Global_Volunteer_Application_Qualifier.ipynb

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    "# Global Volunteer Application Qualifier\n",
    "\n",
    "### A Machine Learning model to identify application most likely to approve.\n",
    "\n",
    "> This notebook is an study and exercise on data science pipeline and modeling, by João Diogo Brites\n",
    "\n",
    "\n",
    "\n",
    "1. Importing packages and dataset\n",
    "2. Selecting features and spliting dataset\n",
    "3. Training XGBoost Model\n",
    "4. K-fold Cross Validation (with and without time series)\n",
    "5. Feature importance\n",
    "6. ROC and P|R Curve\n",
    "7. Recursive Feature Selection with Cross-Validation\n",
    "8. Boruta Feature Selection\n",
    "9. Log. Regression Model\n",
    "- Next Steps\n",
    "---"
   ]
  },
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   "source": [
    "# 1. Importing packages and dataset"
   ]
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import gc\n",
    "import re\n",
    "import sys\n",
    "import traceback\n",
    "import warnings\n",
    "import datetime\n",
    "from time import time\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "from bokeh.palettes import Spectral6\n",
    "from bokeh.plotting import figure, show, output_notebook\n",
    "output_notebook(hide_banner=True)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.options.display.max_columns = None\n",
    "#import lightgbm as lgb\n",
    "import xgboost as xgb\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from category_encoders.target_encoder import TargetEncoder\n",
    "from sklearn.feature_selection import RFECV\n",
    "\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from tqdm import tqdm_notebook\n",
    "from IPython.display import display, HTML\n",
    "import ds_utils\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "gc.enable()\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "np.random.seed(710105)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['approval', 'status', 'required_skills_percentage',\n",
       "       'required_languages_percentage', 'required_backgrounds_percentage',\n",
       "       'date_signup', 'month_signup', 'day_signup', 'abs_date_signup',\n",
       "       'date_applied', 'month_applied', 'day_applied', 'abs_date_applied',\n",
       "       'time_to_apply', 'host_lc', 'host_mc', 'program',\n",
       "       'person_accepted_count', 'person_rejected_count',\n",
       "       'person_applications_count', 'home_lc', 'home_mc',\n",
       "       'profile_completeness_ge', 'profile_completeness_gv',\n",
       "       'profile_completeness_gt', 'professional_experience_in_years',\n",
       "       'english_level', 'spanish_level', 'latest_academic_level.name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../Data/dataset_gv_v2.csv\")\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Selecting features and spliting dataset\n",
    "\n",
    "---\n",
    "\n",
    "We differentiate types of data from our dataset because each one of them will need to be treated differently. This also makes it easier to remove and input variables.\n",
    "\n",
    "- Numerical values dont always need encoding or normalization\n",
    "- Categorical values must be encoded\n",
    "- Boolean values mostly do not need encoding\n",
    "- Date values are better used to generate other numerical variabler\n",
    "- Target is our y\n",
    "\n",
    "We split the dataset in a training and testing set, while also encoding categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = [\n",
    " 'required_skills_percentage',\n",
    " 'required_languages_percentage',\n",
    " 'required_backgrounds_percentage',\n",
    " 'month_signup',\n",
    " 'day_signup',\n",
    " 'abs_date_signup',\n",
    " 'month_applied',\n",
    " 'day_applied',\n",
    " 'abs_date_applied',\n",
    " 'time_to_apply',\n",
    " 'person_accepted_count',\n",
    " 'person_rejected_count',\n",
    " 'person_applications_count',\n",
    " 'professional_experience_in_years',\n",
    " 'english_level',\n",
    " 'spanish_level',\n",
    "]\n",
    "cat_cols = [\n",
    " 'host_lc',\n",
    " 'host_mc',\n",
    "# 'program',\n",
    " 'home_lc',\n",
    "# 'home_mc',\n",
    " 'latest_academic_level.name'\n",
    "]\n",
    "bool_cols = [\n",
    " 'profile_completeness_ge',\n",
    " 'profile_completeness_gv',\n",
    " 'profile_completeness_gt',\n",
    "]\n",
    "date_cols = [\n",
    " 'date_signup',\n",
    " 'date_applied',\n",
    "]\n",
    "target = \"approval\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[num_cols+bool_cols+cat_cols]\n",
    "y = df[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label Encoding\n",
    "for f in X.columns:\n",
    "    if X_train[f].dtype=='object' or X_test[f].dtype=='object':\n",
    "        X_train[f] = X_train[f].fillna('NA')\n",
    "        X_test[f] = X_test[f].fillna('NA')\n",
    "        te = TargetEncoder(smoothing=10, min_samples_leaf=100)\n",
    "        te.fit(X_train[f],y_train)\n",
    "        X_train[f] = te.transform(X_train[f])\n",
    "        X_test[f] = te.transform(X_test[f])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Training XGBoost Classifier\n",
    "\n",
    "---\n",
    "\n",
    "In this example we are using XGBoost. XGBoost is an implementation of gradient boosted decision trees. It is a focused choice that allows better model performance and faster execution when compared to sklearn Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "          'min_child_weight': 0.03454472573214212,\n",
    "          'feature_fraction': 0.3797454081646243,\n",
    "          'bagging_fraction': 0.4181193142567742,\n",
    "          'min_data_in_leaf': 106,\n",
    "          'objective': 'binary:logistic',\n",
    "          'max_depth': 6,\n",
    "          'learning_rate': 0.006883242363721497,\n",
    "          \"boosting_type\": \"gbdt\",\n",
    "          \"bagging_seed\": 11,\n",
    "          \"metric\": 'auc',\n",
    "          'reg_alpha': 0.3899927210061127,\n",
    "          'reg_lambda': 0.6485237330340494,\n",
    "          \"nrounds\":200\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train model\n",
    "\n",
    "clf = xgb.XGBClassifier(**params)\n",
    "_ = clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_clf = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred_clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. K-fold Cross Validation (with and without time series)\n",
    "\n",
    "---\n",
    "K-fold Cross Validation split the defined set in k parts (folds). Uses these folds to train a model and test the rest of the data set accordingly. This results in an array of the desired scoring for these folds. In this example we are using \"roc_auc\", but other models may use \"accuracy\", for example. The average and standard of the k-fold CV **can show us how resilient our model is to futuro data**. Plotting the ROC couve of all these folds can help us better visualize the models consistency.\n",
    "\n",
    "This diagram shows the process for k = 5\n",
    "\n",
    "\n",
    "\n",
    "<img src=\"kfold.png\">\n",
    "\n",
    "\n",
    "In the case of time series, besides the function used, it is important to order the dataset accordingly. if random, the CV will approach the value of the non-time serialized CV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "perda: 0.938 (+/- 0.007)\n"
     ]
    }
   ],
   "source": [
    "# k-fold cross-validation\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "kf = KFold(n_splits=10) \n",
    "\n",
    "cv_scores = cross_val_score(clf,X_train,y_train,scoring = \"roc_auc\",cv=kf)\n",
    "print(\"perda: {0:.3f} (+/- {1:.3f})\".format(cv_scores.mean(), cv_scores.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf = KFold(n_splits=5)\n",
    "\n",
    "tprs,fprs,aucs = [],[],[]\n",
    "\n",
    "\n",
    "for train_index, test_index in kf.split(X):\n",
    "    X_train, X_test = X.iloc[train_index,:], X.iloc[test_index,:]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    # Label Encoding\n",
    "    for f in X.columns:\n",
    "        if X_train[f].dtype=='object' or X_test[f].dtype=='object':\n",
    "            X_train[f] = X_train[f].fillna('NA')\n",
    "            X_test[f] = X_test[f].fillna('NA')\n",
    "            te = TargetEncoder(smoothing=10, min_samples_leaf=100)\n",
    "            te.fit(X_train[f],y_train)\n",
    "            X_train[f] = te.transform(X_train[f])\n",
    "            X_test[f] = te.transform(X_test[f])\n",
    "    clf = xgb.XGBClassifier(**params)\n",
    "    _ = clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict_proba(X_test)[:,1]\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred)\n",
    "    auc = metrics.auc(fpr, tpr)\n",
    "    tprs += [tpr]\n",
    "    fprs += [fpr]\n",
    "    aucs += [auc]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24bec78c550>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1,1)\n",
    "pal = sns.cubehelix_palette(8)\n",
    "sns.set_palette(pal)\n",
    "\n",
    "ax1.plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'grey')\n",
    "\n",
    "for i in range(5):\n",
    "    ax1.plot(fprs[i], tprs[i],\n",
    "             label=r'Fold '+str(i+1)+' ROC (AUC = %0.4f)' % (aucs[i]),lw=2, alpha=1)\n",
    "\n",
    "ax1.set_xlabel('Índice de Falsos Positivos')\n",
    "ax1.set_ylabel('Índice de Verdadeiros Positivos')\n",
    "ax1.set_title('Média da ROC')\n",
    "ax1.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"f3faca03-1b10-4373-9bb9-06e82f3105f4\" data-root-id=\"1001\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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@y)\"]]},\"id\":\"1024\",\"type\":\"HoverTool\"},{\"attributes\":{\"data_source\":{\"id\":\"1111\"},\"glyph\":{\"id\":\"1112\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1113\"},\"selection_glyph\":null,\"view\":{\"id\":\"1115\"}},\"id\":\"1114\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1045\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"line_color\":\"#fc8d59\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1112\",\"type\":\"Line\"},{\"attributes\":{\"label\":{\"value\":\"Fold 1 ROC (AUC = 0.8979)\"},\"renderers\":[{\"id\":\"1041\"}]},\"id\":\"1053\",\"type\":\"LegendItem\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#fc8d59\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1113\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1011\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1002\",\"type\":\"DataRange1d\"},{\"attributes\":{\"source\":{\"id\":\"1111\"}},\"id\":\"1115\",\"type\":\"CDSView\"},{\"attributes\":{\"line_color\":\"#e6f598\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1072\",\"type\":\"Line\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1018\"},{\"id\":\"1019\"},{\"id\":\"1020\"},{\"id\":\"1021\"},{\"id\":\"1022\"},{\"id\":\"1023\"},{\"id\":\"1024\"}]},\"id\":\"1025\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1006\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1021\",\"type\":\"SaveTool\"},{\"attributes\":{\"formatter\":{\"id\":\"1045\"},\"ticker\":{\"id\":\"1011\"}},\"id\":\"1010\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1008\",\"type\":\"LinearScale\"},{\"attributes\":{\"source\":{\"id\":\"1038\"}},\"id\":\"1042\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1132\",\"type\":\"Selection\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1043\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1068\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"label\":{\"value\":\"Fold 5 ROC (AUC = 0.9228)\"},\"renderers\":[{\"id\":\"1114\"}]},\"id\":\"1133\",\"type\":\"LegendItem\"},{\"attributes\":{\"source\":{\"id\":\"1071\"}},\"id\":\"1075\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1151\",\"type\":\"UnionRenderers\"}],\"root_ids\":[\"1001\"]},\"title\":\"Bokeh Application\",\"version\":\"2.0.0\"}};\n",
       "  var render_items = [{\"docid\":\"8fb3685b-6fb0-4168-b741-fececdbf141d\",\"root_ids\":[\"1001\"],\"roots\":{\"1001\":\"f3faca03-1b10-4373-9bb9-06e82f3105f4\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1001"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(plot_width=1000, plot_height=600, tooltips=[(\"(x,y)\", \"(@x, @y)\")])\n",
    "\n",
    "p.line(x = [0, 1], y = [0, 1], color = 'grey', line_dash = 'dashed', line_width = 2)\n",
    "\n",
    "for fold in range(5):\n",
    "    p.line(x = fprs[fold],y = tprs[fold],\n",
    "           line_width = 2,\n",
    "           line_color = Spectral6[fold],\n",
    "           legend_label = r'Fold '+ str(fold+1) +' ROC (AUC = %0.4f)' % aucs[fold])\n",
    "\n",
    "p.legend.location = \"bottom_right\"\n",
    "p.legend.click_policy =\"hide\"\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now the same procedure with timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "\n",
    "tscv = TimeSeriesSplit(n_splits=5)\n",
    "\n",
    "tprs,fprs,aucs = [],[],[]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for train_index, test_index in tscv.split(X):\n",
    "    X_train, X_test = X.iloc[train_index,:], X.iloc[test_index,:]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    # Label Encoding\n",
    "    for f in X.columns:\n",
    "        if X_train[f].dtype=='object' or X_test[f].dtype=='object':\n",
    "            X_train[f] = X_train[f].fillna('NA')\n",
    "            X_test[f] = X_test[f].fillna('NA')\n",
    "            te = TargetEncoder(smoothing=10, min_samples_leaf=100)\n",
    "            te.fit(X_train[f],y_train)\n",
    "            X_train[f] = te.transform(X_train[f])\n",
    "            X_test[f] = te.transform(X_test[f])\n",
    "    clf = xgb.XGBClassifier(**params)\n",
    "    _ = clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict_proba(X_test)[:,1]\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred)\n",
    "    auc = metrics.auc(fpr, tpr)\n",
    "    tprs += [tpr]\n",
    "    fprs += [fpr]\n",
    "    aucs += [auc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24becd24e80>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8er+bzIGGRoKtrdQdOYoKmg5JYrORWjiLhNycIV2nVkaYUHs7KhggHAyYo/ZQkEBD3XV7xYb9vs4TkYidfddReuxr97Kox6tmVPL666+ze/duABYtWsSqVatwDrOBUAdaEYxQWi5VcW7jdgCS8idesxII+gJsf3ojZftPR8scdjOGDcDEogJuf2w1zkRPj3u7RwT11dTSWl5BW0VshBD3+HGkzp6JzdOzje7thX3t5mZnHwQau8Uz6ohtY8S+djhImTIandE4u3hPd0esViz2Lp7RItg83l7jIXWV2eZOwOp2605d0yu333475eXl3HPPPUyaNGmoxekXrQhGGCF/gCt7jlB9+D1QCndmKhNXLIz7/sMb97D3d9t7lCemeVnx6D2kThqHOz2pR6fmq62l7VIlKhSm/UoVRqBvr2TPhPEkTZuKI6XvfQplGASbGwk2N+Gvq45b/htBbDazg3c4omaUFocTR3Kq7sQ1N8yZM2d49913+chHPoLFYsHr9fLpT396RHy3tCIYQYQDQco2vRndF8haOJtxt83FYrXGdX/50XMxSsBmFWw2C/f908dJz+99byHQ2EjrxXKaz5b1vBj5glvdblzpqSCCIzUJw99Gy8UzsUHGe6B6LMnYEvred+gwpHGkRJxtrhZQrGs5gBA76tdobhLt7e28+uqrHDp0CICDBw9y6623AowIJQBaEYwYAk0tnPjln6LnUz+4isS8q28MB9r8nN55nCMb99BS27kBPD4rAZvVQtrUiX0qgbbLV6jZuz96njglH1uCB6vTiWtcNkbAT1vFBcK+tmiQsmBjbdzvyepy40hJQ6w27N5ErM6hN6PTaK6FkydPsnnzZlpaWrBardx5550UFRUNtVjXjFYEI4CQz8+pX22Inhd89N6rOoeFAkHe/sWrnH67M2W0RcDjtpOc4mbBJ9aQOSMPax8hHMJ+P3WHjgBgT/SSOqcw6vzVVnmRhmMHYupbHA4zSJnFgtWTgCM5td81djBj3Gg0I5GWlha2bNnCiRMnAMjNzaW4uJiMjIwhluz60IpgmONvaOLk8yXR84l3LupXCdReqOLNn26i7mJ1jBVQgsfOhEmZrPjqw6bNeWRTWClFoKEx6qXqb2jAX1dPoL4BFQ7jzEgna+lt0Slu2NeOr7rTZNWelILN7cGdPeFmv3WNZthy6tQpTpw4gd1u55577mHRokUjZhmoN7QiGOZ0VQLujFTSZk3tt/6p1w5Qe74KALvNgttlIzHBgcUizH14FdYuMX2Czc3UHT2Ov6b35RxHcjLp8+fFfMFD7Z0B5dLmxr9JrdGMdEKhELbILPfWW2+lvr6eRYsWkZKSMsSS3ThaEQxDgi1tNJVVEOiSHCZ7USHjFs9FrL17jjbXNLL3d9s5u/sUAIkJDpY9uhpPRjKu5AS82aYnozIMqvfsw19X38Nc05WRgT05CWdaKs7UVKyuTpvnkK+dUGsLbRXnzbpZA5soQ6MZLiil2LdvH2+99Raf+cxnSEkxYzetXr16qEW7aWhFMMwIB0O897vNhNo6nZXcGamMX9r3BtSe32zjyOa9MWULH1pJ3rLC6LlSikBjI03vl+KrrgFM+3n3uGw8E8bjHpfde5gIw6Dx/eMxoYyBzjDGGs0opqamhpKSEi5eNE3gjh07xvLly4dYqpuPVgTDiNbLNVx8fXdUCaTPKQCE1Fsm93nPmd0nY5RASpKTSUVTyL+9kFB7O77qGvw1tbRXVcfY/ifPnE7S1Cn9xqZpu1yBryo2e6grcxxis+FIGvnTYY2mL8LhMO+88w5vvvkm4XCYhIQE1q1bx8yZM4datAHhqopARL4MPAs0Az8H5gNfU0q9OsCyjRmUUlQdOEHlrkMQ2eDNLJrBxDv6XoMPtPvZ+7s3ObXNtF1OcNtITXaROXMSt35qHWG/n8o33oxZ/rG63bizMvHkTMCV3n/4ZyMUjFECFoeTpIKZphetRjOKqaqq4qWXXuLyZTNOVlFREWvWrMHtHr2z4Hh+1Z9WSv27iNwLZGLmFn4W0IrgJtF8oZLKnQcBSC8sIHlqLt6c7F7r+lraOf7Kfg689I5ZIOD12ElL83DH338cV4qXUGsrDSdORZVAyqwZuLOzsHm9cVk2GOEQzedKo+fJM+boQGiaMYNSiqqqKpKTk7n//vuZOrV/A43RQDyKoKPnuA94Vil1WEayndQwpPaYGe8ne/Ecxi+Z12sdZSjef+soe3+3HV9zOwAOj5OsNDcSDrPws/fj8Dio2rUnxgooY+ECPBOuvrGrwmECTfUEmxpj4vp4J03VSkAz6qmqqiIzMxMRITs7m4ceeohJkybhcIwNb/R4FMG7IvIqkA98XUQSgeuIuavpjWBrO43nykGEjDkFPa/7Arz9i1eoOltJ02Wzgx43PYe8oqk0nj5PW2UtGTPySJ6QzuU33ybs8yE2G3ZvAknTpl5VCYR97TSdfR8V6hmUzT1uIna9F6AZxfj9fl5//XX27dvHRz/6UWbNmgVAQUHP3+JoJh5F8BmgCDirlGoTkXTM5SHNTaDuxBkwFMlTcrEn9IzSefL1g5S+Y3ovelK8LPjgUvxXaqjadYRwMIQz0UPevByq3tlN2OfD6naTtew27An9RyNVSuGrqqT9SmzEUGd6Fq70LCxO54h2kNForkZpaSkbN26ksbERi8VCQ0PDUIs0ZFxVESilDBHJAR6JdAxvKqU2XOU2TRy019RzZd9RANILeyarUEpx9GUz1k/OnHzu/pv7OfCLzTReMD17U/PHk5njxXepAgCrx8245cuw9pP6riMRS1Ppqc5kKhYL3klTsXt7Rh3VaEYb7e3tvPLKKxw+fBiA8ePHU1xczLhxoy9zXrzEYzX0r8Ai4NeRoi+JyDKl1NcHVLJRTqC5lfd+swmApMkTSZwUG6KhdOdxjr92gLb6FuwuB2v+7i94f/NuGi9cwe52svCz9xOovED7ZVMpJM+4hcQp+X3G9zHCIRpOHoFuWbfsicl4J03RcX80Y4LLly/zq1/9itbWVqxWKytXrmTZsmVYxniKz3iWhu4DipQyh48i8hxwENCK4DoIB0M0navg/MtvRcty7locMxKvPX+FbT/ZCJhhbFd8Zi1ntu7j/FuHEYuFokfX4q8ow1dVjVgsZC9f1m/sf8CMD9RFCdgTk0nIy9fmoJoxRXp6Og6Hg/T0dIqLi0m/ihn1WCHeXiAFqIsc31hW9DFM25Vazvz5DcK+Ti/diXcuiskuVn22kq1PvgSAJyWBCblplP75TVTY7MTnPHg3breFmiozmUtCXm6vSqBjD6AjQ1eHT4AjNQNv7uQBeX8azXBDKcXRo0eZPn06TqcTu93OY489RmJiol4G7UI8iuD/Aw6KyDZMU9I70LOBa8JX20D5jv20XDQdVOxeD660ZCZ9YAU2p4PzB0o5t/c9asou01BRi1KKrIKJpLottFWbG1je7FQm3zGP1JxUqnebnsSurEySp8daN/jra/FVVcbm0+2CI0nrcc3YoKGhgQ0bNnD27FkWLlzIunXrAEhKShpiyYYf8WwW/1ZEtmPuEwjw90qpywMt2Gig+UIllbsP03a5JlqWMi2PSfcujwaPu3j4DK/+8H+i18UizLpnAckeO+V7TWuhKXcv4JYPLMEIhajY+nq0bubihTEhIgJNDbRePNdDDnf2BMRqxZ6YgnWYJs/WaG4WhmGwb98+Xn/9dYLBIG63m9zc3KEWa1gTz2ZxCfBboEQp1TrwIo0OlFKc+VNnp+1KSyZ//UqcKYkAVBw/T/3Fanb9qrPOA4//JWm5mdSVVnDg2c0A5K+cT96iAmoPHqb9yhVU0MwElr18WYwSaL9yKWoK6khJx5093kzKbus98YxGMxqprq6mpKSE8vJyAGbPns0HPvABEq5iTj3WiWdp6P8CDwL/KiJ7gf8GNiqlel970ADQeKYzYe/EOxaSOiMfm8uJUopXnvgjFw+fjalf/M2Pk5DkYd/Tf6bxYlW0PHfhNKre2d1ZUYTs25fgTEuNFgWaGrr4AwieCblXzQ6m0Yw26uvr+elPf0o4HMbr9bJu3TpmzJgx1GKNCOJZGnoTeFNErMDdwGeBXwB6oa0PjFCY8m2dEUEzizq/jPXlNVElMG3ZLFyJbmzhEOe3vUvdmUvRhO6TVsyj4N5FXHpla/Te9AVF2LwJOLskwlBGmJayM9HzlFnztBLQjElSU1OZNWsWNpuNNWvW4OrHn0YTS1w9hoi4gfsxZwYLgOfivG8t8O+AFfi5Uupfu13Pi7SVEqnzNaXU5rilH6acLXmDULs5YZr64XsAqDl3mYMluyjb9z4AKRPSWPGZtbz2Tz+L3icWC1mzJ5Nz22wyZ+TRXHY+em3cyhU4umxyKaVoLS8j1NYKmMojtXBBv2GlNZrRRDAY5M0332TmzJlMnDgRgA9+8INj3ifgeohnj+C/gduAl4GngO0dPgVXuc8aqb8aKAf2iUiJUupEl2r/BPxeKfUTEZkFbAYmX/O7GGa0lJtOXumF0wgYFnb96nWORTyEwQwVcfcXHqBi/6lo2byPryY5NwtPumnVE2pvp/6ImXjelZUZVQLhgJ+2SxcJNsW6wzuS07QS0IwZzp8/z4YNG6itraW0tJS/+qu/QkS0ErhO4pkRPAs8opQKX7VmLIuBUqXUWQAR+R3wANBVESg6l5iSgdjANyMQf2NL9Hj80iJe/fc/UXGsc2S/5ON3c8sdc3AmuGgoNfcRErJSGV8UawZaf6zzY0qefgsAwZZmms++T8cMAACLhaQp07F59GaYZvTj9/t57bXX2L/fHFhlZmayfv167RNwg/SpCETkbqXUG4AHeKD7B62UevEqbU8ELnY5L8ecWXTl28CrIvJFIAG4pw9ZPgd8DiAvL+8qjx1afHUN+P0hysubqPyPkqgSuOWOOSz9xCocHichX4ATL+7gwi5zxD/1HjMBjTIMms+V0VJ2gVCraaCVvqAIZ2oKRihI89n3os/x5k3BnpSiZwGaMcPp06fZuHEjTU1NWCwWli9fzooVK6IJ5TXXT3+f4J3AG5h7A91RwNUUQW8qWnU7fxj4pVLq/4rIUuAFESnsvvSklHoGeAZg4cKF3dsYVrSUX6G0tI621iB1taZNv9PrZuknVmF3O6gtreC9Te/QVG56BmfOnMT4omkow6D2wCHaLnVmBUuaNpWEnIkopWg6fTJanjy9EKtTb4Rpxg4+n48XX3wRn8/HhAkTKC4uJju79+RNmmunT0WglPpW5PA7SqkYLyURyY+j7XKgqxdHDj2Xfj4DrI08b5eIuIAMoIoRhhEOU3e8lPLdR2lrNWP7z7hrHpMXFpA+KZu6M+Wc23YwahrqTEpg7kOrSM0fj4jQWnGJtkuViNVK4tQpJORMxO5NwAiHaL1wDiNo5htOKpillYBmTNARHkVEcLlcrF27ltbWVpYsWaL3Am4y8cyp/gfTUqgrfwRuvcp9+4CCiNKoAB4CHulW5wKwCviliMwEXEB1HDINK6oOnKDq3ROE2n1UXmqOlq/4zFoATm3YSdkOM+StPcHFpNvnknd7IXa3k7DPT9jXTu0BM/ewd3IeSdOm0Fx2GiMQiEkY40hJw+bumbNAoxltNDc3s2nTJvLy8li2bBkA8+b1nr1Pc+P0t0cwA5gNJIvIh7tcSsLssPtFKRUSkb8BXsE0Df2FUuq4iHwH2K+UKgH+DviZiPwt5rLRY6pjGDBCCLX5uPT2AQDCYqG6ug0AT6oXgEsH36dshxk1dPr9y8hdPBOrw46vuoaLr73eo72w30fDiUM9yh0paXjG5wzgO9Fohh6lFAcPHuTVV1/F7/dTXl7OokWLsNu1h/xA0t+MYDqwHtPGv+s+QTOmU9lVifgEbO5W9s0uxyeA2+MVdjhy6R0z6bxSCn9KFh3748Xf/ARNFdUc+/02AGY+sJy8ZYWE/X5aL5ZTe/BwtA2r04kjLRX3uGyCrfWogBmdNCFnMvbEJMRq05vCmlFPfX09GzZs4Nw5cyW6oKCA9evXayUwCPS3R/Bn4M8islQptWsQZRoxBJpbqTtxhrChuNQkXN73LgDT75yL0+PgnZ+VYITCTFw0g9ylszECQSpeeS2mjfF33Yk90Zw9+GqqCCMz0ZAAACAASURBVNSbSiBp2kxtEqoZExiGwZ49e3jjjTcIhUJ4PB7Wrl1LYWGhNgsdJPpbGvqqUurfMFNUPtz9ulLqSwMq2Qig/v0yrlxp4cL5xmiZK9HNskdXc+DZTfjqm0nOzWLWh+5ARGi+cCFaz56YSGrhLOyJXpRSKCOMr8Z0RLO63Fj1XoBmDHHy5ElCoRCFhYWsXbtWB4kbZPpbGuqwV9zfT50xSzgUZtfvd1Jd0akEClYUcudn7+PUxneoK63A4XUz/9G1WO3mx2yETJ88q9vNuJUrEBFC7W00nT4R03bilOl6JKQZ1YTDYfx+Px6PB4vFQnFxMbW1tUyfPn2oRRuT9Lc0tCHyGo0rJCIWwKuUahoE2YYtIX+Q7U9vpLqiHqtVWPDBZUxftQB3kodL774XTSk5/5NrcSV7o/epsKkIvJPzEJGIf0AXJWCxmAnkrTp/sGb0UlFRQUlJCUlJSTzyyCOICBkZGWRkZAy1aGOWeGIN/Qb4X0AYeBfTiuiHSqkfDLRww5VDJe9wbt/7iEDBLenMWb8Eq91GY3k1x/64HTA3h1Pzx8fcF/ab6/8dyWFaL5ZFr3knF+jsYZpRTTAYZNu2bezevRulFMFgkNbWVrxe79Vv1gwo8fgRzFJKNYnIxzEtgP4eUyGMWUVw+i0zNMTEnCSSUjyIRQi0tHPwuS0YoTA5i2eSu3R2j/sC9WagOKvTzEsQaKoHzETyWgloRjNlZWVs2LCBuro6RISlS5dy1113aYugYUI8isAuInbgg8D/U0oFRWRE2frfTMKhMC31ZmC5qbdOY/xthVisVt5/eQ++hhaS87Kjm8NdUeFwNH6QxW4j2NIEhgFiwTt52qC/D41mMFBKsWXLFvbt2wdAVlYWxcXF0bDRmuFBPIrgp0AZcBjYISKTgDG7R9Bc3QgKHA4rGYXTSMw1l3+aL5l5iaeuuhWLrecav7+hc1PZnpxEw3HTacyWkKA3hjWjFhHB6XRisVi44447WL58OVa9BzbsiCdD2Y+BH3cpOi8idw2cSMObpsvmco7LZcPmMtf6my/XRmMIebNTe72v7ZIZZsmbPwkjEIiWu9KzBlJcjWbQaWtro66ujpwc0xP+zjvvZO7cuWRmZg6xZJq+iGezOBn4FnBHpOhN4DtAY583jWIuHSkFwOmyYSjFiZd2cOGdY9HrHYllOjBCIRpOvkfLOTMctSs9nUBDHQA2byKO5N4Vh0Yz0lBKcfz4cbZs2YLFYuHzn/88brcbm82mlcAwJ56loV8Ax4CPRc7/EjNZzYf7vGOU0tbYytGt5pJOQrIHf6s/qgQcXjdzH1oVW//yFeqPHCXs80fLQu1NhFrNwHSecXqdVDM6aGpqYtOmTbz/vpmKNT8/n2AwiNvtHmLJNPEQjyKYqpT6iy7nj4tIz6hoY4Da0vLo8cx1S2mrMSdFrhQvK//xk9FrSikC9Q3U7O30xXOPy8Y9IYtAXVXkfCI2jzab04xslFIcOHCArVu34vf7cTqdrFmzhvnz5+u9rxFEPIqgXUSWK6XeBhCR24H2gRVr+OGrb+T8KzsBSEiw429o4fQrpiXElLtio3S3lJ2n/ujx6HnW7UtwpafTeskMMeFIScOdFetjoNGMREpKSjh0yBwXTp8+nfvuu4+kpKSr3KUZbsSjCP4aeC6yVwBQDzw6cCINT2oOdaaJxGaPKoHp65fF+AwopWg+15mjOGPRrbjS083opDXmbMDuTRwcoTWaAWbOnDmcPn2atWvXMnv2bD0LGKHEYzV0CJgnIkmR8zFnOqoMg5qj72MYpvtEsM2HJSmJuQ+tYty8WB8Af20doRbTz2D8qpXYI8Gz2i51pm+2efWISTMyqaqq4uzZsyxZsgSAKVOm8KUvfQmHwzHEkmluhP6ij96GmSd4KnAU+LRS6mRf9UcrYX+ASzsPEA4bNNSaSWcsViuLPlfcI4QEgAqFABCbLaoEWivO4681E69ZXR6sDucgSa/R3BxCoRBvv/02b731FoZhMGHCBPLy8gC0EhgF9DcjeAr4CrADKAaeBO4dDKGGC0YoRPmO/VQfOU1ddRvt7WYnP+m2Gb0qgWBLC60Vpr+AMz0NAGWEo0oAIGmajq6oGVmUl5dTUlJCdbX5PV64cKFOHD/K6E8RWJRSWyPHfxCRrw+GQMMFZRic+tUG6srraGk0zT+VzQYEmDB7cq/31B0+ir/W9BFwJCVG2umMxpE651a9hqoZMQQCgWiQOIC0tDSKi4uZNGnSEEumudn0pwhSuuUqjjlXSr04cGINPUYwhL+xhbaWTi/gkDI78cwp43q9J9RuGlOlzpmNd7L5Ywm2mFsqYrVqJaAZUbzxxhvs2bMnGiRu5cqVOkjcKKU/RfAmsbmKu54rYFQrgraqOsIhhRE2R/QzHryHrU++hMPjJCkr1hvYV1OLv66OcJupCBJycxERjFCI1gtnARCLjq+iGVmsWLGCqqoq7rnnHiZMmDDU4mgGkP4S03xqMAUZbtSePEvtFdP6J23qRC4cOgOAOykBsXSO7MN+P1W79oAyFYbF4cBis+KrraatotOMNHGq3hvQDG/ee+899u/fz0MPPYTVaiUhIYFPfvKTV79RM+KJx49gzFF39hKlO4539O3MLL6dt543k84vevDOmLq+qmpQCpvHgys7C3dWJkoZXcxFhcQpBdpSSDNsaW1tZcuWLRw/bjpBHj58mAULFlzlLs1oQiuCLihDsf+/NlL7fqfNf+7CAhInZNBcY671p0xIi14L+/3UHjwMgHv8OFJnzwQg2NoMygAgdc4CvTegGZYopTh69Cgvv/wy7e3t2O12Vq1aRVFR0VCLphlktCKIUP3eBY7/YRu+RjN5TMrENOxGgOxZk1CGoiWiCLzppjNYyOej7uCR6P0JOWYAOaUU/jozN4EzPVMrAc2wpLGxkY0bN1JaakbTnTJlCuvXryc1VUfDHYvEE4b6o8DLSqlmEfknYAHwL0qpAwMu3SCglOLkS29xYVcklLQIhR+5k5rdB8BiIXlKDu2NrRihMK5EN3aX6TxzZcdOwj4fAOm3zseRbCqItksXCdTXAmDXHsSaYcqZM2coLS3F5XKxZs0aioqK9KBlDBPPjOAbSqk/iMhyTIeyJ4CfALcNqGSDROnWfVElIBYLd/7DX1K2aTsAzpRE7Ake6iorAPBmmOGWjFAoqgS8+ZNJmGhaVKhwGH+tGU9IbHbsiToPsWb4EAgEol7A8+fPp6mpiVtvvZXERB37aqxjiaNOOPK6DviJUurPwKjxKW+8UBU9vuubj+Gvq8dXY2Yhy5g3A4CWajPctDfDHOEHm1ui93TsCyilaDh1FACLw0nKzLmIJZ6PV6MZWAzDYOfOnTz55JPU15vfbRFh5cqVWglogPhmBBUi8lPgHuD7IuIkPgUy7Gmra6LmPTM09JIv/gWOBBflR83EGinT8sicZ5p8dmwUJ0ZmBOGI45h7XHa0s/fXXEGFzRAUifkFepqtGRZcvnyZkpISKisrATh16hRLly4dYqk0w414FMHHgLXAE0qpBhEZD/yfgRVrcDj+h+0AJOdlk5KXTaC5laYyM1ZQyvT8aL3oRnFGEkopGk+bG2zSJQl3qM3cZHYkp2J1ugZDfI2mT0KhEDt27GDnzp0YhkFycjLr169n2rRpV79ZM+aIJwx1m4icAe4VkXuBt5RSrw68aANLoM0XzTg2aflcAFoqIgnoc7JJmZobrdsSyUSWmJFMy/kLBBtNxZCQ0+ltqcLmCpojNX3ghddo+qGyspIXX3yRmhrTem3RokWsWrUKp1P7smh656pLPCLyZeDXQFbk71ci8sV4GheRtSLynoiUisjX+qjzMRE5ISLHReQ31yL8jVB3urxDACbML8AIh7myz1zjT8yLjSzaHFEECWleGk6cAsx4Qu5IBEZlGNGYQqCXhDRDi81mo76+nvT0dD71qU9x3333aSWg6Zd4loY+A9ymlGoFEJHvA7uA/+jvJhGxYoayXg2UA/tEpEQpdaJLnQLg68DtSql6Ecm6vrdx7VTsNzv0DnPQ1ooq/PVN2Nwu0md3Tp+V6vQhCF65hAqFsHk80aByQNRvAMCmk3VrhoDKykrGjRuHiJCZmcnHP/5xcnNzsdm0q5Dm6sSz6St0Wg4ROY5n2LsYKFVKnVVKBYDfAQ90q/NZ4CmlVD2AUqqKQSIUMDd2swrNvYC6k2YsobTZU7G5O9f4/S3thPxB7E47warLYLGQNm9OdDNYGZ3hJBLypmCxjxqDKs0IoL29nT//+c8888wz0RARAPn5+VoJaOImnm/Ks8AeEXkpcv5B4L/iuG8icLHLeTk9fQ9uARCRnYAV+LZS6uXuDYnI54DPAdGsSDdK40VT59yydgm+ukbq3ysDwJGYEFOvw2LI6TI3hjNunY8rMwMwZwtNZ04BCkRwaL8BzSBy8uRJNm/eTEtLC1arlba2tqEWSTNCiWez+Icish1YjjkT+JRS6mAcbfc2a1Ddzm1AAbASyAHeEpFCpVRDNxmewUybycKFC7u3cc0ow8AIhkDAkejm4uuHo9eS83Ni6nb4ELgT7CROycczvjMXgb+uhnC7+eNzZY6LsSLSaAaKlpYWtmzZwokT5iprbm4uxcXFZGRkDLFkmpFKv4pARCzAEaVUIXCtISXKgdwu5znApV7q7FZKBYFzIvIepmLYd43PuibCkWUhq8NMstF0zvQcvuXBtdi9npi6zdWmTnJ77CROmRwtD7W3RcNM2xOTcWfreO2agefSpUu88MIL+Hw+7HY799xzD4sWLdJ+K5obol9FoJQyROSwiOQppS5cY9v7gAIRyQcqgIeAR7rV+RPwMPBLEcnAXCo6e43PuWZC/iAAVruN5vOXCLX7sHs9uDPTetStO2NaFyWkerF22Qhuq4yseokF7+Sp+oeoGRQyMzNJSEhg4sSJrF+/npSUlKEWSTMKiGePYDxwXET2Aq0dhUqp4v5uUkqFRORvgFcw1/9/oZQ6LiLfAfYrpUoi19aIyAnMTej/o5Sqvc73EjfhgKkIbE47NRFP4vTCaT1CQoR9PhojvgWZs6bFdPYqaLaRkDMJc+Kk0dx8lFIcOHCA2bNn43K5sNvtPPbYYyQkJOjBh+amEY8iePx6G1dKbQY2dyv7ZpdjBfzvyN+g0aEIjEAwuizkzemZh7jhxCnaW82cxWlTY/cODMM0pLIleAdSVM0Ypqamhg0bNnDhwgUqKiooLjbHXl6v/s5pbi7xbBa/ORiCDCYdOQfC/gBg7hMkjIvdaPPX19NaXhFVBB2RRzvo8CS2WLWJnubmEg6H2bVrF9u3byccDuP1eikoKBhqsTSjmD57MRF5Wym1XESaibX2EczB/IgNtt9w4QoAdocVz7gMpn14dcyykDIMag8cJhgIEwoa2Jx2XInumOsYZgYydIRRzU2ksrKSkpISLl++DEBRURFr1qzBrR0VNQNIf8nrl0deR12cWhXpxC1WIaVgEhZbrNln/fGThFpbiRgX4U1Piq7HhtrbaL14zrzf4dDrtJqbRl1dHT//+c8xDIOUlBTWr1/P1KlTh1oszRggrnWNSFKaAqXUsxHrnkSl1LmBFe3mE/YHqDl6mpbyK9Eye0LsSCvU3k7LuTIAJMm0IuqIOhporKf1wjk6JkgJOZMHQ2zNGCEtLY25c+ficDhYtWpVNImMRjPQxJOq8lvAQmA6ppexA/gVcPvAinbzqT99nsp3DtLW4IuWOVNiV7jqDnXmIQ5ZzEBd3owk6o8djCakt7o9ePOm6HDTmhsiEAjw+uuvU1hYSG6u6XJTXFysZ5maQSeeBe4PAcVETEeVUpeAEblcZATNtZ4Op7G0Gfl4sjp9B5Rh4K83HcjS58+jpbYZALfHGlUCzvQskqbN1EpAc0OUlpbyn//5n+zdu5dNmzZhGtChlYBmSIhnaSiglFIiogBEJOFqNwx3zOWgBpxpsZZALecvoEIh7ElJJOTm0FJjOjjbxQ+AzZtIwsSbE+tIMzZpb2/nlVde4fBhM6zJ+PHj9SxAM+TEowh+H0lVmSIinwU+DfxsYMUaGvx1Zj5X7yRzmt4ciTPkSXJjdblJzL9lyGTTjHxOnDjB5s2baW1txWazsXLlSpYuXYpFW55phph4/AieEJHVQBPmPsE3lVJbB1yyAaRzGh77A+zwDbA6nSgjTNOVOgASUhNImjZTj9o0143P52PDhg34fD4mTZrE/fffT3q6zmanGR7EZTUU6fhHdOffFRWOmI/aY81GQ5Ewvla3m5aKCgLtQSxWIXvBgh7hJzSaq6GUQimFxWLB5XKxbt062tvbWbhwoR5UaIYV/TmUdXcki2EkO5QZhvm2rDYrKhym5WI5/ppagk3m5rDN46b2XCTZTKoXqzbj01wjDQ0NbNiwgfz8fJYvXw5AYWHhEEul0fROfw5liQCRIHGXgRcwvYo/zgi1Ggq2xCbuCLW2cnnH2wSbW6JlngnjsTqd0YT13vQR+VY1Q4RhGOzbt4/XX3+dYDBIdXU1S5Ys0dnCNMOaeL6d9yqlumYW+4mI7AH+bYBkGjCaysx0CBL5UbaUlWHNTMCW4CExfzKuzAxskYBeLXXm7MCbMWInPppBprq6mg0bNnDxojmbLCwsZO3atVoJaIY98XxDwyLyccycwwozf0C4/1uGJ4HIyF+FzOijFouQdMs0kgqmYemWXaylzqybmKHjvWv6xzAM3n77bXbs2EE4HCYxMZF169Yxffr0oRZNo4mLeBTBI8C/R/4UsJOeCWZGBpG9ASNsvrrHjyNlRs8fq1KK1nozQqk3O3Xw5NOMSESEs2fPEg6HWbBgAatXr8bl0g6HmpFDPOajZcADAy/KwNMRbM6IvFptfVgCGQZtjWYYisRMPSPQ9CQYDBIIBKIJYu6//36amprIz88fatE0mmsmnlhDLuAzwGwgOsxRSn16AOW66XQoAQAjFMklYO/97QdbW2hrbAcgMTO51zqascv58+cpKSkhJSWFT3ziE4gI6enp2i9AM2KJZ2noBeAUcC/wHUyroZMDKdRAoCLLQmKxYATMZDOOpN4tggItLfhaA4gICanaakhj4vf7ee2119i/fz8AVquVtrY2EhJGfNQVzRgnHkUwTSn1URF5QCn1nIj8BjPX8Iiiw5sYi3QuDTnsvdZtvlwDgCfFg8WqHck0cPr0aTZu3EhTUxMWi4UVK1awfPlybRGkGRXE8y0ORl4bRKQQ06dg8oBJNFBEOn+xWDo9i/vo5JtrtemoxkQpxYYNGzh48CAAEyZMoLi4mOzs7CGWTKO5ecSjCJ4RkVTgG0AJ4AW+2f8tww8VVQQS3SNwJPfe0bc1mI5n3jS9LDTWERGSkpKw2WzcddddLFmyRAeJ04w64rEa+nnk8E1gysCKM3BE9wjEEg0x0T07GZgjwJaaJgC8eqN4TNLc3ExdXR2TJk0CYMWKFcydO5e0tLSr3KnRjEz6izX0v/u7USn1w5svzsChui4NRRRB91zFAIH6WlobTB+CpHHaCmQsoZTi4MGDvPrqq1itVr7whS/g8XiwWq1aCWhGNf3NCDrWRaYDizCXhQDuB3YMpFADQUfnr6TzuLs3sREM0Fpe1ulDkKFnBGOF+vp6NmzYwLlzZiruW265JWpUoNGMdvoLOvc4gIi8CixQSjVHzr8N/GFQpLuZRFJNSsR4SASk24zAiOQjaGvqcCbTimC0YxgGe/bsYdu2bQSDQTweD2vXrqWwsFCHitaMGeLZLM4DAl3OA4xAq6GuMwIwN427zwjA7Bjam/0gkJCurYZGO3/60584evQoAHPmzOHee+/VfgGaMUe8DmV7ReQlzFhDHwKeG1CpBoCoZ3GHO0EfpqPtzX6UUnhSvVh72UPQjC4WLFjA+fPnue+++3SQOM2YJR6roe+KyBZgRaToU0qpgwMr1s2nQxFE01T2oQj0/sDopqKignPnzkWTxUyePJkvfvGL2jFMM6bp99svZlLfI0qpQuDA4Ig0QEQUQMcGoKW3H75S0f0B7Uw2uggGg2zbto3du3ejlCI3NzdqHqqVgGas0+8vQClliMhhEclTSl0YLKEGgo4IE9EE9d3yFSulaL9yKRpszqtnBKOGsrIySkpKqK+vR0RYunQpEyZMGGqxNJphQzxDofHAcRHZC7R2FCqligdMqoFAxeYi6B55NNBQR7CpgbZmP6CXhkYDPp+PrVu3cuCAOZnNysqiuLiYiRMnDrFkGs3wIh5F8Pj1Ni4iazET2liBnyul/rWPeh/BNEldpJTaf73Pi4+ezmThQIC2CnPC4zNXhvTS0Chg27ZtHDhwAIvFwh133MHy5cux9mIpptGMdeLZLH5TRCYBBUqp10TEg9mx94uIWIGngNVAObBPREqUUie61UsEvgTsuZ43EC8dm8QRd4Ko6ahSitbyMpQRxp6YHI0zpH0IRiZKqaj9/5133klDQwOrVq0iKytriCXTaIYvV42eJSKfBf4I/DRSNBH4UxxtLwZKlVJnlVIBzJzHvWU6+2fg3wBfXBLfINFo1JHsZP7aakItTYjVhmfCJFpqI3GGtA/BiEIpxdGjR3n++ecJR/aBPB4PDz/8sFYCGs1ViCeM4heA24EmAKXUaSCeX9ZE4GKX8/JIWRQRmQ/kKqU29teQiHxORPaLyP7q6uo4Ht0LHRqA2PASvurLAHgm5OJr9WOEDVxJHmzO3nMVaIYfTU1N/O53v+PFF1+krKyMI0eODLVIGs2IIp49Ar9SKtAx3RYRG1G3rH7pzT8/el/ENPVHwGNXa0gp9QzwDMDChQvjeXafT+4MOGdBGQZG0Ey34EhOoaG0EtAbxSMFpRTvvvsuW7duJRAI4HQ6WbNmDUVFRUMtmkYzoohHEbwpIv8AuEVkNfB5YEMc95UDuV3Oc4BLXc4TgUJge0TJjANKRKR4IDaMo3sEqnNGEGpvAxRWlxuxWGnuCD+tN4qHPXV1dWzYsIGysjIApk+fzrp160hM1DkkNJprJR5F8DXM5PVHgb8CNgM/7/cOk31AgYjkAxXAQ8AjHReVUo1ARse5iGwHvjLQVkNd9wjC7aY1rNVtxpZpqWkEtCIYCZw/f56ysjISEhL4wAc+wKxZs3SQOI3mOukvH8FXgP9WSl0Efhb5ixulVEhE/gYzv7EV+IVS6riIfAfYr5Qq6b+Fm0x0RmCeitVKqM1UBDaPByA6I9BLQ8MTn8+Hy+UCoKioiLa2NubPn48n8u+n0Wiuj/5mBBOBd0TkHPBb4A9KqZpraVwptRlzBtG1rNc0l0qpldfS9vXSsTRktXVRBB0zguqOGYFWBMOJUCjEW2+9xZ49e/jsZz9Leno6IsLtt98+1KJpNKOC/vIR/G0kS9kdmMs63xCRw5hK4aWO/AQjhU4/gk6HMiPgBxGsbjNlZUutqQi0D8Hwoby8nJKSEjqsxc6cOUN6+uBmjgsGg5SXl+PzDYqFs0ZzQ7hcLnJycrDb47d8vFqsIYWZq/jNyDLPPcC/Ak8DI2s+3s1qSCKGsza3BxELSqnOzWLtQzDkBAKBaJA4gLS0NIqLi6OB4gaT8vJyEhMTmTx5st6H0AxrlFLU1tZSXl5Ofn5+3PfFFXZRROZgzgoeBGqBf7guKYeUjhlBJFOZmK8dG8W+pjbCgRAOjxOHxzk0ImoAs+N98cUXo0Hili1bxp133nlNI5ybic/n00pAMyIQEdLT07lWf6v+NosLMDv/h4EwpmfwGqXU2RsRdKjobj7asWts83RYDEU2ivWy0JDjcrloamoiOzub4uLiYREpVCsBzUjher6r/c0IXsHcD3hQKXX0eoUaNnRbGkKFgE5F0FyrN4qHkgsXLpCbm4uIkJGRwaOPPsqECRN0kDiNZhDoM8SEUmqKUuofR4USAKJLQ9Hk9QqxWrE4zGWgDouhRO1DMKi0trbyxz/+kWeffTYmNERubq5WAl2wWq0UFRVF/zoc6Xpj+/btrF+/vtdrkydPpqamp/HfP/7jP5Kbm4vX6+2z3V/+8pdkZmZSVFTEjBkz+NGPfhRz/ZlnnmHGjBnMmDGDxYsX8/bbb0evBYNBvva1r1FQUEBhYSGLFy9my5YtvT7nIx/5CGfPdi48HDx4EBHhlVdeiZaVlZVRWFgYc9+3v/1tnnjiiej5E088wYwZMygsLGTevHk8//zzfb63eHnuuecoKCigoKCA557rPWPvoUOHWLJkCUVFRSxcuJC9e/cCUF9fz4c+9CHmzp3L4sWLOXbsWPSeT3/602RlZfV4T321tXHjRr71rW/d8PvpIJ5YQ6OCzhWhDs9iC1Z3QnQa1elVrGcEg4FSiiNHjvDUU09x/Phx7HZ7NFicpidut5tDhw5F/yZPnnxT27///vujnUx/PPjggxw6dIidO3fy3e9+l4sXzXBiGzdu5Kc//Slvv/02p06d4umnn+aRRx7h8mUzltc3vvENKisrOXbsGMeOHWPDhg00N/c0PDx+/DjhcJgpU6ZEy37729+yfPlyfvvb38b9fp5++mm2bt3K3r17OXbsGDt27OhcFr5O6urqePzxx9mzZw979+7l8ccfp76+vke9r371q3zrW9/i0KFDfOc73+GrX/0qAN/73vcoKiriyJEjPP/883z5y1+O3vPYY4/x8ssvx93WunXrKCkpoa2t7YbeUwdjJ0dfD4cyiS4LgfYqHkwaGxvZuHEjpaWlAEyZMoX777+flJSUIZbs6tQdGRjH97S5C6/5Hp/Px1//9V+zf/9+bDYbP/zhD7nrrrti6tTW1vLwww9TXV3N4sWL++wMlyxZck3PTk9PZ9q0aVRWVpKbm8v3v/99fvCDH5CRYQYLWLBgAY8++ihPPfUUX//61/nZz37GuXPncDrNGXh2djYf+9jHerT761//mgce6AxSrJTij3/8I1u3bmXFihUxToX98b3vfY9t27aRlGT+npOTk3n00Uev6T1255VXXmH13r6bCgAAIABJREFU6tWkpaUBsHr1al5++WUefvjhmHoiQlOTObBsbGyM7nGdOHGCr3/96wDMmDGDsrIyrly5QnZ2NnfccUevs7y+2hIRVq5cycaNG3v9HK+VeK2G3ECeUuq9G37iUNNlRtDhSAZdNov1jGBAKS8v54UXXiAQCOByubj33nuZN2+e3oy9Cu3t7dFgevn5+bz00ks89dRTABw9epRTp06xZs0a3n///Zj7Hn/8cZYvX843v/lNNm3axDPPPHNT5Llw4QI+n4+5c+cC5kj+1ltvjamzcOFCnnvuOUpLS8nLy4t2yv2xc+fOmI51586d5OfnM3XqVFauXMnmzZv58Ic/3G8bzc3NNDc3M3Xq1Ks+7wc/+AG//vWve5Tfcccd/PjHP44pq6ioIDe3M3xaTk4OFRUVPe598sknuffee/nKV76CYRi88847AMybN48XX3yR5cuXs3fvXs6fP095eTnZ2dl9ytdXW2B+vm+99dbgKAIRuR94AnAA+fL/t3fm8VFV5/9/PyEQdoQQyxKRyp6wJAYEREkUERATo9AviwsiWERcWi3L99tvqxX7FYv+Wi0KLVQFRdQCYkIFgoJGqZCy1iCyyBJCEUNABJMASZ7fH/fOZZJMyCSZLJM579frvnLnzrnnnjMzuc895znP5xGJAp7x11SVjtZQUJAjLWHFEBhncXXQpk0bmjdvTuvWrbntttv8TiSuIk/uvsA1NeTO559/zqOPPgpYT5hXX311CUOQmprKypUrAWs6oWXLlpVqx7vvvsvGjRvZu3cvCxcuvOzTuXuSIG85fvw4YWFhzutly5YxduxYAMaOHcubb77JXXfdVWq9IlKu606fPp3p06d7VdbTaMrTdebPn88f//hHRo0axXvvvcekSZP46KOPmDVrFo8//jhRUVH06tWL6OhogoMvfwsurS6wUq/+5z//uez53uKNj+BprCQz3wOo6k6go0+uXo2U8BGENCCofgMALuSc52LuBYJD6hPStOxhp8F7CgsL2bx5M7m5uQAEBwfzwAMPMGbMGL8zArUNb+e8fTnaGjNmDLt37+azzz7jySefdHwAERERbNu2rUjZ7du3ExERQefOncnIyPDoEyhOo0aNnAjugoICVqxYwTPPPEPHjh159NFHWbNmDWfPniU0NLTE/PypU6do3bo1zZs3p0mTJkUczqUxd+7cIk541/bYY4+VKBseHu74RMAa3Xpa2rx48WJn1PKzn/3M8b00b96c119/nZ07d7JkyRKysrLKDPoqrS6wpgYb2aoIlcUbQ5BvK4X6OUUlJtwTz7j8A83CWpgpCh/y7bffsmjRItatW1dkxYevfryBzuDBg51pjX379pGRkUG3bt1KLbNmzRqPzs2KMHDgQO69915eeuklwHJqzpw5k+zsbMBa7fLGG2/w8MMP07hxYyZNmsRjjz3GhQsXAOvJ/6233ipRb48ePRzf0UcffUSfPn04evQohw8f5siRI4waNYpVq1bRtGlT2rZty8cffwxYRmDt2rXccMMNAPz3f/8306ZNc+bXf/jhB4/TYtOnTy/ihHdtxaeFAIYNG0ZKSgqnT5/m9OnTpKSkMGzYsBLl2rVrx6effgrAhg0b6NKlCwDff/+90/9FixYxePDgMqfLSqsLrO+8+CqjiuKNIUgXkfFAPRHpIiJ/Bv5Z1km1jmJTQ/UaXIoeNtISviU/P58NGzawcOFCjh8/TosWLXz2gzVc4uGHH6agoIBevXoxZswY3njjDccZ6+Kpp54iNTWVa6+9lpSUFDp06OCxrhkzZhAeHk5OTg7h4eE8/fTTZV5/5syZvP7665w9e5aEhAQeeOABrr/+erp3786DDz7IW2+9Rdu2bQF49tlnCQsLIyIigp49e5KYmFhkCsjFyJEj+eSTTwBrWujOO+8s8v6oUaN4++23AViyZAnPPvssUVFR3HzzzTz11FOOX2Dq1KncdNNN9OvXj549exIbG1tpldpWrVrxm9/8hn79+tGvXz9++9vfOo7jyZMns3WrtZBg4cKFPPnkk/Tp04f/+Z//cQzQnj17iIyMpHv37qxZs8YxogDjxo1j4MCB7N27l/DwcP72t79dti6AjRs3MnLkyEr1yYWUNby0k9X/GrgVK+vYOmC2qtaIAlffvn3V9YGXh1NfHyIjZRPf/ecshQVK/8lDaGk/PaWv3coXb31MxC3RDLr/Vl83OaA4evQoSUlJzlr1fv36MWTIkBI3KH9iz5499OjRo6abERDk5uZy0003sWnTJhNHchlOnDjB+PHjnRFRcTz9ZkVkm6p6dHKV6SxW1RwsQ/Dr8je3NlFsRBDiPiIwjmJfcOrUKV5//XVUldDQUBISEkp9AjUYPNGoUSN+97vfcezYMfPbuQwZGRm8+OKLPqvvclpDyVwmN7H/rRqy/7jyEbgZgnMmRaVPaNWqFddeey2NGjUiNja2zBURBoMnPM27G4rSr18/n9Z3uf9UV6z2XVj5hF2enXHAYZ+2olpQywjYBiG4YQPnHcdZbEYE5SI3N5eUlBSioqIceeiRI0cah7vB4GdcLjHNpwAiMltVB7u9lSwiqVXeMh/j7goREcRt/tEkrS8/e/bs4cMPP+TcuXMcP36cKVOmWJ+rMQIGg9/hzdg9TESucclP28noS7r7azuqReQlXDesi3kXOH8ul3r1g2nUosllKjAAnDt3jg8//JA9e/YA0KFDB+Lj440BMBj8GG8MwS+BT0TEFZ3REfh5lbWoqlC9FEwWJOCIzdmO4tDm5mZ2GVSVXbt2sW7dOvLy8mjQoAG33HILffv2NZ+bweDnlBlHoKprgS7A4/bWTVVTqrphVYJrROBmCM5lmWkhb8jLyyMlJYW8vDw6d+7M1KlT6devnzEC1YSRofYfGepdu3YxcOBAevXqRXx8vBPUtn79emJiYujVqxcxMTFs2LDBOScuLo5u3bo53+93330HFP3Mo6KiWLRoEQBZWVkMHz680v1x4dWyDlU9D+zy2VVrAHUbEUi9IMROWuweVWwoiuszCwoKolGjRtx+++1cvHiR3r17GwNQzXjSGvIl8fHxPPLII0UiVz0xZswY5s2bR3Z2Nt26dWP06NFcddVVRWSoW7duzfbt20lMTCQtLY02bdoUkaEOCQnhxIkTTsSsO2XJUHu7oshdhrp58+acOXOGVatWle9DKYZLhnrr1q2ICDExMSQkJJTQb5o8eTIvvPACsbGxvPbaa8ydO5fZs2fTunVrkpOTadeuHenp6QwbNqyIaN3SpUvp27fkMn/XZ+5OWFgYbdu2ZdOmTQwaNKhS/YJAkqHGXXDObWoo24wIPHHy5EmSkpLo3LkzgwdbawUiIiJquFU1T0bSP6qk3g4J5Y8QNTLUpVOTMtR79+51/meGDh3KsGHDmD17NtHR0U6ZyMhI8vLyOH/+fIWDLRMTE1m6dKlPDEHAJKYp4iwOuuQsdmUmaxpqRgRgCX199tlnLFiwgKNHj7Jjxw7y8/NrulkBj0uGOioqypFdcJehXrZsGRMmTHAE21y4ZKh37NhBQkICGRkZPmmPtzLUu3fvLrcMtXs9nmSoy6K8MtTeis55K0Pds2dPkpKSAPj73/9eRKjOxYoVK4iOji5iBCZOnEhUVBSzZ88uYrBXrFhB7969GT16dJG6XDLUvsAbGWoB7gauUdVnRKQD0EZVy05nVItQxRkSBNVz8xGYqSGH48ePk5SU5ChKRkdHM3ToUBMY5kZFntx9gZGh9h8Z6tdee43HHnuMZ555hoSEBBo0aFDk/d27dzNz5kxSUi65WpcuXUr79u05e/Yso0aN4s033+S+++4jPj6ecePGERISwoIFC5gwYYLjW6huGepXgYFYgWQAZ4FXfHL1akWdMGkJCnJbNWSmhgoKCvj4449ZuHAh3377LVdccQX33nsvCQkJRim0FmNkqGunDHX37t1JSUlh27ZtjBs3rsjIJDMzkzvvvJMlS5YUOd6+fXsAmjVrxvjx4x256dDQUGfU8OCDDxb5jKtbhrq/qk4D8gBU9TRWkhr/Qt3zFVtTQ/kX8sk98yNSL4jGLUtfLVHXCQoK4tixY6gq/fv3Z+rUqUWcdYbaiZGhrp0y1K4VP4WFhTz77LM89NBDgCVDPXLkSJ577rki8/r5+fnOSq6LFy+yevVqZ0XU8ePHnXJJSUlFhOSqW4b6oojUw158KSJhQKFPrl6NuMtLSD1rRHDO5Shu1YygoMBxlwCcP3/eeUITEeLj43nggQcYPnx4iaGsoXZiZKhrpwz1smXL6Nq1K927d6ddu3ZMnDgRgHnz5nHgwAFmz55dZJno+fPnGTZsGL179yYqKor27dvz4IMPAvDyyy8TGRlJnz59ePnll3njjTec9lS3DPXdwBjgWmAxMBr4X1X9u09aUE4qKkP93fav+GbdZs6cyqNleAv6PTKG/3x1lDXPv0fbHh24/dfjyq6kjnDgwAFWr15Ny5Ytue+++8xS0DIwMtTVh5Gh9p7BgwfzwQcfePT7VIUM9VIR2QYMwcpHkKiqeyrU8hrGWT5qxxFcEpsLDP9ATk4OKSkp7NplhYQ0btyY3NzcSj8pGQy+wshQe0dWVhZPPPFEpZ3/LrxZNTQA2K2qr9ivm4lIf1Xd4sW5w4GXgHrAIlWdU+z9J4DJQD6QBTygqkfK3w0vKKY1hIibo7hurxhSVUck7scffyQ4OJi4uDgGDhwYcFNihtqPkaEum7CwMBITE31WnzfrAudjTQu5+NHDsRLYfoVXgKFAJvAvEUlS1a/ciu0A+qpqjohMBf6ANQ3lcxTclo/aUcWuGII6PCJQVVauXEl6ejoAV199NfHx8YSGhtZwywwGQ23BG0Mg6uZIUNVCEfHmvOuAA26qpe8AdwCOIVDVjW7lNwP3eNXqilAkoCwIcRsR1OUYAhEhLCyMBg0aMHToUGJiYoxPwGAwFMGbG/pBEXkMaxQA8DBQ9gJdaA+4h9RlAv0vU34S4FGFSkR+jq14WuF5Q70USeCMCLLrZopK1/I21xLQQYMGERUV5VVkp8FgCDy8mSB+CLgeOMalm7k3MtSeHjs9LlESkXuAvsBcT++r6l9Vta+q9vW05MwblEsjgqDgIArzC8g5dQ4RoUmrZhWqs7ZRWFjI5s2bmT9/PsuXL+fHH38ELOVKYwQMBkNpeCND/Z2qjlXVK1X1J6o6XlW/86LuTOAqt9fhQIl4aBG5Bfg1kGCrnFYNRSQmgjh36iyqSuOWTakX7P/L1LKysnj99ddZt24dFy9epFOnTmYKqA5hZKj9X4b68OHDNGrUyPkOXYFmULoMtYvly5cjIk6swpdffsn9999f6f64uFzy+hmq+gcR+TMenuRVtWQMdlH+BXSxM5odA8YC44tdIxr4CzDcS+NScdR9RFDPWTrq747igoICNm3aRGpqKgUFBTRr1oyRI0eWiDA1+DdGhtr/ZagBOnXqVOr3WJoM9dmzZ3n55Zfp3//SzHqvXr3IzMwkIyPDJ8tsL+cjcMUKlD96C1DVfBF5BFiHtXz0NVXdLSLPAFtVNQlrKqgp8Hf76TVDVRMqcj3v2mT9lXr1OOdyFPu5f2DlypV89ZXlf7/22msZOnSoVzK9hoqx8+WSsgi+IOqx8q+TMDLUpVMbZagrym9+8xtmzJhRZLQDlvF+5513mDFjRoXrdlHq1JCqJtt/F3vavKlcVT9U1a6q2klVf28f+61tBFDVW+zppih7q0IjcCkxTb36QZdSVPq5Iejfvz+tWrVylAqNEaibGBnquiFDfejQIaKjo4mNjS0hIe1JhnrHjh0cPXrU41RftchQi0gypTh3Aarypl1lOJHF9Th33CU/7V9TQ4cPH+bw4cPExcUB1iqqadOmmcCwaqIiT+6+wMhQ+78Mddu2bcnIyCA0NJRt27aRmJjI7t27ad68uUcZ6nvuuYdf/vKXRfSF3KkuGeoXgBeBQ0AusNDezgHpPrl6deKevD64nltmMv8YEZw/f57Vq1ezePFiPv30U44cuRSAbYxAYGJkqP1LhjokJMQJ5IyJiaFTp06O4fYkQ3327FnS09OJi4ujY8eObN68mYSEBMdhXC0y1Kr6qap+CkSr6hhVTba38cANPrl6dXJJfBSpX88tM1ntHxHs37+fV199lW3bthEUFERsbCzh4eE13SxDDWNkqP1LhjorK4uCggIADh48yP79+7nmmmtKlaFu0aIFJ0+edGYBBgwYQFJSkuNQrm4Z6jARcVz49iqgii3mr0EsGWp7RCDCuVPW00ltXjWUk5PDypUrefvtt/nhhx9o3749U6ZMIS4uzigzGowMtZ/JUKemptK7d2/69OnD6NGjWbBgAa1atbqsDPXlqG4Z6uHAX7kUTdwRmKKq60o9qQqpqAz1fzbt4Kt/bCH/YiGdh0aycclmGrVowj2vPFIFrfQNa9asIS0tjeDgYG6++Wb69+9vpoFqACNDXX0YGWrvOH/+PLGxsXz++eceU8lWhQz1WhHpAnS3D31dpYFfVYVbHMH5nItA7RwNuDu64uLi+PHHH7n55pudJw+DoS5jZKi9IyMjgzlz5vgsn7i3tcRgjQSCgT62Z77yYXrViCUxYVmCvBxrnrI2xRCoKtu3b2fnzp1MmDCB4OBgGjVqxOjRo2u6aQZDtWJkqMvGFd3sK7zJR/Am0AnYCRTYhxXwK0NgSUxYu3nnLENQW0YEp06dIjk52ZEN2L17N3369KnZRhkMhoDBmxFBXyBCvV2rVltxWz6ae9ZanlbTI4LCwkK2bNnChg0byM/Pp3HjxowYMYLIyMgabZfBYAgsvDEE6UAb4HgVt6VKUTcfQc4PuUDNxhB89913JCUlOZGJvXr1Yvjw4SZtpMFgqHa8MQStga9EJA1wnMT+FlmshYXOfs4ZlyGouamhb7/9lmPHjtGsWTNuv/12unbtWmNtMRgMgY03axGfBhKB/8OKNHZtfkVhgWUIFCXn+xyg+g2BKz8AWCOAESNG8PDDDxsjYCgTI0Pt/zLUaWlpzvfXp08f3n//feecl156iZ49exIZGcmf/vQn5/ipU6cYOnQoXbp0YejQoU5A4OrVq3nqqacq3R8XJQyBiNzs/toVYVx881kLqgnNt/zcqlCQX0BI04Y0aBRSxlm+4eLFi6SkpPDSSy+RlZUFWGH/1113nRGJM3iFS2vItXXs2NGn9cfHx5OWllZmuTFjxrBz5042bdrE73//e0dywV2G+uuvv2bBggWMHz/ekaBwl6FOT08nOTnZo+REWTLU3uIuQ52enk5qaqrXkhyl4ZKh3rJlC2lpafzud7/zGKk9efJk5syZw5dffsmdd97J3LlWvq2ePXuydetWdu7cydq1a5kyZQr5+fmkp6ezcOFC0tLS2LVrF6tXr2b//v0AzJkzhyFDhrB//36GDBnCnDlzACvwLikpiZycnEr1yYWnqaEEEQkDFuFZdE4AVdXaseTGS1wjgsJCq0tNQ6vHP3Do0CGSk5M5ffo0IsKRI0c8RlQa/IO101+tknqHz3243OcYGerSqY0y1O7+v7y8PCdeaM+ePQwYMMB5PzY2lvfff58ZM2bwwQcfOJHWEyZMIC4ujueffx4RIS4ujtWrV3v8HMtLiRGBqv4CeF9Vm6lqcw9bM38zAnDJEOQXWP8IVZ2wPi8vj+TkZJYsWcLp06e58sormTx5ssfEEwZDWRgZ6rohQ71lyxYiIyPp1asXCxYsIDg4mJ49e5Kamkp2djY5OTl8+OGHzjknTpxwZDratm1bJHNZlctQq+oFn9Reiyi0xZ4KbKdxVfoHMjIyWL58OWfPniUoKIjBgwdzww03mJD5OkBFntx9gZGh9n8ZarDyh+zevZs9e/YwYcIERowYQY8ePZg5cyZDhw6ladOm9OnTx6uI4eqSoa5TFNo+gvx8e0RQhUtHmzZtSm5uLuHh4UyZMoXY2FhjBAw+x8hQ+5cMtTs9evSgSZMmpKdbiv6TJk1i+/btpKam0qpVKydq+Cc/+QnHj1sr948fP86VV17p1FEtMtR1DbWnhPILfD8iUFW++eYb5x+zVatWTJw4kYkTJxb54gwGX2JkqP1LhvrQoUPk5+cDcOTIEfbu3es4/V3nZGRksHLlSsfvkJCQ4KxOWrx4cRH/SXXLUNcJtNA1InAZAt+MCM6cOcOyZct46623igzd27VrZ5RCDVWKkaH2Lxnqzz//nD59+jh+nldffdVxro8aNYqIiAji4+N55ZVXnCm8WbNmsX79erp06cL69euZNWuW055qlaGubVRUhjr9zX9wdNdhjp34EVXlvr88TkiTii/dVFW2bdvG+vXruXDhAiEhIdx2222O88xQdzAy1NWHkaH2jhMnTjB+/HhnRFQcn8tQ1xW0oIBCtW7g9RvWp0HjiscQZGdnk5yc7KSL7N69O7fddhvNmjXzVXMNhoDEyFB7R0ZGBi++6Lu43oAxBIWFSoHLPxDavMIOtKNHj7JkyRLy8/Np0qQJI0aMICIiwqcOOYMhkDEy1GXTr18/n9YXMIZACwp84ihu164drVq1om3bttx6661GJM5gMPg9AWMICguVgvzyB5Pl5+fzz3/+k759+9K4cWPq1avHAw88UMIpZzAYDP5KwBgCLSh0GxFc4dU5mZmZJCUlkZWVxcmTJ7nrrrsAjBEwGAx1ioAxBIUFBRR4KS9x4cIFNmzYwJYtWwBLV6V4+LzBYDDUFQJmobsWqFc+goMHDzJ//ny2bNmCiDBo0CAeeughrr766upqqsFQAiND7f8y1OvXrycmJoZevXoRExPDhg0bnHPeffddevfuTWRkJDNmzChS33vvvUdERASRkZGMHz8egKysLIYPH17p/rgInBFBYaGzaqg0eYns7GzefPNNANq0aUNCQoITEGMw1CSetIZ8SXx8PI888kiZCdHHjBnDvHnzyM7Oplu3bowePZqrrrqqiAx169at2b59O4mJiaSlpdGmTZsiMtQhISGcOHGCTz8tqWZflgy1tyuK3GWomzdvzpkzZ1i1alX5PpRiuGSot27diogQExNDQkJCCf2myZMn88ILLxAbG8trr73G3LlzmT17Nq1btyY5OZl27dqRnp7OsGHDOHbsGNnZ2UyfPp1t27YRFhbGhAkT+Pjjjx356eeee45NmzbRsmVLJwI5LCyMtm3bsmnTJgYNGlSpfkEAGYL8i/kUKkiQ0LC555U+oaGh9O/fnyZNmnD99debgBZDCRbe83yV1PvgWzPLfY6RoS6d2ihDHR0d7ZSJjIwkLy+P8+fPc/DgQbp27epEWt9yyy2sWLGCIUOGsHDhQqZNm+YYG3fJmsTERJYuXeoTQxAwU0Pn8yyNj5CQYGfN/7lz51i+fDmHDh1yyg0fPpwbb7zRGAFDrcLIUNcNGWoXK1asIDo6mpCQEDp37szXX3/N4cOHyc/PZ9WqVc45+/btY9++fQwaNIgBAwawdu1ap44ql6Gui1w4bxmCBg2DUVX+/e9/s27dOnJzczl58iRTpkwxQWGGMqnIk7svMDLUdUOGGiyjOXPmTFJSUgBo2bIl8+fPZ8yYMQQFBXH99dc7PpL8/Hz279/PJ598QmZmJjfeeCPp6elcccUV/iNDLSLDRWSviBwQkVke3g8RkXft97eISMeqasvFC/aIoFF93n77bVatWkVubi6dOnVi7NixxggY/A4jQ+1/MtSZmZnceeedLFmypMjx+Ph4tmzZwhdffEG3bt0cX014eDh33HEH9evX56c//SndunVz0lj6hQy1iNQDXgFGABHAOBGJKFZsEnBaVTsDfwSqZgIWuHDBUh8lSDlw4AANGzbkjjvu4O677+aKK7yLKzAYahNGhtq/ZKi///57Ro4cyXPPPVdiXt91zunTp3n11VeZPHkyYPkBNm7cCMDJkyfZt2+f40j3Fxnq64ADqnrQznj2DnBHsTJ3AK41WMuBIVJFj+YXbUMgDYQePXowbdo0oqKizEjA4LcYGWr/kqGeN28eBw4cYPbs2c7Iw2UAHn/8cSIiIhg0aBCzZs2ia9eugGV8QkNDiYiI4KabbmLu3LmEhoYCfiJDLSKjgeGqOtl+fS/QX1UfcSuTbpfJtF9/Y5c5WayunwM/B+jQoUOMS/WzPLwz9U+cO3eeboOu4caHflbRbhkCECNDXX0YGWrvGTx4MB988IFHv09tkqH29Khd3Op4UwZV/SvwV7DyEVSkMWPn/8LKW2xGAAZDrcXIUHtHVlYWTzzxRKWd/y6q0hBkAle5vQ4Hiru4XWUyRSQYaAGcqqoGBZknDIOh1mNkqMsmLCyMxMREn9VXlT6CfwFdROSnItIAGAskFSuTBLiiPEYDG9TfUqYZAgLzszT4CxX5rVaZIVDVfOARYB2wB3hPVXeLyDMikmAX+xsQKiIHgCeAEktMDYaapmHDhmRnZxtjYKj1qCrZ2dleRV+7EzA5iw2GinLx4kUyMzNLRO0aDLWRhg0bEh4eTv369YscNzmLDYZK4ArmMRjqKgGjNWQwGAwGzxhDYDAYDAGOMQQGg8EQ4Pids1hEsoDyhxZbtAZKpmeq25g+Bwamz4FBZfp8taqW1PXADw1BZRCRraV5zesqps+BgelzYFBVfTZTQwaDwRDgGENgMBgMAU6gGYKSguR1H9PnwMD0OTCokj4HlI/AYDAYDCUJtBGBwWAwGIphDIHBYDAEOHXSEIjIcBHZKyIHRKSEoqmIhIjIu/b7W0SkY/W30rd40ecnROQrEfm3iHwsIlfXRDt9SVl9dis3WkRURPx+qaE3fRaR/7K/690i8nZ1t9HXePHb7iAiG0Vkh/37vq0m2ukrROQ1EfnOzuDo6X0RkZftz+PfInJtpS+qqnVqA+oB3wDXAA2AXUBEsTIPAwvs/bHAuzXd7mro801AY3t/aiD02S7XDEgFNgN9a7rd1fA9dwF2AC3t11fWdLuroc9/Baba+xFEFLczAAAHr0lEQVTA4ZpudyX7PBi4Fkgv5f3bgDVYGR4HAFsqe826OCK4DjigqgdV9QLwDnBHsTJ3AIvt/eXAEPHvLPZl9llVN6pqjv1yM1bGOH/Gm+8ZYDbwB6AuaEh70+cHgVdU9TSAqn5XzW30Nd70WYHm9n4LSmZC9CtUNZXLZ2q8A1iiFpuBK0SkbWWuWRcNQXvgqNvrTPuYxzJqJdA5A4RWS+uqBm/67M4krCcKf6bMPotINHCVqq6uzoZVId58z12BriKySUQ2i8jwamtd1eBNn58G7hGRTOBD4NHqaVqNUd7/9zKpi/kIPD3ZF18j600Zf8Lr/ojIPUBfILZKW1T1XLbPIhIE/BG4v7oaVA148z0HY00PxWGN+j4TkZ6q+n0Vt62q8KbP44A3VPVFERkIvGn3ubDqm1cj+Pz+VRdHBJnAVW6vwyk5VHTKiEgw1nDyckOx2o43fUZEbgF+DSSo6vlqaltVUVafmwE9gU9E5DDWXGqSnzuMvf1tf6CqF1X1ELAXyzD4K970eRLwHoCqfgE0xBJnq6t49f9eHuqiIfgX0EVEfioiDbCcwUnFyiQBE+z90cAGtb0wfkqZfbanSf6CZQT8fd4Yyuizqp5R1daq2lFVO2L5RRJU1Z/znHrz216FtTAAEWmNNVV0sFpb6Vu86XMGMARARHpgGYKsam1l9ZIE3GevHhoAnFHV45WpsM5NDalqvog8AqzDWnHwmqruFpFngK2qmgT8DWv4eABrJDC25lpcebzs81ygKfB32y+eoaoJNdboSuJln+sUXvZ5HXCriHwFFADTVTW75lpdObzs85PAQhH5JdYUyf3+/GAnIsuwpvZa236Pp4D6AKq6AMsPchtwAMgBJlb6mn78eRkMBoPBB9TFqSGDwWAwlANjCAwGgyHAMYbAYDAYAhxjCAw+R0RespeqGgwGP8AYAoNPEZEQ4AtV/aim22IwGLzDGAKDT7ED1RaV5xwRiROR1fZ+wuWURCuCe/1elu8oIrkistNta+Cr+suDiHxiK2/usmUjulWgjodE5D57/34Raef23iIRifBlmw3+R52LIzD4N/a68NoQA/CNqkbVdCNs7lbVrSLyc6x4kHLFf9hrz13cD6RjR6Kq6mRfNdLgv5gRgaHKsJ+UPxGR5SLytYgsdam82hrzX4vI58BdbufcLyLz7P2fiMj79tPwLhG53j5+j4ik2U/qfxGReh6uXVr9TWy993/Z+vWeFEtL6891IvJP+7x/eno6F5FYt1HEDhFpZkeAzhWRdBH5UkTG2GXbikiqXTZdRG4sowmpQGf73CF2/V/a/Qmxj8+RS3knXrCPPS0ivxKR0Vg6U0vtazayv5++IjJVRP5Q7Hv4s73/hN2+dBH5hdvn+A/7e0l39cngp9S09rbZ6t4GnLP/xmEpu4ZjPXR8AdyAJQFwFEsDR7B0Ylbb59wPzLP33wV+Ye/Xw9KE6gEkA/Xt468C9xW7/uXq/z/gHnv/CmAf0KTY+R2BXGCnvb1iH28OBNv7twAr3Prpqj8ZGGTvN8UadY8C1tt9+AmWJEJbrIjYX7v1r5mHz/IT7DwKwHT7M3H1r6t9fAnwC6AVlraQK1D0Cvvv08Cvitfn/hoIw5J7dh1fY39XMcCXQBO7P7uBaLtPC93Kt6jp353ZKr6ZEYGhqklT1Uy1lCB3Yt1kuwOHVHW/WneRt0o592ZgPoCqFqjqGSxNmRjgXyKy0359TbHzLlf/rcAs+9xPsG6qHTxc+xtVjbK3afaxFlgSHelYyqaRHs7bBPw/EXkM60acj3VDXWb34QTwKdAPS0dnoog8DfRS1bOlfA5L7fYOAn4FdLP7t89+fzFWMpMfsPIuLBKRu7DkB7xCVbOAgyIyQERC7Wtsstv+vqr+qKrngJXAjVjG4RYReV5EbrS/G4OfYgyBoapxVzkt4JJfqqLaJgIsdrtJd1PVpz2UK61+AUa5nd9BVfd4ee3ZwEZV7QnEYxmRohdVnQNMBhoBm0WkO55lg1ErAclg4BiW9tV9pVz3brutiap69DL15WMlclkBJAJrveyXi3eB/8J62n/fNqKlXWsfl0YLz4nIb8t5LUMtwhgCQ03wNfBTEelkvx5XSrmPsdJqIiL1RKS5fWy0iFxpH28lJfMvX67+dcCjbr6K6HK0uwXWTRtKyXMgIp1U9UtVfR7YijU6SQXG2H0Iw7r5p9nt/k5VF2IJIXqbe/ZroKOIdLZf3wt8KiJNsaZoPsSaKvLk7D6LJdHtiZVYBmQcllHAbnuiiDQWkSbAnVg5DtoBOar6FvBCOdpuqIWYVUOGakdV8+wVMP8QkZPA51i5A4rzOPBXEZmENZqYqqpfiMj/AiliJZ+5CEwDjnhZ/2zgT8C/bWNwGLjdy6b/AVgsIk8AG0op8wsRuclu71dYc+0XgIFY+XYVmKGq34rIBGC6iFwEzgGljQiKYPdvItY0VTDWFNMCLB/BByLSEOtJ/pceTn8DWCAiuXab3Os9LZZqaYSqptnHtovIG0CaXWyRqu4QkWHAXBEpxPoOpnrTdkPtxKiPGgwGQ4BjpoYMBoMhwDGGwGAwGAIcYwgMBoMhwDGGwGAwGAIcYwgMBoMhwDGGwGAwGAIcYwgMBoMhwPn/JLRUZlxfAOoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1)\n",
    "#ROC \n",
    "#f = plt.figure(1)\n",
    "ax1.plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'grey')\n",
    "\n",
    "for i in range(5):\n",
    "    ax1.plot(fprs[i], tprs[i],\n",
    "             label=r'Fold 1 ROC (AUC = %0.4f)' % (aucs[i]),lw=2, alpha=1)\n",
    "\n",
    "ax1.set_xlabel('Índice de Falsos Positivos')\n",
    "ax1.set_ylabel('Índice de Verdadeiros Positivos')\n",
    "ax1.set_title('Média da ROC')\n",
    "ax1.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"74cbf919-e265-4208-b3e2-e0b1232b3d78\" data-root-id=\"1267\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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3 ROC (AUC = 0.9554)\"},\"renderers\":[{\"id\":\"1340\"}]},\"id\":\"1355\",\"type\":\"LegendItem\"}],\"root_ids\":[\"1267\"]},\"title\":\"Bokeh Application\",\"version\":\"2.0.0\"}};\n",
       "  var render_items = [{\"docid\":\"13849a42-8d55-4825-81ec-e33091248021\",\"root_ids\":[\"1267\"],\"roots\":{\"1267\":\"74cbf919-e265-4208-b3e2-e0b1232b3d78\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1267"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(plot_width=1000, plot_height=600, tooltips=[(\"(x,y)\", \"(@x, @y)\")])\n",
    "\n",
    "p.line(x = [0, 1], y = [0, 1], color = 'grey', line_dash = 'dashed', line_width = 2)\n",
    "\n",
    "for fold in range(5):\n",
    "    p.line(x = fprs[fold],y = tprs[fold],\n",
    "           line_width = 2,\n",
    "           line_color = Spectral6[fold],\n",
    "           legend_label = r'Fold '+ str(fold+1) +' ROC (AUC = %0.4f)' % aucs[fold])\n",
    "\n",
    "p.legend.location = \"bottom_right\"\n",
    "p.legend.click_policy =\"hide\"\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perda: 0.919 (+/- 0.044)\n"
     ]
    }
   ],
   "source": [
    "#timeseries kfold\n",
    "\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "\n",
    "tscv = TimeSeriesSplit(n_splits=10)\n",
    "tscv_scores = cross_val_score(clf, X_train, y_train, cv=tscv, scoring='roc_auc')\n",
    "print(\"Perda: {0:.3f} (+/- {1:.3f})\".format(tscv_scores.mean(), tscv_scores.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Feature importance\n",
    "\n",
    "---\n",
    "\n",
    "Here we are ranking every feature in the model according to its importance. Feature importance is calculated as the decrease in node impurity weighted by the probability of reaching that node. The node probability can be calculated by the number of samples that reach the node, divided by the total number of samples. The higher the value the more important the feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_imp = pd.DataFrame(sorted(zip(clf.feature_importances_,X.columns)), columns=['Value','Feature'])\n",
    "\n",
    "plt.figure(figsize=(18, 10))\n",
    "sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False)[:40])\n",
    "plt.title('LightGBM Feature Importance - Top 40')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "#plt.savefig('lgbm_importances.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. ROC and P|R Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict_proba(X_test)[:,1]\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred)\n",
    "auc = metrics.auc(fpr, tpr)\n",
    "precision, recall, th = metrics.precision_recall_curve(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_size_inches(14, 6)\n",
    "#ROC \n",
    "#f = plt.figure(1)\n",
    "ax1.plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'grey')\n",
    "\n",
    "ax1.plot(fpr, tpr, color='blue',\n",
    "         label=r'Fold 1 ROC (AUC = %0.4f)' % (auc),lw=2, alpha=1)\n",
    "\n",
    "ax1.set_xlabel('Índice de Falsos Positivos')\n",
    "ax1.set_ylabel('Índice de Verdadeiros Positivos')\n",
    "ax1.set_title('Média da ROC')\n",
    "ax1.legend(loc=\"lower right\")\n",
    "\n",
    "# PR plt\n",
    "#g = plt.figure(2)\n",
    "ax2.plot([0,1],[1,0],linestyle = '--',lw = 2,color = 'grey')\n",
    "\n",
    "ax2.plot(recall, precision, color='blue',\n",
    "         label=r'Fold 1 P|R')\n",
    "ax2.set_xlabel('Recall')\n",
    "ax2.set_ylabel('Precision')\n",
    "ax2.set_title('Curva P|R')\n",
    "_ = ax2.legend(loc=\"lower left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. RFE w/ Cross Validation\n",
    "\n",
    "---\n",
    "\n",
    "Recurseive Feature Elimination measure the loss in Cross Validation score (AUC) by the removal of the feature with the least importance. it itereates in that to suggest an optimal number of features\n",
    "\n",
    "Here we are plotting the effect of n variables, then listing the optimal number of features suggested."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFECV(cv=5,\n",
       "      estimator=XGBClassifier(bagging_fraction=0.4181193142567742,\n",
       "                              bagging_seed=11, base_score=0.5, booster='gbtree',\n",
       "                              boosting_type='gbdt', colsample_bylevel=1,\n",
       "                              colsample_bynode=1, colsample_bytree=1,\n",
       "                              feature_fraction=0.3797454081646243, gamma=0,\n",
       "                              learning_rate=0.006883242363721497,\n",
       "                              max_delta_step=0, max_depth=6, metric='auc',\n",
       "                              min_child_weight=0.03454472573214212,\n",
       "                              min_data_in_leaf=106, missing=None,\n",
       "                              n_estimators=100, n_jobs=1, nrounds=200,\n",
       "                              nthread=None, objective='binary:logistic',\n",
       "                              random_state=0, reg_alpha=0.3899927210061127,\n",
       "                              reg_lambda=0.6485237330340494, scale_pos_weight=1,\n",
       "                              seed=None, silent=None, subsample=1,\n",
       "                              verbosity=1),\n",
       "      min_features_to_select=1, n_jobs=None, scoring='roc_auc', step=1,\n",
       "      verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RFE with Cross Validation to get optimal number f\n",
    "\n",
    "rfecv = RFECV(estimator=clf, step=1, cv=5,\n",
    "              scoring='roc_auc')\n",
    "rfecv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 8\n"
     ]
    }
   ],
   "source": [
    "#RFECV Viz\n",
    "\n",
    "plt.figure()\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n",
    "plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
    "plt.show()\n",
    "\n",
    "print(\"Optimal number of features : %d\" % rfecv.n_features_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['time_to_apply', 'person_accepted_count', 'person_rejected_count', 'person_applications_count', 'host_lc', 'host_mc', 'home_lc', 'latest_academic_level.name']\n"
     ]
    }
   ],
   "source": [
    "#RFECV Feature List\n",
    "\n",
    "features = [f for f in X.columns if f not in ['TARGET','SK_ID_CURR']]\n",
    "\n",
    "rfecv_features = list()\n",
    "indexes = np.where(rfecv.support_ == True)\n",
    "for x in np.nditer(indexes):\n",
    "    rfecv_features.append(features[x])\n",
    "print(rfecv_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Boruta Feature Selection\n",
    "\n",
    "---\n",
    "\n",
    "Boruta is a R library adapted to python with BorutaPy. It randomizes all data in a dataset, then measure features importance of each feature for each randomization a determined number of times. Then it ranks features according to the loss that randomization of the feature caused in the selected scoring system, then eliminates it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BorutaPy(alpha=0.05,\n",
       "         estimator=XGBClassifier(bagging_fraction=0.4181193142567742,\n",
       "                                 bagging_seed=11, base_score=0.5,\n",
       "                                 booster='gbtree', boosting_type='gbdt',\n",
       "                                 colsample_bylevel=1, colsample_bynode=1,\n",
       "                                 colsample_bytree=1,\n",
       "                                 feature_fraction=0.3797454081646243, gamma=0,\n",
       "                                 learning_rate=0.006883242363721497,\n",
       "                                 max_delta_step=0, max_depth=6, metric='auc',\n",
       "                                 min_child_weight=0.034...\n",
       "                                 min_data_in_leaf=106, missing=None,\n",
       "                                 n_estimators=74, n_jobs=1, nrounds=200,\n",
       "                                 nthread=None, objective='binary:logistic',\n",
       "                                 random_state=776889302,\n",
       "                                 reg_alpha=0.3899927210061127,\n",
       "                                 reg_lambda=0.6485237330340494,\n",
       "                                 scale_pos_weight=1, seed=None, silent=None,\n",
       "                                 subsample=1, verbosity=1),\n",
       "         max_iter=100, n_estimators='auto', perc=100,\n",
       "         random_state=RandomState(MT19937) at 0x24BE6330570, two_step=True,\n",
       "         verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from boruta import BorutaPy\n",
    "\n",
    "boruta = BorutaPy(estimator=clf, n_estimators='auto')\n",
    "boruta.fit(X_train.values, y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['day_applied', 'time_to_apply', 'person_accepted_count', 'person_rejected_count', 'person_applications_count', 'host_lc', 'host_mc', 'home_lc', 'latest_academic_level.name']\n"
     ]
    }
   ],
   "source": [
    "#Boruta feature list\n",
    "\n",
    "features = [f for f in X.columns if f not in ['TARGET','SK_ID_CURR']]\n",
    "\n",
    "boruta_features = list()\n",
    "indexes = np.where(boruta.support_ == True)\n",
    "for x in np.nditer(indexes):\n",
    "    boruta_features.append(features[x])\n",
    "print(boruta_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Logistic Regression\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Via sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_clf = LogisticRegression(random_state = 0)\n",
    "log_clf.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "y_pred_log = log_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Via statsmodels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "regressão com statmodel esta dando erro. `Maximum number of iterations has been exceeded.`\n",
    "\n",
    "Hipótese: Conferir nivel das variavels. Talvez Uma delas esteja muito alto, dificiuldade a convergência. tente remover essas variaveis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.396485\n",
      "         Iterations: 115\n",
      "         Function evaluations: 130\n",
      "         Gradient evaluations: 123\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:               approval   No. Observations:                 7000\n",
      "Model:                          Logit   Df Residuals:                     6978\n",
      "Method:                           MLE   Df Model:                           21\n",
      "Date:                Sat, 18 Apr 2020   Pseudo R-squ.:                  0.3924\n",
      "Time:                        19:24:11   Log-Likelihood:                -2775.4\n",
      "converged:                       True   LL-Null:                       -4567.6\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "====================================================================================================\n",
      "                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "required_skills_percentage           0.0008        nan        nan        nan         nan         nan\n",
      "required_languages_percentage        0.0006        nan        nan        nan         nan         nan\n",
      "required_backgrounds_percentage      0.0035        nan        nan        nan         nan         nan\n",
      "month_signup                        -0.0065        nan        nan        nan         nan         nan\n",
      "day_signup                          -0.0050        nan        nan        nan         nan         nan\n",
      "abs_date_signup                     -0.0007        nan        nan        nan         nan         nan\n",
      "month_applied                        0.0855        nan        nan        nan         nan         nan\n",
      "day_applied                          0.0182        nan        nan        nan         nan         nan\n",
      "abs_date_applied                    -0.0011        nan        nan        nan         nan         nan\n",
      "time_to_apply                       -0.0004        nan        nan        nan         nan         nan\n",
      "person_accepted_count                2.0369        nan        nan        nan         nan         nan\n",
      "person_rejected_count               -0.9551        nan        nan        nan         nan         nan\n",
      "person_applications_count            0.0220        nan        nan        nan         nan         nan\n",
      "professional_experience_in_years     0.0841        nan        nan        nan         nan         nan\n",
      "english_level                        0.0520        nan        nan        nan         nan         nan\n",
      "spanish_level                        0.0649        nan        nan        nan         nan         nan\n",
      "profile_completeness_ge              1.4461        nan        nan        nan         nan         nan\n",
      "profile_completeness_gv             -2.4802        nan        nan        nan         nan         nan\n",
      "profile_completeness_gt             -0.0768        nan        nan        nan         nan         nan\n",
      "host_lc                              0.4700        nan        nan        nan         nan         nan\n",
      "host_mc                              3.1036        nan        nan        nan         nan         nan\n",
      "home_lc                              2.7129        nan        nan        nan         nan         nan\n",
      "latest_academic_level.name           1.6013        nan        nan        nan         nan         nan\n",
      "====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import scipy.stats as st\n",
    "from statsmodels.tools import add_constant as add_constant\n",
    "\n",
    "model=sm.Logit(y_train,X_train)\n",
    "result = model.fit(method='bfgs',maxiter=1000)\n",
    "\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC and AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_log, tpr_log, thresholds_log = metrics.roc_curve(y_test, y_pred_log)\n",
    "auc = metrics.auc(fpr_log, tpr_log)\n",
    "precision, recall, th = metrics.precision_recall_curve(y_test, y_pred_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_size_inches(14, 6)\n",
    "#ROC \n",
    "#f = plt.figure(1)\n",
    "ax1.plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'grey')\n",
    "\n",
    "ax1.plot(fpr_log, tpr_log, color='blue',\n",
    "         label=r'Fold 1 ROC (AUC = %0.4f)' % (auc),lw=2, alpha=1)\n",
    "\n",
    "ax1.set_xlabel('Índice de Falsos Positivos')\n",
    "ax1.set_ylabel('Índice de Verdadeiros Positivos')\n",
    "ax1.set_title('Média da ROC')\n",
    "ax1.legend(loc=\"lower right\")\n",
    "\n",
    "# PR plt\n",
    "#g = plt.figure(2)\n",
    "ax2.plot([0,1],[1,0],linestyle = '--',lw = 2,color = 'grey')\n",
    "\n",
    "ax2.plot(recall, precision, color='blue',\n",
    "         label=r'Fold 1 P|R')\n",
    "ax2.set_xlabel('Recall')\n",
    "ax2.set_ylabel('Precision')\n",
    "ax2.set_title('Curva P|R')\n",
    "_ = ax2.legend(loc=\"lower left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RFECV in the Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 10\n"
     ]
    }
   ],
   "source": [
    "# RFE with Cross Validation to get optimal number f\n",
    "rfecv_log = RFECV(estimator=log_clf, step=1, cv=5,\n",
    "              scoring='roc_auc')\n",
    "rfecv_log.fit(X_train, y_train)\n",
    "\n",
    "#RFECV Viz\n",
    "\n",
    "plt.figure()\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n",
    "plt.plot(range(1, len(rfecv_log.grid_scores_) + 1), rfecv_log.grid_scores_)\n",
    "plt.show()\n",
    "\n",
    "print(\"Optimal number of features : %d\" % rfecv_log.n_features_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['person_accepted_count', 'person_rejected_count', 'professional_experience_in_years', 'spanish_level', 'profile_completeness_ge', 'profile_completeness_gv', 'host_lc', 'host_mc', 'home_lc', 'latest_academic_level.name']\n"
     ]
    }
   ],
   "source": [
    "#RFECV Feature List\n",
    "\n",
    "features = [f for f in X.columns if f not in ['TARGET','SK_ID_CURR']]\n",
    "\n",
    "rfecv_log_features = list()\n",
    "indexes = np.where(rfecv_log.support_ == True)\n",
    "for x in np.nditer(indexes):\n",
    "    rfecv_log_features.append(features[x])\n",
    "print(rfecv_log_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward Stepwise Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Character Variables (Dummies Generated, First Dummies Dropped): []\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.394712\n",
      "         Iterations: 35\n",
      "Eliminated : abs_date_signup\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.394712\n",
      "         Iterations 14\n",
      "Eliminated : month_signup\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.394720\n",
      "         Iterations 13\n",
      "Eliminated : required_languages_percentage\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.394748\n",
      "         Iterations 13\n",
      "Eliminated : host_lc\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.394778\n",
      "         Iterations 14\n",
      "Eliminated : required_skills_percentage\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.394823\n",
      "         Iterations 14\n",
      "Eliminated : profile_completeness_gt\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.394881\n",
      "         Iterations 13\n",
      "Eliminated : day_signup\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.394980\n",
      "         Iterations 13\n",
      "Eliminated : spanish_level\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.395107\n",
      "         Iterations 13\n",
      "Eliminated : latest_academic_level.name\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.395291\n",
      "         Iterations 15\n",
      "Regained :  latest_academic_level.name\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:               approval   No. Observations:                 7000\n",
      "Model:                          Logit   Df Residuals:                     6984\n",
      "Method:                           MLE   Df Model:                           15\n",
      "Date:                Sat, 18 Apr 2020   Pseudo R-squ.:                  0.3945\n",
      "Time:                        19:24:29   Log-Likelihood:                -2765.7\n",
      "converged:                       True   LL-Null:                       -4567.6\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "====================================================================================================\n",
      "                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "intercept                        -1445.7336    294.489     -4.909      0.000   -2022.921    -868.546\n",
      "required_backgrounds_percentage      0.0040      0.001      2.910      0.004       0.001       0.007\n",
      "month_applied                      -21.7322      4.443     -4.892      0.000     -30.440     -13.025\n",
      "day_applied                         -0.6961      0.145     -4.799      0.000      -0.980      -0.412\n",
      "abs_date_applied                     0.7102      0.145      4.898      0.000       0.426       0.994\n",
      "time_to_apply                        0.0003      0.000      2.086      0.037    1.51e-05       0.000\n",
      "person_accepted_count                2.0152      0.062     32.298      0.000       1.893       2.137\n",
      "person_rejected_count               -0.9712      0.043    -22.409      0.000      -1.056      -0.886\n",
      "person_applications_count            0.0335      0.015      2.165      0.030       0.003       0.064\n",
      "professional_experience_in_years     0.0802      0.029      2.770      0.006       0.023       0.137\n",
      "english_level                        0.0598      0.035      1.731      0.083      -0.008       0.127\n",
      "profile_completeness_ge              1.4174      0.557      2.545      0.011       0.326       2.509\n",
      "profile_completeness_gv             -2.5097      0.695     -3.611      0.000      -3.872      -1.148\n",
      "host_mc                              3.2308      0.390      8.295      0.000       2.467       3.994\n",
      "home_lc                              2.7366      0.512      5.348      0.000       1.734       3.740\n",
      "latest_academic_level.name           1.5618      0.978      1.598      0.110      -0.354       3.478\n",
      "====================================================================================================\n",
      "AIC: 5563.499386427784\n",
      "BIC: 5673.158033276383\n",
      "Final Variables: ['intercept', 'required_backgrounds_percentage', 'month_applied', 'day_applied', 'abs_date_applied', 'time_to_apply', 'person_accepted_count', 'person_rejected_count', 'person_applications_count', 'professional_experience_in_years', 'english_level', 'profile_completeness_ge', 'profile_completeness_gv', 'host_mc', 'home_lc', 'latest_academic_level.name']\n"
     ]
    }
   ],
   "source": [
    "import stepwiseSelection as ss\n",
    "\n",
    "final_features, iterations_logs = ss.backwardSelection(X_train,y_train, model_type=\"logistic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Character Variables (Dummies Generated, First Dummies Dropped): []\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.652509\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.650711\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.651443\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.652508\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.651814\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.650281\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.652507\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.642415\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.642788\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.639144\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.652298\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.512146\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.532560\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.628177\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.651048\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.651872\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.652412\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.651321\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.651891\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.650580\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.640064\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.625618\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.640243\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.646909\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.512146\n",
      "         Iterations 6\n",
      "Entered : person_accepted_count \tAIC : 7174.043481932695\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.510093\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.511058\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.512140\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.511097\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.511851\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.510848\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.508827\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.509874\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.507957\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.511386\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408720\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.451791\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.510467\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.510214\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.512042\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.512055\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.511321\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.508275\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.504673\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.496902\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.505108\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.508259\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408720\n",
      "         Iterations 8\n",
      "Entered : person_rejected_count \tAIC : 5728.075636339458\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408702\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408714\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408018\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408702\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408707\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408377\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408610\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.407873\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408472\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408270\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408713\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408303\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408709\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408534\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408540\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408240\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.408719\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.407234\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403983\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.406342\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.407642\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403983\n",
      "         Iterations 8\n",
      "Entered : host_mc \tAIC : 5663.756189234228\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403777\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403795\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403321\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403934\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403948\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403480\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403907\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403121\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403784\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403360\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403775\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403315\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403699\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403781\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403808\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403441\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403922\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.403933\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401973\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.402893\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401973\n",
      "         Iterations 8\n",
      "Entered : home_lc \tAIC : 5637.624580765242\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401741\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401712\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401338\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401904\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401947\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401428\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401911\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401232\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401806\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401311\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401611\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401228\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401537\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401705\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401802\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401476\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401847\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.401923\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400977\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400977\n",
      "         Iterations 9\n",
      "Entered : latest_academic_level.name \tAIC : 5625.680587683628\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400770\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400721\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400358\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400905\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400954\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400445\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400936\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400224\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400844\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400339\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400602\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400247\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400559\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400702\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400977\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400471\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400884\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400926\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400224\n",
      "         Iterations 8\n",
      "Entered : day_applied \tAIC : 5617.141232610618\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399966\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399928\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399577\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400098\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400117\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399620\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400213\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400211\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399573\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399784\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399464\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399725\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399955\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400222\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399661\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400087\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.400181\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399464\n",
      "         Iterations 9\n",
      "Entered : professional_experience_in_years \tAIC : 5608.493729159398\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399293\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399221\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398875\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399368\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399355\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398978\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399443\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399441\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398931\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399081\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399055\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399237\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399461\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398918\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399464\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.399416\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398875\n",
      "         Iterations 9\n",
      "Entered : required_backgrounds_percentage \tAIC : 5602.245621706107\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398762\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398661\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398783\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398770\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398407\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398851\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398849\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398359\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398503\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398481\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398646\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398870\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398349\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398875\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398828\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398359\n",
      "         Iterations 9\n",
      "Entered : time_to_apply \tAIC : 5597.019810481842\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398293\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398192\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398359\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398290\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398323\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398326\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398323\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.398158\n",
      "         Iterations 9\n",
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      "Optimization terminated successfully.\n",
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      "         Iterations 9\n",
      "Entered : profile_completeness_gv \tAIC : 5592.01867840344\n",
      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "         Iterations 9\n",
      "Entered : profile_completeness_ge \tAIC : 5586.607464795013\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.397256\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.397049\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
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      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.397049\n",
      "         Iterations 9\n",
      "Entered : english_level \tAIC : 5584.686193759239\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.397005\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.396984\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.397047\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.396982\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.396986\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.396989\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.396986\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.396918\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.396929\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.397019\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.396996\n",
      "         Iterations 9\n",
      "Break : Significance Level\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.397049\n",
      "         Iterations 9\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:               approval   No. Observations:                 7000\n",
      "Model:                          Logit   Df Residuals:                     6987\n",
      "Method:                           MLE   Df Model:                           12\n",
      "Date:                Sat, 18 Apr 2020   Pseudo R-squ.:                  0.3915\n",
      "Time:                        19:24:54   Log-Likelihood:                -2779.3\n",
      "converged:                       True   LL-Null:                       -4567.6\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "====================================================================================================\n",
      "                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "intercept                           -3.1156      0.597     -5.223      0.000      -4.285      -1.946\n",
      "person_accepted_count                2.0621      0.060     34.252      0.000       1.944       2.180\n",
      "person_rejected_count               -0.9291      0.038    -24.257      0.000      -1.004      -0.854\n",
      "host_mc                              3.1483      0.381      8.265      0.000       2.402       3.895\n",
      "home_lc                              2.6362      0.509      5.184      0.000       1.639       3.633\n",
      "latest_academic_level.name           1.6754      0.973      1.722      0.085      -0.232       3.582\n",
      "day_applied                          0.0144      0.004      3.590      0.000       0.007       0.022\n",
      "professional_experience_in_years     0.0749      0.029      2.607      0.009       0.019       0.131\n",
      "required_backgrounds_percentage      0.0036      0.001      2.641      0.008       0.001       0.006\n",
      "time_to_apply                        0.0003      0.000      2.457      0.014    5.83e-05       0.001\n",
      "profile_completeness_gv             -2.4717      0.696     -3.550      0.000      -3.836      -1.107\n",
      "profile_completeness_ge              1.3949      0.559      2.494      0.013       0.299       2.491\n",
      "english_level                        0.0670      0.034      1.982      0.048       0.001       0.133\n",
      "====================================================================================================\n",
      "AIC: 5584.686193759239\n",
      "BIC: 5673.783844323726\n",
      "Final Variables: ['intercept', 'person_accepted_count', 'person_rejected_count', 'host_mc', 'home_lc', 'latest_academic_level.name', 'day_applied', 'professional_experience_in_years', 'required_backgrounds_percentage', 'time_to_apply', 'profile_completeness_gv', 'profile_completeness_ge', 'english_level']\n"
     ]
    }
   ],
   "source": [
    "import stepwiseSelection as ss\n",
    "\n",
    "final_features, iterations_logs = ss.forwardSelection(X_train,y_train, model_type=\"logistic\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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