From 75aeafe1ab39a1b787c595b9f28da29e658e913e Mon Sep 17 00:00:00 2001
From: Gershon Celniker <gershonc@gmail.com>
Date: Tue, 13 Apr 2021 15:53:31 +0300
Subject: [PATCH] Added new predictions distribution plot

---
 .../octopus_showcase_experimental.ipynb       | 2395 ++++++++++-------
 notebooks/octopus_showcase.ipynb              |  268 +-
 octopus_ml/octopus_ml.py                      |   24 +
 setup.py                                      |    2 +-
 4 files changed, 1706 insertions(+), 983 deletions(-)

diff --git a/notebooks/experimental/octopus_showcase_experimental.ipynb b/notebooks/experimental/octopus_showcase_experimental.ipynb
index 054ee8e..979e96f 100644
--- a/notebooks/experimental/octopus_showcase_experimental.ipynb
+++ b/notebooks/experimental/octopus_showcase_experimental.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 134,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:57:35.530610Z",
@@ -22,15 +22,1082 @@
    },
    "outputs": [
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@@ -64,7 +1131,7 @@
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@@ -92,7 +1159,7 @@
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@@ -106,7 +1173,7 @@
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@@ -117,7 +1184,7 @@
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@@ -263,7 +1330,7 @@
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@@ -281,7 +1348,7 @@
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@@ -297,7 +1364,7 @@
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@@ -308,12 +1375,12 @@
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\n",
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\n",
       "text/plain": [
        "<Figure size 288x216 with 1 Axes>"
       ]
@@ -335,14 +1402,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 437.84x216 with 1 Axes>"
+       "<Figure size 433.34x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -355,14 +1422,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 437.84x216 with 1 Axes>"
+       "<Figure size 433.34x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -382,7 +1449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 74,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.003524Z",
@@ -657,7 +1724,7 @@
        "types         numeric   categorical  numeric  categorical  categorical  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -671,7 +1738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -685,7 +1752,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -709,7 +1776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -742,7 +1809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -761,7 +1828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 78,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.015383Z",
@@ -788,7 +1855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 79,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.115768Z",
@@ -803,7 +1870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 80,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.203536Z",
@@ -818,7 +1885,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 80,
      "metadata": {
       "image/png": {
        "height": 600,
@@ -835,7 +1902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 81,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.420608Z",
@@ -851,9 +1918,9 @@
       "number of negative instance :  549\n",
       "new dataset shape:  (542, 17)\n",
       "Method Name       :\u001b[35;1m sampling\u001b[0m\n",
-      "Current memory usage:\u001b[36m 0.078669MB\u001b[0m\n",
-      "Peak                :\u001b[36m 0.092296MB\u001b[0m\n",
-      "Total time taken:  \u001b[36m 13.501 ms \u001b[0m\n"
+      "Current memory usage:\u001b[36m 0.066895MB\u001b[0m\n",
+      "Peak                :\u001b[36m 0.080649MB\u001b[0m\n",
+      "Total time taken:  \u001b[36m 17.692 ms \u001b[0m\n"
      ]
     }
    ],
@@ -882,152 +1949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm import tqdm\n",
-    "from sklearn.metrics import confusion_matrix, accuracy_score, roc_curve, auc\n",
-    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
-    "from sklearn.metrics import classification_report\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "from sklearn.ensemble import IsolationForest\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.model_selection import KFold\n",
-    "from sklearn.metrics import f1_score\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import warnings\n",
-    "import time\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import lightgbm as lgb\n",
-    "import tracemalloc\n",
-    "\n",
-    "\n",
-    "def cv_adv(X, y, threshold, iterations, shuffle=True, params=None, mode=\"classification\"):\n",
-    "\n",
-    "    # Cross Validation - stratified with and without shuffeling\n",
-    "    arr_f1_weighted = np.array([])\n",
-    "    arr_f1_macro = np.array([])\n",
-    "    arr_f1_positive = np.array([])\n",
-    "    arr_recall = np.array([])\n",
-    "    arr_precision = np.array([])\n",
-    "    prediction_folds = []\n",
-    "    preds_folds = []\n",
-    "    y_folds = []\n",
-    "    stacked_models =[]\n",
-    "    index_column=[]\n",
-    "\n",
-    "    if mode==\"regression\":\n",
-    "        skf = KFold(n_splits=5)\n",
-    "    else:\n",
-    "        skf = StratifiedKFold(n_splits=5, random_state=2, shuffle=shuffle)\n",
-    "\n",
-    "    for train_index, test_index in tqdm(skf.split(X, y)):\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",
-    "\n",
-    "        clf = lgbm(X_train, y_train, X_test, y_test, iterations, params)\n",
-    "        preds = clf.predict(X_test)\n",
-    "\n",
-    "        predictions = []\n",
-    "        predictions = adjusted_classes(preds, threshold)\n",
-    "        stacked_models.append(clf)\n",
-    "        index_column.extend(X_test.index.values.tolist())\n",
-    "        #index_column=np.append(index_column,X_test.index.values.astype(int))\n",
-    "    \n",
-    "        \"\"\" Multiclass \n",
-    "        predictions = clf.predict(X_test)\n",
-    "        predictions_classes = []\n",
-    "        for i in predictions:   \n",
-    "            print (np.argmax(i))\n",
-    "            predictions_classes.append(np.argmax(i))  \n",
-    "        \"\"\"\n",
-    "                               \n",
-    "        if mode==\"regression\":\n",
-    "            prediction_folds.extend(predictions)\n",
-    "            preds_folds.extend(preds)\n",
-    "            y_folds.extend(y_test) \n",
-    "            final_clf = lgbm(X, y, X_test, y_test, iterations, params)\n",
-    "\n",
-    "        else:\n",
-    "            prediction_folds.extend(predictions)\n",
-    "            preds_folds.extend(preds)\n",
-    "            y_folds.extend(y_test)\n",
-    "            arr_f1_weighted = np.append(\n",
-    "                arr_f1_weighted, f1_score(y_test, predictions, average=\"weighted\")\n",
-    "            )\n",
-    "            arr_f1_macro = np.append(\n",
-    "                arr_f1_macro, f1_score(y_test, predictions, average=\"macro\")\n",
-    "            )\n",
-    "            arr_f1_positive = np.append(\n",
-    "                arr_f1_positive, f1_score(y_test, predictions, average=\"binary\")\n",
-    "            )\n",
-    "            final_clf = lgbm(X, y, X_test, y_test, iterations, params)\n",
-    "\n",
-    "    return (\n",
-    "        {'final_clf': final_clf,\n",
-    "        'f1_weighted':arr_f1_weighted,\n",
-    "        'f1_macro':arr_f1_macro,\n",
-    "        'f1_positive': arr_f1_positive,\n",
-    "        'predictions_folds':prediction_folds,\n",
-    "        'predictions_proba': preds_folds,\n",
-    "        'y':y_folds,\n",
-    "        'index':index_column,\n",
-    "        'stacked_models': stacked_models}\n",
-    "    )\n",
-    "\n",
-    "def lgbm(X_train, y_train, X_test, y_test, num, params=None):\n",
-    "    # Training function for LGBM with basic categorical features treatment and close to default params\n",
-    "\n",
-    "    categorical_features = []\n",
-    "    for c in X_train.columns:\n",
-    "        col_type = X_train[c].dtype\n",
-    "        if col_type == \"object\" or col_type.name == \"category\":\n",
-    "            # an option in case the data(pandas dataframe) isn't passed with the categorical column type\n",
-    "            # X[c] = X[c].astype('category')\n",
-    "            categorical_features.append(c)\n",
-    "\n",
-    "    lgb_train = lgb.Dataset(X_train, y_train, categorical_feature=categorical_features)\n",
-    "    lgb_valid = lgb.Dataset(X_test, y_test, categorical_feature=categorical_features)\n",
-    "\n",
-    "    if params == None:\n",
-    "        params = {\n",
-    "            \"objective\": \"binary\",\n",
-    "            \"boosting\": \"gbdt\",\n",
-    "            \"scale_pos_weight\": 0.02,\n",
-    "            \"learning_rate\": 0.005,\n",
-    "            \"seed\": 100\n",
-    "            # 'categorical_feature': 'auto',\n",
-    "            # 'metric': 'auc',\n",
-    "            # 'scale_pos_weight':0.1,\n",
-    "            # 'learning_rate': 0.02,\n",
-    "            # 'num_boost_round':2000,\n",
-    "            # \"min_sum_hessian_in_leaf\":1,\n",
-    "            # 'max_depth' : 100,\n",
-    "            # \"bagging_freq\": 2,\n",
-    "            # \"num_leaves\":31,\n",
-    "            # \"bagging_fraction\" : 0.4,\n",
-    "            # \"feature_fraction\" : 0.05,\n",
-    "        }\n",
-    "\n",
-    "    clf = lgb.train(params, lgb_train, num)\n",
-    "\n",
-    "    return clf\n",
-    "\n",
-    "\n",
-    "def adjusted_classes(y_scores, t):\n",
-    "    # transformation from prediction probabolity to class given the threshold\n",
-    "    return [1 if y >= t else 0 for y in y_scores]\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1036,12 +1958,11 @@
     "    model = LGBMClassifier(**hyperparams)\n",
     "    return model\n",
     "\n",
-    "def kfold_evaluation(X, y, k, hyperparams, esr=100):\n",
+    "def kfold_evaluation(X, y, k, hyperparams, esr=50):\n",
     "    scores = []\n",
     " \n",
     "    kf = KFold(k)\n",
     "    for i, (train_idx, test_idx) in enumerate(kf.split(X)):\n",
-    "        print(f\"\\n-------- FOLD {i} --------\")\n",
     "        \n",
     "        X_train = X.iloc[train_idx]\n",
     "        y_train = y.iloc[train_idx]\n",
@@ -1053,14 +1974,12 @@
     "        train_score = evaluate(model, X_train, y_train)\n",
     "        val_score = evaluate(model, X_val, y_val)\n",
     "        scores.append((train_score, val_score))\n",
-    "        \n",
-    "        #print(f\"Fold {i} | Eval AUC: {val_score}\")    \n",
-    "        \n",
+    "                \n",
     "    scores = pd.DataFrame(scores, columns=['train score', 'validation score']) \n",
     "    return scores\n",
     "\n",
     "# Constant\n",
-    "K = 3\n",
+    "K = 5\n",
     "# Objective function\n",
     "def objective(trial):\n",
     "    # Search spaces\n",
@@ -1077,7 +1996,7 @@
     "    }\n",
     "    \n",
     "    hyperparams.update(best_params)\n",
-    "    scores = kfold_evaluation(X, y, K, hyperparams, 100)\n",
+    "    scores = kfold_evaluation(X, y, K, hyperparams, 10)\n",
     "    return scores['validation score'].mean()\n",
     "\n",
     "def create(hyperparams):\n",
@@ -1085,14 +2004,18 @@
     "    return model\n",
     "\n",
     "def fit(model, X, y):\n",
-    "    model.fit(X, y)\n",
+    "    model.fit(X, y,verbose=-1)\n",
     "    return model\n",
     "\n",
     "def fit_with_stop(model, X, y, X_val, y_val, esr):\n",
+    "    #model.fit(X, y,\n",
+    "    #          eval_set=(X_val, y_val),\n",
+    "    #          early_stopping_rounds=esr, \n",
+    "    #          verbose=-1)\n",
     "    model.fit(X, y,\n",
     "              eval_set=(X_val, y_val),\n",
-    "              early_stopping_rounds=esr, \n",
-    "              verbose=200)\n",
+    "              verbose=-1)\n",
+    "        \n",
     "    return model\n",
     "\n",
     "def evaluate(model, X, y):\n",
@@ -1110,360 +2033,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m[I 2021-04-12 18:00:55,625]\u001b[0m A new study created in memory with name: no-name-51c32856-2da7-459f-b3df-3e5fd6ba1684\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "-------- FOLD 0 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.805863\n",
-      "[400]\tvalid_0's auc: 0.816112\n",
-      "[600]\tvalid_0's auc: 0.818977\n",
-      "[800]\tvalid_0's auc: 0.820063\n",
-      "Early stopping, best iteration is:\n",
-      "[838]\tvalid_0's auc: 0.820211\n",
-      "\n",
-      "-------- FOLD 1 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.862796\n",
-      "Early stopping, best iteration is:\n",
-      "[256]\tvalid_0's auc: 0.863443\n",
-      "\n",
-      "-------- FOLD 2 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m[I 2021-04-12 18:00:56,328]\u001b[0m Trial 0 finished with value: 0.8508852693636412 and parameters: {'reg_alpha': 7.083105901552592, 'reg_lambda': 3.671845169499529, 'num_leaves': 100, 'min_child_samples': 57, 'max_depth': 52, 'colsample_bytree': 0.2773992631290695, 'cat_smooth': 58, 'cat_l2': 9, 'min_data_per_group': 90}. Best is trial 0 with value: 0.8508852693636412.\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Early stopping, best iteration is:\n",
-      "[30]\tvalid_0's auc: 0.869513\n",
-      "\n",
-      "-------- FOLD 0 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.834881\n",
-      "Early stopping, best iteration is:\n",
-      "[237]\tvalid_0's auc: 0.837128\n",
-      "\n",
-      "-------- FOLD 1 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.883575\n",
-      "Early stopping, best iteration is:\n",
-      "[148]\tvalid_0's auc: 0.884962\n",
-      "\n",
-      "-------- FOLD 2 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.882618\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m[I 2021-04-12 18:00:57,374]\u001b[0m Trial 1 finished with value: 0.8688240503575453 and parameters: {'reg_alpha': 0.3491424177084913, 'reg_lambda': 2.9385399799790353, 'num_leaves': 721, 'min_child_samples': 52, 'max_depth': 20, 'colsample_bytree': 0.4758898058150408, 'cat_smooth': 56, 'cat_l2': 7, 'min_data_per_group': 136}. Best is trial 1 with value: 0.8688240503575453.\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Early stopping, best iteration is:\n",
-      "[152]\tvalid_0's auc: 0.884382\n",
-      "\n",
-      "-------- FOLD 0 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.804356\n",
-      "[400]\tvalid_0's auc: 0.810975\n",
-      "[600]\tvalid_0's auc: 0.811617\n",
-      "[800]\tvalid_0's auc: 0.812605\n",
-      "Early stopping, best iteration is:\n",
-      "[789]\tvalid_0's auc: 0.812605\n",
-      "\n",
-      "-------- FOLD 1 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.858936\n",
-      "Early stopping, best iteration is:\n",
-      "[111]\tvalid_0's auc: 0.859421\n",
-      "\n",
-      "-------- FOLD 2 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m[I 2021-04-12 18:00:58,091]\u001b[0m Trial 2 finished with value: 0.846219016902683 and parameters: {'reg_alpha': 7.22629895893493, 'reg_lambda': 0.8794432283474658, 'num_leaves': 723, 'min_child_samples': 91, 'max_depth': 56, 'colsample_bytree': 0.2552625702063128, 'cat_smooth': 61, 'cat_l2': 7, 'min_data_per_group': 86}. Best is trial 1 with value: 0.8688240503575453.\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Early stopping, best iteration is:\n",
-      "[55]\tvalid_0's auc: 0.86677\n",
-      "\n",
-      "-------- FOLD 0 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.818853\n",
-      "[400]\tvalid_0's auc: 0.83093\n",
-      "[600]\tvalid_0's auc: 0.836264\n",
-      "Early stopping, best iteration is:\n",
-      "[568]\tvalid_0's auc: 0.836412\n",
-      "\n",
-      "-------- FOLD 1 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.877196\n",
-      "[400]\tvalid_0's auc: 0.880617\n",
-      "Early stopping, best iteration is:\n",
-      "[356]\tvalid_0's auc: 0.881541\n",
-      "\n",
-      "-------- FOLD 2 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.88982\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m[I 2021-04-12 18:00:59,148]\u001b[0m Trial 3 finished with value: 0.8710283279890798 and parameters: {'reg_alpha': 2.567325409846061, 'reg_lambda': 8.020851867795816, 'num_leaves': 628, 'min_child_samples': 18, 'max_depth': 36, 'colsample_bytree': 0.19808892575600567, 'cat_smooth': 62, 'cat_l2': 16, 'min_data_per_group': 133}. Best is trial 3 with value: 0.8710283279890798.\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[400]\tvalid_0's auc: 0.895503\n",
-      "Early stopping, best iteration is:\n",
-      "[429]\tvalid_0's auc: 0.896042\n",
-      "\n",
-      "-------- FOLD 0 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.816754\n",
-      "[400]\tvalid_0's auc: 0.831448\n",
-      "Early stopping, best iteration is:\n",
-      "[403]\tvalid_0's auc: 0.831596\n",
-      "\n",
-      "-------- FOLD 1 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.869152\n",
-      "[400]\tvalid_0's auc: 0.871857\n",
-      "[600]\tvalid_0's auc: 0.876387\n",
-      "[800]\tvalid_0's auc: 0.876734\n",
-      "Early stopping, best iteration is:\n",
-      "[706]\tvalid_0's auc: 0.877011\n",
-      "\n",
-      "-------- FOLD 2 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.874388\n",
-      "[400]\tvalid_0's auc: 0.877058\n",
-      "[600]\tvalid_0's auc: 0.878552\n",
-      "[800]\tvalid_0's auc: 0.880413\n",
-      "[1000]\tvalid_0's auc: 0.88105\n",
-      "Did not meet early stopping. Best iteration is:\n",
-      "[961]\tvalid_0's auc: 0.881834\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m[I 2021-04-12 18:01:00,311]\u001b[0m Trial 4 finished with value: 0.8634034501972083 and parameters: {'reg_alpha': 0.87374875887037, 'reg_lambda': 9.369754460807714, 'num_leaves': 883, 'min_child_samples': 85, 'max_depth': 59, 'colsample_bytree': 0.2563307054719969, 'cat_smooth': 99, 'cat_l2': 16, 'min_data_per_group': 109}. Best is trial 3 with value: 0.8710283279890798.\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "-------- FOLD 0 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.813568\n",
-      "[400]\tvalid_0's auc: 0.822212\n",
-      "[600]\tvalid_0's auc: 0.823941\n",
-      "[800]\tvalid_0's auc: 0.824237\n",
-      "[1000]\tvalid_0's auc: 0.825768\n",
-      "Did not meet early stopping. Best iteration is:\n",
-      "[926]\tvalid_0's auc: 0.825966\n",
-      "\n",
-      "-------- FOLD 1 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.876479\n",
-      "[400]\tvalid_0's auc: 0.879345\n",
-      "Early stopping, best iteration is:\n",
-      "[432]\tvalid_0's auc: 0.879854\n",
-      "\n",
-      "-------- FOLD 2 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.88056\n",
-      "[400]\tvalid_0's auc: 0.881834\n",
-      "[600]\tvalid_0's auc: 0.883132\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m[I 2021-04-12 18:01:01,732]\u001b[0m Trial 5 finished with value: 0.8629803694371706 and parameters: {'reg_alpha': 4.172517351182977, 'reg_lambda': 3.422853798711023, 'num_leaves': 469, 'min_child_samples': 45, 'max_depth': 13, 'colsample_bytree': 0.20887320847912516, 'cat_smooth': 20, 'cat_l2': 7, 'min_data_per_group': 196}. Best is trial 3 with value: 0.8710283279890798.\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[800]\tvalid_0's auc: 0.883573\n",
-      "Early stopping, best iteration is:\n",
-      "[810]\tvalid_0's auc: 0.883622\n",
-      "\n",
-      "-------- FOLD 0 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.825422\n",
-      "Early stopping, best iteration is:\n",
-      "[207]\tvalid_0's auc: 0.825521\n",
-      "\n",
-      "-------- FOLD 1 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.869499\n",
-      "[400]\tvalid_0's auc: 0.871648\n",
-      "Early stopping, best iteration is:\n",
-      "[332]\tvalid_0's auc: 0.871672\n",
-      "\n",
-      "-------- FOLD 2 --------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m[I 2021-04-12 18:01:02,729]\u001b[0m Trial 6 finished with value: 0.8558867456010176 and parameters: {'reg_alpha': 7.693871009647226, 'reg_lambda': 8.943772817828465, 'num_leaves': 981, 'min_child_samples': 51, 'max_depth': 40, 'colsample_bytree': 0.4677215485116376, 'cat_smooth': 43, 'cat_l2': 8, 'min_data_per_group': 87}. Best is trial 3 with value: 0.8710283279890798.\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.869929\n",
-      "[400]\tvalid_0's auc: 0.870517\n",
-      "Early stopping, best iteration is:\n",
-      "[419]\tvalid_0's auc: 0.870566\n",
-      "\n",
-      "-------- FOLD 0 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.833449\n",
-      "Early stopping, best iteration is:\n",
-      "[218]\tvalid_0's auc: 0.834585\n",
-      "\n",
-      "-------- FOLD 1 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "Early stopping, best iteration is:\n",
-      "[14]\tvalid_0's auc: 0.882812\n",
-      "\n",
-      "-------- FOLD 2 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m[I 2021-04-12 18:01:03,551]\u001b[0m Trial 7 finished with value: 0.8711952086214763 and parameters: {'reg_alpha': 2.028341641327015, 'reg_lambda': 9.058848843938758, 'num_leaves': 725, 'min_child_samples': 33, 'max_depth': 13, 'colsample_bytree': 0.48606542396164654, 'cat_smooth': 51, 'cat_l2': 8, 'min_data_per_group': 199}. Best is trial 7 with value: 0.8711952086214763.\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[200]\tvalid_0's auc: 0.894915\n",
-      "Early stopping, best iteration is:\n",
-      "[244]\tvalid_0's auc: 0.896189\n",
-      "\n",
-      "-------- FOLD 0 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.796503\n",
-      "[400]\tvalid_0's auc: 0.806159\n",
-      "[600]\tvalid_0's auc: 0.810654\n",
-      "[800]\tvalid_0's auc: 0.811617\n",
-      "Early stopping, best iteration is:\n",
-      "[788]\tvalid_0's auc: 0.811667\n",
-      "\n",
-      "-------- FOLD 1 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.858843\n",
-      "[400]\tvalid_0's auc: 0.862241\n",
-      "[600]\tvalid_0's auc: 0.863813\n",
-      "[800]\tvalid_0's auc: 0.865431\n",
-      "Early stopping, best iteration is:\n",
-      "[779]\tvalid_0's auc: 0.865523\n",
-      "\n",
-      "-------- FOLD 2 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "Early stopping, best iteration is:\n",
-      "[51]\tvalid_0's auc: 0.864198\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m[I 2021-04-12 18:01:04,525]\u001b[0m Trial 8 finished with value: 0.8470303256234447 and parameters: {'reg_alpha': 4.830195886698662, 'reg_lambda': 4.325110586174375, 'num_leaves': 791, 'min_child_samples': 96, 'max_depth': 57, 'colsample_bytree': 0.14964647462877562, 'cat_smooth': 42, 'cat_l2': 12, 'min_data_per_group': 122}. Best is trial 7 with value: 0.8711952086214763.\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "-------- FOLD 0 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.834313\n",
-      "Early stopping, best iteration is:\n",
-      "[299]\tvalid_0's auc: 0.835202\n",
-      "\n",
-      "-------- FOLD 1 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.878929\n",
-      "Early stopping, best iteration is:\n",
-      "[159]\tvalid_0's auc: 0.879623\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m[I 2021-04-12 18:01:05,229]\u001b[0m Trial 9 finished with value: 0.8675452959309219 and parameters: {'reg_alpha': 1.796795578438417, 'reg_lambda': 1.64323633900942, 'num_leaves': 35, 'min_child_samples': 56, 'max_depth': 30, 'colsample_bytree': 0.46966113004539933, 'cat_smooth': 75, 'cat_l2': 1, 'min_data_per_group': 72}. Best is trial 7 with value: 0.8711952086214763.\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "-------- FOLD 2 --------\n",
-      "Training until validation scores don't improve for 100 rounds\n",
-      "[200]\tvalid_0's auc: 0.887419\n",
-      "Early stopping, best iteration is:\n",
-      "[145]\tvalid_0's auc: 0.887811\n"
+      "\u001b[32m[I 2021-04-13 15:47:34,752]\u001b[0m A new study created in memory with name: no-name-4ded8b3e-6b88-4839-853d-59afc6b5feef\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:47:36,558]\u001b[0m Trial 0 finished with value: 0.8674641425512183 and parameters: {'reg_alpha': 4.2746949993429695, 'reg_lambda': 8.045602191603217, 'num_leaves': 286, 'min_child_samples': 34, 'max_depth': 53, 'colsample_bytree': 0.3245361071106679, 'cat_smooth': 93, 'cat_l2': 19, 'min_data_per_group': 160}. Best is trial 0 with value: 0.8674641425512183.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:47:38,316]\u001b[0m Trial 1 finished with value: 0.8582319524698712 and parameters: {'reg_alpha': 4.6132732157394285, 'reg_lambda': 4.406229195080214, 'num_leaves': 181, 'min_child_samples': 98, 'max_depth': 16, 'colsample_bytree': 0.3698699340753182, 'cat_smooth': 35, 'cat_l2': 20, 'min_data_per_group': 168}. Best is trial 0 with value: 0.8674641425512183.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:47:40,492]\u001b[0m Trial 2 finished with value: 0.8658998833927194 and parameters: {'reg_alpha': 4.218574544895514, 'reg_lambda': 1.4657944906975642, 'num_leaves': 433, 'min_child_samples': 49, 'max_depth': 34, 'colsample_bytree': 0.22665315027342625, 'cat_smooth': 80, 'cat_l2': 20, 'min_data_per_group': 102}. Best is trial 0 with value: 0.8674641425512183.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:47:42,280]\u001b[0m Trial 3 finished with value: 0.8497744836491897 and parameters: {'reg_alpha': 7.7522267140145384, 'reg_lambda': 5.415889794310271, 'num_leaves': 283, 'min_child_samples': 87, 'max_depth': 52, 'colsample_bytree': 0.40691462028878045, 'cat_smooth': 58, 'cat_l2': 3, 'min_data_per_group': 174}. Best is trial 0 with value: 0.8674641425512183.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:47:46,917]\u001b[0m Trial 4 finished with value: 0.8721478649260332 and parameters: {'reg_alpha': 0.3384095537194454, 'reg_lambda': 1.1900343753522746, 'num_leaves': 875, 'min_child_samples': 71, 'max_depth': 48, 'colsample_bytree': 0.43541424553886043, 'cat_smooth': 66, 'cat_l2': 19, 'min_data_per_group': 120}. Best is trial 4 with value: 0.8721478649260332.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:47:50,408]\u001b[0m Trial 5 finished with value: 0.8732235885433525 and parameters: {'reg_alpha': 1.385900896711225, 'reg_lambda': 0.20367308499224934, 'num_leaves': 510, 'min_child_samples': 29, 'max_depth': 25, 'colsample_bytree': 0.43974746973598533, 'cat_smooth': 32, 'cat_l2': 13, 'min_data_per_group': 184}. Best is trial 5 with value: 0.8732235885433525.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:47:52,868]\u001b[0m Trial 6 finished with value: 0.8649300033525721 and parameters: {'reg_alpha': 5.780275094497645, 'reg_lambda': 3.7143102330776934, 'num_leaves': 785, 'min_child_samples': 18, 'max_depth': 57, 'colsample_bytree': 0.4555495692012851, 'cat_smooth': 12, 'cat_l2': 20, 'min_data_per_group': 177}. Best is trial 5 with value: 0.8732235885433525.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:47:55,714]\u001b[0m Trial 7 finished with value: 0.8582079922775872 and parameters: {'reg_alpha': 9.090669664044404, 'reg_lambda': 9.683604164364613, 'num_leaves': 486, 'min_child_samples': 44, 'max_depth': 24, 'colsample_bytree': 0.32700244031754294, 'cat_smooth': 13, 'cat_l2': 13, 'min_data_per_group': 121}. Best is trial 5 with value: 0.8732235885433525.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:47:58,457]\u001b[0m Trial 8 finished with value: 0.8619848654510237 and parameters: {'reg_alpha': 3.7041375042579743, 'reg_lambda': 8.892155760235866, 'num_leaves': 613, 'min_child_samples': 56, 'max_depth': 16, 'colsample_bytree': 0.2632524954071799, 'cat_smooth': 85, 'cat_l2': 17, 'min_data_per_group': 125}. Best is trial 5 with value: 0.8732235885433525.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:01,211]\u001b[0m Trial 9 finished with value: 0.8602312356160497 and parameters: {'reg_alpha': 8.602414606789647, 'reg_lambda': 8.967075063345913, 'num_leaves': 805, 'min_child_samples': 35, 'max_depth': 5, 'colsample_bytree': 0.48329314449039684, 'cat_smooth': 98, 'cat_l2': 6, 'min_data_per_group': 167}. Best is trial 5 with value: 0.8732235885433525.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:06,679]\u001b[0m Trial 10 finished with value: 0.8639378482676688 and parameters: {'reg_alpha': 0.09545970894171507, 'reg_lambda': 0.06408997604429065, 'num_leaves': 624, 'min_child_samples': 6, 'max_depth': 37, 'colsample_bytree': 0.1160678564041685, 'cat_smooth': 35, 'cat_l2': 11, 'min_data_per_group': 50}. Best is trial 5 with value: 0.8732235885433525.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:11,713]\u001b[0m Trial 11 finished with value: 0.8731836435055869 and parameters: {'reg_alpha': 0.011165260023663637, 'reg_lambda': 0.17898467112315555, 'num_leaves': 995, 'min_child_samples': 72, 'max_depth': 41, 'colsample_bytree': 0.4947832136990945, 'cat_smooth': 62, 'cat_l2': 15, 'min_data_per_group': 91}. Best is trial 5 with value: 0.8732235885433525.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:14,221]\u001b[0m Trial 12 finished with value: 0.8750521676834776 and parameters: {'reg_alpha': 1.6677835941039376, 'reg_lambda': 0.013778720618611295, 'num_leaves': 28, 'min_child_samples': 75, 'max_depth': 38, 'colsample_bytree': 0.49314044110474975, 'cat_smooth': 38, 'cat_l2': 14, 'min_data_per_group': 62}. Best is trial 12 with value: 0.8750521676834776.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:16,884]\u001b[0m Trial 13 finished with value: 0.8716871595901864 and parameters: {'reg_alpha': 2.08979807711966, 'reg_lambda': 2.4210905127331825, 'num_leaves': 134, 'min_child_samples': 19, 'max_depth': 28, 'colsample_bytree': 0.4876397569634047, 'cat_smooth': 36, 'cat_l2': 8, 'min_data_per_group': 200}. Best is trial 12 with value: 0.8750521676834776.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:19,627]\u001b[0m Trial 14 finished with value: 0.8735833971550244 and parameters: {'reg_alpha': 2.296106335795533, 'reg_lambda': 6.963734209884464, 'num_leaves': 389, 'min_child_samples': 66, 'max_depth': 23, 'colsample_bytree': 0.3886535522817055, 'cat_smooth': 48, 'cat_l2': 13, 'min_data_per_group': 69}. Best is trial 12 with value: 0.8750521676834776.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:22,865]\u001b[0m Trial 15 finished with value: 0.8728052029529214 and parameters: {'reg_alpha': 2.4927938988340856, 'reg_lambda': 6.828197659120399, 'num_leaves': 5, 'min_child_samples': 69, 'max_depth': 9, 'colsample_bytree': 0.37671113577177917, 'cat_smooth': 48, 'cat_l2': 9, 'min_data_per_group': 55}. Best is trial 12 with value: 0.8750521676834776.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:25,254]\u001b[0m Trial 16 finished with value: 0.8626221446891165 and parameters: {'reg_alpha': 2.8675993836219673, 'reg_lambda': 6.605504846352161, 'num_leaves': 80, 'min_child_samples': 87, 'max_depth': 64, 'colsample_bytree': 0.16830458844069074, 'cat_smooth': 47, 'cat_l2': 15, 'min_data_per_group': 71}. Best is trial 12 with value: 0.8750521676834776.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:27,799]\u001b[0m Trial 17 finished with value: 0.8618964662401251 and parameters: {'reg_alpha': 6.258135965743827, 'reg_lambda': 6.1675264989357865, 'num_leaves': 281, 'min_child_samples': 60, 'max_depth': 43, 'colsample_bytree': 0.372555015664524, 'cat_smooth': 19, 'cat_l2': 11, 'min_data_per_group': 68}. Best is trial 12 with value: 0.8750521676834776.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:30,574]\u001b[0m Trial 18 finished with value: 0.8701331663236616 and parameters: {'reg_alpha': 1.3914778750779835, 'reg_lambda': 7.496030109824305, 'num_leaves': 372, 'min_child_samples': 86, 'max_depth': 32, 'colsample_bytree': 0.28442576714461426, 'cat_smooth': 23, 'cat_l2': 15, 'min_data_per_group': 82}. Best is trial 12 with value: 0.8750521676834776.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:34,392]\u001b[0m Trial 19 finished with value: 0.8704701026429035 and parameters: {'reg_alpha': 1.108478155277608, 'reg_lambda': 3.3210095496512237, 'num_leaves': 9, 'min_child_samples': 100, 'max_depth': 17, 'colsample_bytree': 0.4060632752089826, 'cat_smooth': 47, 'cat_l2': 13, 'min_data_per_group': 51}. Best is trial 12 with value: 0.8750521676834776.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:37,561]\u001b[0m Trial 20 finished with value: 0.8670979905335315 and parameters: {'reg_alpha': 3.354101492457045, 'reg_lambda': 5.1659240388266285, 'num_leaves': 652, 'min_child_samples': 75, 'max_depth': 23, 'colsample_bytree': 0.3305364588889911, 'cat_smooth': 51, 'cat_l2': 5, 'min_data_per_group': 101}. Best is trial 12 with value: 0.8750521676834776.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:41,412]\u001b[0m Trial 21 finished with value: 0.8728753043791311 and parameters: {'reg_alpha': 1.5746891469217537, 'reg_lambda': 1.26985342466511, 'num_leaves': 559, 'min_child_samples': 58, 'max_depth': 27, 'colsample_bytree': 0.4471496289429904, 'cat_smooth': 28, 'cat_l2': 13, 'min_data_per_group': 200}. Best is trial 12 with value: 0.8750521676834776.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:46,436]\u001b[0m Trial 22 finished with value: 0.8746587948003535 and parameters: {'reg_alpha': 0.90271853352627, 'reg_lambda': 0.19240271980848966, 'num_leaves': 404, 'min_child_samples': 38, 'max_depth': 21, 'colsample_bytree': 0.41429239886582836, 'cat_smooth': 40, 'cat_l2': 17, 'min_data_per_group': 142}. Best is trial 12 with value: 0.8750521676834776.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:51,765]\u001b[0m Trial 23 finished with value: 0.8736637714937938 and parameters: {'reg_alpha': 0.6220439524487782, 'reg_lambda': 2.3043867093697843, 'num_leaves': 368, 'min_child_samples': 45, 'max_depth': 11, 'colsample_bytree': 0.4117370273922249, 'cat_smooth': 41, 'cat_l2': 16, 'min_data_per_group': 141}. Best is trial 12 with value: 0.8750521676834776.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:48:58,054]\u001b[0m Trial 24 finished with value: 0.8752577637035038 and parameters: {'reg_alpha': 0.5061116270010977, 'reg_lambda': 2.129042391247797, 'num_leaves': 209, 'min_child_samples': 43, 'max_depth': 7, 'colsample_bytree': 0.47277122364226126, 'cat_smooth': 40, 'cat_l2': 17, 'min_data_per_group': 149}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:03,948]\u001b[0m Trial 25 finished with value: 0.8715746051512283 and parameters: {'reg_alpha': 0.623620480777357, 'reg_lambda': 0.6174608927025493, 'num_leaves': 179, 'min_child_samples': 41, 'max_depth': 6, 'colsample_bytree': 0.473441631177163, 'cat_smooth': 68, 'cat_l2': 17, 'min_data_per_group': 148}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:15,050]\u001b[0m Trial 26 finished with value: 0.8655647513878559 and parameters: {'reg_alpha': 0.0288322162353985, 'reg_lambda': 2.141554203423388, 'num_leaves': 211, 'min_child_samples': 24, 'max_depth': 12, 'colsample_bytree': 0.49841822260007873, 'cat_smooth': 40, 'cat_l2': 18, 'min_data_per_group': 137}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:19,357]\u001b[0m Trial 27 finished with value: 0.8734521256913007 and parameters: {'reg_alpha': 2.01647365805896, 'reg_lambda': 3.2273805512346536, 'num_leaves': 70, 'min_child_samples': 5, 'max_depth': 38, 'colsample_bytree': 0.46318880847733246, 'cat_smooth': 24, 'cat_l2': 16, 'min_data_per_group': 155}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:23,144]\u001b[0m Trial 28 finished with value: 0.8722632137900176 and parameters: {'reg_alpha': 2.972668115274029, 'reg_lambda': 0.9963235727725037, 'num_leaves': 78, 'min_child_samples': 50, 'max_depth': 31, 'colsample_bytree': 0.42682711339947654, 'cat_smooth': 41, 'cat_l2': 18, 'min_data_per_group': 134}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:28,824]\u001b[0m Trial 29 finished with value: 0.8737743997688531 and parameters: {'reg_alpha': 0.80648356925008, 'reg_lambda': 1.9369130135238484, 'num_leaves': 247, 'min_child_samples': 36, 'max_depth': 20, 'colsample_bytree': 0.3381781264143771, 'cat_smooth': 56, 'cat_l2': 18, 'min_data_per_group': 110}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:32,506]\u001b[0m Trial 30 finished with value: 0.8700365091370609 and parameters: {'reg_alpha': 3.7939502146546524, 'reg_lambda': 0.029710819111842923, 'num_leaves': 316, 'min_child_samples': 13, 'max_depth': 45, 'colsample_bytree': 0.4934166303078282, 'cat_smooth': 28, 'cat_l2': 14, 'min_data_per_group': 150}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:36,774]\u001b[0m Trial 31 finished with value: 0.8738719333275178 and parameters: {'reg_alpha': 0.7786335140189156, 'reg_lambda': 2.033426473181212, 'num_leaves': 235, 'min_child_samples': 36, 'max_depth': 18, 'colsample_bytree': 0.3508358136406101, 'cat_smooth': 54, 'cat_l2': 18, 'min_data_per_group': 113}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:39,495]\u001b[0m Trial 32 finished with value: 0.8726784643457026 and parameters: {'reg_alpha': 1.7987651607774127, 'reg_lambda': 2.7323022091270053, 'num_leaves': 147, 'min_child_samples': 32, 'max_depth': 18, 'colsample_bytree': 0.35542726761757315, 'cat_smooth': 40, 'cat_l2': 19, 'min_data_per_group': 159}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:43,010]\u001b[0m Trial 33 finished with value: 0.8742118741891165 and parameters: {'reg_alpha': 0.9712004357617784, 'reg_lambda': 4.237216803616876, 'num_leaves': 231, 'min_child_samples': 39, 'max_depth': 13, 'colsample_bytree': 0.2876695495522566, 'cat_smooth': 54, 'cat_l2': 17, 'min_data_per_group': 127}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:45,658]\u001b[0m Trial 34 finished with value: 0.862142001910955 and parameters: {'reg_alpha': 5.0470786084419, 'reg_lambda': 4.419426187943311, 'num_leaves': 327, 'min_child_samples': 27, 'max_depth': 13, 'colsample_bytree': 0.24683669301036773, 'cat_smooth': 63, 'cat_l2': 16, 'min_data_per_group': 131}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:49,945]\u001b[0m Trial 35 finished with value: 0.8721249412235407 and parameters: {'reg_alpha': 0.04632715388487263, 'reg_lambda': 0.7154831752241193, 'num_leaves': 455, 'min_child_samples': 42, 'max_depth': 7, 'colsample_bytree': 0.30437882418011125, 'cat_smooth': 71, 'cat_l2': 20, 'min_data_per_group': 141}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:53,607]\u001b[0m Trial 36 finished with value: 0.8750041521688233 and parameters: {'reg_alpha': 1.3347211801658427, 'reg_lambda': 4.369889522596256, 'num_leaves': 147, 'min_child_samples': 53, 'max_depth': 14, 'colsample_bytree': 0.2172256563685932, 'cat_smooth': 73, 'cat_l2': 17, 'min_data_per_group': 167}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:49:57,884]\u001b[0m Trial 37 finished with value: 0.8646534616187596 and parameters: {'reg_alpha': 2.71906943984697, 'reg_lambda': 5.801180032672855, 'num_leaves': 124, 'min_child_samples': 82, 'max_depth': 50, 'colsample_bytree': 0.19257730407274148, 'cat_smooth': 75, 'cat_l2': 19, 'min_data_per_group': 187}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:50:01,553]\u001b[0m Trial 38 finished with value: 0.8612633941121125 and parameters: {'reg_alpha': 4.5299201563039775, 'reg_lambda': 1.554145612205213, 'num_leaves': 12, 'min_child_samples': 64, 'max_depth': 36, 'colsample_bytree': 0.22852441559997502, 'cat_smooth': 80, 'cat_l2': 15, 'min_data_per_group': 166}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:50:04,885]\u001b[0m Trial 39 finished with value: 0.8651712550167294 and parameters: {'reg_alpha': 1.407296749945405, 'reg_lambda': 4.662179410222142, 'num_leaves': 68, 'min_child_samples': 49, 'max_depth': 21, 'colsample_bytree': 0.14094134568605332, 'cat_smooth': 30, 'cat_l2': 12, 'min_data_per_group': 177}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:50:09,041]\u001b[0m Trial 40 finished with value: 0.8747872951492399 and parameters: {'reg_alpha': 0.4130760709879009, 'reg_lambda': 3.568383325684344, 'num_leaves': 417, 'min_child_samples': 54, 'max_depth': 29, 'colsample_bytree': 0.18592676041911887, 'cat_smooth': 87, 'cat_l2': 1, 'min_data_per_group': 149}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:50:12,964]\u001b[0m Trial 41 finished with value: 0.8656364818051298 and parameters: {'reg_alpha': 0.4029058095582985, 'reg_lambda': 3.7986709190523187, 'num_leaves': 419, 'min_child_samples': 50, 'max_depth': 29, 'colsample_bytree': 0.15436796349332946, 'cat_smooth': 91, 'cat_l2': 2, 'min_data_per_group': 148}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:50:19,035]\u001b[0m Trial 42 finished with value: 0.8744015063481667 and parameters: {'reg_alpha': 1.1516314368649248, 'reg_lambda': 2.898467932491239, 'num_leaves': 508, 'min_child_samples': 54, 'max_depth': 33, 'colsample_bytree': 0.20003132293842485, 'cat_smooth': 90, 'cat_l2': 1, 'min_data_per_group': 162}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:50:22,375]\u001b[0m Trial 43 finished with value: 0.8617533102529296 and parameters: {'reg_alpha': 1.7111253019114907, 'reg_lambda': 3.6910975950392095, 'num_leaves': 296, 'min_child_samples': 77, 'max_depth': 26, 'colsample_bytree': 0.11488610088546947, 'cat_smooth': 100, 'cat_l2': 14, 'min_data_per_group': 155}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:50:26,150]\u001b[0m Trial 44 finished with value: 0.8725713000782405 and parameters: {'reg_alpha': 0.31828512412551646, 'reg_lambda': 1.5564571179590372, 'num_leaves': 563, 'min_child_samples': 93, 'max_depth': 14, 'colsample_bytree': 0.19666777571043165, 'cat_smooth': 84, 'cat_l2': 17, 'min_data_per_group': 173}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:50:29,699]\u001b[0m Trial 45 finished with value: 0.8546383989028421 and parameters: {'reg_alpha': 7.42837687672068, 'reg_lambda': 4.961010239696101, 'num_leaves': 179, 'min_child_samples': 60, 'max_depth': 39, 'colsample_bytree': 0.22988761065481633, 'cat_smooth': 96, 'cat_l2': 20, 'min_data_per_group': 144}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:50:33,736]\u001b[0m Trial 46 finished with value: 0.8667069900254282 and parameters: {'reg_alpha': 2.266861209357008, 'reg_lambda': 4.054093658817703, 'num_leaves': 465, 'min_child_samples': 47, 'max_depth': 8, 'colsample_bytree': 0.25976762748471005, 'cat_smooth': 36, 'cat_l2': 9, 'min_data_per_group': 186}. Best is trial 24 with value: 0.8752577637035038.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:50:37,390]\u001b[0m Trial 47 finished with value: 0.8754238596847609 and parameters: {'reg_alpha': 0.1318656240547762, 'reg_lambda': 5.53859320663792, 'num_leaves': 418, 'min_child_samples': 53, 'max_depth': 21, 'colsample_bytree': 0.17715848470107146, 'cat_smooth': 80, 'cat_l2': 14, 'min_data_per_group': 117}. Best is trial 47 with value: 0.8754238596847609.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:50:41,349]\u001b[0m Trial 48 finished with value: 0.8763396596998341 and parameters: {'reg_alpha': 0.11091148697482356, 'reg_lambda': 5.344169952358866, 'num_leaves': 731, 'min_child_samples': 63, 'max_depth': 29, 'colsample_bytree': 0.1800907444791662, 'cat_smooth': 76, 'cat_l2': 14, 'min_data_per_group': 171}. Best is trial 48 with value: 0.8763396596998341.\u001b[0m\n",
+      "\u001b[32m[I 2021-04-13 15:50:45,227]\u001b[0m Trial 49 finished with value: 0.8746089154880596 and parameters: {'reg_alpha': 1.2937729597527057, 'reg_lambda': 5.760545970902446, 'num_leaves': 890, 'min_child_samples': 65, 'max_depth': 10, 'colsample_bytree': 0.2107328361672356, 'cat_smooth': 76, 'cat_l2': 12, 'min_data_per_group': 193}. Best is trial 48 with value: 0.8763396596998341.\u001b[0m\n"
      ]
     }
    ],
@@ -1481,22 +2108,21 @@
     "from sklearn.metrics import roc_auc_score\n",
     "\n",
     "study = optuna.create_study(direction='maximize')\n",
-    "#study.optimize(objective, timeout=360*7)\n",
-    "study.optimize(objective, n_trials=10)\n"
+    "study.optimize(objective, n_trials=50)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.8711952086214763"
+       "0.8763396596998341"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1507,35 +2133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'reg_alpha': 2.028341641327015,\n",
-       " 'reg_lambda': 9.058848843938758,\n",
-       " 'num_leaves': 725,\n",
-       " 'min_child_samples': 33,\n",
-       " 'max_depth': 13,\n",
-       " 'colsample_bytree': 0.48606542396164654,\n",
-       " 'cat_smooth': 51,\n",
-       " 'cat_l2': 8,\n",
-       " 'min_data_per_group': 199}"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "study.best_params"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
@@ -1545,18 +2143,18 @@
        " 'learning_rate': 0.05,\n",
        " 'metric': 'auc',\n",
        " 'verbose': -1,\n",
-       " 'reg_alpha': 2.028341641327015,\n",
-       " 'reg_lambda': 9.058848843938758,\n",
-       " 'num_leaves': 725,\n",
-       " 'min_child_samples': 33,\n",
-       " 'max_depth': 13,\n",
-       " 'colsample_bytree': 0.48606542396164654,\n",
-       " 'cat_smooth': 51,\n",
-       " 'cat_l2': 8,\n",
-       " 'min_data_per_group': 199}"
+       " 'reg_alpha': 2.867574685349458,\n",
+       " 'reg_lambda': 3.2330671930014256,\n",
+       " 'num_leaves': 314,\n",
+       " 'min_child_samples': 38,\n",
+       " 'max_depth': 38,\n",
+       " 'colsample_bytree': 0.3467919628233754,\n",
+       " 'cat_smooth': 71,\n",
+       " 'cat_l2': 14,\n",
+       " 'min_data_per_group': 124}"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1568,7 +2166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1578,7 +2176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 126,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:00.687777Z",
@@ -1590,7 +2188,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "5it [00:04,  1.17it/s]"
+      "5it [00:03,  1.60it/s]"
      ]
     },
     {
@@ -1598,9 +2196,9 @@
      "output_type": "stream",
      "text": [
       "Method Name       :\u001b[35;1m cv_adv\u001b[0m\n",
-      "Current memory usage:\u001b[36m 1.020297MB\u001b[0m\n",
-      "Peak                :\u001b[36m 3.172319MB\u001b[0m\n",
-      "Total time taken:  \u001b[36m 4284.856 ms \u001b[0m\n"
+      "Current memory usage:\u001b[36m 0.940477MB\u001b[0m\n",
+      "Peak                :\u001b[36m 2.750746MB\u001b[0m\n",
+      "Total time taken:  \u001b[36m 3126.462 ms \u001b[0m\n"
      ]
     },
     {
@@ -1660,7 +2258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 127,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:00.944005Z",
@@ -1670,7 +2268,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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0yGG/6tWrZw9mebp3766LFy/qxx9/zFfX9u3bJSnfFwMPP/yww3SLFi307bff6sknn9TSpUt18OBB9e3bV4888sgV97lUqcvf9Vqt1mv9ehz8/SjP9bxelSpV0v3336958+Zp2LBhWr9+vU6cOKFRo0apSZMm9n24cOGCHn74Yc2cOVM//PCDWrVqpaFDh14xmElSiRIl1LNnT3399dc6deqUpMvDLD/77DM9+uijKlGihFxdXfX222+rR48eSktL044dO/T+++/br8R5PUOP816jdu3aOVxcpEuXLg5/167nb9H1yOvzv1+gpk2bNqpUqZLD+0By/Jvs6uqqihUr3vD7AMDN4cgZgFvi9OnTmjhxoj777DNZrVZVq1ZNDRo0kIuLS77LUOcNh8uT9+GkoA96p0+fls1msweFGxUeHq4333xTJUuW1BNPPKG1a9eqWbNm+T54vvPOO1q8eLFOnjypChUqyN/fX2XKlJF0+QhV3lG3vw93K1WqlD2wSFJ6enqBF3aoXLnyFWvM+1D416OLf3Wl82byHne1382ZM2cK3HbeEMu/nn9ypSsD/nUYaEFXrfP09Mz3mubVdObMmZvev5txI/ub9/rmydu3Gw0cBZ1rVr58eQUHB2vfvn2SpI4dOzpc9rxp06b24bLS5aOCV7sgyJkzZwo8mujt7S1J+c4jky6/d6T8Pfv34bWjR4+Wr6+vNmzYoJkzZ2rmzJmqXbu2xowZo9atWxdYT5UqVSRdPupdp06dAtdJT0+Xu7u7Q2/8vVev5/WyWCxatmyZFi5cqM8++0wbN26UxWJRy5YtNWnSJFWrVk2dO3dWTEyM4uLitGzZMi1dulSVKlVSRESEnn322QLry9OrVy8tXLhQn3zyiZ566il9+umnys7OdrhqaFJSkqZOnaoDBw7I3d1dtWvX1t133y3p+u41dubMGdlstgL/fvx93rX+Fl2PvC9PChpK7eXlle82B39/L5QoUeKG3wcAbg7hDMAt8dJLL2n//v1666231KRJE/sHsr9/I36jypYtK4vFUuCQxC1btqhu3bpXvRx+t27d7N+cjxkzRtu3b9ekSZMUFBRk/zD8ySef6I033tDQoUP1xBNP2D/QTJ8+XcnJyZL+76jKyZMnHZ7/0qVL9g/Beev9fZ2CHvdXd911lyTp7bffzjc0TZJD+CvocXnhJ09WVpa+++47BQYGqnz58jpw4EC+x6ampl71uW/E2bNn8w0dO3nypCwWiypWrHjT+3cznLG/f5WTk6OEhATVqFEj34UxsrKy7NtbtGiRw7mJN3rJ8/Lly+vEiRP55l9tv/I+9KelpTmcE/X3fnF1dVVkZKQiIyOVkpKib775RosXL9bQoUOVlJSU74O7dPl97eLioi+//PKKR4Vff/11ffXVV/r666+veE7W9b5eXl5emjBhgiZMmKBDhw5p8+bNWrhwocaPH6933nlH0uUjxmFhYTp//ry+//57vfvuu5o2bZoCAwPVuHHjArcvXX7Ptm7dWhs2bNBTTz2l+Ph4hYSE2N/zv/32mwYOHKg2bdrYh1hbLBZt2bJFGzduvOLz/lWFChVUokSJfK+h1Wp1OAp9PX+Lrkf58uUlXX7t895/eVJTU695FVQAzsOwRgCF9tdhOHm2b9+u9u3bq02bNvZgtnv3bqWnpxfqG1gPDw/5+/tr8+bNDt8a7927V5GRkdq5c+d1P1eZMmU0c+ZM5ebmavjw4bp48aKky+eruLm5afDgwfYPQ7m5ufbzcqxWq3x8fHTvvffm+zD2xRdfOAw1atasmfbs2eNwlCQzM7PAm2Hnadq0qaTLV+MLDAy0/ytdurRmzZrlcCXLv6pVq5YqVqxovwVCni1btigyMlLHjh1Ts2bNdOLEiXxXlcsbkpU3LKwwLl26ZD83Srr87f7GjRvVoEEDeXh4XPf+XW34WZ6Ceu+vnLG/f+Xi4qL58+dr6tSpDv35559/6j//+Y/9XCQ/Pz+Hff/78MRradasmfbu3ZuvFzZs2HDF2wm0aNFCFotFn3zyicP8L774wv6z1WrVI488omnTpkmSfHx81KtXLz311FPKyMhwCA5/VbZsWfXu3Vvx8fEFvgd37Nihzz77TO3bt7/qxTKu5/U6fPiw2rRpY+/z2rVrKzIyUi1bttQff/whSZo4caKeeOIJe21/vYrhX9+LV/L444/rxx9/VHJysnbu3Olw4/Pdu3fr4sWLioiIUK1atexHWPOu7Hk9R7NcXV3VuHFjbdq0yWFI69dff+0wLPJ6/hZJ136v5F1QJyEhwWH+N998o/T09CJ/HwC4eRw5A1BoLi4ucnd3186dO7V9+3Y1btxY9evXV2JiourXr6+qVavqp59+0qJFi2SxWAp97sKIESM0YMAADR48WI8//rgyMjI0d+5c1atX74ZPWK9Xr56GDx+umTNn6vXXX9eUKVPUoEEDrVmzRpMnT1ZoaKjS09O1atUq/e9//5N0OVyVKVNGUVFRGj58uMaMGaOwsDAdPXpU8+bNk/R/Q+L69eunuLg4Pf/88xo6dKjc3Ny0bNkypaenF3iTYUmqU6eOHnnkEUVHR+vPP/9Uw4YNdfz4ccXGxionJ0f16tUr8HElS5bU8OHDNWHCBI0bN06dO3dWamqq/eqHgYGBqlOnjlavXq0RI0ZoyJAhqlWrlr799lstX75cjz322A2HhCsZP368Tpw4oXvuuUfvvfeejhw5ouXLl9/Q/uV9w//VV1+pfPny9guO/FXeEaQtW7YUOJzwkUceKdL9/emnnyRd/R5Sw4YN06hRoxQVFaVevXrpxIkTWrBgge66664rXkXxRj3zzDPasGGDnnvuOQ0ZMkTe3t5KTEzUxx9/rBEjRhQ4JLVatWrq06eP3nrrLUmXL2//5ZdfOtwuoESJEgoODtaqVatUsWJF1a9fX8ePH9eKFSvUqFGjq96na8SIEdq9e7eeeeYZPfnkk/aj5Dt27NCqVatUs2ZNvfrqq1fdr+t5vfKGA0ZHR+vs2bOqWrWq9uzZo6+//lrPPfecpMv3VluzZo3GjBmjrl27Kjs7W//85z911113qU2bNtf8/T700EPy8vLSuHHj5O3trQcffNC+rF69enJxcdHs2bPtVy/99NNP7bcxKOhc2YJERUWpf//+ioyMVL9+/ZSamqp58+bJxcXFvs71/i3Ke6989tlnCgkJyTck9r777lOvXr20ZMkS5eTkqGXLljpy5IhiY2N1//33O5wjCsBYhDMARWLQoEFasmSJIiIi9Mknn2jatGmaPHmypk6dqkuXLqlatWoaOnSoDh48aL+P0LUueX8lDz74oJYuXar58+dr2LBhuuuuuxQSEqKRI0fe1HM+//zz2rp1q95//301bdpUPXr00LFjx/T+++9r3bp18vLyUrNmzfTcc89p4MCB+uGHH9S5c2d17txZb775phYvXqyEhARVr15d0dHRGjZsmH2YWtmyZbVy5Uq98cYbGjt2rNzc3NS9e3dVqlRJP//88xVrmjJlimrVqqV//etfWrhwoTw9PdWsWTONGDHiqueU9e7dW2XLltXSpUu1YcMGVa5c2X5Zfeny+X0rV67U7Nmz9dZbb+nMmTOqUaOG/SqJRWXy5MmaMWOGUlJS5O/vr3/+85/2I2bXu38PPPCAunTpori4OH3zzTf66KOP8m3n8ccfV1JSkgYPHqxhw4blu0BLUe/vkCFDJMnhyODf9ejRQ+7u7lq6dKmGDh0qV1dXtW7dWi+99JJ9eFlhVa5cWe+9955mz56t6dOnKzMzU7Vr19abb7551Q/a48ePV+XKlbV+/XotWbJEwcHBGjNmjP0S8ZL0yiuvyMPDQ+vXr1dsbKzKlSuntm3bauTIkVetqWzZsnrnnXe0evVqffzxx/r3v/+t7OxsVatWTREREerfv/8Vz2PMcz2vl8Vi0ZIlSzRr1izNnj1bp0+flq+vrwYNGqQXXnhBkhQaGqo333xTK1asUGJioiwWixo1aqSVK1de120RSpUqpZ49e2rx4sUaNGiQw5GpGjVqKCYmRvPmzdPgwYN11113yd/fX6tXr1ZERIS2b9+uLl26XHMbjRo10rJlyzR79mwNHz5cXl5eGjNmjMNNuq/3b1Hr1q3VvHlzzZo1S9u2bdOSJUvybS86Olo1atTQ+++/r3feeUeVKlVSt27dNHz48HzniAIwjsV2vWeTAgAcfPTRR/L393f4lvrgwYPq2rWr3nrrLbVt21Y//vijTp06pYceesjhsY8++qiqVKliP9J2p4iNjdX8+fO1a9cu+32T7iSnTp3Sk08+me+m2gAAFAWOnAHATUpISNCMGTMUFRWlqlWr6s8//9Rbb72lOnXqqFWrVpIun2s0fPhwvfDCC2rZsqVycnL0ySef6KeffrKfA4Pbx7x58656GwQAAAqDI2cAcJNOnz6t2bNna8uWLfbLXD/00EOKiopyGDq1bt06xcXF6ddff1XJkiVVr149DRo06LpuVHu7udOPnO3du1f+/v7XvBAJAAA3g3AGAAAAACbAV38AAAAAYAJ3xDlnVqtVFy5ckIuLi/3y1QAAAABgJjabTTk5OfLw8ChwiPwdEc4uXLigAwcOGF0GAAAAAFzT/fffr3LlyuWbf0eEs7wbNt5///03fd8k5Ldnzx4FBAQYXQaKMXoQRqMHYTR6EEajB4tWdna2Dhw44HDD+b+6I8JZ3lBGV1fXO/LqYEbi9wmj0YMwGj0Io9GDMBo9WPSudCoWFwQBAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAJ3xAVBAAAAgNvV2bNnlZqaqpycHKNLyadUqVLat2+f0WXcVjw8PFS1atUC72N2LYQzAAAAwCBnz55VSkqKqlSpojJlylzxKn5GuXDhgjw8PIwu47ZhtVp17NgxnThxQt7e3jf8eIY1AgAAAAZJTU1VlSpV5O7ubrpghhtXokQJ+fj46MyZMzf3+CKuBwAAAMB1ysnJUZkyZYwuA0XIxcVFubm5N/VYwhkAAABgII6Y3VkK83oSzgAAAADABLggCAAAAIAb0q5dOx07dizf/Dp16uijjz5ymLdhwwa99957WrNmjbPKu20RzgAAAACTOZd1SblWm9O2V6qEReXcSt7QY0aPHq2uXbs6Pk8px3jx3XffacKECfL39y90jcUB4QwAAAAwmVyrTcM/+dNp25sbdvcNP6Zs2bLy8vK64vL58+dr8eLFuvfeewtRWfHCOWcAAAAAitwPP/ygZcuWKTQ09Jrr+vn56eOPP1aXLl1Uv359jRw5Ur/99pv69eun+vXrq2/fvkpNTbWvv2TJErVv314BAQFq3bq15s6da1926dIlzZs3T23atFGjRo00cOBA+2P79eun6OhodezYUW3atFF6erqOHz+u4cOHq2nTpmrWrJmio6OVlZVV9L+Q60A4AwAAAFDk3n33XTVp0uS61583b56mTp2qt956S4mJiXryySfVt29frV69WseOHdOyZcskXT6HbdmyZZo8ebISExM1ePBgLVy4ULt27ZIkxcbGat26dZo8ebLWr1+vrKwsjRo1yr6d+Ph4TZ06VQsXLlTZsmX19NNPKyMjQ++++67mzp2rr7/+WtOmTSvaX8Z1YlgjAAAAgBsWHR2tN954w2He559/rkqVKt3U8/Xv318NGjSQdPlIWp06ddSpUydJUvv27XXkyBFJko+Pj6ZOnaoWLVpIkp588kktWLBABw8eVGBgoNauXasXX3xRDz74oCRp4sSJev/992W1WiVJISEhaty4sSTpiy++0PHjx7V27Vp5enpKkl577TUNGDBAL774osqVK3dT+3KzCGcAAOCO5eyLKhS1e+6tbXQJwBUNGTJEnTt3dpiXF3BuRrVq1ew/u7m56Z577rFPly5dWtnZ2ZKk5s2b68cff9SsWbN0+PBh7du3T2lpabJarTp16pTS09NVr149+2OrV6+uF1980T5dpUoV+8+HDx9W9erVHepu1KiRLl26pF9++UWBgYE3vT83g3AGAADuWM6+qEJRm9Hh5o5AAM5QsWJF1ahRo8ie7+9XeixRouAzsNavX6833nhDvXr1UmhoqEaNGqX+/ftLklxcXK65HVdXV/vPbm5u+ZZfunRJkuxH2pyJc84AAAAA3DbWrFmjAQMGaNy4cerRo4cqVKigkydPymazqVy5cqpYsaJ++ukn+/q//PKLWrZsqdOnT+d7rvvuu09Hjx51WPbf//5XJUuWVPXq1Z2wN444cnYLMZQCAAAAKFoVKlTQtm3b1LFjR2VkZCgmJkY5OTn2YY/9+/dXbGys7r77bvn6+mrKlCl64IEHChxy2bJlS91777165ZVX9OKLL+rMmTOaPHmywsLCVKFCBSfvGeHslmIoBQAAQPF2rS/rrTZbgctt//+fs9ikAutwKWDYn9HGjh2rcePG6ZFHHlGFChXUpUsXeXh42I+WRURE6OzZsxo5cqRycnLUunVrvfbaawU+V4kSJbRgwQK9/vrreuKJJ+Tu7q7w8HCNHDnSmbtkZ7HZbLfvoZ3/LysrS3v27FFAQECB40aNcioz97YPZz7l3Y0uA8VYcnKygoODjS4DxRg9ePvj/2IY7Vo9GHHvaVWt5ZdvfllX5599dD47/zlWVcuVlEupkk6v5Xa3b98++fv755t/rdzCkTMAAADAZAoKSrjzcUEQAAAAADABwhkAAAAAmADhDAAAAABMwKnhLCEhQWFhYQoNDVVcXFy+5Xv37tWjjz6qbt266YUXXtDZs2edWR4AAAAAGMZp4SwlJUUxMTFavXq1PvjgA61du1aHDh1yWGfKlCkaNmyYPvzwQ9WsWVP//Oc/nVUeAAAAABjKaeEsKSlJzZs3l6enp9zd3dWpUyclJiY6rGO1WnXhwgVJUmZmpkqXLu2s8gAAAADAUE4LZ6mpqfLy8rJPe3t7KyUlxWGd0aNHa/z48WrdurWSkpLUu3dvZ5UHAAAAAIZy2n3OrFarLBaLfdpmszlMX7x4UePGjdOKFSsUFBSk5cuXa9SoUVqyZMl1b2PPnj1FWnNhVa3trwsZF4wuoxAqKTk52egiUMzRgzAaPXh74/9iGO1aPWiz2nTJesmJFd2okvaRbbh+2dnZN/XedVo48/X11Y4dO+zTaWlp8vb2tk8fOHBAbm5uCgoKkiQ98cQTmjt37g1t40p32jbKqcxcebh7GF1GoQQHBxtdAoqx5ORkehCGogdvf/xfDKNdqwctJXJUskRJJ1Z04zw88tffrl07HTt2LN/8OnXq6KOPPnKYt2HDBr333ntas2bNLavxSmJjY5WUlGTfdmJiooKDg+Xl5ZVvWVFydXVV/fr1883Pysq66gElp4Wzli1bKjY2Vunp6SpTpow2bdqk119/3b68Ro0aOn78uI4cOaJatWrpiy++UGBgoLPKAwDcAueyLinXajO6jJt2z721jS4BQDFVtkyObBar07ZnsZXQ+UyXG3rM6NGj1bVrV4d5pUo5xovvvvtOEyZMkL+/f6FrvBn/+Mc/1K9fP0nSsWPHNHz4cG3atCnfMrNwWjjz8fFRVFSU+vfvr5ycHPXq1UtBQUGKiIjQsGHDFBgYqKlTp2rEiBGy2WyqVKmS3njjDWeVBwC4BXKtNg3/5E+jy7hpMzpUMroEAMWUzWLVgAPXf3pPYb11f+QNP6Zs2bIO15T4u/nz52vx4sW69957C1FZ4fz1qJ/NZrviMrNw6n3OwsPD9dFHH2njxo2KiIiQJC1dutR+hOzBBx/Uhx9+qISEBK1YsULVqlVzZnkAAAAAisgPP/ygZcuWKTQ09Jrr+vn5ad26derYsaMaNmyoF198UefPn7cvP3z4sJ577jk1atRIrVu3VmxsrKzWy0cWz58/rxEjRqhp06Zq1KiRhgwZorS0NEmXhzU++eSTkqT27dtLkkJDQxUfH29fZrVa1aZNG61fv96hprCwMPu9mXfs2GE/uPTwww/rgw8+KPTvpyBODWcAAAAAiod3331XTZo0ue71582bp7Fjx+rdd9/VwYMHNX78eElSenq6+vTpI29vb61fv14TJ05UXFycli1bJkmaM2eOjh07ppUrV2rdunU6efKkpk6dmu/588LX2rVrFRYWZp9fokQJde7cWRs3brTPO3DggH755Rd16tRJaWlpioyMVHh4uBISEjR48GBNnjxZmzdvvqnfy9U4bVgjAAAAgDtHdHR0vtOQPv/8c1WqdHNDwp9//nm1bdtWkjRu3Dg9++yzOn36tD766CO5ubkpOjpaLi4uuu+++5SWlqa5c+fq+eef17Fjx+Tu7q6qVavKw8ND06dP19mzZ/M9f8WKFSVJFSpUyHc/5a5du+qpp57SuXPnVK5cOSUmJqpZs2aqXLmy5syZo2bNmunpp5+WdPlaGUeOHNE777yjdu3a3dS+XgnhDAAAAMANGzJkiDp37uwwz9PT86afr2HDhvafAwICZLVa9fPPP+vw4cN64IEH5OLi4rDuqVOnlJ6ersjISEVGRqpFixZq1qyZOnbsqB49etzQtuvXry8fHx9t3rxZ3bt3V2Jiop599llJ0pEjR/TNN9841Jebm2sPe0WJcAYAAADghlWsWFE1atQosucrWfL/bimQdz5ZiRIlCrxVVt5yq9Wqhg0b6ssvv9TmzZu1ZcsWTZs2TQkJCVq5cuUNbf/hhx/Wpk2b9MADD+jo0aP2c+Vyc3P18MMPa9CgQQ7rlyhR9GeIEc5wRaVKW5Wec/7aK5qYi6WkypUqY3QZAAAAtyVLCSm3gJtk22w2XbJaC1z2d1abVTYV/Dx/tfenn1T3gcuX3P9x148q5VJK1e+toXtr1VRiYqIysy7aj54l/+c/Kl++vO7yLK9ly5erdu3aCuv6sMK6Pqzk5GT179tPx1NTZP3LFRotFstVtx8WFqYnn3xS999/v1q3bq3y5ctLkmrWrKkdO3Y4BNG4uDilpqYqKirqmvt/IwhnuCKbbBpyaKnRZRTKgto3fllYAADM4nb/opQvSW9/Nkm/ZaXlm59rsyo995yOFrDs787kZijLmnPNdefNnyc333JyLe2mmClvqt3DoTrlelH1OzZTduw8vfzqaPV6urf+OHpM8+bPU9hj3fR7zkkdPHZEce+tVtTEUapQuaLe37Be3nf76Jx77v/fg8vc3d0lSfv371flypXzbb9u3bq65557tGLFCk2aNMk+v0+fPlq5cqVmzZqlRx99VPv379eMGTP0yiuvXHPfbxThDAAAwKRu9y9K+ZIUN6Jjty6aPWGazp09p4e6tNcLLw2RJJVxd9fkBTO0aPo8DekdofIVyqtHn1564rm+kqR+g55TxvkLio4ap8yMDPkFPKCJ86Y6DJOULl8IpGfPnho5cqRefvnlAmt4+OGHtXjxYvtl9yWpSpUqWrx4sWbOnKnly5fLy8tLQ4cOVZ8+fYr8d2Cx/f1ubLehrKws7dmzRwEBAQWOSTXKqczc2/rmq7O7emrokdv3PwTp8n8KFV3KGl0GblJycrKCg4ONLgOFcLv/HZzRoZJ8yrsbXQYK4Xbvwdv9/2L+H752D0bce1pVa/nlm1+2TI5sFuutLM2BxVZC5zNd8s2vWr5kgUfOilqXBg/qjbdmqWHzxkX6vNXdvFSqRMlrr1jE9u3bJ39//3zzr5VbOHIGAAAAmExBQQl3Pm5CDQAAAAAmwJEzAAAAAIb69L9bjC7BFDhyBgAAAAAmwJEz4A52LuuScq237zV/7rm3ttElAABwS9lsl+8Zdq17cOH2UZjrLRLOgDtYrtV2W1+lbEaHSkaXAADALZVxqYQuZV9UKTfuB3enyMnJUalSNxezGNYIAAAAGOTbE25K+fOYcrMyC3XEBeZgtVqVkpKi8uXL39TjOXIGAAAAGOTXi27a+IfUKvt3uZe0ymyjGy+UKaH03PNGl3HTMkudVAmLc49HeXh4qHLlyjf1WMIZAAAAYKBfL7rp19/z35DYDGZ39dQUboTuNAxrBAAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAmUMroAAADMqlRpq9JzzhtdRqG4WEqqXKkyRpcBALgOhDMAAK7AJpuGHFpqdBmFsqB2pNElAACuE8MaAQAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAESjlzYwkJCVq0aJFyc3P19NNP66mnnrIv27dvn0aPHm2fTk9PV/ny5fXRRx85s0QAAAAAMITTwllKSopiYmIUHx8vV1dX9e7dW82aNVPt2rUlSf7+/tqwYYMkKTMzU4899pgmTpzorPIAAAAAwFBOG9aYlJSk5s2by9PTU+7u7urUqZMSExMLXHfx4sVq0qSJGjdu7KzyAAAAAMBQTjtylpqaKi8vL/u0t7e3du3alW+9c+fOad26dUpISHBWaQAAAABgOKeFM6vVKovFYp+22WwO03k+/PBDdejQQZUqVbrhbezZs6dQNRa1qrX9dSHjgtFlFIKnLmRkGF1EoWRnZyt5V7LRZRjm9u/BSkpOLr6v353g9u9B/g7e7uhBYxX3/pPoQaPdbj3otHDm6+urHTt22KfT0tLk7e2db73PP/9cL7zwwk1tIyAgQG5ubjddY1E7lZkrD3cPo8soFA93d6NLKBRXV1cFBwcbXYZh7oQeLM6v353gTuhB/g7e3uhBYxX3/pPoQaOZrQezsrKuekDJaeectWzZUtu2bVN6eroyMzO1adMmhYSEOKxjs9m0d+9eNWzY0FllAQAAAIApOC2c+fj4KCoqSv3791ePHj3UtWtXBQUFKSIiQrt375Z0+fL5Li4upjr6BQAAAADO4NT7nIWHhys8PNxh3tKlS+0/V6pUSd9++60zSwIAAAAAU3BqOAOAG1GqtFXpOeeNLqNQXCwlVa5UGaPLAAAAtwHCGQDTssmmIYeWXntFE1tQO9LoEgAAwG3CaeecAQAAAACujHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJODWcJSQkKCwsTKGhoYqLi8u3/MiRI+rXr5+6deum5557TmfOnHFmeQAAAABgGKeFs5SUFMXExGj16tX64IMPtHbtWh06dMi+3GazaeDAgYqIiNCHH34of39/LVmyxFnlAQAAAIChnBbOkpKS1Lx5c3l6esrd3V2dOnVSYmKiffnevXvl7u6ukJAQSdKAAQP01FNPOas8AAAAADCU08JZamqqvLy87NPe3t5KSUmxTx89elSVK1fW2LFj9cgjj2jChAlyd3d3VnkAAAAAYKhSztqQ1WqVxWKxT9tsNofp3Nxc/fDDD1q1apUCAwM1Z84cTZs2TdOmTbvubezZs6dIay6sqrX9dSHjgtFlFIKnLmRkGF1EoWRnZyt5V7LRZRiGHjQePUgPGo0epAeNVNz7T6IHjXa79aDTwpmvr6927Nhhn05LS5O3t7d92svLSzVq1FBgYKAkqWvXrho2bNgNbSMgIEBubm5FU3AROJWZKw93D6PLKBSP2/zopaurq4KDg40uwzD0oPHoQXrQaPQgPWik4t5/Ej1oNLP1YFZW1lUPKDltWGPLli21bds2paenKzMzU5s2bbKfXyZJDRs2VHp6uvbv3y9J2rx5s+rVq+es8gAAAADAUE47cubj46OoqCj1799fOTk56tWrl4KCghQREaFhw4YpMDBQCxYs0Pjx45WZmSlfX19Nnz7dWeUBAAAAgKGcFs4kKTw8XOHh4Q7zli5dav+5fv36ev/9951ZEgAAAACYglNvQg0AAAAAKBjhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAScGs4SEhIUFham0NBQxcXF5Vs+f/58tW3bVt27d1f37t0LXAcAAAAA7kSlnLWhlJQUxcTEKD4+Xq6ururdu7eaNWum2rVr29fZs2ePZs+erYYNGzqrLAAAAAAwBacdOUtKSlLz5s3l6ekpd3d3derUSYmJiQ7r7NmzR4sXL1Z4eLiio6OVlZXlrPIAAAAAwFBOC2epqany8vKyT3t7eyslJcU+feHCBfn7++vll1/Wv//9b509e1YLFy50VnkAAAAAYCinDWu0Wq2yWCz2aZvN5jDt4eGhpUuX2qf/8Y9/aOzYsYqKirrubezZs6doii0iVWv760LGBaPLKARPXcjIMLqIQsnOzlbyrmSjyzAMPWg8epAeNBo9SA8aqbj3n0QPGu1260GnhTNfX1/t2LHDPp2WliZvb2/79B9//KGkpCT16tVL0uXwVqrUjZUXEBAgNze3oim4CJzKzJWHu4fRZRSKh7u70SUUiqurq4KDg40uwzD0oPHoQXrQaPQgPWik4t5/Ej1oNLP1YFZW1lUPKDltWGPLli21bds2paenKzMzU5s2bVJISIh9eenSpTVjxgz99ttvstlsiouLU8eOHZ1VHgAAAAAYymnhzMfHR1FRUerfv7969Oihrl27KigoSBEREdq9e7cqVqyo6OhoDRw4UJ07d5bNZtOzzz7rrPIAAAAAwFBOG9YoSeHh4QoPD3eY99fzzDp16qROnTo5syQAAAAAMAWn3oQaAAAAAFAwwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwARKXW1hnz59ZLFYruuJ4uLiiqQgAAAAACiOrhrOnnjiCU2cOFHVqlVTaGios2oCAAAAgGLnquGse/fu8vHxUWRkpJo3b67GjRs7qy4AAAAAKFauec5Z8+bNFRkZqfnz5zujHgAAAAAolq565CzPkCFDbnUdAAAAAFCsXfXIWXh4uM6cOeMwLz09XTab7ZYWBQAAAADFzVXD2cGDB5Wbm+swr0OHDvr9999vaVEAAAAAUNzc8H3OOGoGAAAAAEWPm1ADAAAAgAkQzgAAAADABK55tcaPPvpIHh4e9mmr1apPP/1UFStWdFivV69eRV8dAAAAABQTVw1n99xzj9555x2HeZUqVdJ7773nMM9isRDOAAAAAKAQrhrONm/e7Kw6AAAAAKBY45wzAAAAADABwhkAAAAAmIBTw1lCQoLCwsIUGhqquLi4K6731VdfqV27dk6sDAAAAACMdc2rNRaVlJQUxcTEKD4+Xq6ururdu7eaNWum2rVrO6x34sQJvfnmm84qCwAAAABMwWlHzpKSktS8eXN5enrK3d1dnTp1UmJiYr71xo8fryFDhjirLAAAAAAwBacdOUtNTZWXl5d92tvbW7t27XJY591339UDDzyg+vXr39Q29uzZU6gai1rV2v66kHHB6DIKwVMXMjKMLqJQsrOzlbwr2egyDEMPGo8epAeNRg/Sg0Yq7v0n0YNGu9160GnhzGq1ymKx2KdtNpvD9IEDB7Rp0yatWLFCx48fv6ltBAQEyM3NrdC1FpVTmbnycPe49oom5uHubnQJheLq6qrg4GCjyzAMPWg8epAeNBo9SA8aqbj3n0QPGs1sPZiVlXXVA0pOG9bo6+urtLQ0+3RaWpq8vb3t04mJiUpLS9Ojjz6qyMhIpaamqk+fPs4qDwAAAAAM5bRw1rJlS23btk3p6enKzMzUpk2bFBISYl8+bNgwbdy4URs2bNCSJUvk7e2t1atXO6s8AAAAADCU08KZj4+PoqKi1L9/f/Xo0UNdu3ZVUFCQIiIitHv3bmeVAQAAAACm5LRzziQpPDxc4eHhDvOWLl2ab72qVatq8+bNzioLAAAAAAzn1JtQAwAAAAAKRjgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAaeGs4SEBIWFhSk0NFRxcXH5ln/22WcKDw/Xww8/rNGjRys7O9uZ5QEAAACAYZwWzlJSUhQTE6PVq1frgw8+0Nq1a3Xo0CH78oyMDEVHR2v58uX6+OOPlZWVpX//+9/OKg8AAAAADOW0cJaUlKTmzZvL09NT7u7u6tSpkxITE+3L3d3dtXnzZlWuXFmZmZk6efKk7rrrLmeVBwAAAACGKuWsDaWmpsrLy8s+7e3trV27djms4+Lioi1btuiVV16Rt7e3WrdufUPb2LNnT5HUWlSq1vbXhYwLRpdRCJ66kJFhdBGFkp2dreRdyUaXYRh60Hj0ID1oNHqQHjRSce8/iR402u3Wg04LZ1arVRaLxT5ts9kcpvM8+OCD+v777zV79mxNnDhRs2bNuu5tBAQEyM3NrUjqLQqnMnPl4e5hdBmF4uHubnQJheLq6qrg4GCjyzAMPWg8epAeNBo9SA8aqbj3n0QPGs1sPZiVlXXVA0pOG9bo6+urtLQ0+3RaWpq8vb3t06dPn9bWrVvt0+Hh4frf//7nrPIAAAAAwFBOC2ctW7bUtm3blJ6erszMTG3atEkhISH25TabTS+//LL++OMPSVJiYqIaNWrkrPIAAAAAwFBOG9bo4+OjqKgo9e/fXzk5OerVq5eCgoIUERGhYcOGKTAwUK+//rpeeOEFWSwW1a5dW5MmTXJWeQAAAABgKKeFM+nyUMXw8HCHeUuXLrX/3KFDB3Xo0MGZJQEAAACAKTj1JtQAAAAAgIIRzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEzAqeEsISFBYWFhCg0NVVxcXL7ln3/+ubp3765u3bpp0KBBOnPmjDPLAwAAAADDOC2cpaSkKCYmRqtXr9YHH3ygtWvX6tChQ/bl58+f18SJE7VkyRJ9+OGH8vPzU2xsrLPKAwAAAABDOS2cJSUlqXnz5vL09JS7u7s6deqkxMRE+/KcnBxNmDBBPj4+kiQ/Pz/9+eefzioPAAAAAAxVylkbSk1NlZeXl33a29tbu3btsk9XqFBBHTt2lCRdvHhRS5YsUb9+/W5oG3v27CmaYotI1dr+upBxwegyCsFTFzIyjC6iULKzs5W8K9noMgxDDxqPHqQHjUYP0oNGKu79J9GDRrvdetBp4cxqtcpisdinbTabw3Sec+fOafDgwapbt64eeeSRG9pGQECA3NzcCl1rUTmVmSsPdw+jyygUD3d3o0soFFdXVwUHBxtdhmHoQePRg/Sg0ehBetBIxb3/JHrQaGbrwaysrKseUHLasEZfX1+lpaXZp9PS0uTt7e2wTmpqqvr06SM/Pz9NmTLFWaUBAAAAgOGcFs5atmypbdu2KT09XZmZmdq0aZNCQkLsyy9duqQBAwaoS5cuGjduXIFH1QAAAADgTuW0YY0+Pj6KiopS//79lZOTo169eikoKEgREREaNmyYjh8/rp9++kmXLl3Sxo0bJV0epsgRNAAAAADFgdPCmSSFh4crPDzcYd7SpUslSYGBgdq/f78zywEAAAAA03DqTagBAAAAAAUjnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJiAU8NZQkKCwsLCFBoaqri4uCuu98orryg+Pt6JlQEAAACAsZwWzlJSUhQTE6PVq1frgw8+0Nq1a3Xo0KF86wwYMEAbN250VlkAAAAAYApOC2dJSUlq3ry5PD095e7urk6dOikxMdFhnYSEBLVv315dunRxVlkAAAAAYAqlnLWh1NRUeXl52ae9vb21a9cuh3Wef/55SVJycvJNbWPPnj03X+AtULW2vy5kXDC6jELw1IWMDKOLKJTs7Gwl77q5froT0IPGowfpQaPRg/SgkYp7/0n0oNFutx50WjizWq2yWCz2aZvN5jBdFAICAuTm5lakz1kYpzJz5eHuYXQZheLh7m50CYXi6uqq4OBgo8swDD1oPHqQHjQaPUgPGqm4959EDxrNbD2YlZV11QNKThvW6Ovrq7S0NPt0WlqavL29nbV5AAAAADA1p4Wzli1batu2bUpPT1dmZqY2bdqkkJAQZ20eAAAAAEzNaeHMx8dHUVFR6t+/v3r06KGuXbsqKChIERER2r17t7PKAAAAAABTcto5Z5IUHh6u8PBwh3lLly7Nt960adOcVRIAAAAAmIJTb0INAAAAACgY4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAEnBrOEhISFBYWptDQUMXFxeVbvm/fPvXs2VOdOnXSuHHjlJub68zyAAAAAMAwTgtnKSkpiomJ0erVq/XBBx9o7dq1OnTokMM6L7/8sl577TVt3LhRNptN69atc1Z5AAAAAGCoUs7aUFJSkpo3by5PT09JUqdOnZSYmKghQ4ZIko4dO6aLFy+qQYMGkqSePXtq3rx56tOnzzWf22azSZKys7NvSe03Kyf7ksqVumR0GTctNztHd1ndjC6jUHKyc5RlzTK6DMPQg8ajB+lBo9GD9KCRinv/SfSg0czWg3l5JS+//J3FdqUlRWzx4sXKyMhQVFSUJGn9+vXatWuXXn/9dUnSzp07NX36dK1Zs0aS9OuvvyoyMlIbN2685nOfO3dOBw4cuHXFAwAAAEARuf/++1WuXLl885125MxqtcpisdinbTabw/S1ll+Nh4eH7r//frm4uFz3YwAAAADAmWw2m3JycuTh4VHgcqeFM19fX+3YscM+nZaWJm9vb4flaWlp9ukTJ044LL+aEiVKFJg8AQAAAMBMSpcufcVlTrsgSMuWLbVt2zalp6crMzNTmzZtUkhIiH15lSpV5ObmpuTkZEnShg0bHJYDAAAAwJ3MaeecSZcvpb948WLl5OSoV69eioiIUEREhIYNG6bAwEDt379f48eP1/nz51WvXj1NnTpVrq6uzioPAAAAAAzj1HAGAAAAACiYU29CDQAAAAAoGOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwhn+zsbL366qtq0qSJWrVqpaVLlxpdEoqp7Oxsde3aVUlJSUaXgmLm6NGjGjBggJo0aaKQkBBNmzZNWVlZRpeFYuTw4cN65pln1LBhQ7Vt21Zvv/220SWhmBo3bpz69etndBnFBuEM+UyfPl07d+7U8uXLNWnSJC1atEgff/yx0WWhmMnKytKLL76ogwcPGl0Kipns7GwNGDBArq6ueu+99zRz5kx9/vnniomJMbo0FBM5OTmKiIjQ3XffrQ8++ECvvfaaFi5cqA8//NDo0lDMbNu2Te+//77RZRQrhDM4yMjI0Lp16zR27FgFBASoQ4cOev7557Vq1SqjS0MxcujQIT3++OM6evSo0aWgGNq1a5eOHj2qqVOn6r777lPTpk01fPhwJSQkGF0aiomUlBQFBQVpwoQJqlGjhtq2bauWLVtq+/btRpeGYiQjI0OvvvqqGjVqZHQpxQrhDA7279+v7OxsBQcH2+cFBwdr9+7dys3NNbAyFCc7duxQq1attHbtWqNLQTFUq1YtLVmyRB4eHvZ5FotF2dnZBlaF4qRq1aqaM2eOSpcuLZvNpuTkZG3fvl0tWrQwujQUIzExMWratKmaNm1qdCnFSimjC4C5pKWlqXz58nJzc7PPq1y5snJycpSeni5vb28Dq0Nx0bt3b6NLQDFWsWJFtWzZ0j5ttVq1atUqhy+tAGcJCQlRamqq2rZtq06dOhldDoqJnTt3KjExUR999JGWLVtmdDnFCkfO4CAzM1Ourq4O8/Km+dYYQHE0depU7du3TyNHjjS6FBRDCxcu1MKFC7V3715NnTrV6HJQDGRnZ2vcuHEaO3asypcvb3Q5xQ5HzuDAzc0tXwjLmy5TpowRJQGAIWw2m6ZMmaI1a9Zo7ty5qlOnjtEloRgKDAyUJF28eFGjRo3SK6+8ku9LVKAoLViwQDVq1FCXLl2MLqVYIpzBgY+Pj86ePavs7Gz7H/+0tDS5urry7QmAYsNqtWrcuHFKSEhQTEyMOnToYHRJKEZSUlK0Z88etW/f3j7vvvvuU05Ojs6fP6+KFSsaWB3udAkJCUpLS1PDhg0lXb566KVLl9SwYUPt3LnT4OrufIQzOPD395eLi4t27typZs2aSZKSk5NVr149lSpFuwAoHqZNm6aEhATFxsaqbdu2RpeDYubw4cMaOnSovvnmG1WqVEmStHfvXlWsWJFghltu5cqVDheBW7Fihfbs2aOZM2caWFXxwTlncFCmTBn16NFDkyZN0q5du/TFF19o2bJl6t+/v9GlAYBT/Pe//9U777yjYcOGKSAgQGlpafZ/gDM0adJE9913n0aPHq3Dhw/ryy+/1KxZszRgwACjS0MxUKVKFdWoUcP+76677lLp0qVVo0YNo0srFjgUgnzGjBmjiRMn6umnn5aHh4cGDx6ssLAwo8sCAKfYuHGjJGnWrFmaNWuWw7K9e/cyigC3nIuLixYvXqzo6Gg99thj8vDw0NNPP80XpUAxYLHZbDajiwAAAACA4o5hjQAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQDuWO3atZOfn1++f127dr2ux65fv77AZb/++qv8/Pz0+++/F3XJAIBijDtpAgDuaKNHj84XxriRNADAjPjfCQBwRytbtqy8vLyMLgMAgGtiWCMAoNiKj49XWFiYgoKC1LNnT33//fcFrpeTk6Po6Gg1btxYDz74oL755huH5YmJiQoLC1NgYKBCQ0P1r3/9yxnlAwDuMIQzAECxFB8fr+joaEVGRmrDhg1q1aqVIiMj9ccff+RbNzY2Vl999ZUWLVqkOXPmaOXKlfZlJ0+e1EsvvaRnnnlGiYmJeuGFFzR+/HgdPnzYmbsDALgDEM4AAHe06OhoNWzY0OHfyZMntXLlSj311FPq0aOHatasqZEjR6pu3boOwUuSbDab1q9fryFDhqhJkyZq2LChRo8ebV+ekpKinJwc+fj4qEqVKnr00Ue1bNkyVa5c2dm7CgC4zXHOGQDgjjZkyBB17tzZYZ6np6cOHz6sgQMHOsxv0KCBjhw54jDv1KlTSk9PV926de3zAgIC7D/7+/ura9euioyMVPXq1dW2bVv17NlT5cuXvwV7AwC4k3HkDABwR6tYsaJq1Kjh8K9kyZIqXbp0vnUvXbqkS5cuFfg8NpvN/vNfr/ZosVg0a9YsxcfHq1u3btq+fbt69eqlrVu3Fv3OAADuaIQzAECxVKtWLf34448O83788UfVrFnTYV6FChVUuXJl7d692z5v37599p8PHz6sqVOnql69eho6dKj+/e9/q3Hjxvrss89u7Q4AAO44DGsEABRLzz77rEaPHq06deqofv36io+P1759+/TGG284rGexWNSnTx/Nnz9f1apVU7ly5TRt2jT78rvuuktr165V2bJl1aNHDx07dkz/+9//9PDDDzt7lwAAtznCGQCgWOrUqZPS0tI0b948paWlyd/fX8uXL1edOnXyrTtw4EBdvHhRL774okqWLKnBgwcrOjpakuTl5aXY2FjNmjVLS5cuVfny5fXkk0+qV69ezt4lAMBtzmL76yB6AAAAAIAhOOcMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEzg/wH+k9iGZ40S0wAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 936x504 with 1 Axes>"
       ]
@@ -1697,7 +2295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 128,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:01.132424Z",
@@ -1711,12 +2309,12 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.84      0.91      0.87       549\n",
-      "           1       0.82      0.71      0.76       342\n",
+      "           0       0.83      0.89      0.86       549\n",
+      "           1       0.80      0.71      0.75       342\n",
       "\n",
-      "    accuracy                           0.83       891\n",
-      "   macro avg       0.83      0.81      0.82       891\n",
-      "weighted avg       0.83      0.83      0.83       891\n",
+      "    accuracy                           0.82       891\n",
+      "   macro avg       0.82      0.80      0.81       891\n",
+      "weighted avg       0.82      0.82      0.82       891\n",
       "\n"
      ]
     }
@@ -1739,7 +2337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 129,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:01.481931Z",
@@ -1749,7 +2347,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x432 with 1 Axes>"
       ]
@@ -1776,7 +2374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 100,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:02.186153Z",
@@ -1795,7 +2393,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 504x396 with 2 Axes>"
       ]
@@ -1814,7 +2412,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x396 with 2 Axes>"
       ]
@@ -1841,7 +2439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 101,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:03.194703Z",
@@ -1851,7 +2449,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1152x540 with 1 Axes>"
       ]
@@ -1866,7 +2464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 102,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:03.201650Z",
@@ -1881,7 +2479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 103,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:03.213082Z",
@@ -1917,67 +2515,67 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>550.759131</td>\n",
+       "      <td>500.408576</td>\n",
        "      <td>Sex</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>115.342144</td>\n",
+       "      <td>Fare</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>140.194760</td>\n",
+       "      <td>97.101224</td>\n",
        "      <td>Pclass</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>135.147257</td>\n",
+       "      <td>87.826828</td>\n",
        "      <td>Age</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>78.042008</td>\n",
-       "      <td>Fare</td>\n",
+       "      <th>10</th>\n",
+       "      <td>37.837198</td>\n",
+       "      <td>Deck</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>49.728347</td>\n",
+       "      <td>34.473590</td>\n",
        "      <td>relatives</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>42.700521</td>\n",
+       "      <td>29.795026</td>\n",
        "      <td>Cabin</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>27.265482</td>\n",
-       "      <td>Deck</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>21.635730</td>\n",
+       "      <td>19.757797</td>\n",
        "      <td>Age_fare</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>13.955465</td>\n",
-       "      <td>Embarked</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>13.915507</td>\n",
+       "      <td>16.183201</td>\n",
        "      <td>Fare_adj</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>7.719619</td>\n",
-       "      <td>Parch</td>\n",
+       "      <th>9</th>\n",
+       "      <td>14.306096</td>\n",
+       "      <td>Embarked</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>7.640122</td>\n",
+       "      <td>13.764521</td>\n",
        "      <td>SibSp</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10.423508</td>\n",
+       "      <td>Parch</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>0.085272</td>\n",
+       "      <td>4.829766</td>\n",
        "      <td>not_alone</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1996,24 +2594,24 @@
       ],
       "text/plain": [
        "         Value    Feature\n",
-       "2   550.759131  Sex      \n",
-       "0   140.194760  Pclass   \n",
-       "3   135.147257  Age      \n",
-       "7   78.042008   Fare     \n",
-       "11  49.728347   relatives\n",
-       "8   42.700521   Cabin    \n",
-       "10  27.265482   Deck     \n",
-       "13  21.635730   Age_fare \n",
-       "9   13.955465   Embarked \n",
-       "14  13.915507   Fare_adj \n",
-       "5   7.719619    Parch    \n",
-       "4   7.640122    SibSp    \n",
-       "12  0.085272    not_alone\n",
+       "2   500.408576  Sex      \n",
+       "7   115.342144  Fare     \n",
+       "0   97.101224   Pclass   \n",
+       "3   87.826828   Age      \n",
+       "10  37.837198   Deck     \n",
+       "11  34.473590   relatives\n",
+       "8   29.795026   Cabin    \n",
+       "13  19.757797   Age_fare \n",
+       "14  16.183201   Fare_adj \n",
+       "9   14.306096   Embarked \n",
+       "4   13.764521   SibSp    \n",
+       "5   10.423508   Parch    \n",
+       "12  4.829766    not_alone\n",
        "1   0.000000    Name     \n",
        "6   0.000000    Ticket   "
       ]
      },
-     "execution_count": 54,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2036,7 +2634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 104,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:04.346868Z",
@@ -2046,7 +2644,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 792x648 with 2 Axes>"
       ]
@@ -2070,7 +2668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
@@ -2079,9 +2677,9 @@
      "text": [
       "-> \u001b[32m Passed the data leakage test - no duplicate intstances detected \u001b[0m\n",
       "Method Name       :\u001b[35;1m data_leakage\u001b[0m\n",
-      "Current memory usage:\u001b[36m 0.020817MB\u001b[0m\n",
-      "Peak                :\u001b[36m 0.190289MB\u001b[0m\n",
-      "Total time taken:  \u001b[36m 9.733 ms \u001b[0m\n"
+      "Current memory usage:\u001b[36m 0.020105MB\u001b[0m\n",
+      "Peak                :\u001b[36m 0.189193MB\u001b[0m\n",
+      "Total time taken:  \u001b[36m 9.369 ms \u001b[0m\n"
      ]
     }
    ],
@@ -2098,7 +2696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
@@ -2117,7 +2715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
@@ -2152,70 +2750,70 @@
        "      <th>0</th>\n",
        "      <td>297</td>\n",
        "      <td>0</td>\n",
-       "      <td>1.066759</td>\n",
+       "      <td>1.122291</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>312</td>\n",
+       "      <td>498</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.980033</td>\n",
+       "      <td>0.953806</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>177</td>\n",
+       "      <td>312</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.937899</td>\n",
+       "      <td>0.931236</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>41</td>\n",
+       "      <td>177</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.932652</td>\n",
+       "      <td>0.912762</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>498</td>\n",
+       "      <td>852</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.893928</td>\n",
+       "      <td>0.901367</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>357</td>\n",
+       "      <td>41</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.876932</td>\n",
+       "      <td>0.856118</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>852</td>\n",
+       "      <td>772</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.869694</td>\n",
+       "      <td>0.851535</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>772</td>\n",
+       "      <td>205</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.868053</td>\n",
+       "      <td>0.842565</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>854</td>\n",
+       "      <td>140</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.838546</td>\n",
+       "      <td>0.785630</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>205</td>\n",
+       "      <td>357</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.784718</td>\n",
+       "      <td>0.780998</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2224,19 +2822,19 @@
       ],
       "text/plain": [
        "   index  label  preds_proba  preds_class\n",
-       "0  297    0      1.066759     1          \n",
-       "1  312    0      0.980033     1          \n",
-       "2  177    0      0.937899     1          \n",
-       "3  41     0      0.932652     1          \n",
-       "4  498    0      0.893928     1          \n",
-       "5  357    0      0.876932     1          \n",
-       "6  852    0      0.869694     1          \n",
-       "7  772    0      0.868053     1          \n",
-       "8  854    0      0.838546     1          \n",
-       "9  205    0      0.784718     1          "
+       "0  297    0      1.122291     1          \n",
+       "1  498    0      0.953806     1          \n",
+       "2  312    0      0.931236     1          \n",
+       "3  177    0      0.912762     1          \n",
+       "4  852    0      0.901367     1          \n",
+       "5  41     0      0.856118     1          \n",
+       "6  772    0      0.851535     1          \n",
+       "7  205    0      0.842565     1          \n",
+       "8  140    0      0.785630     1          \n",
+       "9  357    0      0.780998     1          "
       ]
      },
-     "execution_count": 58,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2247,7 +2845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -2282,70 +2880,70 @@
        "      <th>0</th>\n",
        "      <td>338</td>\n",
        "      <td>1</td>\n",
-       "      <td>-0.013960</td>\n",
+       "      <td>0.014749</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>107</td>\n",
+       "      <td>400</td>\n",
        "      <td>1</td>\n",
-       "      <td>-0.007528</td>\n",
+       "      <td>0.016676</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>65</td>\n",
+       "      <td>107</td>\n",
        "      <td>1</td>\n",
-       "      <td>-0.007385</td>\n",
+       "      <td>0.021673</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>709</td>\n",
        "      <td>1</td>\n",
-       "      <td>-0.007385</td>\n",
+       "      <td>0.042933</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>444</td>\n",
+       "      <td>65</td>\n",
        "      <td>1</td>\n",
-       "      <td>-0.006948</td>\n",
+       "      <td>0.042933</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>400</td>\n",
+       "      <td>127</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.010595</td>\n",
+       "      <td>0.048231</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>570</td>\n",
+       "      <td>271</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.032801</td>\n",
+       "      <td>0.051815</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>127</td>\n",
+       "      <td>664</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.037790</td>\n",
+       "      <td>0.052865</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>455</td>\n",
+       "      <td>414</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.042101</td>\n",
+       "      <td>0.062155</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>510</td>\n",
+       "      <td>391</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.045326</td>\n",
+       "      <td>0.066308</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2354,19 +2952,19 @@
       ],
       "text/plain": [
        "   index  label  preds_proba  preds_class\n",
-       "0  338    1     -0.013960     0          \n",
-       "1  107    1     -0.007528     0          \n",
-       "2  65     1     -0.007385     0          \n",
-       "3  709    1     -0.007385     0          \n",
-       "4  444    1     -0.006948     0          \n",
-       "5  400    1      0.010595     0          \n",
-       "6  570    1      0.032801     0          \n",
-       "7  127    1      0.037790     0          \n",
-       "8  455    1      0.042101     0          \n",
-       "9  510    1      0.045326     0          "
+       "0  338    1      0.014749     0          \n",
+       "1  400    1      0.016676     0          \n",
+       "2  107    1      0.021673     0          \n",
+       "3  709    1      0.042933     0          \n",
+       "4  65     1      0.042933     0          \n",
+       "5  127    1      0.048231     0          \n",
+       "6  271    1      0.051815     0          \n",
+       "7  664    1      0.052865     0          \n",
+       "8  414    1      0.062155     0          \n",
+       "9  391    1      0.066308     0          "
       ]
      },
-     "execution_count": 59,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2377,7 +2975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2396,7 +2994,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
@@ -2464,10 +3062,33 @@
        "      <td>1.0</td>\n",
        "      <td>2.0</td>\n",
        "      <td>41</td>\n",
-       "      <td>0.932652</td>\n",
+       "      <td>0.856118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>140</td>\n",
+       "      <td>141</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Boulos, Mrs. Joseph (Sultana)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2678</td>\n",
+       "      <td>15.2458</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.785630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
        "      <td>177</td>\n",
        "      <td>178</td>\n",
        "      <td>0</td>\n",
@@ -2487,10 +3108,10 @@
        "      <td>4.0</td>\n",
        "      <td>2.0</td>\n",
        "      <td>177</td>\n",
-       "      <td>0.937899</td>\n",
+       "      <td>0.912762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>3</th>\n",
        "      <td>205</td>\n",
        "      <td>206</td>\n",
        "      <td>0</td>\n",
@@ -2510,10 +3131,10 @@
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>205</td>\n",
-       "      <td>0.784718</td>\n",
+       "      <td>0.842565</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>4</th>\n",
        "      <td>297</td>\n",
        "      <td>298</td>\n",
        "      <td>0</td>\n",
@@ -2533,10 +3154,10 @@
        "      <td>0.0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>297</td>\n",
-       "      <td>1.066759</td>\n",
+       "      <td>1.122291</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>5</th>\n",
        "      <td>312</td>\n",
        "      <td>313</td>\n",
        "      <td>0</td>\n",
@@ -2556,10 +3177,10 @@
        "      <td>1.0</td>\n",
        "      <td>2.0</td>\n",
        "      <td>312</td>\n",
-       "      <td>0.980033</td>\n",
+       "      <td>0.931236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>6</th>\n",
        "      <td>357</td>\n",
        "      <td>358</td>\n",
        "      <td>0</td>\n",
@@ -2579,10 +3200,10 @@
        "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>357</td>\n",
-       "      <td>0.876932</td>\n",
+       "      <td>0.780998</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>7</th>\n",
        "      <td>498</td>\n",
        "      <td>499</td>\n",
        "      <td>0</td>\n",
@@ -2602,10 +3223,10 @@
        "      <td>1.0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>498</td>\n",
-       "      <td>0.893928</td>\n",
+       "      <td>0.953806</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>8</th>\n",
        "      <td>772</td>\n",
        "      <td>773</td>\n",
        "      <td>0</td>\n",
@@ -2625,10 +3246,10 @@
        "      <td>4.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>772</td>\n",
-       "      <td>0.868053</td>\n",
+       "      <td>0.851535</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
+       "      <th>9</th>\n",
        "      <td>852</td>\n",
        "      <td>853</td>\n",
        "      <td>0</td>\n",
@@ -2648,30 +3269,7 @@
        "      <td>0.0</td>\n",
        "      <td>2.0</td>\n",
        "      <td>852</td>\n",
-       "      <td>0.869694</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>854</td>\n",
-       "      <td>855</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Carter, Mrs. Ernest Courtenay (Lilian Hughes)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>244252</td>\n",
-       "      <td>26.0000</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>854</td>\n",
-       "      <td>0.838546</td>\n",
+       "      <td>0.901367</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2680,54 +3278,54 @@
       "text/plain": [
        "   key_0  PassengerId  Survived  Pclass  \\\n",
        "0  41     42           0         2        \n",
-       "1  177    178          0         1        \n",
-       "2  205    206          0         3        \n",
-       "3  297    298          0         1        \n",
-       "4  312    313          0         2        \n",
-       "5  357    358          0         2        \n",
-       "6  498    499          0         1        \n",
-       "7  772    773          0         2        \n",
-       "8  852    853          0         3        \n",
-       "9  854    855          0         2        \n",
+       "1  140    141          0         3        \n",
+       "2  177    178          0         1        \n",
+       "3  205    206          0         3        \n",
+       "4  297    298          0         1        \n",
+       "5  312    313          0         2        \n",
+       "6  357    358          0         2        \n",
+       "7  498    499          0         1        \n",
+       "8  772    773          0         2        \n",
+       "9  852    853          0         3        \n",
        "\n",
        "                                                       Name     Sex   Age  \\\n",
        "0  Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)  female  27.0   \n",
-       "1  Isham, Miss. Ann Elizabeth                                female  50.0   \n",
-       "2  Strom, Miss. Telma Matilda                                female  2.0    \n",
-       "3  Allison, Miss. Helen Loraine                              female  2.0    \n",
-       "4  Lahtinen, Mrs. William (Anna Sylfven)                     female  26.0   \n",
-       "5  Funk, Miss. Annie Clemmer                                 female  38.0   \n",
-       "6  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)           female  25.0   \n",
-       "7  Mack, Mrs. (Mary)                                         female  57.0   \n",
-       "8  Boulos, Miss. Nourelain                                   female  9.0    \n",
-       "9  Carter, Mrs. Ernest Courtenay (Lilian Hughes)             female  44.0   \n",
+       "1  Boulos, Mrs. Joseph (Sultana)                             female NaN     \n",
+       "2  Isham, Miss. Ann Elizabeth                                female  50.0   \n",
+       "3  Strom, Miss. Telma Matilda                                female  2.0    \n",
+       "4  Allison, Miss. Helen Loraine                              female  2.0    \n",
+       "5  Lahtinen, Mrs. William (Anna Sylfven)                     female  26.0   \n",
+       "6  Funk, Miss. Annie Clemmer                                 female  38.0   \n",
+       "7  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)           female  25.0   \n",
+       "8  Mack, Mrs. (Mary)                                         female  57.0   \n",
+       "9  Boulos, Miss. Nourelain                                   female  9.0    \n",
        "\n",
        "   SibSp  Parch       Ticket      Fare    Cabin Embarked Deck  relatives  \\\n",
        "0  1      0      11668        21.0000   nan      S        0.0  1           \n",
-       "1  0      0      PC 17595     28.7125   C49      C        3.0  0           \n",
-       "2  0      1      347054       10.4625   G6       S        7.0  1           \n",
-       "3  1      2      113781       151.5500  C22 C26  S        3.0  3           \n",
-       "4  1      1      250651       26.0000   nan      S        0.0  2           \n",
-       "5  0      0      237671       13.0000   nan      S        0.0  0           \n",
-       "6  1      2      113781       151.5500  C22 C26  S        3.0  3           \n",
-       "7  0      0      S.O./P.P. 3  10.5000   E77      S        5.0  0           \n",
-       "8  1      1      2678         15.2458   nan      C        0.0  2           \n",
-       "9  1      0      244252       26.0000   nan      S        0.0  1           \n",
+       "1  0      2      2678         15.2458   nan      C        0.0  2           \n",
+       "2  0      0      PC 17595     28.7125   C49      C        3.0  0           \n",
+       "3  0      1      347054       10.4625   G6       S        7.0  1           \n",
+       "4  1      2      113781       151.5500  C22 C26  S        3.0  3           \n",
+       "5  1      1      250651       26.0000   nan      S        0.0  2           \n",
+       "6  0      0      237671       13.0000   nan      S        0.0  0           \n",
+       "7  1      2      113781       151.5500  C22 C26  S        3.0  3           \n",
+       "8  0      0      S.O./P.P. 3  10.5000   E77      S        5.0  0           \n",
+       "9  1      1      2678         15.2458   nan      C        0.0  2           \n",
        "\n",
        "   not_alone  Age_fare  Fare_adj  index  preds_proba  \n",
-       "0  0          1.0       2.0       41     0.932652     \n",
-       "1  1          4.0       2.0       177    0.937899     \n",
-       "2  0          0.0       1.0       205    0.784718     \n",
-       "3  0          0.0       3.0       297    1.066759     \n",
-       "4  0          1.0       2.0       312    0.980033     \n",
-       "5  1          2.0       1.0       357    0.876932     \n",
-       "6  0          1.0       3.0       498    0.893928     \n",
-       "7  1          4.0       1.0       772    0.868053     \n",
-       "8  0          0.0       2.0       852    0.869694     \n",
-       "9  0          2.0       2.0       854    0.838546     "
+       "0  0          1.0       2.0       41     0.856118     \n",
+       "1  0         NaN        2.0       140    0.785630     \n",
+       "2  1          4.0       2.0       177    0.912762     \n",
+       "3  0          0.0       1.0       205    0.842565     \n",
+       "4  0          0.0       3.0       297    1.122291     \n",
+       "5  0          1.0       2.0       312    0.931236     \n",
+       "6  1          2.0       1.0       357    0.780998     \n",
+       "7  0          1.0       3.0       498    0.953806     \n",
+       "8  1          4.0       1.0       772    0.851535     \n",
+       "9  0          0.0       2.0       852    0.901367     "
       ]
      },
-     "execution_count": 61,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2745,7 +3343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
@@ -2813,7 +3411,7 @@
        "      <td>NaN</td>\n",
        "      <td>2.0</td>\n",
        "      <td>65</td>\n",
-       "      <td>-0.007385</td>\n",
+       "      <td>0.042933</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -2836,7 +3434,7 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>107</td>\n",
-       "      <td>-0.007528</td>\n",
+       "      <td>0.021673</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -2859,44 +3457,44 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>127</td>\n",
-       "      <td>0.037790</td>\n",
+       "      <td>0.048231</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>338</td>\n",
-       "      <td>339</td>\n",
+       "      <td>271</td>\n",
+       "      <td>272</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
-       "      <td>Dahl, Mr. Karl Edwart</td>\n",
+       "      <td>Tornquist, Mr. William Henry</td>\n",
        "      <td>male</td>\n",
-       "      <td>45.0</td>\n",
+       "      <td>25.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>7598</td>\n",
-       "      <td>8.0500</td>\n",
+       "      <td>LINE</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>nan</td>\n",
        "      <td>S</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>338</td>\n",
-       "      <td>-0.013960</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>271</td>\n",
+       "      <td>0.051815</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>400</td>\n",
-       "      <td>401</td>\n",
+       "      <td>338</td>\n",
+       "      <td>339</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
-       "      <td>Niskanen, Mr. Juha</td>\n",
+       "      <td>Dahl, Mr. Karl Edwart</td>\n",
        "      <td>male</td>\n",
-       "      <td>39.0</td>\n",
+       "      <td>45.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>STON/O 2. 3101289</td>\n",
-       "      <td>7.9250</td>\n",
+       "      <td>7598</td>\n",
+       "      <td>8.0500</td>\n",
        "      <td>nan</td>\n",
        "      <td>S</td>\n",
        "      <td>0.0</td>\n",
@@ -2904,100 +3502,100 @@
        "      <td>1</td>\n",
        "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>400</td>\n",
-       "      <td>0.010595</td>\n",
+       "      <td>338</td>\n",
+       "      <td>0.014749</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>444</td>\n",
-       "      <td>445</td>\n",
+       "      <td>391</td>\n",
+       "      <td>392</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
-       "      <td>Johannesen-Bratthammer, Mr. Bernt</td>\n",
+       "      <td>Jansson, Mr. Carl Olof</td>\n",
        "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>21.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>65306</td>\n",
-       "      <td>8.1125</td>\n",
+       "      <td>350034</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>nan</td>\n",
        "      <td>S</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>444</td>\n",
-       "      <td>-0.006948</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>391</td>\n",
+       "      <td>0.066308</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>455</td>\n",
-       "      <td>456</td>\n",
+       "      <td>400</td>\n",
+       "      <td>401</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
-       "      <td>Jalsevac, Mr. Ivan</td>\n",
+       "      <td>Niskanen, Mr. Juha</td>\n",
        "      <td>male</td>\n",
-       "      <td>29.0</td>\n",
+       "      <td>39.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>349240</td>\n",
-       "      <td>0.0000</td>\n",
+       "      <td>STON/O 2. 3101289</td>\n",
+       "      <td>7.9250</td>\n",
        "      <td>nan</td>\n",
-       "      <td>C</td>\n",
+       "      <td>S</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>455</td>\n",
-       "      <td>0.042101</td>\n",
+       "      <td>400</td>\n",
+       "      <td>0.016676</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>510</td>\n",
-       "      <td>511</td>\n",
+       "      <td>414</td>\n",
+       "      <td>415</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
-       "      <td>Daly, Mr. Eugene Patrick</td>\n",
+       "      <td>Sundman, Mr. Johan Julian</td>\n",
        "      <td>male</td>\n",
-       "      <td>29.0</td>\n",
+       "      <td>44.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>382651</td>\n",
-       "      <td>0.0000</td>\n",
+       "      <td>STON/O 2. 3101269</td>\n",
+       "      <td>7.9250</td>\n",
        "      <td>nan</td>\n",
-       "      <td>Q</td>\n",
+       "      <td>S</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>510</td>\n",
-       "      <td>0.045326</td>\n",
+       "      <td>414</td>\n",
+       "      <td>0.062155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>570</td>\n",
-       "      <td>571</td>\n",
+       "      <td>664</td>\n",
+       "      <td>665</td>\n",
        "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Harris, Mr. George</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Lindqvist, Mr. Eino William</td>\n",
        "      <td>male</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>S.W./PP 752</td>\n",
-       "      <td>10.5000</td>\n",
+       "      <td>STON/O 2. 3101285</td>\n",
+       "      <td>7.9250</td>\n",
        "      <td>nan</td>\n",
        "      <td>S</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>570</td>\n",
-       "      <td>0.032801</td>\n",
+       "      <td>664</td>\n",
+       "      <td>0.052865</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -3020,7 +3618,7 @@
        "      <td>NaN</td>\n",
        "      <td>2.0</td>\n",
        "      <td>709</td>\n",
-       "      <td>-0.007385</td>\n",
+       "      <td>0.042933</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -3031,52 +3629,52 @@
        "0  65     66           1         3        \n",
        "1  107    108          1         3        \n",
        "2  127    128          1         3        \n",
-       "3  338    339          1         3        \n",
-       "4  400    401          1         3        \n",
-       "5  444    445          1         3        \n",
-       "6  455    456          1         3        \n",
-       "7  510    511          1         3        \n",
-       "8  570    571          1         2        \n",
+       "3  271    272          1         3        \n",
+       "4  338    339          1         3        \n",
+       "5  391    392          1         3        \n",
+       "6  400    401          1         3        \n",
+       "7  414    415          1         3        \n",
+       "8  664    665          1         3        \n",
        "9  709    710          1         3        \n",
        "\n",
        "                                                Name   Sex   Age  SibSp  \\\n",
        "0  Moubarek, Master. Gerios                           male NaN    1       \n",
        "1  Moss, Mr. Albert Johan                             male NaN    0       \n",
        "2  Madsen, Mr. Fridtjof Arne                          male  24.0  0       \n",
-       "3  Dahl, Mr. Karl Edwart                              male  45.0  0       \n",
-       "4  Niskanen, Mr. Juha                                 male  39.0  0       \n",
-       "5  Johannesen-Bratthammer, Mr. Bernt                  male NaN    0       \n",
-       "6  Jalsevac, Mr. Ivan                                 male  29.0  0       \n",
-       "7  Daly, Mr. Eugene Patrick                           male  29.0  0       \n",
-       "8  Harris, Mr. George                                 male  62.0  0       \n",
+       "3  Tornquist, Mr. William Henry                       male  25.0  0       \n",
+       "4  Dahl, Mr. Karl Edwart                              male  45.0  0       \n",
+       "5  Jansson, Mr. Carl Olof                             male  21.0  0       \n",
+       "6  Niskanen, Mr. Juha                                 male  39.0  0       \n",
+       "7  Sundman, Mr. Johan Julian                          male  44.0  0       \n",
+       "8  Lindqvist, Mr. Eino William                        male  20.0  1       \n",
        "9  Moubarek, Master. Halim Gonios (\"William George\")  male NaN    1       \n",
        "\n",
        "   Parch             Ticket     Fare Cabin Embarked Deck  relatives  \\\n",
        "0  1      2661               15.2458  nan   C        0.0  2           \n",
        "1  0      312991             0.0000   nan   S        0.0  0           \n",
        "2  0      C 17369            0.0000   nan   S        0.0  0           \n",
-       "3  0      7598               8.0500   nan   S        0.0  0           \n",
-       "4  0      STON/O 2. 3101289  7.9250   nan   S        0.0  0           \n",
-       "5  0      65306              8.1125   nan   S        0.0  0           \n",
-       "6  0      349240             0.0000   nan   C        0.0  0           \n",
-       "7  0      382651             0.0000   nan   Q        0.0  0           \n",
-       "8  0      S.W./PP 752        10.5000  nan   S        0.0  0           \n",
+       "3  0      LINE               0.0000   nan   S        0.0  0           \n",
+       "4  0      7598               8.0500   nan   S        0.0  0           \n",
+       "5  0      350034             0.0000   nan   S        0.0  0           \n",
+       "6  0      STON/O 2. 3101289  7.9250   nan   S        0.0  0           \n",
+       "7  0      STON/O 2. 3101269  7.9250   nan   S        0.0  0           \n",
+       "8  0      STON/O 2. 3101285  7.9250   nan   S        0.0  1           \n",
        "9  1      2661               15.2458  nan   C        0.0  2           \n",
        "\n",
        "   not_alone  Age_fare  Fare_adj  index  preds_proba  \n",
-       "0  0         NaN        2.0       65    -0.007385     \n",
-       "1  1         NaN       NaN        107   -0.007528     \n",
-       "2  1          1.0      NaN        127    0.037790     \n",
-       "3  1          2.0       1.0       338   -0.013960     \n",
-       "4  1          2.0       1.0       400    0.010595     \n",
-       "5  1         NaN        1.0       444   -0.006948     \n",
-       "6  1          1.0      NaN        455    0.042101     \n",
-       "7  1          1.0      NaN        510    0.045326     \n",
-       "8  1          4.0       1.0       570    0.032801     \n",
-       "9  0         NaN        2.0       709   -0.007385     "
+       "0  0         NaN        2.0       65     0.042933     \n",
+       "1  1         NaN       NaN        107    0.021673     \n",
+       "2  1          1.0      NaN        127    0.048231     \n",
+       "3  1          1.0      NaN        271    0.051815     \n",
+       "4  1          2.0       1.0       338    0.014749     \n",
+       "5  1          1.0      NaN        391    0.066308     \n",
+       "6  1          2.0       1.0       400    0.016676     \n",
+       "7  1          2.0       1.0       414    0.062155     \n",
+       "8  0          1.0       1.0       664    0.052865     \n",
+       "9  0         NaN        2.0       709    0.042933     "
       ]
      },
-     "execution_count": 62,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3084,27 +3682,6 @@
    "source": [
     "filter_fns_with_prediction"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/octopus_showcase.ipynb b/notebooks/octopus_showcase.ipynb
index 0626472..babd929 100644
--- a/notebooks/octopus_showcase.ipynb
+++ b/notebooks/octopus_showcase.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:57:35.530610Z",
@@ -357,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 35,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.003524Z",
@@ -398,6 +398,11 @@
        "      <th>Fare</th>\n",
        "      <th>Cabin</th>\n",
        "      <th>Embarked</th>\n",
+       "      <th>Deck</th>\n",
+       "      <th>relatives</th>\n",
+       "      <th>not_alone</th>\n",
+       "      <th>Age_fare</th>\n",
+       "      <th>Fare_adj</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -415,6 +420,11 @@
        "      <td>891</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>891</td>\n",
+       "      <td>891</td>\n",
+       "      <td>714</td>\n",
+       "      <td>665</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
@@ -427,9 +437,14 @@
        "      <td>0.523008</td>\n",
        "      <td>0.381594</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>32.2042</td>\n",
+       "      <td>30.4437</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.904602</td>\n",
+       "      <td>0.602694</td>\n",
+       "      <td>1.45938</td>\n",
+       "      <td>2.00301</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
@@ -442,9 +457,14 @@
        "      <td>1.10274</td>\n",
        "      <td>0.806057</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>49.6934</td>\n",
+       "      <td>50.6607</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>1.61346</td>\n",
+       "      <td>0.489615</td>\n",
+       "      <td>1.09655</td>\n",
+       "      <td>0.810319</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
@@ -460,6 +480,11 @@
        "      <td>0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
@@ -472,9 +497,14 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>7.9104</td>\n",
+       "      <td>3.9625</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
@@ -490,6 +520,11 @@
        "      <td>14.4542</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
@@ -505,6 +540,11 @@
        "      <td>31</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
@@ -520,6 +560,11 @@
        "      <td>512.329</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>counts</th>\n",
@@ -533,8 +578,13 @@
        "      <td>891</td>\n",
        "      <td>891</td>\n",
        "      <td>891</td>\n",
-       "      <td>204</td>\n",
+       "      <td>891</td>\n",
        "      <td>889</td>\n",
+       "      <td>891</td>\n",
+       "      <td>891</td>\n",
+       "      <td>891</td>\n",
+       "      <td>714</td>\n",
+       "      <td>665</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>uniques</th>\n",
@@ -547,8 +597,13 @@
        "      <td>7</td>\n",
        "      <td>7</td>\n",
        "      <td>681</td>\n",
-       "      <td>248</td>\n",
-       "      <td>147</td>\n",
+       "      <td>208</td>\n",
+       "      <td>148</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -563,8 +618,13 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>687</td>\n",
+       "      <td>0</td>\n",
        "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>177</td>\n",
+       "      <td>226</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>missing_perc</th>\n",
@@ -578,8 +638,13 @@
        "      <td>0%</td>\n",
        "      <td>0%</td>\n",
        "      <td>0%</td>\n",
-       "      <td>77.10%</td>\n",
+       "      <td>0%</td>\n",
        "      <td>0.22%</td>\n",
+       "      <td>0%</td>\n",
+       "      <td>0%</td>\n",
+       "      <td>0%</td>\n",
+       "      <td>19.87%</td>\n",
+       "      <td>25.36%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>types</th>\n",
@@ -595,6 +660,11 @@
        "      <td>numeric</td>\n",
        "      <td>categorical</td>\n",
        "      <td>categorical</td>\n",
+       "      <td>categorical</td>\n",
+       "      <td>numeric</td>\n",
+       "      <td>bool</td>\n",
+       "      <td>numeric</td>\n",
+       "      <td>numeric</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -616,23 +686,38 @@
        "missing_perc  0%          0%        0%        0%      0%    19.87%   0%         \n",
        "types         numeric     bool      numeric   unique  bool  numeric  numeric    \n",
        "\n",
-       "                 Parch       Ticket     Fare        Cabin     Embarked  \n",
-       "count         891       NaN          891      NaN          NaN          \n",
-       "mean          0.381594  NaN          32.2042  NaN          NaN          \n",
-       "std           0.806057  NaN          49.6934  NaN          NaN          \n",
-       "min           0         NaN          0        NaN          NaN          \n",
-       "25%           0         NaN          7.9104   NaN          NaN          \n",
-       "50%           0         NaN          14.4542  NaN          NaN          \n",
-       "75%           0         NaN          31       NaN          NaN          \n",
-       "max           6         NaN          512.329  NaN          NaN          \n",
-       "counts        891       891          891      204          889          \n",
-       "uniques       7         681          248      147          3            \n",
-       "missing       0         0            0        687          2            \n",
-       "missing_perc  0%        0%           0%       77.10%       0.22%        \n",
-       "types         numeric   categorical  numeric  categorical  categorical  "
+       "                 Parch       Ticket     Fare        Cabin     Embarked  \\\n",
+       "count         891       NaN          891      NaN          NaN           \n",
+       "mean          0.381594  NaN          30.4437  NaN          NaN           \n",
+       "std           0.806057  NaN          50.6607  NaN          NaN           \n",
+       "min           0         NaN          0        NaN          NaN           \n",
+       "25%           0         NaN          3.9625   NaN          NaN           \n",
+       "50%           0         NaN          14.4542  NaN          NaN           \n",
+       "75%           0         NaN          31       NaN          NaN           \n",
+       "max           6         NaN          512.329  NaN          NaN           \n",
+       "counts        891       891          891      891          889           \n",
+       "uniques       7         681          208      148          3             \n",
+       "missing       0         0            0        0            2             \n",
+       "missing_perc  0%        0%           0%       0%           0.22%         \n",
+       "types         numeric   categorical  numeric  categorical  categorical   \n",
+       "\n",
+       "                     Deck relatives not_alone Age_fare  Fare_adj  \n",
+       "count         NaN          891       891       714      665       \n",
+       "mean          NaN          0.904602  0.602694  1.45938  2.00301   \n",
+       "std           NaN          1.61346   0.489615  1.09655  0.810319  \n",
+       "min           NaN          0         0         0        1         \n",
+       "25%           NaN          0         0         1        1         \n",
+       "50%           NaN          0         1         1        2         \n",
+       "75%           NaN          1         1         2        3         \n",
+       "max           NaN          10        1         4        3         \n",
+       "counts        891          891       891       714      665       \n",
+       "uniques       8            9         2         4        3         \n",
+       "missing       0            0         0         177      226       \n",
+       "missing_perc  0%           0%        0%        19.87%   25.36%    \n",
+       "types         categorical  numeric   bool      numeric  numeric   "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -646,21 +731,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Cabin       0.771044\n",
+       "Fare_adj    0.253648\n",
        "Age         0.198653\n",
+       "Age_fare    0.198653\n",
        "Embarked    0.002245\n",
-       "Fare        0.000000\n",
-       "Ticket      0.000000\n",
+       "SibSp       0.000000\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -684,7 +769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -717,7 +802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -736,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 39,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.015383Z",
@@ -763,7 +848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 40,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.115768Z",
@@ -778,7 +863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 41,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.203536Z",
@@ -793,7 +878,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 41,
      "metadata": {
       "image/png": {
        "height": 600,
@@ -810,7 +895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 42,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.420608Z",
@@ -826,9 +911,9 @@
       "number of negative instance :  549\n",
       "new dataset shape:  (542, 17)\n",
       "Method Name       :\u001b[35;1m sampling\u001b[0m\n",
-      "Current memory usage:\u001b[36m 0.064101MB\u001b[0m\n",
-      "Peak                :\u001b[36m 0.077783MB\u001b[0m\n",
-      "Total time taken:  \u001b[36m 13.155 ms \u001b[0m\n"
+      "Current memory usage:\u001b[36m 0.066183MB\u001b[0m\n",
+      "Peak                :\u001b[36m 0.079865MB\u001b[0m\n",
+      "Total time taken:  \u001b[36m 12.406 ms \u001b[0m\n"
      ]
     }
    ],
@@ -857,7 +942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 43,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:00.687777Z",
@@ -869,7 +954,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "5it [00:03,  1.56it/s]"
+      "5it [00:03,  1.44it/s]"
      ]
     },
     {
@@ -877,9 +962,9 @@
      "output_type": "stream",
      "text": [
       "Method Name       :\u001b[35;1m cv_adv\u001b[0m\n",
-      "Current memory usage:\u001b[36m 0.957038MB\u001b[0m\n",
-      "Peak                :\u001b[36m 3.307132MB\u001b[0m\n",
-      "Total time taken:  \u001b[36m 3207.140 ms \u001b[0m\n"
+      "Current memory usage:\u001b[36m 0.962317MB\u001b[0m\n",
+      "Peak                :\u001b[36m 3.311668MB\u001b[0m\n",
+      "Total time taken:  \u001b[36m 3475.836 ms \u001b[0m\n"
      ]
     },
     {
@@ -939,7 +1024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 44,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:00.944005Z",
@@ -949,7 +1034,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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pwIEDGeras2ePJGX4YeDFF1+0mq5fv75+/vlnvfLKK1q4cKF+/fVX9erVS507d77rMefNe/u3XpPJdL+nx8o/e3my8noVLVpUzz77rGbPnq1BgwZp3bp1unjxooYNG6batWtbjuH69et68cUXNW3aNO3evVsNGzZUSEjIXYOZJDk6OqpLly7avn27Ll26JOn2MMtvv/1WL730khwdHeXs7KxFixapU6dOSkhI0N69e7V+/XrLlTizMvQ4/TVq1qyZ1cVF2rZta/W5lpXPoqxIb+f/vEBN48aNVbRoUav3gWT9mezs7CwPD48Hfh8AyB56zgA8EpcvX9aHH36ob7/9ViaTSaVKlVK1atXk5OSU4TLU6cPh0qV/Ocnsi97ly5dlNpstQeFBtW/fXh999JHy5Mmjl19+WWvXrlXdunUzfPFctmyZFixYoL/++ktFihSRr6+v8ufPL+l2D1V6r9s/h7vlzZvXElgkKTExMdMLOxQrVuyuNaZ/Kbyzd/FOdztvJv1x93purly5kum+04dY3nn+yd2uDHjnMNDMrlrn7u6e4TVNr+nKlSvZPr7seJDjTX9906Uf24MGjszONStcuLBq1qypY8eOSZJatmxpddnzOnXqWIbLSrd7Be91QZArV65k2pvo5eUlSRnOI5Nuv3ekjG32n8Nrhw8fruLFi2vjxo2aNm2apk2bpgoVKmjEiBFq1KhRpvWUKFFC0u1e74oVK2a6TmJiolxdXa3axj/balZeLwcHBy1evFjz5s3Tt99+q82bN8vBwUENGjTQuHHjVKpUKbVp00YRERFatWqVFi9erIULF6po0aIKDAzUG2+8kWl96bp27ap58+Zp06ZNevXVV/XNN98oOTnZ6qqhsbGxCg8P14kTJ+Tq6qoKFSroqaeekpS1e41duXJFZrM508+Pf86732dRVqT/eJLZUGpPT88Mtzn453vB0dHxgd8HALKHcAbgkXj33Xd1/Phxffzxx6pdu7blC9k/fxF/UAUKFJCDg0OmQxK3bdumSpUq3fNy+B06dLD8cj5ixAjt2bNH48aNU5UqVSxfhjdt2qRJkyYpJCREL7/8suULzZQpU7Rv3z5J/9er8tdff1ltPy0tzfIlOH29f66T2ePuVKhQIUnSokWLMgxNk2QV/jJ7XHr4SZeUlKRdu3bJ399fhQsX1okTJzI89sKFC/fc9oO4evVqhqFjf/31lxwcHOTh4ZHt48sOWxzvnVJSUhQdHa0yZcpkuDBGUlKSZX/z58+3OjfxQS95XrhwYV28eDHD/HsdV/qX/oSEBKtzov7ZXpydnRUUFKSgoCDFx8frp59+0oIFCxQSEqLY2NgMX9yl2+9rJycn/fDDD3ftFR4/frx+/PFHbd++/a7nZGX19fL09NTYsWM1duxYnTx5Ulu3btW8efM0evRoLVu2TNLtHuOAgABdu3ZNv/zyi5YvX67JkyfL399ftWrVynT/0u33bKNGjbRx40a9+uqr2rBhg5o0aWJ5z//555/q37+/GjdubBli7eDgoG3btmnz5s133e6dihQpIkdHxwyvoclksuqFzspnUVYULlxY0u3XPv39l+7ChQv3vQoqANthWCOAh3bnMJx0e/bsUfPmzdW4cWNLMDt06JASExMf6hdYNzc3+fr6auvWrVa/Gh85ckRBQUHav39/lreVP39+TZs2TampqQoNDdWtW7ck3T5fxcXFRQMHDrR8GUpNTbWcl2MymeTt7a1nnnkmw5ex77//3mqoUd26dXX48GGrXpKbN29mejPsdHXq1JF0+2p8/v7+ln/58uXT9OnTra5keady5crJw8PDcguEdNu2bVNQUJDOnj2runXr6uLFixmuKpc+JCt9WNjDSEtLs5wbJd3+dX/z5s2qVq2a3Nzcsnx89xp+li6ztncnWxzvnZycnDRnzhyFh4dbtc9z587p3//+t+VcJB8fH6tj/+fwxPupW7eujhw5kqEtbNy48a63E6hfv74cHBy0adMmq/nff/+95W+TyaTOnTtr8uTJkiRvb2917dpVr776qm7cuGEVHO5UoEAB9ejRQxs2bMj0Pbh37159++23at68+T0vlpGV1ysuLk6NGze2tPMKFSooKChIDRo00P/+9z9J0ocffqiXX37ZUtudVzG88714N927d9eBAwe0b98+7d+/3+rG54cOHdKtW7cUGBiocuXKWXpY06/smZXeLGdnZ9WqVUtbtmyxGtK6fft2q2GRWfksku7/Xkm/oE50dLTV/J9++kmJiYk5/j4AkH30nAF4aE5OTnJ1ddX+/fu1Z88e1apVS1WrVlVMTIyqVq2qkiVL6ujRo5o/f74cHBwe+tyFwYMHq1+/fho4cKC6d++uGzduaNasWapcufIDn7BeuXJlhYaGatq0aRo/frwmTpyoatWqac2aNZowYYJatWqlxMRErVy5Uv/9738l3Q5X+fPn15AhQxQaGqoRI0YoICBAp0+f1uzZsyX935C43r17a9WqVerbt69CQkLk4uKixYsXKzExMdObDEtSxYoV1blzZ4WFhencuXOqXr26zp8/r8jISKWkpKhy5cqZPi5PnjwKDQ3V2LFjNWrUKLVp00YXLlywXP3Q399fFStW1OrVqzV48GAFBwerXLly+vnnn7VkyRJ169btgUPC3YwePVoXL17U008/rU8//VSnTp3SkiVLHuj40n/h//HHH1W4cGHLBUfulN6DtG3btkyHE3bu3DlHj/fo0aOS7n0PqUGDBmnYsGEaMmSIunbtqosXL2ru3LkqVKjQXa+i+KBef/11bdy4UW+99ZaCg4Pl5eWlmJgYff311xo8eHCmQ1JLlSqlnj176uOPP5Z0+/L2P/zwg9XtAhwdHVWzZk2tXLlSHh4eqlq1qs6fP6+lS5eqRo0a97xP1+DBg3Xo0CG9/vrreuWVVyy95Hv37tXKlStVtmxZffDBB/c8rqy8XunDAcPCwnT16lWVLFlShw8f1vbt2/XWW29Jun1vtTVr1mjEiBFq166dkpOT9cknn6hQoUJq3LjxfZ/fF154QZ6enho1apS8vLz0/PPPW5ZVrlxZTk5OmjFjhuXqpd98843lNgaZnSubmSFDhqhPnz4KCgpS7969deHCBc2ePVtOTk6WdbL6WZT+Xvn222/VpEmTDENiy5cvr65duyoqKkopKSlq0KCBTp06pcjISD377LNW54gCsC/CGYAcMWDAAEVFRSkwMFCbNm3S5MmTNWHCBIWHhystLU2lSpVSSEiIfv31V8t9hO53yfu7ef7557Vw4ULNmTNHgwYNUqFChdSkSRMNHTo0W9vs27evduzYofXr16tOnTrq1KmTzp49q/Xr1+uzzz6Tp6en6tatq7feekv9+/fX7t271aZNG7Vp00YfffSRFixYoOjoaJUuXVphYWEaNGiQZZhagQIFtGLFCk2aNEkjR46Ui4uLOnbsqKJFi+q33367a00TJ05UuXLl9Pnnn2vevHlyd3dX3bp1NXjw4HueU9ajRw8VKFBACxcu1MaNG1WsWDHLZfWl2+f3rVixQjNmzNDHH3+sK1euqEyZMparJOaUCRMmaOrUqYqPj5evr68++eQTS49ZVo/vueeeU9u2bbVq1Sr99NNP+uqrrzLsp3v37oqNjdXAgQM1aNCgDBdoyenjDQ4OliSrnsF/6tSpk1xdXbVw4UKFhITI2dlZjRo10rvvvmsZXvawihUrpk8//VQzZszQlClTdPPmTVWoUEEfffTRPb9ojx49WsWKFdO6desUFRWlmjVrasSIEZZLxEvS+++/Lzc3N61bt06RkZEqWLCgmjZtqqFDh96zpgIFCmjZsmVavXq1vv76a/3rX/9ScnKySpUqpcDAQPXp0+eu5zGmy8rr5eDgoKioKE2fPl0zZszQ5cuXVbx4cQ0YMEBvv/22JKlVq1b66KOPtHTpUsXExMjBwUE1atTQihUrsnRbhLx586pLly5asGCBBgwYYNUzVaZMGUVERGj27NkaOHCgChUqJF9fX61evVqBgYHas2eP2rZte9991KhRQ4sXL9aMGTMUGhoqT09PjRgxwuom3Vn9LGrUqJHq1aun6dOna+fOnYqKisqwv7CwMJUpU0br16/XsmXLVLRoUXXo0EGhoaEZzhEFYD8O5qyeTQoAsPLVV1/J19fX6lfqX3/9Ve3atdPHH3+spk2b6sCBA7p06ZJeeOEFq8e+9NJLKlGihKWn7UkRGRmpOXPm6ODBg5b7Jj1JLl26pFdeeSXDTbUBAMgJ9JwBQDZFR0dr6tSpGjJkiEqWLKlz587p448/VsWKFdWwYUNJt881Cg0N1dtvv60GDRooJSVFmzZt0tGjRy3nwODxMXv27HveBgEAgIdBzxkAZNPly5c1Y8YMbdu2zXKZ6xdeeEFDhgyxGjr12WefadWqVfrjjz+UJ08eVa5cWQMGDMjSjWofN096z9mRI0fk6+t73wuRAACQHYQzAAAAADAAfvoDAAAAAAN4Is45M5lMun79upycnCyXrwYAAAAAIzGbzUpJSZGbm1umQ+SfiHB2/fp1nThxwt5lAAAAAMB9PfvssypYsGCG+U9EOEu/YeOzzz6b7fsmIaPDhw/Lz8/P3mUgF6MNwt5og7A32iDsjTaYs5KTk3XixAmrG87f6YkIZ+lDGZ2dnZ/Iq4PZE88n7I02CHujDcLeaIOwN9pgzrvbqVhcEAQAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAATwRFwQBAAAAHldXr17VhQsXlJKSYu9SMsibN6+OHTtm7zIeK25ubipZsmSm9zG7H8IZAAAAYCdXr15VfHy8SpQoofz589/1Kn72cv36dbm5udm7jMeGyWTS2bNndfHiRXl5eT3w4xnWCAAAANjJhQsXVKJECbm6uhoumOHBOTo6ytvbW1euXMne43O4HgAAAABZlJKSovz589u7DOQgJycnpaamZuuxhDMAAADAjugxe7I8zOtJOAMAAAAAA+CCIAAAAAAeSLNmzXT27NkM8ytWrKivvvrKat7GjRv16aefas2aNbYq77FFOAMAAAAM5u+kNKWazDbbX15HBxV0yfNAjxk+fLjatWtnvZ281vFi165dGjt2rHx9fR+6xtyAcAYAAAAYTKrJrNBN52y2v1kBTz3wYwoUKCBPT8+7Lp8zZ44WLFigZ5555iEqy1045wwAAABAjtu9e7cWL16sVq1a3XddHx8fff3112rbtq2qVq2qoUOH6s8//1Tv3r1VtWpV9erVSxcuXLCsHxUVpebNm8vPz0+NGjXSrFmzLMvS0tI0e/ZsNW7cWDVq1FD//v0tj+3du7fCwsLUsmVLNW7cWImJiTp//rxCQ0NVp04d1a1bV2FhYUpKSsr5JyQLCGcAAAAActzy5ctVu3btLK8/e/ZshYeH6+OPP1ZMTIxeeeUV9erVS6tXr9bZs2e1ePFiSbfPYVu8eLEmTJigmJgYDRw4UPPmzdPBgwclSZGRkfrss880YcIErVu3TklJSRo2bJhlPxs2bFB4eLjmzZunAgUK6LXXXtONGze0fPlyzZo1S9u3b9fkyZNz9snIIoY1AgAAAHhgYWFhmjRpktW87777TkWLFs3W9vr06aNq1apJut2TVrFiRbVu3VqS1Lx5c506dUqS5O3trfDwcNWvX1+S9Morr2ju3Ln69ddf5e/vr7Vr1+qdd97R888/L0n68MMPtX79eplMJklSkyZNVKtWLUnS999/r/Pnz2vt2rVyd3eXJI0ZM0b9+vXTO++8o4IFC2brWLKLcAYAAADggQUHB6tNmzZW89IDTnaUKlXK8reLi4uefvppy3S+fPmUnJwsSapXr54OHDig6dOnKy4uTseOHVNCQoJMJpMuXbqkxMREVa5c2fLY0qVL65133rFMlyhRwvJ3XFycSpcubVV3jRo1lJaWpt9//13+/v7ZPp7sIJwBAIAnlq2veJfTnn6mgr1LAO7Kw8NDZcqUybHt/fNKj46OmZ+BtW7dOk2aNEldu3ZVq1atNGzYMPXp00eS5OTkdN/9ODs7W/52cXHJsDwtLU2SLD1ttkQ4AwAATyxbX/Eup01tkb3hYcCTbM2aNerXr5/efvttSdLVq1f1119/yWw2q2DBgvLw8NDRo0f13HPPSZJ+//139ezZU5s2bcqwrfLly+v06dO6fPmypffsP//5j/LkyaPSpUvb7JjSEc4AAAAAPDaKFCminTt3qmXLlrpx44YiIiKUkpJiGfbYp08fRUZG6qmnnlLx4sU1ceJEPffcc5kOuWzQoIGeeeYZvf/++3rnnXd05coVTZgwQQEBASpSpIiNj4xwBgAAAOAxMnLkSI0aNUqdO3dWkSJF1LZtW7m5ueno0aOSpMDAQF29elVDhw5VSkqKGjVqpDFjxmS6LUdHR82dO1fjx4/Xyy+/LFdXV7Vv315Dhw615SFZOJjN5sd3IPb/l5SUpMOHD8vPzy/TcaPInn379qlmzZr2LgO5GG0Q9kYbfPxdupn62A9r9C7sau8y8AgdO3ZMvr6+Gebb+nzJvI4OKuiSJ8P869evy83NzWZ1PCnu9rreL7fQcwYAAAAYTGZBCU8+bkINAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAA4IE0a9ZMPj4+Gf61a9cuw7obN27UK6+8YocqpcjISKt9x8TEKCEhIdNlRpDX3gUAAAAAsPZ36k2lmNNstj8nhzwqmDf/Az1m+PDhGcJY3rzW8WLXrl0aO3asfH19H7rG7HjzzTfVu3dvSdLZs2cVGhqqLVu2ZFhmFIQzAAAAwGBSzGkaeDLKZvubWyHogR9ToEABeXp63nX5nDlztGDBAj3zzDMPUdnDcXNzs/xtNpvvuswoCGeP0N9JaUo1me+/okE9/UwFe5cAAECuljefSYkp1+xdRrZlpzcGT47du3dr8eLF2rVrl2JjY++5ro+Pj8aPH6+FCxfq4sWLatq0qcLCwlSgQAFJUlxcnCZNmqT9+/fL1dVVL7/8sgYOHChHR0ddu3ZNo0ePVmxsrFJTU9WgQQONHTtWnp6eioyMVGxsrNasWaPmzZtLklq1aqXw8HCdPXtWsbGxWrVqlZ5//nkNGjRI3bp1s9QUEBCgV199Va+++qr27t2ryZMn68SJEypVqpQCAwPVqVOnHH/OCGePUKrJrNBN5+xdRrZNbVHU3iUAAJCrmWVW8MmF9i4j27LTG4Mnx/LlyyXdHtqYFbNnz9b48eNVrFgxjRw5UqNHj9bMmTOVmJionj17qlmzZlq3bp1+++03jR49Wvnz51ffvn01c+ZMnT17VitWrFCePHn0wQcfKDw8XDNmzLDa/rp169StWzetXbtWlSpV0sKFt99bjo6OatOmjTZv3mwJZydOnNDvv/+u1q1bKyEhQUFBQQoNDdULL7ygI0eOaMyYMSpUqJCaNWuWg88Y4QwAAABANoSFhWnSpElW87777jsVLZq9H/j79u2rpk2bSpJGjRqlN954Q5cvX9ZXX30lFxcXhYWFycnJSeXLl1dCQoJmzZqlvn376uzZs3J1dVXJkiXl5uamKVOm6OrVqxm27+HhIUkqUqSI8uXLZ7WsXbt2evXVV/X333+rYMGCiomJUd26dVWsWDHNnDlTdevW1WuvvSZJKlOmjE6dOqVly5YRzgAAAADYX3BwsNq0aWM1z93dPdvbq169uuVvPz8/mUwm/fbbb4qLi9Nzzz0nJycnq3UvXbqkxMREBQUFKSgoSPXr11fdunXVsmXLBx5yWLVqVXl7e2vr1q3q2LGjYmJi9MYbb0iSTp06pZ9++smqvtTUVEvYy0mEMwAAAAAPzMPDQ2XKlMmx7eXJk8fyt8lkknR7yKGLi0uGddOXm0wmVa9eXT/88IO2bt2qbdu2afLkyYqOjtaKFSseaP8vvviitmzZoueee06nT59Wq1atJN0OYi+++KIGDBhgtb6jY87flYz7nAEAAACwu2PHjln+Pnz4sJycnFSuXDmVL19eR48eVUpKimX5/v375e7uLg8PDy1dulQHDhxQhw4dNH36dEVFRWn37t26ePGi1fYdHBzuuf+AgADFxsZq06ZNatSokQoXLixJKlu2rH7//XeVKVPG8m/Hjh1av359Dh79bTYNZ9HR0QoICFCrVq20atWqDMuPHDmil156SR06dNDbb7+d6VhRAAAAAE+eOXPm6JdfftGBAwc0ceJEdejQQQULFlS7du1kMpk0ZswYxcXF6fvvv1dkZKR69OghR0dHnT9/XuPHj9e///1v/fnnn4qOjtbTTz+tIkWKWG3f1dVVknT8+HFdv349w/4rVaqkp59+WkuXLlVAQIBlfs+ePXXs2DFNnz5dv//+u2JiYjR16lR5e3vn+HNgs3AWHx+viIgIrV69Wl988YXWrl2rkydPWq0zceJEDRo0SF9++aXKli2rTz75xFblAQAAALCjzp07a8SIEXrzzTdVq1YtjR07VtLt+5EtWrRIf/75pzp16qSwsDD16dNHoaGhkqTQ0FDVqlVLAwcO1IsvvqhTp07p448/thomKd2+EEiXLl00dOjQu/Z6vfjiizKZTJbL7ktSiRIltGDBAsXGxqpdu3b66KOPFBISop49e+b4c2Czc85iY2NVr149y0mCrVu3VkxMjIKDgy3rmEwmS4q9efOmpSsRAAAAyE2cHPLY9FYETg557r/SHbZu3ZrldUNCQhQSEnLf9erUqaPBgwdnuqxSpUpauXJlpsvy58+vCRMmaMKECffdd3h4uMLDw62W32nAgAEZzi2TpPr16+vzzz+/7zE8LJuFswsXLljdQdzLy0sHDx60Wmf48OF68803NWnSJOXPn1+fffaZrcoDAAAADIObd+dONgtnJpPJ6iQ8s9lsNX3r1i2NGjVKS5cuVZUqVbRkyRINGzZMUVFRWd7H4cOHc7Tmh1Wygq+u38g4nvXxUVT79u2zdxHI5WiDsDfa4OPt8f+/2F3Xb9ywdxHZlpycrH0HeQ/dS968eTM9/8lIbFXfrVu3DP9cZFVycnK2/v+wWTgrXry49u7da5lOSEiQl5eXZfrEiRNycXFRlSpVJEkvv/yyZs2a9UD78PPzy/RSm/Zy6Waq3Fzd7F3GQ6lZs6a9S8BD+DspTakms73LyLZbN67rqaIMb36c0QZhb0/C/8Vu//8iBo8jZ2dnvkvcx7Fjx+TmZtw2ev36dZvU99///veR78OWnJ2dVbVq1Qzzk5KS7tmhZLNw1qBBA0VGRioxMVH58+fXli1bNH78eMvyMmXK6Pz58zp16pTKlSun77//Xv7+/rYqD3gipZrMCt10zt5lZNvUFkXtXQIeEm0QAICss1k48/b21pAhQ9SnTx+lpKSoa9euqlKligIDAzVo0CD5+/srPDxcgwcPltlsVtGiRTVp0iRblQcAAADkuPuNIDCZpZQ0033vwWUvTgYalfa4MJuzP2LEZuFMktq3b6/27dtbzVu4cKHl7+eff17PP/+8LUsCAAAAHpn7jSDoWdIk54S/ldfFmBcAKVnwwa7iCCklJUV582YvZtn0JtQAAAAA/s/PF10Uf+6sUpNuPlSPC4zBZDIpPj4+27cEs2nPGQAAAID/88ctF23+n9Qw+Yxc85hktNGN1/M5Ko8j/TkPws3NTcWKFcvWYwlnAAAAgB39cctFf5wx5rldU1sUlXfhx/eKoY8bYjAAAAAAGAA9Z7irvPlMSky5Zu8yHoqTQx4VzGvME2wBAACAOxHOcFdmmRV8cuH9VzSwuRWC7F0CAAAAkCUMawQAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADCAvPYuAAAAo8qbz6TElGv2LuOhODnkUcG8+e1dBgAgCwhnAADchVlmBZ9caO8yHsrcCkH2LgHAY+xx/5HqcfuBinAGAAAAIFOP+49Uj9sPVJxzBgAAAAAGQDgDAAAAAANgWCMAw3rcx7lLj99YdwAAYD+EMwCG9biPc5cev7HuAADAfhjWCAAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABiATcNZdHS0AgIC1KpVK61atSrD8lOnTql3797q0KGD3nrrLV25csWW5QEAAACA3dgsnMXHxysiIkKrV6/WF198obVr1+rkyZOW5WazWf3791dgYKC+/PJL+fr6KioqylblAQAAAIBd2SycxcbGql69enJ3d5erq6tat26tmJgYy/IjR47I1dVVTZo0kST169dPr776qq3KAwAAAAC7slk4u3Dhgjw9PS3TXl5eio+Pt0yfPn1axYoV08iRI9W5c2eNHTtWrq6utioPAAAAAOwqr612ZDKZ5ODgYJk2m81W06mpqdq9e7dWrlwpf39/zZw5U5MnT9bkyZOzvI/Dhw/naM0Pq2QFX12/cd3eZTwEd12/ccPeRTyU5ORk7Tu4z95l2A1t0P5og7RBe6MN0gbtKbe3P4k2aG+PWxu0WTgrXry49u7da5lOSEiQl5eXZdrT01NlypSRv7+/JKldu3YaNGjQA+3Dz89PLi4uOVNwDrh0M1Vurm72LuOhuD3mvZfOzs6qWbOmvcuwG9qg/dEGaYP2RhukDdpTbm9/Em3Q3ozWBpOSku7ZoWSzYY0NGjTQzp07lZiYqJs3b2rLli2W88skqXr16kpMTNTx48clSVu3blXlypVtVR4AAAAA2JXNes68vb01ZMgQ9enTRykpKeratauqVKmiwMBADRo0SP7+/po7d65Gjx6tmzdvqnjx4poyZYqtygMAAAAAu7JZOJOk9u3bq3379lbzFi5caPm7atWqWr9+vS1LAgAAAABDsOlNqAEAAAAAmSOcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAHnvtbBnz55ycHDI0oZWrVqVIwUBAAAAQG50z3D28ssv68MPP1SpUqXUqlUrW9UEAAAAALnOPcNZx44d5e3traCgINWrV0+1atWyVV0AAAAAkKvc95yzevXqKSgoSHPmzLFFPQAAAACQK92z5yxdcHDwo64DAAAAAHK1e/actW/fXleuXLGal5iYKLPZ/EiLAgAAAIDc5p7h7Ndff1VqaqrVvBYtWujMmTOPtCgAAAAAyG0e+D5n9JoBAAAAQM7jJtQAAAAAYACEMwAAAAAwgPterfGrr76Sm5ubZdpkMumbb76Rh4eH1Xpdu3bN+eoAAAAAIJe4Zzh7+umntWzZMqt5RYsW1aeffmo1z8HBgXAGAAAAAA/hnuFs69attqoDAAAAAHI1zjkDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAOwaTiLjo5WQECAWrVqpVWrVt11vR9//FHNmjWzYWUAAAAAYF95bbWj+Ph4RUREaMOGDXJ2dlaPHj1Ut25dVahQwWq9ixcv6qOPPrJVWQAAAABgCDbrOYuNjVW9evXk7u4uV1dXtW7dWjExMRnWGz16tIKDg21VFgAAAAAYgs3C2YULF+Tp6WmZ9vLyUnx8vNU6y5cv13PPPaeqVavaqiwAAAAAMASbDWs0mUxycHCwTJvNZqvpEydOaMuWLVq6dKnOnz+frX0cPnz4oevMSSUr+Or6jev2LuMhuOv6jRv2LuKhJCcna9/BffYuw25og/ZHG6QN2httkDZoT7m9/Um0QXt73NqgzcJZ8eLFtXfvXst0QkKCvLy8LNMxMTFKSEjQSy+9pJSUFF24cEE9e/bU6tWrs7wPPz8/ubi45GjdD+PSzVS5ubrZu4yH4ubqau8SHoqzs7Nq1qxp7zLshjZof7RB2qC90QZpg/aU29ufRBu0N6O1waSkpHt2KNlsWGODBg20c+dOJSYm6ubNm9qyZYuaNGliWT5o0CBt3rxZGzduVFRUlLy8vB4omAEAAADA48xm4czb21tDhgxRnz591KlTJ7Vr105VqlRRYGCgDh06ZKsyAAAAAMCQbDasUZLat2+v9u3bW81buHBhhvVKliyprVu32qosAAAAALA7m96EGgAAAACQOcIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAJuGs+joaAUEBKhVq1ZatWpVhuXfffedOnbsqA4dOmjAgAG6cuWKLcsDAAAAALuxWTiLj49XRESEVq9erS+++EJr167VyZMnLcuvXbumDz/8UFFRUfryyy/l4+OjyMhIW5UHAAAAAHZls3AWGxurevXqyd3dXa6urmrdurViYmIsy1NSUjR27Fh5e3tLknx8fHTu3DlblQcAAAAAdmWzcHbhwgV5enpapr28vBQfH2+ZLlKkiFq2bClJunXrlqKiotSiRQtblQcAAAAAdpXXVjsymUxycHCwTJvNZqvpdH///bcGDhyoSpUqqXPnzg+0j8OHDz90nTmpZAVfXb9x3d5lPAR3Xb9xw95FPJTk5GTtO7jP3mXYDW3Q/miDtEF7ow3SBu0pt7c/iTZob49bG7RZOCtevLj27t1rmU5ISJCXl5fVOhcuXNBbb72levXqaeTIkQ+8Dz8/P7m4uDx0rTnl0s1Uubm62buMh+Lm6mrvEh6Ks7Ozatasae8y7IY2aH+0QdqgvdEGaYP2lNvbn0QbtDejtcGkpKR7dijZbFhjgwYNtHPnTiUmJurmzZvasmWLmjRpYlmelpamfv36qW3btho1alSmvWoAAAAA8KSyWc+Zt7e3hgwZoj59+iglJUVdu3ZVlSpVFBgYqEGDBun8+fM6evSo0tLStHnzZkm3e8ImTpxoqxIBAAAAwG5sFs4kqX379mrfvr3VvIULF0qS/P39dfz4cVuWAwAAAACGYdObUAMAAAAAMkc4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADsGk4i46OVkBAgFq1aqVVq1ZlWH7s2DF16dJFrVu31qhRo5SammrL8gAAAADAbmwWzuLj4xUREaHVq1friy++0Nq1a3Xy5Emrdd577z2NGTNGmzdvltls1meffWar8gAAAADArvLaakexsbGqV6+e3N3dJUmtW7dWTEyMgoODJUlnz57VrVu3VK1aNUlSly5dNHv2bPXs2fO+2zabzZKk5OTkR1J7dqUkp6lg3jR7l5FtqckpKmRysXcZDyUlOUVJpiR7l2E3tEH7ow3SBu2NNkgbtKfc3v4k2qC9Ga0NpueV9PzyTw7muy3JYQsWLNCNGzc0ZMgQSdK6det08OBBjR8/XpK0f/9+TZkyRWvWrJEk/fHHHwoKCtLmzZvvu+2///5bJ06ceHTFAwAAAEAOefbZZ1WwYMEM823Wc2YymeTg4GCZNpvNVtP3W34vbm5uevbZZ+Xk5JTlxwAAAACALZnNZqWkpMjNzS3T5TYLZ8WLF9fevXst0wkJCfLy8rJanpCQYJm+ePGi1fJ7cXR0zDR5AgAAAICR5MuX767LbHZBkAYNGmjnzp1KTEzUzZs3tWXLFjVp0sSyvESJEnJxcdG+ffskSRs3brRaDgAAAABPMpudcybdvpT+ggULlJKSoq5duyowMFCBgYEaNGiQ/P39dfz4cY0ePVrXrl1T5cqVFR4eLmdnZ1uVBwAAAAB2Y9NwBgAAAADInE1vQg0AAAAAyBzhDAAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcIYPk5GR98MEHql27tho2bKiFCxfauyTkUsnJyWrXrp1iY2PtXQpymdOnT6tfv36qXbu2mjRposmTJyspKcneZSEXiYuL0+uvv67q1auradOmWrRokb1LQi41atQo9e7d295l5BqEM2QwZcoU7d+/X0uWLNG4ceM0f/58ff311/YuC7lMUlKS3nnnHf3666/2LgW5THJysvr16ydnZ2d9+umnmjZtmr777jtFRETYuzTkEikpKQoMDNRTTz2lL774QmPGjNG8efP05Zdf2rs05DI7d+7U+vXr7V1GrkI4g5UbN27os88+08iRI+Xn56cWLVqob9++Wrlypb1LQy5y8uRJde/eXadPn7Z3KciFDh48qNOnTys8PFzly5dXnTp1FBoaqujoaHuXhlwiPj5eVapU0dixY1WmTBk1bdpUDRo00J49e+xdGnKRGzdu6IMPPlCNGjXsXUquQjiDlePHjys5OVk1a9a0zKtZs6YOHTqk1NRUO1aG3GTv3r1q2LCh1q5da+9SkAuVK1dOUVFRcnNzs8xzcHBQcnKyHatCblKyZEnNnDlT+fLlk9ls1r59+7Rnzx7Vr1/f3qUhF4mIiFCdOnVUp04de5eSq+S1dwEwloSEBBUuXFguLi6WecWKFVNKSooSExPl5eVlx+qQW/To0cPeJSAX8/DwUIMGDSzTJpNJK1eutPrRCrCVJk2a6MKFC2ratKlat25t73KQS+zfv18xMTH66quvtHjxYnuXk6vQcwYrN2/elLOzs9W89Gl+NQaQG4WHh+vYsWMaOnSovUtBLjRv3jzNmzdPR44cUXh4uL3LQS6QnJysUaNGaeTIkSpcuLC9y8l16DmDFRcXlwwhLH06f/789igJAOzCbDZr4sSJWrNmjWbNmqWKFSvauyTkQv7+/pKkW7duadiwYXr//fcz/IgK5KS5c+eqTJkyatu2rb1LyZUIZ7Di7e2tq1evKjk52fLhn5CQIGdnZ349AZBrmEwmjRo1StHR0YqIiFCLFi3sXRJykfj4eB0+fFjNmze3zCtfvrxSUlJ07do1eXh42LE6POmio6OVkJCg6tWrS7p99dC0tDRVr15d+/fvt3N1Tz7CGaz4+vrKyclJ+/fvV926dSVJ+/btU+XKlZU3L80FQO4wefJkRUdHKzIyUk2bNrV3Ochl4uLiFBISop9++klFixaVJB05ckQeHh4EMzxyK1assLoI3NKlS3X48GFNmzbNjlXlHpxzBiv58+dXp06dNG7cOB08eFDff/+9Fi9erD59+ti7NACwif/85z9atmyZBg0aJD8/PyUkJFj+AbZQu3ZtlS9fXsOHD1dcXJx++OEHTZ8+Xf369bN3acgFSpQooTJlylj+FSpUSPny5VOZMmXsXVquQFcIMhgxYoQ+/PBDvfbaa3Jzc9PAgQMVEBBg77IAwCY2b94sSZo+fbqmT59utezIkSOMIsAj5+TkpAULFigsLEzdunWTm5ubXnvtNX4oBXIBB7PZbLZ3EQAAAACQ2zGsEQAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAMATq1mzZvLx8cnwr127dll67Lp16zJd9scff8jHx0dnzpzJ6ZIBALkYd9IEADzRhg8fniGMcSNpAIAR8b8TAOCJVqBAAXl6etq7DAAA7othjQCAXGvDhg0KCAhQlSpV1KVLF/3yyy+ZrpeSkqKwsDDVqlVLzz//vH766Ser5TExMQoICJC/v79atWqlzz//3BblAwCeMIQzAECutGHDBoWFhSkoKEgbN25Uw4YNFRQUpP/9738Z1o2MjNSPP/6o+fPna+bMmVqxYoVl2V9//aV3331Xr7/+umJiYvT2229r9OjRiouLs+XhAACeAIQzAMATLSwsTNWrV7f699dff2nFihV69dVX1alTJ5UtW1ZDhw5VpUqVrIKXJJnNZq1bt07BwcGqXbu2qlevruHDh1uWx8fHKyUlRd7e3ipRooReeuklLV68WMWKFbP1oQIAHnOccwYAeKIFBwerTZs2VvPc3d0VFxen/v37W82vVq2aTp06ZTXv0qVLSkxMVKVKlSzz/Pz8LH/7+vqqXbt2CgoKUunSpdW0aVN16dJFhQsXfgRHAwB4ktFzBgB4onl4eKhMmTJW//LkyaN8+fJlWDctLU1paWmZbsdsNlv+vvNqjw4ODpo+fbo2bNigDh06aM+ePeratat27NiR8wcDAHiiEc4AALlSuXLldODAAat5Bw4cUNmyZa3mFSlSRMWKFdOhQ4cs844dO2b5Oy4uTuHh4apcubJCQkL0r3/9S7Vq1dK33377aA8AAPDEYVgjACBXeuONNzR8+HBVrFhRVatW1YYNG3Ts2DFNmjTJaj0HBwf17NlTc+bMUalSpVSwYEFNnjzZsrxQoUJau3atChQooE6dOuns2bP673//qxdffNHWhwQAeMwRzgAAuVLr1q2VkJCg2bNnKyEhQb6+vlqyZIkqVqyYYd3+/fvr1q1beuedd5QnTx4NHDhQYWFhkiRPT09FRkZq+vTpWrhwoQoXLqxXXnlFXbt2tfUhAQAecw7mOwfRAwAAAADsgnPOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAA/h/zMXP3V5EtCwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 936x504 with 1 Axes>"
       ]
@@ -976,7 +1061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1013,7 +1098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 46,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:01.481931Z",
@@ -1023,7 +1108,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x432 with 1 Axes>"
       ]
@@ -1050,7 +1135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 47,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:02.186153Z",
@@ -1069,7 +1154,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x396 with 2 Axes>"
       ]
@@ -1088,7 +1173,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 504x396 with 2 Axes>"
       ]
@@ -1115,7 +1200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 48,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:03.194703Z",
@@ -1140,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 49,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:03.201650Z",
@@ -1155,7 +1240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 50,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:03.213082Z",
@@ -1287,7 +1372,7 @@
        "6   0.000000     Ticket   "
       ]
      },
-     "execution_count": 93,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1310,7 +1395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 51,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:04.346868Z",
@@ -1320,7 +1405,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 792x648 with 2 Axes>"
       ]
@@ -1344,7 +1429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1353,9 +1438,9 @@
      "text": [
       "-> \u001b[32m Passed the data leakage test - no duplicate intstances detected \u001b[0m\n",
       "Method Name       :\u001b[35;1m data_leakage\u001b[0m\n",
-      "Current memory usage:\u001b[36m 0.036915MB\u001b[0m\n",
-      "Peak                :\u001b[36m 0.187622MB\u001b[0m\n",
-      "Total time taken:  \u001b[36m 9.715 ms \u001b[0m\n"
+      "Current memory usage:\u001b[36m 0.029208MB\u001b[0m\n",
+      "Peak                :\u001b[36m 0.188993MB\u001b[0m\n",
+      "Total time taken:  \u001b[36m 9.849 ms \u001b[0m\n"
      ]
     }
    ],
@@ -1372,7 +1457,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'Predictions Distribution'}>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "oc.preds_distribution(metrics['y'], metrics['predictions_proba'], bins=40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1391,7 +1506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -1510,7 +1625,7 @@
        "9  854    0      0.919287     1          "
       ]
      },
-     "execution_count": 97,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1521,7 +1636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -1640,7 +1755,7 @@
        "9  338    1      0.022364     0          "
       ]
      },
-     "execution_count": 98,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1651,7 +1766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1670,7 +1785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -2001,7 +2116,7 @@
        "9  0          2.0       2.0       854    0.919287     "
       ]
      },
-     "execution_count": 100,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2019,7 +2134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -2350,7 +2465,7 @@
        "9 NaN        2.0       709    0.007480     "
       ]
      },
-     "execution_count": 101,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2358,6 +2473,13 @@
    "source": [
     "filter_fns_with_prediction"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/octopus_ml/octopus_ml.py b/octopus_ml/octopus_ml.py
index aabe8f9..d739ee8 100644
--- a/octopus_ml/octopus_ml.py
+++ b/octopus_ml/octopus_ml.py
@@ -173,6 +173,30 @@ def cv_plot(arr_f1_weighted, arr_f1_macro, arr_f1_positive, AxisName):
     plt.legend(["F1 macro", "F1 positive"], loc="upper right", fontsize=14)
     plt.grid(True)
 
+def preds_distribution(y_true, y_pred, bins=100, title='Predictions Distribution', normalize=False, ax=None,
+                            figsize=None, title_fontsize='large', max_y=None):
+    sns.set_style("whitegrid")
+    if ax is None:
+        fig, ax = plt.subplots(1, 1, figsize=(10,6))
+    
+    predictions_proba=np.array(y_pred)
+    y_bool=np.array(y_true)>0
+    y_pred_true = predictions_proba[y_bool]
+    y_pred_false = predictions_proba[~y_bool]
+    
+    #print (y_pred_true)
+    # matplotlib normalize is using the bin width, just calculate it by our own...
+    weights_false = np.ones(len(y_pred_false)) / len(y_pred_false) if normalize else None
+    weights_true = np.ones(len(y_pred_true)) / len(y_pred_true) if normalize else None
+
+    ax.hist(y_pred_false, bins=bins, color='r', alpha=0.5, label='negative', weights=weights_false)
+    ax.hist(y_pred_true, bins=bins, color='g', alpha=0.5, label='positive', weights=weights_true)
+    ax.set_title(title, fontsize=title_fontsize)
+    #_set_lim(max_y, ax.set_ylim)
+    ax.legend(loc='best')
+
+    return ax
+
 
 def lgbm(X_train, y_train, X_test, y_test, num, params=None):
     # Training function for LGBM with basic categorical features treatment and close to default params
diff --git a/setup.py b/setup.py
index 9fdb700..59d57da 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name="octopus-ml",
-    version="0.1.7",
+    version="0.1.9",
     description="A collection of handy ML and data validation tools",
     long_description=long_description,
     long_description_content_type="text/markdown",