0-error:0.039551\n",
            "[111]\tvalidation_0-auc:0.834109\tvalidation_0-error:0.039551\n",
            "[112]\tvalidation_0-auc:0.834185\tvalidation_0-error:0.039551\n",
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            "[116]\tvalidation_0-auc:0.834383\tvalidation_0-error:0.039551\n",
            "[117]\tvalidation_0-auc:0.834422\tvalidation_0-error:0.039551\n",
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            "[124]\tvalidation_0-auc:0.835038\tvalidation_0-error:0.039551\n",
            "[125]\tvalidation_0-auc:0.835172\tvalidation_0-error:0.039551\n",
            "[126]\tvalidation_0-auc:0.835232\tvalidation_0-error:0.039551\n",
            "[127]\tvalidation_0-auc:0.835335\tvalidation_0-error:0.039551\n",
            "[128]\tvalidation_0-auc:0.835369\tvalidation_0-error:0.039551\n",
            "[129]\tvalidation_0-auc:0.835522\tvalidation_0-error:0.039551\n",
            "[130]\tvalidation_0-auc:0.835502\tvalidation_0-error:0.039551\n",
            "[131]\tvalidation_0-auc:0.835722\tvalidation_0-error:0.039551\n",
            "[132]\tvalidation_0-auc:0.835751\tvalidation_0-error:0.039551\n",
            "[133]\tvalidation_0-auc:0.835702\tvalidation_0-error:0.039551\n",
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            "[932]\tvalidation_0-auc:0.847795\tvalidation_0-error:0.039551\n",
            "[933]\tvalidation_0-auc:0.847776\tvalidation_0-error:0.039551\n",
            "[934]\tvalidation_0-auc:0.847781\tvalidation_0-error:0.039551\n",
            "[935]\tvalidation_0-auc:0.84777\tvalidation_0-error:0.039551\n",
            "[936]\tvalidation_0-auc:0.847836\tvalidation_0-error:0.039551\n",
            "[937]\tvalidation_0-auc:0.847793\tvalidation_0-error:0.039551\n",
            "[938]\tvalidation_0-auc:0.847829\tvalidation_0-error:0.039551\n",
            "[939]\tvalidation_0-auc:0.847832\tvalidation_0-error:0.039551\n",
            "[940]\tvalidation_0-auc:0.847864\tvalidation_0-error:0.039551\n",
            "[941]\tvalidation_0-auc:0.847874\tvalidation_0-error:0.039551\n",
            "[942]\tvalidation_0-auc:0.847849\tvalidation_0-error:0.039551\n",
            "[943]\tvalidation_0-auc:0.847826\tvalidation_0-error:0.039551\n",
            "[944]\tvalidation_0-auc:0.847824\tvalidation_0-error:0.039551\n",
            "CPU times: user 13min 27s, sys: 1.63 s, total: 13min 28s\n",
            "Wall time: 6min 56s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=0.6, gamma=0,\n",
              "              learning_rate=0.01, max_delta_step=0, max_depth=6,\n",
              "              min_child_weight=1, missing=None, n_estimators=945, n_jobs=-1,\n",
              "              nthread=-1, objective='binary:logistic', random_state=0,\n",
              "              reg_alpha=0.1, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=0.7, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9ZI4N9TM-V3D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "ef2fb4bc-9c5e-4314-ae6c-c4ae63cc0988"
      },
      "source": [
        "y_pred = model.predict_proba(X_val_200)[:,1]\n",
        "label=\"XGBoost Classifer(top 250)\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "i+=1\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t XGBoost Classifer(top 250) on Log Transformed RE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6qs7mzZvDFy1atHnp0qVbmjVrVvDiiy82LL3Nk08+mdCuXbu80stmzZoV53K5LEuWLNm8ZMmSzR6Ph5kzZx73s1qe6hr4Mw+YxomnGLoJaAm0AmKBX5VSy7XWe6o+PCGEEJXlyx8yYlesy6rQl66/+lwQk3HlRXGZJ9vuyiuvPLJ48eK6kyZNarx+/fqI0aNHp9SvX98NMGPGjAaDBw/OGDhw4NGS7evXr+9+4oknDpT3WoWFhdbi4mJLnTp1PAC5ubnWcePGJW3dujUcYODAgRkPPvhgGsD27dtDxo8f3+To0aN2m81m3H///Qf69++fk5+fb33ggQea7tmzJ8xmsxlJSUmFb7755q4nn3wyKTU1Nbh///7tEhISCmfOnLmr9LFnzpxZ77zzzsu56aabskqWNWzY0H3zzTcfcw1mz54d88EHH8S73W4rwMMPP5zSp0+fXI/Hw2OPPZa0du3ayKCgICMsLMw7b968rWlpafb777+/eXZ2th2gU6dOuU899VTKu+++G7tq1aromTNn7rrmmmtUcXGx5ZZbbmndpUuXnEmTJu1/7733YufMmVPP4/FYIiIiPJMmTdrbpk2bonfffTd2wYIFsQ6Hw7N///7QZ599dteWLVvCzj333DyHw2FkZGTYXn/99UZOp9PWv3//duedd17ulClTUhYvXhw1bdq0xl6vl+joaPeTTz65t1WrVkXffPNN5NNPP53YsmVL57Zt28JDQ0O9U6ZM2d2+ffvCsuc+ePDgP97Lrl275mVkZAR7PB5stuPn4mlpafbt27c7rrvuuiyA6667Lmvq1KlJ6enp9vj4ePfQoUOzhg4d2m7cuHEHy9s/IyPD9uuvv0YtWLBg0+DBgzscOnTI3rBhQ/dxD1jKa6+91vD2228/2LlzZ2fJsvPPP78AKPBnf4AVK1bEPPXUU7sBWrduXdS6dev8JUuWRA8bNiy79HYWiwWXy2UtLCy0Wq1Wr9PptCUkJPwxJ+7q1asjUlJSQm+77ba0qVOnOkrvV1hYaHW5XBaAwsJCW8OGDV3+xgfVlGRqrb8DUOqElfbrgTe11l7gsFJqHnAtMLXqIxRCiIozDAO5M++Z69///ve+3r17n3vBBRfkXHvttX988W7bts3Rr1+/7BPtC/DAAw+0CA4ONg4dOhTSqVOnnMsvvzwH4LnnnmtoGAZLlizZnJubax02bFjbNm3aFAwYMCBn9OjRzYYOHZrxt7/9LWPz5s2ht956q7rgggs2f//99xH5+fm25cuXbwbIysqyAYwfP37f1KlTGy9YsOD38mLQWju6du2a48/5XnbZZUevu+66LKvVytatW0Nuv/121adPn40bNmwIW7duXeSyZcs222y2P449Z86cmISEhKKPP/54W+mYSvvss8+0Uir5k08+2RoZGeldvXp1xJIlS+rOnTtXh4aGGosWLYp67LHHms2bN28rwNatW8M//fTTLS1btiwCePvtt+PPPffcfIC4uDjPXXfddbAkgQUz0ZswYUKzt99+W7dv377w7bffjhs9enSz+fPnbwXYs2dP2NixY/f17Nlzz3vvvRc7ZsyYZse7ViXeeuut+C5duhwtnWD+5z//afzSSy8ltGzZsmDcuHH7Gzdu7EpJSQmOi4tz2e1mKmS324mNjXWlpKQEx8fHuxs2bOi22+3G77//Htq2bdtjEtuPP/44tmvXrkcaNGjgvuSSS7I/+uijuNGjR6f6817t2LHDkZycvO9460eOHNn8wIEDoeWt++yzz353OBxGenp6cJMmTYpLljdo0KD40KFDx8zhOGjQoCM//vhjZPfu3TuGhIR4ExMTC6dMmbIPIC8vzzplypTEN954Y8eOHTv+crzbb7/98IYNGyIuuuiijgCdOnXKueGGG7LKvv6JnElTGCUBe0s93wckBigWIUQN5/YYZOa4MCqYJRYUeflJ57Buey46xUmRK3BZpgUvvYJ+5urgbzjS7lpITg5YLKVdeVFcpj/Vxqr29ddfRzkcDs++fftCCwsLLaGhoeW+WY899lji+vXrI48cOWKfM2fO70lJSS4wm8vPOeecwoKCAsuoUaNavPLKK/H33ntv+s8//xw1duzYFKvVSnR0tLdv375Za9asibr44ovzdu3a5bjlllsyANq3b1/YokWLgv/973/h55xzjnPq1KmhjzzySFKXLl1yBwwYcLS8WE7Hzp07Q+67776EjIyMYJvNZmRnZwcdOnTI3rx582KPx2P5xz/+0bRr1645JcdOTk7Onz17dv3HH3+88YUXXphbkkSfyPLly+vs2rXLcdVVV7UF84dWXl7eH9lc+/bt80oSTID09PTgevXqHfdcf/rpp/DmzZsXlFQnb7nlloypU6cmlfT9a9iwYVHPnj3zAIYPH545ZcqUJkePHrVGR0d7y3u9jz/+uO7SpUtjP/zww60ly1544YVdSUlJLrfbzQsvvNDwvvvua/7555/71fQfExPjOnDgQFB5Seb8+fPjxowZkwIwdOjQjMcff7xpSZJpsVjK/axZLP516y1b0T4d69atc+zZsyd01apVGyMjIz3jxo1LnDBhQuKzzz67b+LEiY2vv/769MaNG7vKJpkrVqyIAlizZs0GgP/7v/9rPm3atPoPPPBAmr/HPpOSzEqxadOmU9537dq1lRjJ2UHOuXaoCefsNeCIE47k+/OPtIVdi9b9Zd9N+y3k+b4mPF7Yl2mhyH3q4zga1TU4v4mBIzgwSWaUK5NLsufRsGgPh0KbExZXr0a8z5UlPT3d/vzzzye+9tpr29944436U6ZMafSvf/3rAECrVq2cGzduDB8yZMgRgClTpqQAdOrUqaPH4znmQxEWFmZceumlR1evXh197733HnNXE3+0aNGieNGiRZu//vrrqFWrVkW/8sorCYsWLdp8sv2UUs5NmzaF+3OMMWPGNB89evT+IUOGHPF4PHTs2PGCwsJCa8OGDYsXL168edWqVZFr1qyJevnllxt//vnnW7p165b/xRdfbFmxYkXUl19+GTtz5swG/iRfgwYNyhg/fny5TchhYWF/mW81JCTEW1RUVC3jP+bNm1dn+vTpCW+//fa2Bg0a/NFsXfKjwW63c/fdd6fNmjWrkcfjITExsTgjIyPI7XZjt9txu91kZmYGJSYm/lEdLC4utoSGhh6T0P7yyy+Offv2hT7xxBNNn3jiCQCysrKCvvvuu/Du3bvn16lTx13SDaHE0aNHg+Li4lwALVu2dK5bty7c10R+DH8qmfHx8cV79+4Njo+PdwOkpqYGd+nSJbfs9nPnzo3r3LlzTkl3j6uvvjprwoQJTQE2btwY8eOPP0a/9dZbjYqLi615eXm2fv36tVuyZMmWjz/+uN6VV16ZGRYWZgD0798/68svv4wFzsokcx/QBPjZ97xsZdMvHTp0OKUJideuXUvyGVIFqC5yzrVDIM7Z4zXYm1bIvrRCXB7/krBit5ffduWTcriw3Cbo7Dw3R/L86u50XNHhdmKj7GCF7ueEcU6zcKzWiiWaQTYL9WOCaZvk1/d+pTO8Hop+mkfB6vex2IIIG3g/bTtezrp1607pfS4qKjqtH+dnqvHjxyddeeWVhzt27FgwefLklCuuuKLdoEGDsjt37uy86667UkeNGtW6c+fOuf37988BczSx1+st98Pg8Xj45ZdfIpOSkooAOnfunDNnzpy4iy++OC8vL8+6bNmymIceeiglKirK27x5c+f7778fe+utt2Zu2bIldNeuXWFdunTJT0lJCYqJifEMGTLkyGWXXZbTvXv3c7OysuyRkZGe/Pz843YcvP322w9fffXV7WbPnh1T0lSZlpZmX7ZsWXTZfpn5+fn2Jk2aFAG8++67cSV96dLT0+12u93o169fTp8+fXLWrFkTvWvXrpCCggJr48aNXddff332xRdfnDdgwIBzPJ4Tz8nfp0+fI//85z+bjRgx4nBiYqLL7Xazfv16R6dOnZzlbd+yZcuCXbt2/ZEslT3fLl265E+aNCmspEn6/fffj23RooUzKirKC5CamhqyevXqiEsvvTTv448/jmnatGlBeVXMr776Kvr5559P/O9//7utWbNmfySJLpeLzMxMe0nSOXfu3JimTZsW2Gw26tev727RokXBnDlzYoYPH541Z86cmJYtWzpLkja3282hQ4dCOnToUF5TedyNN96YOmHChD+S7eeff77B3Llz47p3755/0UUX5cyfPz922LBhWWFhYcb69evD9u3bF9q5c+d8gLvuuuvQ5MmTm5533nnO5ORkJ8CGDRvC0tLSgi6//PIcfyqZvXv3zv7www/rde7cee+2bdtCtNbhl19++TH7NW7cuOiHH36IKi4uTg8ODjZWrlwZ3axZswKAJUuWbCnZ7ptvvoks3XWjUaNGRd99913U0KFDsw3D4Ntvv41u0aKF331G4cxKMucCo5RSn2EO/LkK8166QoizgGEYrPg1m09Wp3MwsxiX+9QqfB2bR+AIPbbwkRgfSstGYbRMCMN2ksRw2zZN69Z/9gG3WKBRbAixUX4P3DwjudN341wwDc+h7QS17oqj3z1YI2NPvmMtM3fu3LopKSmhr7766m6AmJiYktHmTefPn//7eeedV/Dyyy/vmDZtWqMnn3yySZ06ddx2u9176623HmrUqNEfAxtK+mS63W5Ls2bNCh5++OGDAA8//PChcePGJfXr1689wIABAzJLktUXXnhh9/jx45t8+OGH9W02mzF58uTd8fHx7kWLFkW9+OKLjcG8Fd8tt9ySmpCQ4IqPj3clJiYW9u3bt31SUlJB2eQiISHB9c477+gpU6YkTJ8+PSE0NNQTFhbmHTly5DF9/x588MF99913X8vIyEh3165dcyIjI90AKSkpwRMmTGji8XgsXq/X0rVr16Ndu3bNf//992M/+OCD+larFcMwGDNmzN4TDZQB6NGjR94999xz4K677mrp8Xgsbrfb0rt37+zjJZkDBw7MnjhxYhPgIECvXr1y33nnnQb9+vVrd/755+dOmTIlZfLkybsfeuihZh6PxxIdHe1+/vnnd5fs36RJk4I5c+bETZ48uUloaKj32Wef3V3ecSZOnNjUbrcb99133x/TVL3//vs6NDTUGDlyZCu3220xDIO4uDjXf/7zn50l20yePHnvmDFjmr755puNIiIi3FOnTv3j9desWRPRpk2b/JIKYImCggLLypUrY957772tpZcPHTo06+qrr26Xl5eXcuutt2YcPHgweMiQIW2tVishISHeZ599dldcXJwHoH///jler3fvpEmTknJycux2u91o0KBB0ZgxY/bjp/vuuy/1H//4R9OePXt2sFqt/POf/9xbkpw/+eSTjeLj412jRo06PGrUqPQdO3aEDRgwoJ3NZjPi4+OLp0yZctIi3sMPP3zo0UcfbVLyOW/RooVz9OjRFbr9rKWi/ZFOhVLqJeAaoAGQAWRqrdsrpRYCj2utf1FK2YBXgMt9uz2jtX7D32OsXbu2KbBbKpn+k3OuHSpyzoZhUFjsxVnkJb/QQ0GRl/2HC9mTVlhuFdHtMdiXXkhWrpscpxuvr75w2fl1advEQdsm4ThC/J9pJSTISp2I0//tW9PeZ8PtonDNbAp/mIslNBJHv7sJatP9L/27TvWcS1UymyUnJ+851Rg3bNiwp2PHjhmnur+ouW688cZWjzzyyP4LLrigQlWwspW16nb33Xc3u/baazP69OlzTBO0+KsNGzbEdezYsWnZ5dU1uvx+4P5ylg8s9dgD/L064hGitvN4DNbvzKPY7cXrNdh1qIBvfzvK/oyicpuq7TYLMZHH/nNhtVhIiAtBJTqIctiJiQzivJYRJMWX25VInAL3ga3kL5iGN2MfwR16E3bZKKyOqECHJYTfJk6cuM83qKRCSWYgFRYWWpKTk/MkwTw9Z1JzuRCiEni8Bocyi8nIKSbX6WHTLgsphYcByMxxsXRtFjn5x/a7SogL5qpuccREBeEIseEItRIaZKVRXAihwVbi6xwzM4aoQkZxIQWr3qXo5y+xRMURcf1Eglp0CnRYQlSYbw7HopNv+Vc9e/bM7dmzZ0CqmKGhocaoUaMOB+LYNYkkmUKc5Y7kuSh2G+xJLeTTb9PZugNBQtUAACAASURBVM9J8V/6Q9pg3V8HgnZsHkG7pg66tTfvQla/TjCRDvnn4Ezh2r0e56KX8B5JI+SCQYT1+huWEMfJdxRCiDOIfKsIcZbIOFLMrzvz2L7fSWau2T/y8JFith/4swUqMszGoC6xNGsYRmiwlcb1QtizYwudkjv+sU2QzUpocLXMKCIqyFuYR8GKmRRvWIo1phERNz9DUFK5d+IVQogzniSZQlSRzXvyeXPBQTze0x9cdzCzCGeROaom2G6hYWwIFguEBVu5rV8Don2DZZo1CKN1479WvLIOQGSY/K9+pive9gPOxdMx8o8QctEwwroPxxJU8UGMQghxppBvHiEqgWEY6BQn67bnAeacjx9/Y87ZfKGKNOfQOQ0xkXaC7FaGdIujTZKDILtUImsKb142zmWv4/r9W2zxzXBc+zj2hq0CHZYQQpw2STKFOA170wr58vsMlvySiafM9MB2m4W/X5nAwAtlHkNxLMMwKN70NQXL38AoLiC0xwhCuw7FYpN/litLVlaWrUePHh0HDx58+Omnn04BePrppxs5nU7r5MmT/5iPcMaMGfU2b94c/vLLL+8B0FqHTJkypfHOnTsdkZGR7qCgIOO2225LLblDkD+eeeaZhgsXLowFGDhwYOajjz5a7vyCSqnkpk2bFpRMR/Xss8/uPvfcc8+aUdhCnIj8aybEKdiXXsjBjCImvrcHgNioIOpFB3Fj7/okt44EwAIVvpuMqB28R9PJX/wq7p2/YEtoQ/igB7DFJQU6rBpn7ty5MW3atMlbsWJFTFFR0f6QkJCT9l05ePBg0IgRI9rcf//9KbNmzdoJcOjQIfuKFSui/T3uqlWrIlauXFm35LaRV199dduuXbvm9ujRI6+87T/55JOtkZGR5d6LW4izmSSZQvgpLbuYV+btZ+s+J3mFf04BdMfAhgy9JD6AkYmzhWF4KVq3kIKv3wbDIKzvXYQkD8Ji9X/CeuG/L774Iu6hhx7a/8YbbzScP39+nWHDhmWfbJ+ZM2fWO++883JuuummrJJlDRs2dJe9heOJfPXVVzEDBw7MdDgcBpiVzK+++irmeEmmEDWVJJlCHMfu1ALWbDpKYbGX1RuPcPioebe5ji0iiI0KYnCXWEKCrTStLxOPi5PzZB7AuXAa7pTN2Judj2PAfdjq1A90WFWi8Jf5scW/rYiritcOPqdPRminK06a8G3YsCEsNzfX3qtXr9z09PSgzz//PM6fJFNr7ejatWvO8dY///zzDZYtWxZT3rpx48btu/TSS/PS0tKCu3Tp8sck3o0aNSpet25d5PFe84YbblAej8dy0UUXHX300UcPhoaGVv2t+ISoBpJkClGGy+1l4U+ZvD7/IAbmrQ4BWjcO48Ze9enazu9WMyEwvB6K/vcZBas/wBIUjGPwPwg+57K/3BJSVL7Zs2fH9e/fP9NqtTJkyJDs5557Lmn//v1BFovltBK4hx56KPWhhx465r7hp2rZsmUbk5KSXEePHrXef//9zaZOndpwwoQJB0++pxBnPkkyhShl/+Eixv93J+lHXATZLTx3V8tjpgQSwl/utF04F7yIJ3UnQaobjn5/xxpRbhGsRgntdEWmP9XGqlJUVGRZvnx5TFBQkLF48eJYALfbbfnoo49iY2Ji3AcOHPjL3FDZ2dlBMTExLgCllHPTpk3hx3ttfyqZ9evXLz5w4MAft8g6ePBgcHx8fHF5+yQlJbkAoqOjvcOGDct49913a2Z5W9RKkmQK4bN6YzYzvjqIy2Xw6PVJtG8WTr1ouZWiqDjDXUzhd7Mp/PETLGGRhF8zjuA2Fwc6rFpj/vz5dRo3blz4+eef65Jl33//ffi4ceOaTZ8+fcd///vfhikpKUGJiYmuzMxM2/Lly+uOGzduH8Dtt99++Oqrr243e/bsmBtuuCELIC0tzb5s2bLom2++OdOfSuagQYOyn3766cRRo0alAyxcuDB27Nix+8pul5WVZQsNDfU6HA7D5XKxZMmSuq1atZKR5aLGkCRT1Gp5BR6+WZ/Ngv9lsietkPg6QTx8bRIXtDpu9ykhTsi9fwv5C6bhzdxP8Dl9CLtsFNYw+TxVp88//zxuwIABWaWXdevWLd8wDNLT0+2jR49Oueuuu1p6vV6LYRhcf/316X369MkFSEhIcL3zzjt6ypQpCdOnT08IDQ31hIWFeUeOHOl3E3nPnj1zf/jhhyP9+/fvADBgwIDMnj175gHMnz8/esWKFXVefPHFvVu3bg2dOHFiEwCPx2Pp0KFD3tixYw9U3pUQIrAkyRS1UrHby5SP9rJuey5FLoOmDUL52+X1ubJbPcJCZKSvqDijuICCb96l6Jf5WKPiiLhhEkHNkwMdVq30wQcfbC9v+apVqzaVPL7uuuuOOwiobdu2he+8887O04lh7NixB8eOHXtM38orrrji6BVXXHEUzMR3yZIlW07nOEKcySTJFLWCx2vw2648Zq228sqK30nLLsYAEuNDuHNQIzq1jgp0iOIs5tq1Dueil/EePUxI8iDCet6KJUT68gohajdJMkWN5PEaLP0liw9XpnE0343Ha+D1gs1qoW1SEOe3ikAlOujVsS7BQXKLRnFqvAW5FKx4i+KNy7HGNCbylmewJ7YPdFhCCHFGkCRT1Dgbdubx+lcH2J1aSLsmDnqdVwerxUJ8nSBCivfS55KWgQ5R1ADFW9fgXPIahvMood2uI7T7jVjsMlBMCCFKSJIpaoS8AjfvLUtl16FCNu3JJ75OEGNvbMIl50T/ZT7CtWv3BjBKURN487JwLpmBS6/BVr8FjhsmYq/fItBhnQm8Xq/XYrVaZSJxIWoRr9drAcq9LaokmeKst2FHLrOWHGLbgQJaNgrjb/0acNXF9f6YRF2IymAYBsW/Ladg+VsYriLCet5KSJdrsNjkn1GfTYcPH25Xr169o5JoClE7eL1ey+HDh6OBTeWtl38dxVntvWWH+HBlOsF2C7f1a8C1PWQeY1H5PEfScC56Bffuddgbt8cx6H5ssY0DHdYZxe1235GamvpWampqB0B+4QlRO3iBTW63+47yVkqSKc5aOw44WbXhCKHBVt4f25bwUPk4i8plGF6KfvmKgm/eAYuFsMv/TkjyQCwWyaHKSk5OTgeuDHQcQogzh3wri7PCj1uOkp3nZv/hItbvzKWgyMuhLPMubdf3jJcEU1Q6T0YK+QtfwrN/C/bmyTgG3IstOj7QYQkhxFlDvpnFGcUwDD5Ykcbin7PIK/AA5nREbo/Zxctus3BOs3Aaxdrofk40XdtG0zIhLJAhixrG8Lgp/PFTCr/7EEtwGI4rHiK4Q6+/DCATQghxcpJkijNGfqGHf0zfzv7DRTSuF0LPjnX+WBceZuP8FhE0qR8qd+QRVcadugPngml40nYR1KY7jsvvxhpRN9BhCSHEWUmSTBFweQUeNuzM5Y0FB0k/4uKqi+O4rV8DgoMkmRTVw3AVUfjdRxT++CkWRzThQ8cTrLoFOiwhhDirSZIpAirX6eau/2iy89wA3D24EUMurhfgqERt4k7ZTP6CaXizDhDcsS9hvUdiDYsMdFhCCHHWkyRTBIRhGBzIKOaFT/aRnefm7sGNSG4dSUJcSKBDE7WEUeSk4Jt3KFr7Fdbo+kTc+G+Cmp0f6LCEEKLGkCRTVDtnkYcn3t7Npj35AIzo20Cql6JauXb+Qv6iVzByMgjpPISwHiOwBIcGOiwhhKhRJMkU1Sa3wM1Pv+ew6OcsNu/JZ3DXWJJbRdK1XXSgQxO1hNeZQ8HyNynetBJrbCIRI6Zib9w20GEJIUSNJEmmqDYvfbaf7zYdBaBnxzrcc2WCTAsjqoVhGLi2fodzyQyMwlxCL76B0ItvwGIPCnRoQghRY0mSKaqM12uwdG0Wm3bnU+z28t2mo5zTLJxxw5tSJ0I+eqJ6ePOycC6ejmvbD9gatMQx/N/Y45sFOiwhhKjxqu2bXinVGngHiAUygRFa6+1ltokHZgGJQBDwNXC/1tpdXXGKU3Mkz82Wvfm4PF5WbThCXoGHzBwXBzOLqRthJzjISkJsMHcMbCQJpqgWhmFQvHEZBcvfwvC4COt9OyEXXoXFKlNjCSFEdajOb/sZwKta6/eVUjcDrwO9y2wzDvhdaz1IKRUEfAdcA8ypxjhFBeU63dz14lZy8s079NSJsJNYL4TGcSHc1KcB5zYPJy46OMBRitrE7swi76PxuPdswJ7YAceg+7HFJAQ6LCGEqFWqJcn0VSgvAPr6Fn0EvKKUqqe1PlxqUwOIVEpZgRAgGDhQHTEK/331QwavfvnXt8VqhZv61OeidtEkxIUQGmwNUHSiNjO8Hop+mU/j79/GbbPj6P9/BJ/fH4tFPo9CCFHdLIZhVPlBlFLJwLta6/allm0BbtZaryu1LAb4FGgHhAOvaK0f8+cYa9eubQrsrsy4xbG2p8I735m/TZKbeony3Ta8cYwX1TCAgYlaLygvnXqb5hF6dD/OuFYcbnclnjCZuaACmiUnJ+8JdBBCiJrjTOscdy2wEegDRAKLlFLDtNaf+PsCHTp0ICSk4hN6r127luTk5ArvdzY7lXOeN2sXVmsurz2gSIo/++YVlPe55jE8Lgp/+ITCH2djCXbgGPIIuwoiSO7UKdChVatTfZ+LiorYtGlTFUQkhKjtqqsNKQVIUErZAHz/beRbXtp9wAdaa6/W+ijwBdCrmmIUJ3Akz8WEWbv4ZVsunVtHnpUJpqh53Ie2kzvrQQpXv0+Q6kbUnTMIbt8TZGosIYQIuGpJMrXW6cB64EbfohuBX8v0xwSzubs/gFIqGLgMkJ/YZ4BZi1P5ZVsuF7aJZMwNTQIdjqjlDFchzpX/Jfft0XidOYQPm0DEVY9iDZfmcSGEOFNUZ3P53cA7SqnHgWxgBIBSaiHwuNb6F+AfwAyl1G+ADXMKozerMUZRxo4DTt5blspPOpcOTcOZeGvzQIckajnX3t9wLnwJb/ZBgs/rh6P3SCyh4YEOSwghRBnVlmRqrbcCXcpZPrDU4538OQJdBNhvu/KY8PYugu1WrusRzxUXxQU6JFGLGUVOnCtnUfzrQqx1GhAx/CmCmnYMdFhCCCGO40wb+CPOAIZh8OHKND5ckUZ4mI3n7mxBUv2wQIclajHXjp/IX/QqRl4WIRdeTViPm7EESb9gIYQ4k0mSKf7iQEYRL3ySwpa9+QTZLcx4oDUxUTKRuggMr/MoBcveoHjzN1jjmhBxzVjsCW0CHZYQQgg/+J1kKqX6AjcA8VrrK5RSnYAorfXKKotOVLuHX9/BkTw3KtHB4zc3JSYqKNAhiVrIMAxcW1bjXDoDo8hJaPfhhF58HRabfB6FEOJs4VeSqZS6D3gAeAsY5ltcALwEdKua0ER127grjyN5bs5tHs4zo1oGOhxRS3lzM3Auno5r+/+wNWxN+KAHsMU3DXRYQgghKsjfSuY/gD5a6z1KqUd9y7YCqmrCEtXJWeTh5605vDxvPyFBFh68JjHQIYlayDAMitcvwblyJng8hPUZSUjnIVistkCHJoQQ4hT4m2RG8ufE6SX3oQwCiis9IlFtilww6b3d/O/3HLwGhIdamXpnCxrEVvyOSUKcDk/2IZwLX8K9dyP2pHNwDLwfW0yjQIclhBDiNPibZK4GHgOeLLXsfsx5LMVZavFvVn7ZlUPf5Lqc3yqStknh1K8rg3xE9TG8Hop+/pKCVe+BzYZjwH0En3c5Fkt13YxMCCFEVfE3ybwPmK+UGgVEKqU0kAsMrrLIRJX54vvDLPopi71pVi5oFcGDw5ICHZKohTzpe8hfOA3PwW0EtboQR///wxopc7EKIURN4VeSqbU+pJTqDHQGmmA2nf+ktfZWZXCi8qVmFTFj/kEAurXycu+1kmCK6mV4XBR+P5fCNR9jCXEQPmQMQe0uxSL3GxdCiBrF39HlX2ithwA/+f5Kln+mtb6mqoITlev3vfmMnbkLgMm3NcOSu526kTIljKg+7oOa/AXT8B7eS3D7noT1vROrQ+43LoQQNZG/zeW9jrO8ZyXFIarYroMFTHp/D0UuLxNvbUan1lGsXRvoqERtYbgKKVj9PkU/fYEloi7h1z5BcKsLAx2WEEKIKnTCJFMpNcn3MLjU4xLNgb1VEpWoNEUuL1M+2stPW3MIC7HyrxHNuLBNVKDDErWIa88GnAtfwnskleDzB+DodRuW0PBAhyWEEKKKnaySWTJhorXUYzCnMUoB/lUFMYlK8rPO4YPlqej9BZzXIoJHrk8iRprHRTUxCvNxrpxJ8folWOs2JOKmpwlqcm6gwxJCCFFNTphkaq1vA1BKfa+1frN6QhKna+WvWby7LJW0bBcAg7rEcs+VCVitMrBCVI/i7f/DuehVjPxsQrpcQ9ilN2EJCg10WEIIIaqRv6PL3wRQSkUCcYCl1LpdVROaOBVp2cVMnZOCzQpXd49jcJc4GsXJ5Oqienjzj+JcNgPXltVY6zUhYtg/sTdqHeiwhBBCBIC/o8vbAh8CHTGbyi38eecfuefbGcLl9vLip+aNmR6/RfpeiupjGAauLatwLn0do8hJ6KU3E3rRMCw26Z4hhBC1lb+jy1/DvLtPL2A30BR4Gvi+asISFWEYBgcyinj9q4Os35nHDT3jJcEU1cabcxjn4ldx7fgZWyNF+KAHsNVrEuiwhBBCBJi/SWZHoK/W2qWUsmitjyqlHgE2Ae9XXXjCH28uOMjnazIASIgL5tZ+DQMckagNDMNL8a+Lca78Lxhewi4bRUinK7BYpXFDCCGE/0lmIRAEuIAMpVQSkA3EVlVgwj9ffn+Yz9dkYLdaeO7uljSuJ/0vRdXzZB3AufBl3Pt+w960I44B92GrKz9uhBBC/MnfJPNb4DrgbeATYBFQBKysmrCEP3SKk7cWHSLYbuHl+1qTFC+jd0XVMrwein6aR8Hq97HYgnAMvJ/gjpfLLSGFEEIcw9/R5deVejoO2AxEAO9URVDi5NZty+Hpj/YRbLcw5Y4WkmCKKudO341zwTQ8h7YT1Lorjn73YI2UxgwhhBDl87eS+QettRd4TykVDIwCXq30qMQJZee6eOqjveQXenlmVAtaJjgCHZKowQy3i8I1syn8YS6W0EjCr36MoDbdpXophBDihE6aZCql+gDnATu01l8opezAPcCjQBaSZFa7NxYcJL/Qy79va8a5zSMCHY6owdwHtpK/YBrejH0Ed+hN2GWjsDpk5gIhhBAnd7J7lz8KTMBsHm+vlJoO9MTsj3mn1npBlUcojrFlbz7ntYggubV82YuqYRQXUrDqXYp+/hJLVBwR108kqEWnQIclhBDiLHKySuZdQA+t9VqlVFdgDfCQ1vrFqg9NlCevwEPGURdd2kqCKaqGa/evOBe+jPdoGiEXDCKs19+whEiXDCGEEBVzsiQzTmu9FkBr/aNSqgiYVvVhieOZMf8AXgM6SjO5qGTewjwKVrxF8YZlWGMaEXHzMwQldQh0WEIIIc5S/vTJtGDeRtKCOV8mSilryXrfQCBRDTbuymXFr9m0Sgjj4g51Ah2OqEGK9Q84l0zHyD9C6EXXEtr9RixBMueqEEKIU3eyJDMCcJd6bin1vOT+5XJ7j2qwauMRpny0l+hwG5P+1izQ4YgawpuXjXPpDFxbv8MW3wzHtY9jb9gq0GEJIYSoAU6WZEo2E0A6xcm6Hbn8b0sOer8TgHHDm1InIijAkYmznWEYFG/6moLlb2AUFxDaYwShXYdisVV4VjMhhBCiXCf8RtFa762uQMRf/eeTFJauzQIgymGjf6cYunWIlimLxGnzHk0nf9EruHetxda4LeED78cWlxTosIQQQtQw1Va2UEq1xrxDUCyQCYzQWm8vZ7vrMKdNKmmOv0xrnVZdcZ4JjuS5Wbo2i1YJYfzfkMa0bhwmE1+L02YYXorWLqTgm7fBMAjrexchnQZjsVhPuq8QQghRUdX57TIDeFVr3RpzAvfXy26glOoE/Avoq7XuAHQHjlZjjGeEZb4K5rU94lGJDkkwxWnzZO4n773HKFj6GvaENkSNmk5o5yslwRRCCFFlqqWSqZSKBy4A+voWfQS8opSqp7U+XGrTB4HntNapAFrrWpdgAny9PpuQIAvdO0QHOhRxljO8HqJ3rSZn+SosQcE4Bv+D4HMukx8uQgghqlyFkkylVCKQoLX+sYLHSQQOaK09AFprj1LqoG956SSzHbBbKbUac2T7Z8CTWmujgsc7a23f7yTlcBF9zq8riYA4Le60nTgXTCM2dSdBqhuOfn/HGhET6LCEEELUEn4lmUqpJMzq43mY/SQjlFLDgP5a6zsqMR4bcC5mxTMYWAzsA9719wU2bdp0ygdfu3btKe97urwGfPSDla2HLATboEO9dNauTa/y4wbynAOlpp+zxeOizq5V1Nn9HZ4gB5kdrye/QXvQu4HdgQ6v2tT097k8tfGchRBnLn8rma8DC4BLMAftACwDnvdz/xQgQSll81UxbUAj3/LS9gGfaK2LgCKl1BfAhVQgyezQoQMhIRWfRHrt2rUkJydXeL/K8tHKNH4/mEq39tHcOySBupFVP01RoM85EGr6ObtTtpC/4A28WfsJPvcywvrcwb4t22r0OZenpr/P5TnVcy4qKjqtH+dCCHE8/vb6vxCY4ru7jwF/9Jf0q9Og1jodWA/c6Ft0I/Brmf6YAB8ClyulLEqpIKAPsMHPGM9aW/bk8+6yVFo2CmP88CbVkmCKmsUoLsC5dAa5740BTzERN0wmfPCDWMMiAx2aEEKIWsrfSmYa0BLYVrJAKdUOs/Lor7uBd5RSjwPZwAjf6ywEHtda/wLMBjoBWwAvsASYWYFjnJU+XmXO0HTvVY2xWqUfpqgY1651OBe9jPfoYUI6DSas561YgsMCHZYQQohazt8k8zngK6XU04BdKXUjMA6Y4u+BtNZbgS7lLB9Y6rEXGO37qzX2pBbSsXkEKtER6FDEWcRbkEvBirco3rgca0xjIm95Fntiu0CHJYQQQgB+Jpla6/8qpTKBuzD7UY4AJmit51VlcLWB22OQleumk4oKdCjiLFK8dQ3OJdMxnDmEdruO0O43YrEHBzosIYQQ4g/+ji63aa2/AL6o4nhqnQ07cnF7DM5tFh7oUMRZwJuXhXPJa7j099jqt8BxwyTs9VsEOiwhhBDiGP42l6cqpeYCH2it11RlQLVNVp4LgOYNpQ+dOD7DMCj+bTkFy9/EcBUT1utvhHS5BovVFujQhBBCiHL5m2Rejjki/COllAdzgM6HWuvfqiyyWqDY7eWjleZcmFHh1XYbeXGW8RxJw7noZdy7f8XeuD2OQfdji20c6LCEEEKIE/K3T+avwK/AGKVUD8yEc6VS6pDW+tyqDLAm+3BFGoeyiunWPopoSTJFGYbXQ9HaBRR88w5YLIT1+zshFwyU+40LIYQ4K5xKZrMV+B1z+qJWlRtO7eH2GCz6KZNWCWH886amgQ5HnGE8GfvIX/gSnv2/Y2+ejGPAvdii4wMdlhBCCOE3fwf+1AGGAsOBrsBS4Bngy6oLrWZbvyOXHKeHB4fWl3uUiz8YHjeFP35C4XcfYQkOw3HFQwR36CWfESGEEGcdfyuZB4HvMe/IM1RrfaTqQqodVm08QniolQtayx1ZhMmdugPnVy/iSd9NUJvuOC6/G2tE3UCHJYQQQpwSf5PMFlrrQ1UaSS2ydlsuy9dl0ze5LsF26V9X2xmuIgq++5CiHz/D4ogmfOh4glW3QIclhBBCnJbjJplKqUu11qt9T9sqpdqWt53WemWVRFaDvb8iFYCrL64X4EhEoLn2bcK58CW8WQcI7ng5Yb1vl/uNCyGEqBFOVMmcDnTwPT7e/cMNoHmlRlQL7E8vonuHaJrJ3Ji1llHkpODrtylatwBrdH0ibvw3Qc3OD3RYQgghRKU5bpKpte5Q6nGz6gmn5svKdZFX6CEmSqYsqq1cO38hf9ErGDkZhHQeQliPEViCQwMdlhBCCFGp/OoQqJQq93aSSqnPKjecms3jNRj92nasFugs9yqvdbzOHPK/fJ68j5/AEhRK5IipOPreKQmmEEKIGsnfclqv4yzvWUlx1Aq5Tjdp2S6G965Pp9aSZNYWhmHg+v07nEtfwyjMI/TiGwi9+AYs9qBAhyaEEEJUmRMmmUqpSb6HwaUel2gO7K2SqGqoHQcKAGjXxBHgSER18eZm4lwyHde2H7E1aInjxn9jry/dmIUQQtR8J6tkJvr+ay31GMwBPynAv6ogphqpoMjDxPf2AKASwwMbjKhyhmFQvGEpBStmYnhchPW6jZAuV2Ox2gIdmhBCCFEtTphkaq1vA1BKfa+1frN6QqqZXvgkBbfHoEfHOkSESaJRk3myD+Fc+DLuvRuwJ3bAMeh+bDEJgQ5LCCGEqFYnmiezqdZ6j+/pCqVUuW18WutdVRFYTbP/cBEAj93QJMCRiKpieD0U/TKfglXvgsWKo///EXx+fywWmXBfCCFE7XOiSuZvQMms0Dswm8jL3kDZAKQsdxLOIg970wsZ0Dkm0KGIKuI5vJf8BdPwHNTYW3QifMC9WKNksn0hhBC114nmyYws9VhKMafht135GAZ0aCZ9MWsaw+Oi8IdPKPxuNpYQB44rHya4fU8slrK/x4QQQoja5ZRmBPc1nXtLNaeL4/B6Dd5eYt72XaYtqlncB7fhXDANz+E9BLW7FEffu7CG1wl0WEIIIcQZwd/J2D9SSnXzPb4N2AxsVkqNrMrgaoI1m4+yJ62QPufXJSpc7vJTExiuQpwrZpL7zkN4C3IJHzaBiKselQRTCCGEKMXfrKcPcKvv8WjgMuAIMI/j39dcAJ9/dxi7zcK9V8no4prAtXcjzoUv480+SPB5/XD0HoklVLpBCCGEEGX5m2QGa62LlVIJQIzWeg2AUqp+1YV29vN6DXanFtKtXTShwTI+6mxmFObj/HoWxb8uwlqnARHDnyKoacdAE4bJRQAAH79JREFUhyWEEEKcsfxNMtcrpcYCTYAFAL6EM6eqAqsJ9qUXUljsJblVRKBDEafBteMn8he9gpGXTUiXqwm79GYsQXK/cSGEEOJE/E0yRwKTARcwxrfsIuCDqgiqptiy1wlAh2aSZJ6NvPlHKVj+BsWbv8FarwkRQ8djb6QCHZYQQghxVvArydRa7wSGl1n2CfBJVQRVU2zZm0+dCDsNY4MDHYqoAMMwcG1ZjXPpDIwiJ6GX3ERot2ux2IICHZoQQghx1vB7uLNvVPktQAJwAHhPaz2rqgKrCbbszadtkkPmTDyLeHP+v707D5OqOvc9/q2qngcQmQRkUIHXAYeAiqg4RFGDJkejRtFoJjV6nGKSm+SYozGD90nuTW4ElSMaNV6nGM2JyRUU51mjtmhE9AWUeWxm6Oqu7q6q+8cuYtt209VtdVV11+/zPDxSm917/VZ1W7ysvdZeG4jOvZWmRW8QGTqWyqlXExk0KtexREREepy0ikwz+ylwIfA7YBnB3MwfmdlQd7+xG/P1WJu3N7FmUyNTJ/bPdRRJQzKZoPGduUSfvQviccpPuIjSw75CKKwFWyIiIl2R7kjmRcBx7r5s5wEzmwu8CKjIbMOCZXUAHDBSj7fJd/FNq4nOmUHz8vcoGnkQFVOvItJvSK5jiYiI9GjpFpmVQG2rYxuB8szG6T0WLI9SXBRin2F6i/JVMhEn9ubfqH/hPohEqJh6FSUHn6TpDSIiIhmQbpH5BHC/mf0EWE5wu/xGYG66DZnZWOAeoD9BgXqhuy9q51wD5gEz3f2H6baRTxYsrWPMsHJKirTtez6Kr19K3ezpxNcspHjM4VSccjnh6gG5jiUiItJrpFsBXQFsB/4J7ADeAeqAKzvR1m3Are4+FrgVmNXWSWYWSf3Zo524dl6JNSVYvLqe/XWrPP8kmql/8X623XU1ia3rqDz9x1Sedb0KTBERkQzrcCTTzPoC+wCXA98EBgAb3D2RbiNmNggYD0xJHXoQuMXMBrp769vwPwEeA6pSv3qcRauiNMeTKjLzTPNqZ8/XbqNhx3pKDjiO8imXEK7om+tYIiIivdIui0wzOxX4M8Hcy+3A6e7+XBfaGQ6scvc4gLvHzWx16vi/ikwzOxg4GTgeuK4L7TB//vyufBkANTU1Xf7all72EBChactiMnTJbpOpPuezUHMj/RY/S99lrxEuq2bN+POpH2jwweJcR8uaQvg+t6Y+i4jkVkcjmb8EfgzcBVxMMA/zyO4IYmbFwO3At1JFaJeuM27cOEpLSzv9dTU1NUyYMKFLbbb27OLl9KvezjFH5vfe1pnsc75qWvou0TkzSWxZS+n4qXzY7xDGTzwq17GyqhC+z62pz+mLxWKf6x/nIiLt6WhO5t7ufou7RwnmUY7uYjsrgGGp+ZY7510OTR3faQjBbfk5ZrYU+B5wsZnd3sU2c2L+kh28tXAbQ3bXLj+5lGjYQd2cGex44FoIhak6/9dUnHI5ySLtOS4iIpINHY1k/qsIdfdmM0t7h6CW3H29mb0DTAPuS/13Xsv5mO6+nGC+JwBmdgNQ1dNWlz/6yga2ReOcf8IeuY5SsBoXvk70iZkk6zZTesSZlE8+n1Bx50e3RUREpOs6KhorzOzFFq+rW73G3Y9Js61LgXvM7HpgM8EOQpjZHOB6d38rzevktdqtjYzds5zxY6pzHaXgJOq2EH1yFk0fvEhk4Cgqzr6OoiFjch1LRESkIHVUZH6n1es7u9qQu38ITGzj+NR2zr+hq23lSl1DnEUr6zn1CG0lmU3JZJLG95+n/qnbSTZGKTvmAsomnUkoUpzraCIiIgVrl0Wmu9+TrSC9weJVUZLAXnto3l+2JLbVUvf4LTR/9BaRYftSOfVqIgNH5DqWiIhIwevSHEtp2/3PrKO0OMRR4/Tsxe6WTCZofPtxos/dDckE5SdeQumhpxEKR3IdTURERFCRmVHvLamjb2WEvpW6Tdud4ptWEZ09g+YV8ykadQgVU68kspsWWomIiOQTFZkZsnl7EwCH79snx0l6r2QiTuwff6X+pfsJRYqpOPVqSg6aQigUynU0ERERaUVFZgbUx+Jce+fHAJw6UYt+ukPzuo+Jzp5OfO1iisdOouLkywhX670WERHJV2kVmWZWClxP8HzL/u7e18xOAsa6+y3dGbAneOX9rSxd18AXRldhw7VfeSYlm5toeOVPNLz2MKGyairP+A+K9z1Ko5ciIiJ5Lt2RzN8Dw4DzgcdTx95PHS/oIvOZtzfxu4dXMHJwGb/61t65jtOrNK/8gLrZ00lsXEHJgSdQfsJFhCs0HUFERKQnSLfIPAMY7e51ZpYAcPdVZjas+6L1DDP+upIBfYv5wdnDCYc1upYJycZ66l+4l9ibfyfcZwBV5/yc4n0OzXUsERER6YR0i8zG1uea2UBgY8YT9SBPvLmRxuYkUyfuzphhFbmO0ys0LZlHdM7NJLauo3TCaZQf9w1CpXpvRUREepp0i8yHCbaEvAbAzIYANwF/6q5g+W7Zugam//dKqssjnHHUoFzH6fES9dupf/ZOGt99ivDuw6j6+m8oHjEu17FERESki9ItMq8FfgO8B1QAi4A7gJ93U66896ZvA+CK0/ekrCSc4zQ9W6O/SvSJmSSjWymbdDZlk88jVFSS61giIiLyOaRVZLp7I3ANcE3qNvkGd092a7I8997HOygvDTP5QO3u01WJHZuJPnkbTR++TGTw3lSccwNFe4zOdSwRERHJgHQfYdR62XS1mQHg7h9nOlS+W7q2njd8O2OGletROl2QTCZpnP8s9U/dTrIpRtlx36Bs4lcJRfTYVhERkd4i3b/VFwNJoGVFtXMks6A2i94ebebymxcC8P2zhuc4Tc8T37qe6OO30PxxDZE996Ny6tVEBuh9FBER6W3SvV3+qUmHZrYH8DPgpe4Ilc8ee30jiQQcZtWM2qM813F6jGQyQaxmDvXP/xGSScqnfJfSQ08jFNJ8VhERkd6oS/cn3X2tmX0PWAg8kNlI+e31D7Ziwyv4xTf14PV0xTeuJDp7Bs0r36dor/FUfOkKIrsNznUsERER6UafZxKcEaw0Lxgfra5n4cp6vnHSHrmO0iMk483E/vHf1L/0AKHiUipOu4aSA0/QPFYREZECkO7Cn5f4ZA4mBMXlAcAvuiNUvnpvyQ4AJu6nrQ070rz2I6KzpxNf9xHF+x5FxUmXEq7aPdexREREJEvSHcn8Q6vXdcC77r4ow3ny2sKVUcpLwowaXJbrKHkr2dxIw8sP0vDaI4Qq+lD51Wsp2feoXMcSERGRLOuwyDSzCPBF4BJ3j3V/pPy1bG0DIwaV6nZvO5pXLKBu9nQSm1ZSctAUyk/4DuHy6lzHEhERkRzosMh097iZnQQkspAnb22ta+bjtQ2cdczAXEfJO8lYlPrn7yFWM5tw34FUnftLivcen+tYIiIikkPp3i7/PfBzM/uZuzd1Z6B8taK2AYDhA3WrvKWmj2uIzrmZxLYNlB76ZcqPu5BQiR7tJCIiUuh2WWSa2TR3fxC4EtgD+L6Z1dJiEZC7j+jeiPlhW10cgL2HqMgESNRvp/7pO2h87xnC/fek+oL/RdHw/XMdS0RERPJERyOZs4AHga9nIUte2zmS2adSWx82fvgK0bkzSdZvp+zIcyg7+lxCRSW5jiUiIiJ5pKOKKQTg7i9kIUteW72hEYDdqgq3yEzs2ER07n/R5K8S2WMfKs79BUWD98l1LBEREclDHVVMETM7nk/vWf4p7v5sZiPlp1UbYvSrKqKkqPC2QUwmkzS+9zT1T99BsqmR8uO/SenErxIKF9S29SIiItIJHRWZpcCdtF9kJoGC2F/xozX1DOxbnOsYWRffso7o4zfTvGQeRcMPoGLq1UT6D8t1LBEREclzHRWZde5eEEXkrsSaEjQ0JthvRGWuo2RNMhEnVjOb+ufvgVCI8pMvo3T8VEKhwhvJFRERkc4r3AmGnbBmY/AM+tHDCuPRPPENy6mbM4P4yg8o2udQKk+5nHDfQbmOJSIiIj1IWgt/Ct2ydcHKchtekeMk3SsZb6bh9UdoePlBQiXlVHzlB5QccLx2OBIREZFO22WR6e4Z2xPQzMYC9wD9gY3Aha33Pjez64BzgTjQBFzr7nMzlaGrXnl/KwDDBpTmOEn3aV67mOhjNxFfv4Ti/SZTcdKlhCt3y3UsERER6aGyOcHuNuBWdx8L3ErwDM7W3gAOc/eDgG8DD5lZzu9Rr94QY/fqIirLet9q6mRTjOhzd7P97mtIRLdSeeZ/UnXGT1RgioiIyOeSlTmZZjYIGA9MSR16ELjFzAa6e+3O81qNWv6T4HZ9f2BlNnK2ZWtdMx+t6Z17lpdtXsq2O2eR2LSKkoNPovyE7xAuq8p1LBEREekFsrXwZziwyt3jAO4eN7PVqeO17XzNhcBH7p6zAhPgLd8GwJH7981ljIxKxqLUP/dHhr49G3YbTNW0Gyne65BcxxIREZFeJC9Xl5vZscAv+WTkM23z58/vcrs1NTWfOfbUG2GKIyG2r/+Qmg1dvnTeKK9dyMAF/49Iwza2jpzE5tEnkNwUh02f7Xtv1db3ubdTnwtDIfZZRPJXtorMFcAwM4ukRjEjwNDU8U8xs0nAfcC/ubt3tqFx48ZRWtr5BTo1NTVMmDDhM8enP7WAA0aVcvhhPXv7xER0G/VP30Hj/GcJDxhB5TnXs2RtXZt97s3a+z73ZupzYehqn2Ox2Of6x7mISHuysvDH3dcD7wDTUoemAfNazscEMLPDgIeAs9z97Wxk25XVG2PUbm1iWP+SXEfpsmQySeOCl9h2+6U0LniBsqOn0efbMygatm+uo4mIiEgvls3b5ZcC95jZ9cBmgjmXmNkc4Hp3fwuYCZQDs8xs59dd4O7vZTHnv6zf3AjA/qN65k4/ie0bic6dSdPC14kMGUPFeTdSNGivXMcSERGRApC1ItPdPwQmtnF8aovfH5atPOnYtK0JgL2H5PwpSp2STCZpfPdJ6p+5k2S8ifIvfpvSw08nFO59j2ASERGR/JSXC3/yxb3PrAOgf5/iHCdJX3zzGqJzbqZ52bsUjTiQiqlXEdl9aK5jiYiISIFRkdmOeCLJxm1NjB5WTnVF/r9NyUSc2Ft/p/6FeyEUpuKUKyj5wsmEQtl83r6IiIhIIP+rpxzZtK2JpuYkXzwk/3e+idcuo272dOKrneLRh1FxyhWE+wzIdSwREREpYCoy27F+SzAfc2j/shwnaV8y3kTDqw/T8MpDhEorqPy3/0Hx/scSCoVyHU1EREQKnIrMdqzeGANgcL/8nI/ZvHoh0dnTidcupXj/Y6mY8l3Clb1nVyIRERHp2VRktmNjamV5vi36STY1UP/i/cTeeJRQVT8qz76ekjGfWbQvIiIiklMqMtuxaXsT4TBUluXPY3+alv2T6JybSWxeTckhp1DxxW8TKuuZz/AUERGR3k1FZjs2bWumqixCOJz7+Y3Jhjqiz91N47zHCfcbQtV5/5PiUQfnOpaIiIhIu1RktmPjtkYqy3M/itm46A2iT9xCcsdmSid+lfJjzidUnL+LkURERERARWabVm9swFfUc9qk/jnLkKjbSvSpWTQteIHwwJFUnflTioZax18oIiIikgdUZLbh7YU7SAJfPiL7z5pMJpM0LXiB6JOzSMailE0+n7IjzyYUya8FSCIiIiK7oiKzDcvWNxAOwZD+pVltN7FtA9EnbqVp8RtEho6lcurVRAaNymoGERERkUxQkdmGlbUxBvQtpiiSnUU/yWSCxnfmEn32LojHKT/xIkoP/QqhcO7nhIqIiIh0hYrMNqzZFGOP3Uuy0lZ802qic2bQvPw9ikYeTMXUK4n0G5KVtkVERES6i4rMVprjSWq3NjFhbJ9ubSeZiBN782/Uv3AfRCJUTL2KkoNP0paQIiIi0iuoyGxl7aYYiQSMGtx9jwmKr19K3ezpxNcspHjMRCpO+XfC1dlfZCQiIiLSXVRktrJkbQMAew/JfJGZbG6i4dWHaHj1z4TKqqg8/ccU7zdZo5ciIiLS66jIbGXZuqDIHDm4PKPXbV71IXWzZ5DYsIySccdTfuLFhCv6ZrQNERERkXyhIrOV9VsaKSkKUVkWzsj1ko0N1L94L7E3/kaouj9VX/sZxaMPz8i1RURERPKViswWdtTHefejHexWVZSRW9hNS98lOmcGiS1rKR0/lfLjv0WotCIDSUVERETym4rMFh57fQPrtzRx4ZQ9Ptd1Eg07qH/2LhrfmUt496FUff03FI8Yl6GUIiIiIvlPRWYL6zY3AnDmMQO7fI3Gha8RfWImybotlB5xJuWTzydUnN2dg0RERERyTUVmCytrGxiyewklRZ2fj5mo20L0yVk0ffAikUF7UXH29RQNGdMNKUVERETyn4rMFtZtaWL4gM6NOiaTSRrff476p24n2VhP2TEXUDbpLEIRvbUiIiJSuFQJpTTFoXZLE0fsl/5OP4lttdQ9fgvNH71FZNi+VE69msjAEd2YUkRERKRnUJGZsmBVsJq8f5/iDs9NJhM0vv040efuhmSC8hMvofTQ0wiFI90dU0RERKRHUJGZsqQ2KDKP2G/XD0iPb1xFdM4MmlfMp2jUIVRMvZLIbp9vNbqIiIhIb6MiM2XFxhCjBpcxsp09y5OJOLF//JX6l+4nFCmm4tSrKTloiraEFBEREWmDisyUHTEYPbykzT9rXvcx0dk3EV/7EcVjJ1Fx8mWEq/tnOaGIiIhIz6EiE2iOJ6lvhP59Pl1kJpubaHjlTzS89jChsmoqz/gPivc9SqOXIiIiIh1QkQms2RgjkQwxcvAnjy9qXvkBdbOnk9i4gpIDT6D8hIsIV6S/8lxERESkkGWtyDSzscA9QH9gI3Chuy9qdU4EmAGcAiSBX7v7H7o729a6ZgAG9ysh2VhP/Qv3Envz74T7DKDqnJ9TvM+h3R1BREREpFfp/NY2XXcbcKu7jwVuBWa1cc75wGhgDDAJuMHMRnV3sGgsAUDfTe+z7Y7Lib35N0onnEqfi2eqwBQRERHpgqwUmWY2CBgPPJg69CAw3sxabxJ+DnCHuyfcvRZ4FDi7u/NFt+/g4tK/0P/ZX0GkiKqv/4aKky8jVFrR3U2LiIiI9ErZul0+HFjl7nEAd4+b2erU8doW540AlrV4vTx1Ttrmz5/f6XANH3zAMcXzWD9iMnVjjyNZG4Pamk5fpyeqqSmMfrakPhcG9VlEJLd63cKfcePGUVrayf3Hx3+BV1/am6OOObKbUuWnmpoaJkyYkOsYWaU+Fwb1OX2xWKxL/zgXEelItuZkrgCGpRb27FzgMzR1vKXlwMgWr0e0cU7GhUJhyio7V5iKiIiISPuyUmS6+3rgHWBa6tA0YF5q3mVLDwMXm1k4NV/zdOCRbGQUERERkczJ5uryS4ErzWwhcGXqNWY2x8x2LuG+F/gYWAS8DvzC3ZdkMaOIiIiIZEDW5mS6+4fAxDaOT23x+zhwWbYyiYiIiEj3yOZIpoiIiIgUCBWZIiIiIpJxKjJFREREJON603MyIwCNjY1dvkAsFstYmJ5CfS4M6nNh6EqfW3xmRjIaRkQKXiiZTOY6Q0bU1NQcDbyU6xwiIj3U5AkTJryc6xAi0nv0ppHMN4HJwBognuMsIiI9RQQYQvAZKiKSMb1mJFNERERE8ocW/oiIiIhIxqnIFBEREZGMU5EpIiIiIhmnIlNEREREMk5FpoiIiIhknIpMEREREck4FZkiIiIiknG96WHsHTKzscA9QH9gI3Chuy9qdU4EmAGcAiSBX7v7H7KdNVPS7PN1wLkED7FvAq5197nZzpop6fS5xbkGzANmuvsPs5cys9Lts5l9DbgOCBH8fJ/o7uuymTVT0vzZHgTcDQwHioHngKvcvTnLcT83M/stcCYwCjjQ3ee3cU6v+vwSkZ6t0EYybwNudfexwK3ArDbOOR8YDYwBJgE3mNmorCXMvHT6/AZwmLsfBHwbeMjMyrOYMdPS6fPOv5BnAY9mMVt36bDPZnYocAMwxd3HAUcDW7MZMsPS+T5fC3yQ+tk+CJgAfDV7ETPqUeAYYNkuzultn18i0oMVTJGZGtEYDzyYOvQgMN7MBrY69RzgDndPuHstwQf72dlLmjnp9tnd57p7NPXynwSjXP2zFjSDOvF9BvgJ8BiwMEvxukUn+nwN8Ft3Xwvg7lvdvSF7STOnE31OAtVmFgZKgRJgVdaCZpC7v+zuKzo4rdd8folIz1cwRSbB7bJV7h4HSP13dep4SyP49EjB8jbO6SnS7XNLFwIfufvKLOTrDmn12cwOBk4Gfp/1hJmX7vd5f2BvM3vRzN42s/80s1CWs2ZKun3+JTAWWAOsBea6+yvZDJplvenzS0R6uEIqMqUDZnYswV/K03KdpTuZWTFwO3DpziKlQEQIbhlPAY4FvgRckNNE3e9sgtH5IcAw4BgzOyu3kURECkMhFZkrgGGpeXg75+MNTR1vaTkwssXrEW2c01Ok22fMbBJwH3C6u3tWU2ZWOn0eAuwDzDGzpcD3gIvN7PbsRs2YzvxsP+LuMXffDvwNODyrSTMn3T5fCdyfun28laDPx2c1aXb1ps8vEenhCqbIdPf1wDt8Mko3DZiXmrfU0sMEBUc4Nb/rdOCR7CXNnHT7bGaHAQ8BZ7n729lNmVnp9Nndl7v7AHcf5e6jgJsI5rFdkvXAGdCJn+0HgJPMLJQazT0BeDd7STOnE31eQrDSGjMrAU4EPrMquxfpNZ9fItLzFUyRmXIpcKWZLSQY4bgUwMzmpFbeAtwLfAwsAl4HfuHuS3IRNkPS6fNMoByYZWbvpH4dmJu4GZFOn3ubdPr8J2A9sICgQHsfuDMHWTMlnT5/D5hsZu8R9HkhcEcuwn5eZjbDzFYCewJPm9n7qeO9+fNLRHqwUDKZzHUGEREREellCm0kU0RERESyQEWmiIiIiGScikwRERERyTgVmSIiIiKScSoyRURERCTjVGRKj2Zmz5vZRbnOsStmdr6ZPbmLP59sZj35AfgiIiKfoUcYSd5I7b4zGGi51eNYd1+9i695HrjP3f+QwRzPA0cAzUAD8CJwubuvydD1k8AYd1+cievtop0bgJ8CMYK+LAB+4O6vpfn1WckpIiK9k0YyJd982d2rWvxqt8DsZle4exUwFtgN+H2OcnxeD6X6MQB4jmBHGBERkW5XlOsAIrtiZv0IdjGZSPDz+gpwqbuvbOPc0QQ72BwCNAHPuPs5qT/bF7gZmADUAte5+587at/dN5nZX4DLUtc5EphOUHwuBK5291dTf/ZN4HpgILAB+E93vz91/CJ3P9rMXkxd+t3USOF3gHUEo7F7mtmPgcPc/awW/ZoOhNz9KjPrC/wfYCqQAO4GfubuLUd/2+pHs5ndD1xrZgPdvdbMDk/1ZT+gHvgL8H13b2wrp7s/ZGanAb8CRhGMjF7q7v/s6H0UEZHCo5FMyXdhgkJqJDCCoBi6pZ1zfwk8CfQj2HrvZgAzqwSeIti7exBwLjDTzPbvqHEzGwCcCcwzs92B2cAMoD9BsTfbzPqn2pgBfMndq4EjCbYx/BR3Pyb124NTI7UPtTrlT8BUM6tOtR8BvpbKDvBHglvfo4EvACcBHc5JTe3bfSGwEdicOhwHriEY5ZxEsJf5v7eX08y+ANwFfDfV/1nA382stKP2RUSk8GgkU/LNo2bWnPr98+5+OsEIGwBmdiPBbd+2NBEUo0NTI50vp46fBix197tTr+elRifPBn7ezrVmmNlvgTrgeeD7wKnAIne/N3XOg2Z2FfBlgtvQCWCcmS1Pzd/s9BxOd19mZm8DZwD/F/giEHX3181sMMEI5m7uXg/UmdnvgUsICr62fC01+lgNbAHOdPfmVFs1Lc5bamazgGOBm9q51iXALHf/R+r1PWZ2LcH81Rc621cREendVGRKvjnd3Z/e+cLMKgjmQ55CMEIJUG1mkTZuEf+IYDTzDTPbDPzO3e8iKDwnmtmWFucWEdyGb89VrRcTmdlQYFmr85YBw9y9zszOAX4I3GlmrxAssvkwjT639gAwjaDIPI9PRjFHAsXAGjPbeW4YWLGLa/3Z3b+eGpH9C8F0gedT/RlLMBp7KFBB8J7UtHOdne1/w8yubHGsBBiabsdERKRwqMiUfPcDwICJ7r7WzA4B5gGh1ie6+1rgYgAzOxp4OjW3cAXwgrtP+ZxZVhMUWi2NAJ5ItT8XmGtm5QTzFu8AJnehnYeB35nZngQjmpNSx1cQrBQfsHM0Ml3uvsHMLgHeMrMHUiOt/0XwXk5z9+1m9j3grF1cZgVwo7vf2Mn+iIhIAVKRKfmummAe5pbUnMiftXeimZ0NvJa6Vb4ZSBLcwn4M+LWZXUAw5xGCxUE73P2DTmSZA9xsZucBfyaYq7k/8FjqVvYRwNOpvDtSbbdlHbA30OajgVKLcp4nmIu6ZGdGd1+Tet7m78zsulQbewF7unuHt6vd3c1sLsGI7zUE7+02YEdqYdRlBIui2st5B/BXM3saeINg9PM44EV3395R+yIiUli08Efy3U1AOcFq7ddJjRq24zDgH2a2A/g7wcrvj1MF0EkEC35WA2uB3wCdWrDi7hsJ5nf+gGABzY+A09x9A8H/S99PXX8TwdzGy9q51A0E8xm3mNnX2jnnAeBEPrlVvtOFBLeoFxAU0o8AQzrRjf8NXGJmgwhu7Z8HbCcoIFsvQvpUTnd/i2Ck+JZU24uBb3aibRERKSB6GLuIiIiIZJxGMkVEREQk41RkioiIiEjGqcgUERERkYxTkSkiIiIiGaciU0REREQyTkWmiIiIiGScikwRERERyTgVmSIiIiKScSoyRURERCTj/j8tIoLaLZ/HiAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.133 and 0.848 for XGBoost Classifer(top 250) on Log Transformed RE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LN2FW-s2-V3G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "811e3118-9a35-4a51-fa09-eb8334dc6b2c"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.524004</td>\n",
              "      <td>0.823513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.141823</td>\n",
              "      <td>0.803198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.136824</td>\n",
              "      <td>0.836372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.132661</td>\n",
              "      <td>0.846845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LGBM Classifer</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.133117</td>\n",
              "      <td>0.845982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest Classifier (top 250)</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.136456</td>\n",
              "      <td>0.837322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XGBoost Classifer(top 250)</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.132627</td>\n",
              "      <td>0.847824</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Model             Dataset  log_loss       auc\n",
              "0                  Logisic Regression  Log Transformed RE  0.524004  0.823513\n",
              "1            Decision Tree Classifier  Log Transformed RE  0.141823  0.803198\n",
              "2            Random Forest Classifier  Log Transformed RE  0.136824  0.836372\n",
              "3                   XGBoost Classifer  Log Transformed RE  0.132661  0.846845\n",
              "4                      LGBM Classifer  Log Transformed RE  0.133117  0.845982\n",
              "5  Random Forest Classifier (top 250)  Log Transformed RE  0.136456  0.837322\n",
              "6          XGBoost Classifer(top 250)  Log Transformed RE  0.132627  0.847824"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mYW7_x88-V3J"
      },
      "source": [
        "# LightGBM Classifier (top 250)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "EYaevQf8-V3J",
        "colab": {}
      },
      "source": [
        "model = lgb.LGBMClassifier(n_jobs=-1,\n",
        "                      nthread=-1,\n",
        "                      scale_pos_weight=1,\n",
        "                      learning_rate=0.01,  \n",
        "                      colsample_bytree = 0.5, \n",
        "                      subsample = .8,\n",
        "                      objective='binary', \n",
        "                      n_estimators=447,\n",
        "                      reg_alpha = .3,\n",
        "                      max_depth=5\n",
        "                     )\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JhKg3-Lv-V3M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ae951142-3f6f-4c0d-cc24-fbe5ee326a23"
      },
      "source": [
        "#https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n",
        "\"\"\"\n",
        "manually changed each parameters 1 by 1 and compared the validation auc\n",
        "and selected the best parameter value\n",
        "\"\"\"\n",
        "eval_metric = ['auc','logloss']\n",
        "%time model.fit(X_train_200,y_train,eval_set=[(X_val_200,y_val)],eval_metric=eval_metric,verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]\tvalid_0's auc: 0.797802\tvalid_0's binary_logloss: 0.165817\n",
            "[2]\tvalid_0's auc: 0.801098\tvalid_0's binary_logloss: 0.165197\n",
            "[3]\tvalid_0's auc: 0.800429\tvalid_0's binary_logloss: 0.164549\n",
            "[4]\tvalid_0's auc: 0.800665\tvalid_0's binary_logloss: 0.163933\n",
            "[5]\tvalid_0's auc: 0.818105\tvalid_0's binary_logloss: 0.163439\n",
            "[6]\tvalid_0's auc: 0.817232\tvalid_0's binary_logloss: 0.162938\n",
            "[7]\tvalid_0's auc: 0.818755\tvalid_0's binary_logloss: 0.162415\n",
            "[8]\tvalid_0's auc: 0.82007\tvalid_0's binary_logloss: 0.161866\n",
            "[9]\tvalid_0's auc: 0.821862\tvalid_0's binary_logloss: 0.161375\n",
            "[10]\tvalid_0's auc: 0.821913\tvalid_0's binary_logloss: 0.160893\n",
            "[11]\tvalid_0's auc: 0.821918\tvalid_0's binary_logloss: 0.160427\n",
            "[12]\tvalid_0's auc: 0.823639\tvalid_0's binary_logloss: 0.15994\n",
            "[13]\tvalid_0's auc: 0.827841\tvalid_0's binary_logloss: 0.159453\n",
            "[14]\tvalid_0's auc: 0.828315\tvalid_0's binary_logloss: 0.159\n",
            "[15]\tvalid_0's auc: 0.829432\tvalid_0's binary_logloss: 0.158547\n",
            "[16]\tvalid_0's auc: 0.830931\tvalid_0's binary_logloss: 0.158114\n",
            "[17]\tvalid_0's auc: 0.830934\tvalid_0's binary_logloss: 0.157695\n",
            "[18]\tvalid_0's auc: 0.831262\tvalid_0's binary_logloss: 0.157267\n",
            "[19]\tvalid_0's auc: 0.831284\tvalid_0's binary_logloss: 0.156884\n",
            "[20]\tvalid_0's auc: 0.83209\tvalid_0's binary_logloss: 0.156497\n",
            "[21]\tvalid_0's auc: 0.831874\tvalid_0's binary_logloss: 0.156141\n",
            "[22]\tvalid_0's auc: 0.832007\tvalid_0's binary_logloss: 0.155792\n",
            "[23]\tvalid_0's auc: 0.831565\tvalid_0's binary_logloss: 0.155462\n",
            "[24]\tvalid_0's auc: 0.831603\tvalid_0's binary_logloss: 0.155154\n",
            "[25]\tvalid_0's auc: 0.831518\tvalid_0's binary_logloss: 0.154805\n",
            "[26]\tvalid_0's auc: 0.830815\tvalid_0's binary_logloss: 0.154483\n",
            "[27]\tvalid_0's auc: 0.831571\tvalid_0's binary_logloss: 0.154148\n",
            "[28]\tvalid_0's auc: 0.832504\tvalid_0's binary_logloss: 0.15383\n",
            "[29]\tvalid_0's auc: 0.832812\tvalid_0's binary_logloss: 0.153511\n",
            "[30]\tvalid_0's auc: 0.832732\tvalid_0's binary_logloss: 0.153197\n",
            "[31]\tvalid_0's auc: 0.833267\tvalid_0's binary_logloss: 0.152895\n",
            "[32]\tvalid_0's auc: 0.83309\tvalid_0's binary_logloss: 0.152611\n",
            "[33]\tvalid_0's auc: 0.833004\tvalid_0's binary_logloss: 0.152332\n",
            "[34]\tvalid_0's auc: 0.83291\tvalid_0's binary_logloss: 0.152098\n",
            "[35]\tvalid_0's auc: 0.83304\tvalid_0's binary_logloss: 0.151843\n",
            "[36]\tvalid_0's auc: 0.833244\tvalid_0's binary_logloss: 0.151559\n",
            "[37]\tvalid_0's auc: 0.833631\tvalid_0's binary_logloss: 0.151264\n",
            "[38]\tvalid_0's auc: 0.833971\tvalid_0's binary_logloss: 0.150999\n",
            "[39]\tvalid_0's auc: 0.833999\tvalid_0's binary_logloss: 0.150719\n",
            "[40]\tvalid_0's auc: 0.834592\tvalid_0's binary_logloss: 0.150435\n",
            "[41]\tvalid_0's auc: 0.834668\tvalid_0's binary_logloss: 0.150181\n",
            "[42]\tvalid_0's auc: 0.834836\tvalid_0's binary_logloss: 0.149951\n",
            "[43]\tvalid_0's auc: 0.834906\tvalid_0's binary_logloss: 0.149761\n",
            "[44]\tvalid_0's auc: 0.834689\tvalid_0's binary_logloss: 0.149541\n",
            "[45]\tvalid_0's auc: 0.834744\tvalid_0's binary_logloss: 0.149345\n",
            "[46]\tvalid_0's auc: 0.83462\tvalid_0's binary_logloss: 0.149134\n",
            "[47]\tvalid_0's auc: 0.834491\tvalid_0's binary_logloss: 0.148938\n",
            "[48]\tvalid_0's auc: 0.834484\tvalid_0's binary_logloss: 0.148712\n",
            "[49]\tvalid_0's auc: 0.834837\tvalid_0's binary_logloss: 0.148519\n",
            "[50]\tvalid_0's auc: 0.834908\tvalid_0's binary_logloss: 0.148304\n",
            "[51]\tvalid_0's auc: 0.834638\tvalid_0's binary_logloss: 0.148112\n",
            "[52]\tvalid_0's auc: 0.834881\tvalid_0's binary_logloss: 0.147942\n",
            "[53]\tvalid_0's auc: 0.835029\tvalid_0's binary_logloss: 0.147742\n",
            "[54]\tvalid_0's auc: 0.835166\tvalid_0's binary_logloss: 0.147541\n",
            "[55]\tvalid_0's auc: 0.835538\tvalid_0's binary_logloss: 0.147325\n",
            "[56]\tvalid_0's auc: 0.835639\tvalid_0's binary_logloss: 0.14712\n",
            "[57]\tvalid_0's auc: 0.835736\tvalid_0's binary_logloss: 0.146934\n",
            "[58]\tvalid_0's auc: 0.835891\tvalid_0's binary_logloss: 0.14675\n",
            "[59]\tvalid_0's auc: 0.835812\tvalid_0's binary_logloss: 0.146579\n",
            "[60]\tvalid_0's auc: 0.835986\tvalid_0's binary_logloss: 0.14639\n",
            "[61]\tvalid_0's auc: 0.83606\tvalid_0's binary_logloss: 0.146229\n",
            "[62]\tvalid_0's auc: 0.836088\tvalid_0's binary_logloss: 0.146053\n",
            "[63]\tvalid_0's auc: 0.836183\tvalid_0's binary_logloss: 0.145889\n",
            "[64]\tvalid_0's auc: 0.836422\tvalid_0's binary_logloss: 0.145712\n",
            "[65]\tvalid_0's auc: 0.836528\tvalid_0's binary_logloss: 0.145565\n",
            "[66]\tvalid_0's auc: 0.836758\tvalid_0's binary_logloss: 0.145388\n",
            "[67]\tvalid_0's auc: 0.836745\tvalid_0's binary_logloss: 0.145234\n",
            "[68]\tvalid_0's auc: 0.836461\tvalid_0's binary_logloss: 0.14511\n",
            "[69]\tvalid_0's auc: 0.836466\tvalid_0's binary_logloss: 0.144993\n",
            "[70]\tvalid_0's auc: 0.83658\tvalid_0's binary_logloss: 0.14484\n",
            "[71]\tvalid_0's auc: 0.836721\tvalid_0's binary_logloss: 0.144703\n",
            "[72]\tvalid_0's auc: 0.836725\tvalid_0's binary_logloss: 0.144549\n",
            "[73]\tvalid_0's auc: 0.836624\tvalid_0's binary_logloss: 0.144432\n",
            "[74]\tvalid_0's auc: 0.836673\tvalid_0's binary_logloss: 0.144303\n",
            "[75]\tvalid_0's auc: 0.836574\tvalid_0's binary_logloss: 0.144182\n",
            "[76]\tvalid_0's auc: 0.836659\tvalid_0's binary_logloss: 0.144036\n",
            "[77]\tvalid_0's auc: 0.836668\tvalid_0's binary_logloss: 0.143889\n",
            "[78]\tvalid_0's auc: 0.836736\tvalid_0's binary_logloss: 0.143744\n",
            "[79]\tvalid_0's auc: 0.836796\tvalid_0's binary_logloss: 0.14362\n",
            "[80]\tvalid_0's auc: 0.837047\tvalid_0's binary_logloss: 0.14349\n",
            "[81]\tvalid_0's auc: 0.837024\tvalid_0's binary_logloss: 0.143382\n",
            "[82]\tvalid_0's auc: 0.837066\tvalid_0's binary_logloss: 0.14329\n",
            "[83]\tvalid_0's auc: 0.83746\tvalid_0's binary_logloss: 0.143153\n",
            "[84]\tvalid_0's auc: 0.837413\tvalid_0's binary_logloss: 0.143039\n",
            "[85]\tvalid_0's auc: 0.837393\tvalid_0's binary_logloss: 0.142924\n",
            "[86]\tvalid_0's auc: 0.837616\tvalid_0's binary_logloss: 0.142796\n",
            "[87]\tvalid_0's auc: 0.837788\tvalid_0's binary_logloss: 0.142696\n",
            "[88]\tvalid_0's auc: 0.837844\tvalid_0's binary_logloss: 0.142571\n",
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            "[93]\tvalid_0's auc: 0.838118\tvalid_0's binary_logloss: 0.141995\n",
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            "[108]\tvalid_0's auc: 0.839224\tvalid_0's binary_logloss: 0.140532\n",
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            "[430]\tvalid_0's auc: 0.846808\tvalid_0's binary_logloss: 0.133184\n",
            "[431]\tvalid_0's auc: 0.846806\tvalid_0's binary_logloss: 0.133182\n",
            "[432]\tvalid_0's auc: 0.846793\tvalid_0's binary_logloss: 0.133184\n",
            "[433]\tvalid_0's auc: 0.8468\tvalid_0's binary_logloss: 0.13318\n",
            "[434]\tvalid_0's auc: 0.846786\tvalid_0's binary_logloss: 0.133178\n",
            "[435]\tvalid_0's auc: 0.846759\tvalid_0's binary_logloss: 0.133182\n",
            "[436]\tvalid_0's auc: 0.846777\tvalid_0's binary_logloss: 0.133177\n",
            "[437]\tvalid_0's auc: 0.846774\tvalid_0's binary_logloss: 0.133174\n",
            "[438]\tvalid_0's auc: 0.846778\tvalid_0's binary_logloss: 0.133173\n",
            "[439]\tvalid_0's auc: 0.846731\tvalid_0's binary_logloss: 0.133172\n",
            "[440]\tvalid_0's auc: 0.84676\tvalid_0's binary_logloss: 0.133169\n",
            "[441]\tvalid_0's auc: 0.846762\tvalid_0's binary_logloss: 0.13317\n",
            "[442]\tvalid_0's auc: 0.846757\tvalid_0's binary_logloss: 0.13317\n",
            "[443]\tvalid_0's auc: 0.846788\tvalid_0's binary_logloss: 0.133163\n",
            "[444]\tvalid_0's auc: 0.846787\tvalid_0's binary_logloss: 0.133164\n",
            "[445]\tvalid_0's auc: 0.846875\tvalid_0's binary_logloss: 0.133152\n",
            "[446]\tvalid_0's auc: 0.846872\tvalid_0's binary_logloss: 0.133147\n",
            "[447]\tvalid_0's auc: 0.846871\tvalid_0's binary_logloss: 0.133145\n",
            "CPU times: user 40.6 s, sys: 171 ms, total: 40.8 s\n",
            "Wall time: 21.4 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.5,\n",
              "               importance_type='split', learning_rate=0.01, max_depth=5,\n",
              "               min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
              "               n_estimators=447, n_jobs=-1, nthread=-1, num_leaves=31,\n",
              "               objective='binary', random_state=None, reg_alpha=0.3,\n",
              "               reg_lambda=0.0, scale_pos_weight=1, silent=True, subsample=0.8,\n",
              "               subsample_for_bin=200000, subsample_freq=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OFf2WKQC-V3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "9702c74d-4771-4695-cd0e-0353da2e9161"
      },
      "source": [
        "y_pred = model.predict_proba(X_val_200)[:,1]\n",
        "label=\"LightGBM Classifer(top 250)\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "i+=1\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t LightGBM Classifer(top 250) on Log Transformed RE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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r167dvnv37qDRo0e37dGjx8/h4eHusustXry43o4dO0KXLVu2IyAggCeffDL+tddei3vllVcOl67zwgsvNGnfvn3+3r17TyXtzz777FHgKEBaWpq1T58+lw4fPjzL13OE6htwtBCYztmnQrodaAW0BqKBLUqpVVrrg1UfnhBCiMry1caM6NWbs2KqYt99rojK+MNVMZnnu/2111576YwZM/ZceumlRTt27LA9/vjjzYuKiiytW7e2HzlyJOj+++8/XpqMbNq0KXzmzJmNMjMzA3v37p317LPPHp01a1b0nj17QqZOndr0zTffdD366KMpGzduDO/UqVPumDFjTsUVERHhnjhx4tHyYnA6naaioiJzZGTkqaQtODjY3aJFi8Kvv/46ctCgQTnz5s2LGTx4cOZrr70WUt4+Zs6cGdupU6fc22+//dSXflxcnPOOO+743bWZO3du1EcffdTA6XSaAR599NGUPn365LlcLh5//PGmycnJ4QEBAUZwcLB74cKFu06cOGEdN25cy+zsbCtA586d81588cWU2bNnR69bty5y5syZ+2+88UZVUlJiGj16dJuuXbvmTp48+ciHH34YPX/+/FiXy2UKCwtzTZ48+VDbtm2LZ8+eHb1kyZLokJAQ15EjR2xTp07dv3PnzuDLLrssPyQkxMjIyLD85z//aWy32y0DBgxo36lTp7wpU6akLF++PGL69OnxbrebyMhI5wsvvHCodevWxWvXrg1/6aWXElq1amXfvXt3qM1mc0+ZMuVAhw4dik4/97KJZbdu3fIzMjICXS4XFsuZC5cnTpyw7tmzJ+Tmm2/OArj55puzXnnllaZpaWnWBg0aOEeMGJE1YsSI9mdKPjMyMixbtmyJWLJkyfYbbrgh8fjx49a4uDifEvR///vfcffcc8+x0sQT4PLLLy8EfEo8AVavXh314osvHgBo06ZNcZs2bQpWrFgROXLkyN8k9iaTCYfDYS4qKjKbzWa33W63NGnS5NRcv+vXrw9LSUmx3X333SdeeeWVcj+H8+bNi77iiivyfD2/UtWSfGqtvwFQ6qyV+VuAd7TWbiBdKbUQuAl4peojFEKIqud2y3ici8ljjz3W4o477jgxatSorB9++CFkzJgx7cq+npqaGvjpp5/qvLw8S9++fRNHjRqVceedd2YuWrQo5q677kotTWw++OCDht26dcut6HilCWt6enpgfHx80e233/6bRHHEiBGZn3zySeyAAQNyVq1aFTV//vxfXnvttYTy9qW1DvHlmAB9+/bNufnmm7PMZjO7du0Kuueee1SfPn22bd26NXjz5s3hK1eu3GGxWMjKyrIAzJ8/P6pJkybF8+bN2w2cWl7W559/rpVSSZ9++umu8PBw9/r168NWrFhRf8GCBdpmsxnLli2LePzxx1ssXLhwF8CuXbtCP/vss52tWrUq9l6zBpdddlkBQExMjOuBBx44VprYgicBnDhxYosPPvhAd+jQoeiDDz6Iefjhh1ssWrRoF8DBgweDn3jiicM9e/Y8+OGHH0aPHz++xZIlS34523V49913G3Tt2jWnbOL5f//3f/Gvv/56k1atWhVOmDDhSHx8vCMlJSUwJibGYbV6UiSr1Up0dLQjJSUlsEGDBs64uDin1Wo1fvnlF1u7du1+l/DOmzcvulu3bicbNWrkvPbaa7M/+eSTmNIKd0X27t0bkpSUdPhMr48dO7bl0aNHf9edBODzzz//JSQkxEhLSwts1qxZSenyRo0alRw/fvx3c1AOHjz45HfffRfevXv3jkFBQe6EhISiKVOmHAbIz883T5kyJeHtt9/eu3fv3nKPB7Bo0aLoP//5z2esAJ/JxTTVUlPgUJnnh4Fy/9MJIUR1MgyDzFwnTpf7t8uBZZsyKXFUnFQezSgmeU8eRiXmnybc9Ar4geGBaznZ/iZISqq8nV+AP1wVk3kh1cnqkJOTYz506FDwLbfckgXQpUsXe/PmzX9TXerfv3+2xWKhXr16rqZNmxbt378/qE2bNhXeBWratGmNVq5cGZWXl2edOnXqvmuuuaYAfm3idTqdPPbYY80mT54c/+KLL6aUbtejR4+8559/vulXX31Vr0WLFoUxMTGVcre+ffv2BT300ENNMjIyAi0Wi5GdnR1w/Phxa8uWLUtcLpfpb3/7W/Nu3brlDhw4MAcgKSmpYO7cuQ2ffvrp+CuvvDKvX79+FSa5q1atqrd///6QYcOGtQPP/5n8/PxTWV6HDh3ySxNPgLS0tMDY2NjfNXeX2rRpU2jLli0LS6uZo0ePznjllVea5ubmmgHi4uKKe/bsmQ8watSozClTpjTLyckxR0ZGusvb37x58+p//fXX0R9//PGu0mWvvvrq/qZNmzqcTievvvpq3EMPPdTyiy++0BWdK0BUVJTj6NGjAeUln4sWLYoZP358CsCIESMynn766ealyafJZCr3N4DJZPLlsJQm55Vh8+bNIQcPHrStW7duW3h4uGvChAkJEydOTJg6derhSZMmxd9yyy1p8fHxjjMln99//33IyZMnAwYNGuRzt4JSF1PyWSm2b/9dd1KfJScnV2IkNYOcc91wsZ5zRh7kFvr2S/fcmNi/bDMA6Xmw94TpgpK+tFwTWQVnj9MWUPEBokKhY9NyvxvPWYQjk2uzFxJXfJDjtpYEx8RetO/zxexsX/pBQUGn3iyz2YzT6Sx3ZaVU4Y4dO0JLnz/yyCOpjzzySOrgwYPbORyO3/XbtFqt9OvXL/vVV1/9TYHFbDbTp0+f7Oeff77ZM888c/BscSul7Nu3bw892zqlxo8f3/Lhhx8+MnTo0JMul4uOHTteUVRUZI6LiytZvnz5jnXr1oVv2LAh4o033oj/4osvdl599dUFX3755c7Vq1dHfPXVV9EzZ85s5EtSNnjw4Iwnn3yy3CpYcHDwbxLpoKAgd3FxcbUMel64cGG9GTNmNPnggw92N2rU6FTzcNOmTR3geT8efPDBE++//35jl8tFQkJCSUZGRoDT6cRqteJ0OsnMzAxISEg4VU0sKSkx2Wy23/1n/vHHH0MOHz5se+aZZ5o/88wzAGRlZQV88803od27dy+oV6+es7Q7Q6mcnJyAmJgYB0CrVq3smzdvDvU2tf+OL5XPBg0alBw6dCiwtC9uampqYNeuXfNOX3/BggUxXbp0ya1Xr54LYPjw4VkTJ05sDrBt27aw7777LvLdd99tXFJSYs7Pz7f079+//YoVK3aWbj9//vyY/v37ZwYEBJy+6wpdTMnnYaAZ8IP3+emVUJ8kJiae10TLycnJJF0kVYPqIudcN1ys57xmSzavrThj61Klaxl3xpajCtWLgKhIE0OuiuH0XCXAauLq9pEEBlTP5CGG20XxpoUUrp+DyRJA8KBxtOvYj82bN5/X+1xcXHxBf7TXVJGRke5mzZoVzp8/P+rWW2/NSk5ODjl48KBPI4pDQ0NdeXl5p6p699xzT9rw4cPbf/TRR1GlfTCdTicOh+OMme3GjRsj4uPjf1c1GzNmTHpwcLBrwIABZ6wKeo+ZPnz48PZz586NuvXWW7PA01S9cuXKyNP7fRYUFFibNWtWDDB79uyY0rjS0tKsVqvV6N+/f26fPn1yN2zYELl///6gwsJCc3x8vOOWW27Jvuaaa/IHDhx4qct19iJsnz59Tj711FMtxowZk56QkOBwOp389NNPIZ07d7aXt36rVq0K9+/ff+o/ZXh4uKugoODUNe3atWvB5MmTg0ubtufMmRN9ySWX2CMiItwAqampQevXrw+77rrr8ufNmxfVvHnzwvKqnosXL46cNm1awnvvvbe7RYsWp5JHh8NBZmamtTQZXbBgQVTz5s0LLRYLDRs2dF5yySWF8+fPjxo1alTW/Pnzo1q1amUvTeacTifHjx8PSkxMLK/JPea2225LnThx4qkkfNq0aY0WLFgQ071794Krrroqd9GiRdEjR47MCg4ONn766afgw4cP27p06VIA8MADDxx/7rnnmnfq1MmelJRkB9i6dWvwiRMnAvr165frS+Wzd+/e2R9//HFsly5dDu3evTtIax3ar1+/320XHx9fvHHjxoiSkpK0wMBAY82aNZEtWrQoBCibZK5duzb8lVdeiS/brcFut5vWrFkTNWfOnF2n79cXF1PyuQC4Tyn1OZ4BR8Pw3GtYCHGRKSpx43R5Kn2HThTx0948fC0sFjvcfJ2cRW6BiwCLiYdHJhAVce5/OZ/N7t2aNm1+7WPeoF4AjaJq/h2PnGkHsC+Zjuv4HgLadCOk/58wh0dXvGEdde+99yqLxXLqo7l48eIdZV9/+eWXD0yYMKHFe++916hly5aFzZs3L4yIiKiwqfumm25Kf/XVVxNmzZrV6NFHH03p27dv3uzZs3dNnTo1/q233moSERHhDAgIMHr06HHy8ssvLyjdrrTPp8vlMjVo0KDkhRde+F2BJT4+3vH3v//9REUxNGnSxDFr1iw9ZcqUJjNmzGhis9lcwcHB7rFjx/6ub+Hf//73ww899FCr8PBwZ7du3XLDw8OdACkpKYETJ05s5nK5TG6329StW7ecbt26FcyZMyf6o48+amg2mzEMg/Hjxx862wAdgB49euT/6U9/OvrAAw+0crlcJqfTaerdu3f2mZLPQYMGZU+aNKkZcAygV69eebNmzWrUv3//9pdffnnelClTUp577rkDjzzySAuXy2WKjIx0Tps27UDp9t4/HGKee+65ZjabzT116tQD5R1n0qRJza1Wq/HQQw9dUrpszpw52mazGWPHjm3tdDpNhmEQExPj+L//+799pes899xzh8aPH9/8nXfeaRwWFuZ85ZVXTu1/w4YNYW3bti0orRiWKiwsNK1Zsybqww8//E1CNmLEiKzhw4e3z8/PT7nzzjszjh07Fjh06NB2ZrOZoKAg99SpU/eXdrEYMGBArtvtPjR58uSmubm5VqvVajRq1Kh4/PjxR876BpTx0EMPpf7tb39r3rNnz0Sz2cxTTz11qDRpf+GFFxo3aNDAcd9996Xfd999aXv37g0eOHBge4vFYjRo0KBkypQpPhX9vvzyy/oJCQlF5Q3y8oXJqMwOSGeglHoduBFoBGQAmVrrDkqppcDTWusfvVMtvQn08272stb6bV+PkZyc3Bw4IJVP38k51w3ne86GYXA8s4Qfd+ex5+iv3x8p6cXolHK/T3xmMkGPy+ox9OoY2jb1qeXwnNS299lwOijaMJeijQsw2cIJ6f8gAW27/6bJ+HzPuUzls0VSUtLB841x69atBzt27Jhxvtv7Q15enjk0NNRtNpvZsWOH7e6771bLly/fHhUVVSl9LcXZ3Xbbba0fe+yxI1dccYXPI7mh/EpcdXrwwQdb3HTTTRl9+vT5XVO2+K2tW7fGdOzYsfnpy6trtPs4YFw5yweVeewC/lgd8QhR2zldBj8fyKeoxM3eoybWHTjMieySijf0bptT4CIn30l+kec7OCzYQkjQr83KYcEWhl4dQ6jNQliwhfbNQmkSU/Mrixcj59FdFCyZjjvjMIGJvQnuex/mkAh/h1UrbNy4MWzatGmnJk1/6qmnDkniWX0mTZp02DuY5ZyST38qKioyJSUl5UvieWEupmZ3IUQ59h0v5Of9+RWuV+xwo1PsHMko5lhGMb8OzLYQFJBD6ybB4MPYngCrmUviAogMtdK0gY0msUEkxAYRE/m7mTpEFTJKiihcN5viH77CFBFD2C2TCLiks7/DqlX69euX269fv50VrymqgncOygpnDzhdz54983r27OmXqqfNZjPuu+++dH8cuzaR5FOIi1BGjoN127LZui+f5N15+Do9ZP0wK+2ahdK1bQTREQEkNg/ll19+4bqrOhIZKv/dawrHgZ+wL3sd98kTBF0xmOBed2EKKneOZyGEqHHk20gIPytxutlzpJCNO3M4llnM/uNFp5rIbYFmruoQwQODG2MLOnuHf4CQIAsW82/LmzmpSOJZQ7iL8ilcPZOSrV9jjmpM2B0vE9A00d9hCSFEpZJvJCGqWInTzYbtORxMLeJIRjHpJ0t+M+fk3mOe7k4mEzRrYKNFIxsDr4yidZMQrmgd7qeoRXUr2b0R+/IZGAUnCbpqJMHdR2EKkH60QojaR7yI4/8AACAASURBVJJPISqRYRgUlrjZsjefQ6lFuA2DtVtPcjSjGIsZGkcH0bB+IOYy1cmukQEktQ6nZVwwHZpX/shvcXFz52djX/kfHL/8D0uDFoTc9DTWuNb+DksIIaqMJJ9CVAKXy2DVliyWfZ+FPvLbaYgiQ608M7o5nVUEVktV3M1H1ESGYVCy/b8Urnobo6QQW48x2LqNwGSRX8uVJSsry9KjR4+ON9xwQ/pLL72UAvDSSy81ttvt5ueee+7UvIlvvfVW7I4dO0LfeOONgwBa66ApU6bE79u3LyQ8PNwZEBBg3H333alDhw71+TaCL7/8ctzSpUujAQYNGpT5j3/843h56ymlkpo3b15YOm3W1KlTD1x22WU1ZvS3EOdDfssJcQE278kjeXceyXvyOHSiiFCbmRu6RRNbL5Bh18RgMZswwW8qnUK4c9IoWP4vnPt+xNKkLaGD/4olpqm/w6p1FixYENW2bdv81atXRxUXFx8JCgqqcOjesWPHAsaMGdN23LhxKe+///4+gOPHj1tXr14d6etx161bF7ZmzZr6y5Yt2wEwfPjwdt26dcvr0aNHudNWfPrpp7vCw8Mr576rQtQAknwKcY4Mw2Du2jT+uyWblHTPLCHxsUEM6hrNX4Y2Oeu9okXdZhhuijcvpfC/H4BhEHz9AwQlDcZkrngwmTh3X375Zcwjjzxy5O23345btGhRvZEjR2ZXtM3MmTNjO3XqlFt6m0yAuLg45+m3rDybxYsXRw0aNCgzJCTEAE/lc/HixVFnSj6FqGsk+RSiAi63QXaeEwODn/bms3BDOvuPF2ELNNOvcxRjB8QRIaPJRQVcmUexL52OM2UH1haXEzLwISz1Gvo7rCpR9OOi6JKfV8dUxb4DL+2TYes8pMJEcOvWrcF5eXnWXr165aWlpQV88cUXMb4kn1rrkG7duuWe6fVp06Y1WrlyZVR5r02YMOHwddddl3/ixInArl27npqEvHHjxiWbN28+4+jBW2+9VblcLtNVV12V849//OOYzWar+lsPCuFH8o0pxBmUONx8tTGDT9enk1PgPLU8ITaIP/2hCV1UeK24X7ioWobbRfH3n1O4/iNMAYGE3PA3Ai/tKxXyKjZ37tyYAQMGZJrNZoYOHZr9z3/+s+mRI0cCTCbTBSV2jzzySOojjzzyu/unn6+VK1dua9q0qSMnJ8c8bty4Fq+88krcxIkTj1XW/oW4GEnyKcRp3G6D9dtO8v6K46SddNC5TThd20VgMZuIjQygWUMbsfXkbj+iYs4T+7EveQ1X6j4C1NWE9P8j5rByi2a1iq3zkExfqpNVpbi42LRq1aqogIAAY/ny5dEATqfT9Mknn0RHRUU5jx49+pu/GrOzswOioqIcAEop+/bt28847YQvlc+GDRuWHD169NQviWPHjgU2aNCg3PvbNm3a1AEQGRnpHjlyZMbs2bNrZzlciDIk+RSijF8OFfDW4qPsPlJIyzgbfxuRwOWtZK5NcW4MZwlF38yl6LtPMQWHE3rjBALbXuPvsOqMRYsW1YuPjy/64osvdOmyb7/9NnTChAktZsyYsfe9996LS0lJCUhISHBkZmZaVq1aVX/ChAmHAe6555704cOHt587d27UrbfemgVw4sQJ68qVKyPvuOOOTF8qn4MHD85+6aWXEu677740gKVLl0Y/8cQTh09fLysry2Kz2dwhISGGw+FgxYoV9Vu3bi0j3UWtJ8mnqPOKSlyknXSgU+z832cp1Au18vDIBHpfXv93dwsSoiLOIzspWDIdd+YRAi/tQ3Df+zAHyx8w1emLL76IGThwYFbZZVdffXWBYRikpaVZH3744ZQHHnigldvtNhmGwS233JLWp0+fPIAmTZo4Zs2apadMmdJkxowZTWw2mys4ONg9duxYn5vae/bsmbdx48aTAwYMSAQYOHBgZs+ePfMBFi1aFLl69ep6r7322qFdu3bZJk2a1AzA5XKZEhMT85944omjlXclhLg4SfIp6rRlP2Ty5sIjuL2TnDRvaOPJ25sRH2vzb2CixjFKCilcO5viHxdhjogh7NbJBLRM8ndYddJHH320p7zl69at2176+Oabbz7j4KN27doVzZo1a9+FxPDEE08ce+KJJ37Xd3PIkCE5Q4YMyQFPQrxixYqdF3IcIWoiST5FnWIYsGlXLmknSziQWsSy7zMJsZn589B4zGa4/JJwGbkuzplj/2bsy97AnZNOUNJggnveiSkoxN9hCSHERUm+ZUWtV1js4j+Lj7JuWw4lDgtu4wAAAVYTvTrV547rGxIno9bFeXAX5lG4+l1Ktq3CHBVP+OiXsSZ08HdYQghxUZPkU9RKW/fmsUnncSSjmF8OFZBX6KJ7YiQmRzZJiQm0jAumWUMbgVazv0MVNVTJrg3YV/wbw56D7eqbsXW/DZNVZkEQQoiKSPIpapXMXAffbD/J+8uPU+wwaN7QRmKLUAZ3jSapTQTJyZkkJUX7O0xRg7nzs7CveAuH3oCl4SWE3DoJa8NL/B3WxcDtdrtNZrNZJkgXQuB2u01AubeNleRT1FhOl4FhGBQWu9mVYmf5D5ls3Om5MUl0hJVXH2xJy8bBfo5S1BaGYVDy8yoKV72L4SgmuOedBHW9EZNFfo16bU9PT28fGxubIwmoEHWb2+02paenRwLby3tdfmuKGmfCzH3sO15IboHrN8sjQiz0vaI+fa6oT8eWYXIHGVFpXCdPYF/2Js4Dm7HGdyBk8Dgs0fH+Duui4nQ6701NTX03NTU1EZD+LELUbW5gu9PpvLe8FyX5FDXK1z9msmVvPuHBFkb3bYTZDEEBZprEBJHQIEgGDolKZRhuin9cTOHaWWAyEdzvjwQlDcJkktzqdElJSWnAH/wdhxDi4ifJp6gRcgucvL7wCBu25wAw46+KmMgAP0clajNXRgoFS1/HdWQn1pZJhAz8C5bIBv4OSwghajxJPsVFzTAMlm7K5IPlqRQ53HRrF8G1l0ZK4imqjOFyUvTdZxR98zGmwGBChjxCYGIv6cYhhBCVRJJP4XeHThRxOK2IH3QuTtdvxykcyyhBH7ED8OofW9Guaag/QhR1hDN1L/Yl03Gd2E9A2+6E9HsQc1h9f4clhBC1iiSfwm/y7E7mrk3j8/+ln1oWF/XbeRKDg8zc1b8R/TtHUS9Mqp2iahiOYoq++YSi7z7DFBJJ6IgnCVRX+zssIYSolST5FH6xbls20xak4HAaqIQQbroulsYxQbRoJFMjierlTNlBwZLpuLOOEtjxeoJ7j8UcHO7vsIQQotaS5FNUK5fbYPLsA2zSeQD8a1wbWsZJwimqn1Fsp3DtLIqTF2OObEjYbc8T0OJyf4clhBC1niSfolo98tZedIqdkCAzL99/iSSewi8c+36kYNmbGLkZBHUZSnCPMZgCbf4OSwgh6gRJPkW1+XR9GjrFTs+O9Rh9fSMaR8ucnKJ6ue25FK56h5LtazBHJxA25hWs8e38HZYQQtQpknyKauF2GyzckE5wkJmHhscTEmTxd0iiDjEMA8eub7CveAujKA/bNbdiu+ZWTFYZxCaEENVNkk9R5U5klzDxg/1k5jrp2bGeJJ6iWrnzs7Avn4Fj90YsjVoRMup5rA1a+DssIYSos6ot+VRKtQFmAdFAJjBGa73ntHUaAO8DCUAA8F9gnNbaWV1xisqRkeNg9xE7P+3LY9HGTACuah/BXf0b+TkyUVcYhkHJtpUUrnoXw+UguPc9BF05DJNZ/vgRQgh/qs7K51vAv7TWc5RSdwD/AXqfts4E4Bet9WClVADwDXAjML8a4xTnKSvXwf+2nyQr18n8dWmnlkdHWBk3PIEWcTZiIwPPsgchKofVnkX+J0/iPLgVa0IiIYPHYYlq4u+whBBCUE3Jp7eieQVwvXfRJ8CbSqlYrXV6mVUNIFwpZQaCgEDgaHXEKM7P0Yxilv+QyX9/yiYz99cCtcUM9w1uTGLzMBrWDyQsWKpNouoZbhfFPy4i/tsPcFqshAz4M4GXD8BkMvs7NCGEEF4mwzAqXusCKaWSgNla6w5llu0E7tBaby6zLAr4DGgPhAJvaq0f9+UYycnJzYEDlRm3OLvDGTBzvQW3GyJDoEMTgw7xbmLDPclnoPQoFtUoID+N2O0LseUcwR7TmvT2f8AVHOnvsGqSFklJSQf9HYQQova72NKDm4BtQB8gHFimlBqptf7U1x0kJiYSFHTuU/gkJyeTlJR0ztvVZBdyzt/9ksPbaw8CMPmuFnRREZUYWdWR97n2MVwOijZ+StF3czEFhhAy9DH2F4aR1Lmzv0OrVuf7PhcXF7N9+/YqiEgIIcpXXclnCtBEKWXRWruUUhagsXd5WQ8B92it3UCOUupLoBfgc/IpqpZhGCzckMHbS44RFGDmrzfG15jEU9Q+zuN7sC+ZjivtAAHtryPk+gcxh0ZCcrK/QxNCCHEG1ZJ8aq3TlFI/AbcBc7z/bjmtvyd4ms0HAJuUUoFAX+Dz6ohRVMzlNpj84UE27cqlXdMQnru7JaE26cspqp/hKKLwfx9T/P0XmELrEzpyIoFtuvk7LCGEED6ozmb3B4FZSqmngWxgDIBSainwtNb6R+BvwFtKqZ8BC56plt6pxhjFGRQ73LzxxRE27cplSLdohnWPlcRT+IXj0M/Yl76OO/sYgZ36E9J7LCZbqL/DEkII4aNqSz611ruAruUsH1Tm8T5+HREvLgJrf8pm9spUjmeVANAoKpAHhzTBbDb5OTJR1xjFduxr3qdky1LM9RoRNupFApp39HdYQgghztHFNuBI+NmhE0Ws2ZJNscPNrsN29BE7jaIC6d2pPlariT8PlcRTVD/H3k0ULPsXRn4WQVcOJ7jHHZgCbP4OSwghxHmQ5FMAsG5rNm9+eZT8QhcAwYFmGscE8YerYhjUNZpmDeWLXlQ/tz2HwpVvU7JjLeaYZoTd+ATWJm39HZYQQogL4HPyqZS6HrgVaKC1HqKU6gxEaK3XVFl0osoZhsH3u3L5z+JjGIbB/YMb0/3SSLkTkfArwzBw7FyP/eu3MIrt2LqPwnbNzZgsAf4OTQghxAXyKflUSj0E/BV4FxjpXVwIvA5cXTWhiarmcMHbi4+x8NsMAJ64rSnXXVbfz1GJus6dl4F9+Qwce77HEteG0MF/xdKgub/DEkIIUUl8rXz+DeijtT6olPqHd9kuQFVNWKI6zP7GzIH0DBrWD+SfD7QiJlKqSsJ/DMOg5KcV2NfMBJeL4D5jCeoyFJNZZlUQQojaxNfkM5xfJ4QvvR9nAFBS6RGJKmUYBqs2Z/Hp+nQOp5sZ0CWKccPjMZlkEJHwH1f2cexLX8d5aBvWppcSMmgclqjG/g5LCCFEFfA1+VwPPA68UGbZODzzcIoapLSZvWH9QPolurh/cGNJPIXfGG4XxT98ReG6D8FiIWTgQwR26ofJZPZ3aEIIIaqIr8nnQ8AipdR9QLhSSgN5wA1VFpmodKlZxSz5PpOk1mFMvqslW7ZsJjhImjSFf7jSDlKwdDquY7sJaH0lIQP+jDk8xt9hCSGEqGI+JZ9a6+NKqS5AF6AZnib4Td57sIsaIDvfwTOzDmAywQM3yFydwn8Ml4OibxdQtGEepqAQQoeOJ6D9dVKBF0KIOsLX0e5faq2HApu8P6XLP9da31hVwYnKYRgGj761l9SsEv74hyYkNJA5O4V/OI9pCpZMx51+iMAOPQm+/n7MIZH+DksIIUQ18rXZvdcZlvespDhEFZq57DjHMksYOzCOG7pJs6aofoajiML1cyje9CWmsPqE3vQMga2v9HdYQggh/OCsyadSarL3YWCZx6VaAoeqJCpRKdxug49Wn+Cz/6VzfVJ9buwe6++QRB3kOLgV+9LXcZ9MJfDygYT0uhuTLdTfYQkhhPCTiiqfCd5/zWUeg2e6pRTg2SqISVygdduyWZWczY+78wBoGWfjL8PipZ+nqFZGUQH2NTMp+WkF5vpxhN3+EgHNLvN3WEIIIfzsrMmn1vpuAKXUt1rrd6onJHGh5v03jRPZJbSJD8ZkMvHM6OYEWmXqGlF9SvZ8j33ZvzAKsgnqeiPB192OKUD6GgshhPB9tPs7AEqpcCAGMJV5bX/VhCbOx8HUQg6eKKJ/5yj+emNCxRsIUYncBTnYV76FY+d6zLHNCBv5FNbGbfwdlhBCiIuIr6Pd2wEfAx3xNLmb+PVORzJR5EXk+19yMQwYerUMLBLVxzAMHDvXYf/6PxjFdmzX3YHtqpGYLHLLViGEEL/l62j3f+O5m1Ev4ADQHHgJ+LZqwhLnIyvXweLvMomJCKBZQ2niFNXDnZuOffm/cOz9AUtjRejgv2KJbebvsIQQQlykfE0+OwLXa60dSimT1jpHKfUYsB2YU3XhiXPx6qcpnCxw8tK9LWXCblHlDMNNyZbl2Ne8B4ab4L73EdR5CCazNIYIIYQ4M1+TzyIgAHAAGUqppkA2EF1VgYlz43QZ7DxUQIdmoSQ2D/N3OKKWc2Udxb70DZyHf8bavCMhAx/CUj/O32EJIYSoAXxNPv8H3Ax8AHwKLAOKgTVVE5Y4V298kUJhiZtener5OxRRixluF8WbFlK4fg4mSwAhg8YR2LGfVNqFEEL4zNfR7jeXeToB2AGEAbOqIihxbtJzSvjvTye5UoXTr3OUv8MRtZQz7QD2JdNxHd9DQJtuhPT/E+ZwafwQQghxbnytfJ6itXYDHyqlAoH7gH9VelTCZzkFTv7x9j4sFhN3D4iTCpSodIbTQdGGuRRtXIDJFk7o8McJaNtdPmtCCCHOS4XJp1KqD9AJ2Ku1/lIpZQX+BPwDyEKST78pcboZ+89fKChyM+nOFjRvFOzvkEQt4zy6i4Il03FnHCYwsTfBfe/DHBLh77CEEELUYBXd2/0fwEQ8zewdlFIzgJ54+nver7VeUuURinJl5Tr4yxu7KShy0+fy+lzZVhICUXmMkiIK182m+IevMEXEEHbLJAIu6ezvsIQQQtQCFVU+HwB6aK2TlVLdgA3AI1rr16o+NHE27yw9Rna+kyHdormlV0N/hyNqEceBLdiXvoE75wRBVwwmuNddmIJC/B2WEEKIWqKi5DNGa50MoLX+TilVDEyv+rDE2SzflMnarSe57rJ6/GlovL/DEbWEuyifwtXvUrJ1JeaoxoTd8TIBTRP9HZYQQohaxpc+nyY8t9M04ZnvE6WUufR17wAkUQ2KHW7WbzvJ9C+OEBFi4S/Dmvg7JFFLlOiN2FfMwCg4ie2qm7B1vw1TQJC/wxJCCFELVZR8hgHOMs9NZZ6X3t9dbmdSDYodbm5/cQcFRW6iwq1MvqsF4cHnPFmBEL/hzs/G/vVbOHZ9g6VBC0JuehprXGt/hyWEEKIWqyh7aVEtUYgKfbsjxzu4qB539osjtl6gv0MSNZhhGJRs/y+Fq97GKCnE1mMMtm4jMFnkDxohhBBV66zfNFrrQ9UViDgzwzB47bMUmsQE8ZdhCdgCzRVvJMQZuHPSKFj2Js79yVji2xE6aByWmKb+DksIIUQdUW1lDqVUGzx3RIoGMoExWus95ax3M57pnUqb9ftqrU9UV5wXm4IiF3+fsYcSp8EfroqWxFOcN8NwU5y8lMK1H4BhEHz9AwR1vgGTST5TQgghqk91trG9BfxLaz1HKXUH8B+gd9kVlFKdgWeB3lrrVKVUJJ45ReukOatS+Wi1J+/u3zmKgVfKrQzF+XFlHsG+5HWcR3ZgbXE5IQMfwlJPpugSQghR/aol+VRKNQCuAK73LvoEeFMpFau1Ti+z6t+Bf2qtUwG01jnVEd/FpsTh5vtduXy0+gRtE0JoEWfjoWHxcjtDcc4Mt4vI/evJXbUOU0AgITf8jcBL+8pnSQghhN+cU/KplEoAmmitvzvH4yQAR7XWLgCttUspdcy7vGzy2R44oJRaj2ek/efAC1pr4xyPVyMZhsFHq08wb20aTpeBLdDMi2NbEhwkEwqIc+c8sQ/7kulEp+4jQF1NSP8/Yg6L8ndYQggh6jifkk+lVFM81cpOePphhimlRgIDtNb3VmI8FuAyPBXSQGA5cBiY7esOtm/fft4HT05OPu9tK8P2Iybmfmch3GbQp5ObcJuTndt/qtJj+vuc/aG2n7PJ5aDe/nXUO/ANroAQMjveQkGjDqAPAAf8HV61qe3vc3nq4jkLIWoeXyuf/wGWANfiGSwEsBKY5uP2KUATpZTFW/W0AI29y8s6DHyqtS4GipVSXwJXcg7JZ2JiIkFB5z45dnJyMklJSee8XWVatecQcJK3Hu5AVHhAlR/vYjjn6lbbz9mZspOCJW/jzjpC4GV9Ce5zL4d37q7V51ye2v4+l+d8z7m4uPiC/mgXQohz5esw1yuBKd67GRlwqj9mpC8ba63TgJ+A27yLbgO2nNbfE+BjoJ9SyqSUCgD6AFt9jLFGc7kNtuzNo33TkGpJPEXtYpQUYv/6LfI+HA+uEsJufY7QG/6OOTjc36EJIYQQv+Fr5fME0ArYXbpAKdUeT6XSVw8Cs5RSTwPZwBjvfpYCT2utfwTmAp2BnYAbWAHMPIdj1Fh7jxaSU+Di7gHSJ0+cG8f+zdiXvYE7J52gzjcQ3PNOTIHB/g5LCCGEKJevyec/gcVKqZcAq1LqNmACMMXXA2mtdwFdy1k+qMxjN/Cw96fOKHa4WbAuDYBLm4f5ORpRU7gL8yhc/S4l21ZhjoonfPRUrAnt/R2WEEIIcVY+JZ9a6/eUUpnAA3j6aY4BJmqtF1ZlcHVBxskSHn5rL+k5DhKbh9I45tz7q4q6p2TXBuwrZmDYc7FdfTO27rdhssotV4UQQlz8fB3tbtFafwl8WcXx1Dkfrj5Beo6DuwfEMeLaWH+HIy5y7vws7Cv+jUN/i6XhJYTcOhlrw0v8HZYQQgjhM1+b3VOVUguAj7TWG6oyoLokv9DF1z9m0axhEDddFysTf4szMgyDkp9XUbjqHQxHCcG97iKo642YzDIHrBBCiJrF1+SzH54R6p8opVx4BgZ9rLX+ucoiqwN2HS4AYNg1kniKM3OdPIF92Rs4D2zBGt+BkMHjsETH+zssIYQQ4rz42udzC7AFGK+U6oEnEV2jlDqutb6sKgOsrQzD4M0vjxARYqFnx3r+DkdchAy3i+LkJRSunQUmE8H9/0jQFYMwmXydIU0IIYS4+JzPvd13Ab/gmWapdeWGU3es23qSE9kObu4Riy1Qmk7Fb7kyDlOw9HVcR37B2jKJkIF/wRLZwN9hCSGEEBfM1wFH9YARwCigG/A18DLwVdWFVnu53QYvz/NMkXp9UrSfoxEXE8PlpOi7Tyn65hNMgcGEDHmEwMRe0i1DCCFEreFr5fMY8C2eOxCN0FqfrLqQar8te/MBGHltLPGxMrWS8HCm7sW++DVcaQcIaNudkH4PYg6r7++whBBCiErla/J5idb6eJVGUocs3ZRJZKiF0f0a+TsUcREwHMUUfvMxxd99jikkktARTxKorvZ3WEIIIUSVOGPyqZS6Tmu93vu0nVKqXXnraa3XVElktVRGjoPvfslhxLWxBFpl4Ehd5zi8HfvS13FnHSWwYz+Ce98j92MXQghRq52t8jkDSPQ+PtP91Q2gZaVGVMut+DETtxsGXil9Pesyo9hO4X8/oHjzEsyRDQm77XkCWlzu77CEEEKIKnfG5FNrnVjmcYvqCad2c7kNlm3KIql1OHFR0tezrnLs+5GCZW9i5GYQ1GUowT3GYAq0+TssIYQQolr41O6rlCr3tppKqc8rN5zabe/RQjJzHXRPjPR3KMIP3PZcCr6aRv68ZzAF2Agf8woh198viacQQog6xdcBR73OsLxnJcVRJ/x8wDPKPamN9OmrSwzDwPHLN9i//jdGUT62a27Fds2tmKwB/g5NCCGEqHZnTT6VUpO9DwPLPC7VEjhUJVHVUt/9kktEiIXYeoH+DkVUE3deJvYVM3Ds/g5Lo1aE3PY81obSTVoIIUTdVVHlM8H7r7nMY/AMNEoBnq2CmGqlPLuTHQcLaBknTax1gWEYlGz9msLVMzFcDoJ73U1Q1+GYzHI3KyGEEHXbWZNPrfXdAEqpb7XW71RPSLXTj7vzALitd0M/RyKqmiv7OPalb+A8tBVrQiIhg8dhiWri77CEEEKIi8LZ5vlsrrU+6H26WilVbluh1np/VQRW22TkOgBo3zTUz5GIqmK4XRT/uIjCdbPBZCZkwJ8JvHwAJpPM5yqEEEKUOlvl82egdGTMXjxN7affYNoApB3RBxk5JZhNEBnq6xgvUZO40g9RsGQ6rmMa6yWdCR34F8wRsf4OSwghhLjonG2ez/Ayj6V0cwHcboMfdR6XNA7GYjk9fxc1meFyULTxU4q+mYspKISQPzxKYIeemEzyPgshhBDlOa8ynLcJ3l2mWV6cRfKePI5lljD+lqb+DkVUIuex3diXTMeVfpCA9tcRcv0DmEPr+TssIYQQ4qLm6yTznyilrvY+vhvYAexQSo2tyuBqi4Ub0okKt8rk8rWE4SjCvnomebMewV2YR+jIiYQN+4cknkIIIYQPfK189gHu9D5+GOgLnAQWcub7vgvgeFYxm/fkM+b6RgRYpfdCTec4tA370jdwZx8jsFN/QnqPxWSTQWRCCCGEr3xNPgO11iVKqSZAlNZ6A4BSSuYNqsC2fZ67Gl0jVc8azSgqwP7f9ynZsgxzvUaEjXqRgOYd/R2WEEIIUeP4mnz+pJR6AmgGLAHwJqK5VRVYbbH9YAERoRYSYoP8HYo4T469myhY9iZGfjZBXYcTfN0dmALkZgFCCCHE+fA1+RwLPAc4gPHeZVcBH1VFULXJ9oMFdGgWKqOfayB3QQ6Fq96mZMdazLHNCBvxJNbGyt9hCSGEEDWaT8mn1nofMOq0ZZ8Cn1ZFULVFZq6D1KwShnSL8Xco4hwYhoFj53rsX7+FUWzHdu3t2K6+CZMlwN+hCSGEEDWez1MteUe5QctfSwAAHvJJREFUjwaaAEeBD7XW71dVYLXBjoMFAHRoLgNSagp3bgb2Ff/CsWcTlsZtCB30VywNmvs7LCGEEKLW8Cn5VEo9CYwBpgGH8PT9HK+Uaqy1fqEK46vRth/MxxZoplXjYH+HIipgGG5KflqBfc174HIR3Odegrr8AZNZbuAlhBBCVCZfK5/3Aj211odKFyilVgDrAUk+z2DHwQLaNQ2Ruxpd5FxZx7AvfR3n4Z+xNruMkEHjsNSP83dYQgghRK3ka/IZCqSftiwTkJLeGeQXujiQWsTtvWU2qouV4f7/9u48TKr6zvf4u6p6Z5NVAUWiyNcoigJqUHGJ4oJmxrij0WRMNDhGjUnuJONEY5LrPMmd5EZRGdGo43WLS25MRlDUqLhvDS6IfkGRRdmaHbq6q7urzvxxith2uulqrD5V3f15PQ+P1OHUOZ9fd1t8+Z3fkib1xp+pm3svJBJUTbmCsrEnaHKYiIhIJ8q1+HwCuM/MfgIsJ3zsfj0wJ9cbmdlo4G5gIGHheqG7L27jXAPmAzPc/Ue53qOYLFxWSxDA/l/SeM9ilF67lNpZN5JetYjSfQ6l6qTLiPfRxDAREZHOluuWO98DtgLvANuAt4Ba4PIO3OtW4BZ3Hw3cAsxs7SQzS2T/7NEOXLvovLe0lkQc9t1DxWdRyTRR9/x9bLnzSjKb19DrtB/T68xrVXiKiIhEpN2eTzPrB+wNXAZ8CxgErHP3TK43MbMhwDhgcvbQA8DNZjbY3Vs+zv8J8BjQO/urywmCgNc+2MLewyqpKNOWmsWiaaWz+yu3Ur9tLWX7H0Pl5EuIV2nnKRERkSjtsPg0s1OAhwjHdm4FTnP3Z3fiPnsAn7p7GsDd02a2Mnv8b8WnmY0FTgSOBa7ZifuwYMGCnXkbANXV1Tv93uZWb4Jla0qYvH86b9fsLMWeLx9iTQ30//AZ+i17hXhFH1aNO5+6wQbvf1joaJHpCd/nltRmEZHi1F7P5y+BHwN3AhcTjvM8vDOCmFkpcBvwT9nidKeuM2bMGMrLO76VZXV1NePHj9+pe7b0+9kricVquOi0A9ild/EuTJ7PNherxqVvk5w9g8ym1ZSPm8IH/Q9i3GFHFDpWpHrC97kltTl3qVTqC/2jXUSko9p7JryXu9/s7knCcZqjdvI+K4Dh2fGc28d1Dsse324o4eP92Wa2FPg+cLGZ3baT9yyY95bWMmxgWVEXnt1dpn4btbOns+3+qyEWp/f5v6LqpMsISrQnu4iISCG11/P5t+LU3ZvMLOcdkZpz97Vm9hYwFbg3+9/5zcd7uvtywvGkAJjZdUDvrjbb/Z0l2/hwZR3Hj+tf6Cg9VsOiV0k+MYOgdiPlXzmDyknnEyvteG+4iIiI5F97xWSVmT3f7HWfFq9x96NyvNc04G4zuxbYSLhjEmY2G7jW3d/M8TpFbeZjnzKgTwnnaX3PyGVqN5F8ciaN7z9PYvBIqs66hpKh+xQ6loiIiDTTXvH57Rav79jZG7n7B8BhrRyf0sb51+3svQqlZlMDS1bV8+2ThzJ4l7JCx+kxgiCg4b3nqHvqNoKGJBVHXUDFxDOIJTTsQUREpNjssPh097ujCtIdPDR3LQCH7tu3wEl6jsyWGmofv5mmj94kMXxfek25ksTgEYWOJSIiIm3YqTGc0rottU0AjBiiSS2dLQgyNMx7nOSzd0GQofL4SyifcCqxeKLQ0URERGQHVHzm0fKaFPvtWVXoGN1eesOnJGdNp2nFAkpGHkTVlMtJ7LJboWOJiIhIDlR85snHq+tYurqeCyerCOosQSZN6rU/UffCfcQSpVSdciVlB04mFosVOpqIiIjkSMVnnvx13kYATpwwoMBJuqemNUtIzrqR9OoPKR09kaoTLyXeZ2ChY4mIiEgH5VR8mlk5cC3h+pwD3b2fmZ0AjHb3mzszYFcQBAFPzdtA314JBvTVDOt8CpoaqX/pD9S/8jCxij70+vq/UrrvEertFBER6aJy7fn8HTAcOB94PHvsvezxHl98rqhJsaU2rUfuedb0yfvUzrqRzPoVlB1wHJXHfYd4lVYSEBER6cpyLT6/Doxy91ozywC4+6dmNrzzonUdb/pWAO1qlCdBQx11c+8h9cZfiPcdRO9zfk7p3hMKHUtERETyINfis6HluWY2GFif90Rd0LzFWxk6oEwLy+dB48fzSc6+iczmNZSPP5XKY75JrFwrCIiIiHQXuRafDxNujXkVgJkNBW4A/tBZwbqKxqYM7y2r5aC9exc6SpeWqdtK3TN30PD2U8QHDKf3N35N6YgxhY4lIiIieZZr8Xk18GvgXaAKWAzcDvy8k3J1GdWLt1LfkNEs9y+gwV8m+cQMguRmKiaeRcWk84iVqBdZRESkO8qp+HT3BuAq4Krs4/Z17h50arIuINWY4T8eXE7fqgQTTBNhOiqzbSPJJ2+l8YMXSey6F1XnXEfJbqMKHUtEREQ6Ua5LLe3V4lAfMwPA3ZfkO1RXcd9fV5NMZfjaxIGUJLT0T66CIKBhwTPUPXUbQWOKimO+ScVhpxNLaNlZERGR7i7Xv+0/BAKgeYW1veezR26mXZdK8/DcGgb0KeG7p2rSf67Sm9eSfPxmmpZUk9j9y/SaciWJQXsUOpaIiIhEJNfH7vHmr81sN+BnwAudEaorWLOxAYAjxvQjEVevZ3uCIEOqejZ1z/0XBAGVk79L+YRTicXi7b5XREREuo+des7p7qvN7PvAIuD+/EbqGpatrQdgzEjNcm9Pev0nJGdNp+mT9yj50jiqTv4eiV12LXQsERERKYAvMsjOCGe+90gfLE8CMFZLLLUpSDeReu3/U/fC/cRKy6k69SrKDjhOW2OKiIj0YLlOOHqBz8Z4Qlh07g/8ojNCFbt0JuDJNzdw+P796NdLk2Ra07T6I5KzbiS95iNK9z2CqhOmEe+t5ahERER6ulwrp9+3eF0LvO3ui/Ocp0u4/r6lJFMZjhm7S6GjFJ2gqYH6Fx+g/pVHiFX1pdfpV1O27xGFjiUiIiJFot3i08wSwFeBS9w91fmRituyNfW8snALE0b34Yj9+xU6TlFpWrGQ2lk3ktnwCWUHTqbyuG8Tr+xT6FgiIiJSRNotPt09bWYnAJkI8hS9eYu3AjDta8OJa5Y7AEEqSd1zd5OqnkW832B6n/tLSvcaV+hYIiIiUoRyfez+O+DnZvYzd2/szEDFLJMJ+OMLa9m1fylDB2j7R4DGJdUkZ99EZss6yid8jcpjLiRWVlnoWCIiIlKkdlh8mtlUd38AuBzYDfiBmdXQbPKRu4/o3IjF45WFm1m/pYmLTxnW43s9M3VbqXv6dhre/SvxgbvT54L/Q8ke+xU6loiIiBS59no+ZwIPAN+IIEvRW14TDnk9+ZCePWu74YOXSM6ZQVC3lYrDz6HiyHOJlagnWERERNrXXvEZA3D3uRFkKXor16UY2LeEyvIeuaMomW0bSM75Txr9ZRK77U3Vub+gZNe9Cx1LREREupD2is+EmR3L5/d0/xx3fya/kYrXyvUphg0sL3SMyAVBQMO7T1P39O0EjQ1UHvstyg87nVi8ZxbhIiIisvPaKz7LgTtou/gMgL3ymqhIbalt4qOVdRx1YM9a2zO9aQ3Jx2+i6eP5lOyxP1VTriQxcHihY4mIiEgX1V7xWevuPaK4bM+s19aTagw47uD+hY4SiSCTJlU9i7rn7oZYjMoTL6V83BRisXiho4mIiEgXpr0hc/Tigk3YHlWM3bv7L5qeXrec2tnTSX/yPiV7T6DXSZcR7zek0LFERESkG8hpwlFPt3R1HUtW1XP6kYMLHaVTBekm6l99hPoXHyBWVknVP/yQsv2PJRbTj4GIiIjkxw6LT3fPWzefmY0G7gYGAuuBC1vuDW9m1wDnAmmgEbja3efkK8POWrKqHoATJnTfJZaaVn9I8rEbSK/9mNIvT6LqhGnEe/Ws8a0iIiLS+aIcwHcrcIu7jwZuIVxDtKXXgUPc/UDgIuBBMyv4djmfrEsRj8HQgd1vLcugMUXy2bvYetdVZJKb6XXGT+n99Z+o8BQREZFOEcmYTzMbAowDJmcPPQDcbGaD3b1m+3ktejnfIXzsPxD4JIqcrQmCgCffXM9uA8ooK+lek20qNi5lyx0zyWz4lLKxJ1B53LeJV/QudCwRERHpxqKqpvYAPnX3NED2vyuzx9tyIfCRuxes8ATYsLWJ9Vua2H1w91nfM0glST4xg2Gv3wmZJnpPvZ5ep1ypwlNEREQ6XVHOdjezo4Ff8llPac4WLFiw0/etrq7+u2P/PT8OxBnVf1Orf97VVNYsYvDC/yZRv4XNe05k46jjCDakYUPXb1uuusP3saPU5p6hJ7ZZRLqeqIrPFcBwM0u4e9rMEsCw7PHPMbOJwL3AP7q7d/RGY8aMoby8472U1dXVjB8//u+O3/GiA/WcffJBlJd23cfumeQW6p6+nYYFzxAfNIJe51zLx6trW21zd9bW97k7U5t7hp1tcyqV+kL/aBcR6ahIqil3Xwu8BUzNHpoKzG8+3hPAzA4BHgTOdPd5UWTbkSAIWLOxga8e3L/LFp5BENCw8AW23DaNhoVzqThyKn0vmk7J8H0LHU1ERER6oCgfu08D7jaza4GNhGM6MbPZwLXu/iYwA6gEZprZ9vdd4O7vRpjzbzbXNpFMZRi5a9cc75nZup7knBk0LnqVxNB9qDrvekqGfKnQsURERKQHi6z4dPcPgMNaOT6l2e8PiSpPLpasqgNgr6EFX+2pQ4IgoOHtJ6n76x0E6UYqv3oR5YeeRiyeKHQ0ERER6eGKcsJRsfgou7j8iF0rCpwkd+mNq0jOvommZW9TMuIAqqZcQWLAsELHEhEREQFUfO7QstX1lJfGGNS3tNBR2hVk0qTe/At1c++BWJyqk75H2cEnEot1zbGqIiIi0j2p+NyBFTX17Nq/rOj3Nk/XLKN21o2kVzqlow6h6qTvEe87qNCxRERERP6Ois8d2LStib2HFe94zyDdSP3LD1P/0oPEyqvo9Y//i9L9ji76YllERER6LhWfO7C1Ls2QXYpzP/emlYtIzrqRdM1SSvc7mqrJ3yXeq1+hY4mIiIjskIrPNtSl0tSlMgwosvGeQWM9dc/fR+r1R4n17k+vs66lbJ+/W0RAREREpCip+GzDs29vAmDX/sVTfDYue4fk7JvIbFxJ2UEnUfXVi4hV9Cp0LBEREZGcqfhsw7xFWwGYMLpvgZNAUF9L8tm7aJj/OPH+Q+l93r9TOnJsoWOJiIiIdJiKz1ak0wEvvbeZ4w7uT6+Kwi7M3rD4dZJP3EywbSPlh51O5VHnEyvtOuuOioiIiDSn4rMVH68OdzYq5Ez3TO1mkk/NpHHhXOKD96T3Gf9GyTBr/40iIiIiRUzFZytee38LAONH94n83kEQ0LhwLsknZxKkklRMOp+Kw88iliiesaciIiIiO0vFZysWLq8FYI/B5ZHeN7NlHcknbqHxw9dJDBtNrylXkhgyMtIMIiIiIp1JxWcLjU0Z5i3ehu1RFdli7UGQoeGtOSSfuRPSaSqP/w7lE/6BWLyw401FRERE8k3FZwvvL08CMOmAaBZsT29YSXL2dJqWv0vJnmOpmnI5if5DI7m3iIiISNRUfLawZGU42eiYsf079T5BJk3qjT9TN/deSCSomnIFZWNP0NaYIiIi0q2p+Gzho1V1lJbE6N+787406bVLqZ11I+lViyjd5zCqTvpn4n0Gddr9RERERIqFis8WlqyqY6/dKojH898DGTQ1Uv/yg9S//BCxit70Ou3HlH55kno7RUREpMdQ8dlMqjHDsjX1nDFpSN6v3fTpB9TOmk5m3TLKxhxL5fEXE6+KZlypiIiISLFQ8dnMC+9uIp2B3fO4xFLQUE/d8/eQev3PxPoMpPfZP6N01KF5u76IiIhIV6Lis5l3lmwDYN8RVXm5XuPSt0nOnk5m02rKx02h8th/Ilaen2uLiIiIdEUqPptZuKyW3fqXscfgL7Z3eqZ+G3XP3EnDW3OIDxhG72/8mtIRY/KUUkRERKTrUvGZ1ZSBdZsbGTX8i/VMNix6heQTMwhqN1H+lTOonHQ+sdJod0oSERERKVYqPrNWrIdUY8AZkwbv1PsztZtIPjmTxvefJzHkS1SddS0lQ/fJc0oRERGRrk3FZ1bNlnC5o72GVnbofUEQ0PDes9Q9dRtBQx0VR11AxcQziSX0pRURERFpSRVSVs3WGPE4DOpXmvN7MltqqH38Zpo+epPE8H3pNeVKEoNHdGJKERERka5NxWfW1nrYdZcyEjksLh8EGRrmPU7y2bsgyFB5/CWUTziVWDwRQVIRERGRrkvFZ1bNlhhDBrbf65le/ynJ2dNpWrGAkpEHUTXlchK77BZBQhEREZGuT8Vn1qYk2Mi2vxxBJk3qtT9R98J9xBKlVJ1yJWUHTtbWmCIiIiIdoOITSGcCUk0xBvVtveezac0SkrNuIL36I0pHT6TqxEuJ9xkYcUoRERGRrk/FJ5BMpQHoU/X5L0fQ1Ej9S3+g/pWHiVX0odfX/5XSfY9Qb6eIiIjITlLxCdTWhcVn36rPJgw1ffI+tbNuJLN+BWUHHEflcd8hXtW3UBFFREREuoXIik8zGw3cDQwE1gMXuvviFuckgOnASUAA/Mrdf9/Z2bbVfdbzGTTUUTf3HlJv/IV430H0PufnlO49obMjiIiIiPQI8QjvdStwi7uPBm4BZrZyzvnAKGAfYCJwnZmN7Oxg2+rD4nPIlvfYcvtlpN74M+XjT6HvxTNUeIqIiIjkUSTFp5kNAcYBD2QPPQCMM7OWe1meA9zu7hl3rwEeBc7q7Hxra7ZycfkfGTL33yFRQu9v/JqqEy8lVv7F9nkXERERkc+L6rH7HsCn7p4GcPe0ma3MHq9pdt4IYFmz18uz5+RswYIFHQ6X+uB9jiqdz7o9J7F1n2MIalJQU93h63RF1dU9o53Nqc09g9osIlKcut2EozFjxlBeXt6h94wbdzCvvLgXh086vJNSFafq6mrGjx9f6BiRUpt7BrU5d6lUaqf+0S4isrOiGvO5AhienVC0fWLRsOzx5pYDezZ7PaKVc/IuFotTXtWxglVEREREOi6S4tPd1wJvAVOzh6YC87PjOpt7GLjYzOLZ8aCnAY9EkVFEREREOl+Us92nAZeb2SLg8uxrzGy2mW2fUn4PsARYDLwK/MLdP44wo4iIiIh0osjGfLr7B8BhrRyf0uz3aeDSqDKJiIiISLSi7PkUERERkR5OxaeIiIiIREbFp4iIiIhEpjut85kAaGho2OkLpFKpvIXpKtTmnkFt7hl2ps3NPjMTeQ0jItKGWBAEhc6QF9XV1UcCLxQ6h4hIFzVp/PjxLxY6hIh0f92p5/MNYBKwCkgXOIuISFeRAIYSfoaKiHS6btPzKSIiIiLFTxOORERERCQyKj5FREREJDIqPkVEREQkMio+RURERCQyKj5FREREJDIqPkVEREQkMio+RURERCQy3WmR+XaZ2WjgbmAgsB640N0XtzgnAUwHTgIC4Ffu/vuos+ZLjm2+BjiXcHH+RuBqd58TddZ8yaXNzc41YD4ww91/FF3K/Mq1zWZ2NnANECP8+T7e3ddEmTVfcvzZHgLcBewBlALPAle4e1PEcb8wM/sNcAYwEjjA3Re0ck63+vwSke6pp/V83grc4u6jgVuAma2ccz4wCtgHmAhcZ2YjI0uYf7m0+XXgEHc/ELgIeNDMKiPMmG+5tHn7X9QzgUcjzNZZ2m2zmU0ArgMmu/sY4Ehgc5Qh8yyX7/PVwPvZn+0DgfHA6dFFzKtHgaOAZTs4p7t9folIN9Rjis9sD8g44IHsoQeAcWY2uMWp5wC3u3vG3WsIP/DPii5p/uTaZnef4+7J7Mt3CHvFBkYWNI868H0G+AnwGLAoonidogNtvgr4jbuvBnD3ze5eH13S/OlAmwOgj5nFgXKgDPg0sqB55O4vuvuKdk7rNp9fItJ99Zjik/Cx26fungbI/ndl9nhzI/h8z8LyVs7pKnJtc3MXAh+5+ycR5OsMObXZzMYCJwK/izxh/uX6fd4P2MvMnjezeWb2UzOLRZw1X3Jt8y+B0cAqYDUwx91fijJoxLrT55eIdFM9qfiUdpjZ0YR/WU8tdJbOZGalwG3AtO3FSw+RIHz0PBk4GjgZuKCgiTrfWYS9+UOB4cBRZnZmYSOJiPRsPan4XAEMz47z2z7eb1j2eHPLgT2bvR7RyjldRa5txswmAvcCp7m7R5oyv3Jp81Bgb2C2mS0Fvg9cbGa3RRs1bzrys/2Iu6fcfSvwZ+DQSJPmT65tvhy4L/sYejNhm4+NNGm0utPnl4h0Uz2m+HT3tcBbfNarNxWYnx0X1dzDhIVIPDt+7DTgkeiS5k+ubTazQ4AHgTPdfV60KfMrlza7+3J3H+TuI919JHAD4Ti5SyIPnAcd+Nm+HzjBzGLZ3t/jgLejS5o/HWjzx4QzvzGzMuB44O9miXcj3ebzS0S6rx5TfGZNAy43s0WEPSLTAMxsdnYmMMA9wBJgMfAq8At3/7gQYfMklzbPACqBmWb2VvbXAYWJmxe5tLm7yaXNfwDWAgsJC7f3gDsKkDVfcmnz94FJZvYuYZsXAbcXIuwXZWbTzewTYHfgaTN7L3u8O39+iUg3FAuCoNAZRERERKSH6Gk9nyIiIiJSQCo+RURERCQyKj5FREREJDIqPkVEREQkMio+RURERCQyKj6lSzOz58zsO4XOsSNmdr6ZPbmDP59kZl15YX8REZGcaaklKRrZ3YZ2BZpveTna3Vfu4D3PAfe6++/zmOM54CtAE1APPA9c5u6r8nT9ANjH3T/Mx/V2cJ/rgH8DUoRtWQj80N1fyfH9keQUEZGeRT2fUmy+5u69m/1qs/DsZN9z997AaGAX4HcFyvFFPZhtxyDgWcIdcERERAqmpNABRHbEzPoT7tpyGOHP60vANHf/pJVzRxHu2HMQ0Aj81d3Pyf7ZvsBNwHigBrjG3R9q7/7uvsHM/ghcmr3O4cCNhEXpIuBKd385+2ffAq4FBgPrgJ+6+33Z499x9yPN7Pnspd/O9ix+G1hD2Hu7u5n9GDjE3c9s1q4bgZi7X2Fm/YD/C0wBMsBdwM/cvXlvcWvtaDKz+4CrzWywu9eY2aHZtnwZqAP+CPzA3Rtay+nuD5rZqcD/BkYS9qROc/d32vs6ioiIbKeeTyl2ccICa09gBGGRdHMb5/4SeBLoT7gF4U0AZtYLeIpwb/MhwLnADDPbr72bm9kg4AxgvpkNAGYB04GBhEXgLDMbmL3HdOBkd+8DHE64nePnuPtR2d+OzfbsPtjilD8AU8ysT/b+CeDsbHaA/yJ8hD4KOBg4AWh3zGt2X/MLgfXAxuzhNHAVYa/oRMK93v+5rZxmdjBwJ/DdbPtnAn8xs/L27i8iIrKdej6l2DxqZk3Z3z/n7qcR9sgBYGbXEz4+bk0jYZE6LNsz+mL2+KnAUne/K/t6frY38yzg521ca7qZ/QaoBZ4DfgCcAix293uy5zxgZlcAXyN8nJ0BxpjZ8uz40A6PEXX3ZWY2D/g68P+ArwJJd3/VzHYl7PHcxd3rgFoz+x1wCWEh2Jqzs72VfYBNwBnu3pS9V3Wz85aa2UzgaOCGNq51CTDT3V/Lvr7bzK4mHB87t6NtFRGRnknFpxSb09z96e0vzKyKcLzlSYQ9mgB9zCzRyqPmfyHs/XzdzDYCv3X3OwkL0sPMbFOzc0sIH+e35YqWk5jMbBiwrMV5y4Dh7l5rZucAPwLuMLOXCCf3fJBDm1u6H5hKWHyex2e9nnsCpcAqM9t+bhxYsYNrPeTu38j24P6RcNjBc9n2jCbsvZ0AVBF+TarbuM72+3/TzC5vdqwMGJZrw0RERFR8SrH7IWDAYe6+2swOAuYDsZYnuvtq4GIAMzsSeDo7dnEFMNfdJ3/BLCsJC7DmRgBPZO8/B5hjZpWE4yJvBybtxH0eBn5rZrsT9oBOzB5fQThzfdD23stcufs6M7sEeNPM7s/2zP4n4ddyqrtvNbPvA2fu4DIrgOvd/foOtkdERORvVHxKsetDOM5zU3bM5c/aOtHMzgJeyT5y3wgEhI/CHwN+ZWYXEI6phHBS0jZ3f78DWWYDN5nZecBDhGNB9wMeyz4S/wrwdDbvtuy9W7MG2AtodQmj7GSg5wjHun68PaO7r8quF/pbM7sme48vAbu7e7uPvd3dzWwOYQ/xVYRf2y3AtuyErEsJJ2O1lfN24E9m9jTwOmFv6THA8+6+tb37i4iIgCYcSfG7AagknD3+KtlexjYcArxmZtuAvxDORF+SLYxOIJxotBJYDfwa6NBEGXdfTzh+9IeEE3f+BTjV3dcR/r/0g+z1NxCOnby0jUtdRzhecpOZnd3GOfcDx/PZI/ftLiR81L2QsMB+BBjagWb8B3CJmQ0hHCJwHrCVsLBsOfnpcznd/U3CnuWbs/f+EPhWB+4tIiKiReZFREREJDrq+RQRERGRyKj4FBEREZHIqPgUERERkcio+BQRERGRyKj4FBEREZHIqPgUERERkcio+BQRERGRyKj4FBEREZHIqPgUERERkcj8Dx6+IbWSdyzTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.133 and 0.847 for LightGBM Classifer(top 250) on Log Transformed RE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F8r0vIjs9i27",
        "colab_type": "text"
      },
      "source": [
        "# Combined"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfDFKhQ9DajM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "40211d21-5bf5-4c6a-f82b-92a802bf81a1"
      },
      "source": [
        "plot_auc(y_val,y_preds,labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWJqDqYVCUJv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "808350c4-0e00-4fd0-f361-fc10c0432ac5"
      },
      "source": [
        "result_df.sort_values(by='auc',ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XGBoost Classifer(top 250)</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.132627</td>\n",
              "      <td>0.847824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LightGBM Classifer(top 250)</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.133145</td>\n",
              "      <td>0.846871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.132661</td>\n",
              "      <td>0.846845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LGBM Classifer</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.133117</td>\n",
              "      <td>0.845982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest Classifier (top 250)</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.136456</td>\n",
              "      <td>0.837322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.136824</td>\n",
              "      <td>0.836372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.524004</td>\n",
              "      <td>0.823513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed RE</td>\n",
              "      <td>0.141823</td>\n",
              "      <td>0.803198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Model             Dataset  log_loss       auc\n",
              "6          XGBoost Classifer(top 250)  Log Transformed RE  0.132627  0.847824\n",
              "7         LightGBM Classifer(top 250)  Log Transformed RE  0.133145  0.846871\n",
              "3                   XGBoost Classifer  Log Transformed RE  0.132661  0.846845\n",
              "4                      LGBM Classifer  Log Transformed RE  0.133117  0.845982\n",
              "5  Random Forest Classifier (top 250)  Log Transformed RE  0.136456  0.837322\n",
              "2            Random Forest Classifier  Log Transformed RE  0.136824  0.836372\n",
              "0                  Logisic Regression  Log Transformed RE  0.524004  0.823513\n",
              "1            Decision Tree Classifier  Log Transformed RE  0.141823  0.803198"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lF-jgX2JfCuI"
      },
      "source": [
        "# Saving all the datasets created"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7X50CQtb2SL5",
        "colab": {}
      },
      "source": [
        "filename = 'log_train_re.pkl'\n",
        "X_train.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'log_val_re.pkl'\n",
        "X_val.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'log_test_re.pkl'\n",
        "X_test.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'log_train_re_250.pkl'\n",
        "X_train_200.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'log_val_re_250.pkl'\n",
        "X_val_200.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'log_test_re_250.pkl'\n",
        "X_test_200.to_pickle(os.path.join(file_path,'best_models',filename))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}