credCardConfIntV1h_import.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First Part is from \"How To Deal With Imbalanced Classification, Without Re-balancing the Data\"  by David Rosen  \n",
    "\n",
    "https://towardsdatascience.com/how-to-deal-with-imbalanced-classification-without-re-balancing-the-data-8a3c02353fe3\n",
    "## Second Part is down below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-09-12 14:34:55.466138\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "print(datetime.datetime.now())\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "from sklearn import metrics, model_selection\n",
    "from sklearn.experimental import enable_hist_gradient_boosting\n",
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "df=pd.read_csv('creditcard.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(492, 0.001727485630620034)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Class.sum(),df.Class.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## split into train and test and train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = model_selection.train_test_split(\n",
    "        df.loc[:,'V1':'Amount'], df.Class,  stratify=df.Class, \n",
    "        test_size=0.3, random_state=42)\n",
    "gbc=HistGradientBoostingClassifier(learning_rate=0.01, \n",
    "        max_iter=2000, max_leaf_nodes=6, validation_fraction=0.2, \n",
    "        n_iter_no_change=15, random_state=42).fit(Xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[pandas.core.frame.DataFrame,\n",
       " pandas.core.frame.DataFrame,\n",
       " pandas.core.series.Series,\n",
       " pandas.core.series.Series]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[type(x) for x in (Xtrain, Xtest, ytrain, ytest)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(249927    0\n",
       " 214082    0\n",
       " 106005    0\n",
       " 58619     0\n",
       " 191638    0\n",
       "          ..\n",
       " 3441      0\n",
       " 233802    0\n",
       " 85418     0\n",
       " 29062     0\n",
       " 17677     0\n",
       " Name: Class, Length: 199364, dtype: int64,\n",
       " 186882    0\n",
       " 165754    0\n",
       " 235285    0\n",
       " 101271    0\n",
       " 5832      0\n",
       "          ..\n",
       " 64725     0\n",
       " 16688     0\n",
       " 183868    0\n",
       " 45011     0\n",
       " 150527    0\n",
       " Name: Class, Length: 85443, dtype: int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain,ytest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## apply the full model to the test data as a hard classifier with (implicit) decision threshold 0.5\n",
    "\n",
    "In other words, this classifier tells us which class is more probable than the other according to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred 0(Approve as Legit)</th>\n",
       "      <th>Pred 1(Deny as Fraud)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True 0(Legit)</th>\n",
       "      <td>TN = 85285 (TNR = 99.99%)</td>\n",
       "      <td>FP = 10 (FPR = 0.01%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True 1(Fraud)</th>\n",
       "      <td>FN = 42 (FNR = 28.38%)</td>\n",
       "      <td>TP = 106 (TPR = 71.62%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Pred 0(Approve as Legit)    Pred 1(Deny as Fraud)\n",
       "True 0(Legit)  TN = 85285 (TNR = 99.99%)    FP = 10 (FPR = 0.01%)\n",
       "True 1(Fraud)     FN = 42 (FNR = 28.38%)  TP = 106 (TPR = 71.62%)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hardpredtst=gbc.predict(Xtest)\n",
    "def conf_matrix(y,pred):\n",
    "    ((tn, fp), (fn, tp)) = metrics.confusion_matrix(y, pred)\n",
    "    ((tnr,fpr),(fnr,tpr))= metrics.confusion_matrix(y, pred, \n",
    "            normalize='true')\n",
    "    return pd.DataFrame([[f'TN = {tn} (TNR = {tnr:1.2%})', \n",
    "                                f'FP = {fp} (FPR = {fpr:1.2%})'], \n",
    "                         [f'FN = {fn} (FNR = {fnr:1.2%})', \n",
    "                                f'TP = {tp} (TPR = {tpr:1.2%})']],\n",
    "            index=['True 0(Legit)', 'True 1(Fraud)'], \n",
    "            columns=['Pred 0(Approve as Legit)', \n",
    "                            'Pred 1(Deny as Fraud)'])\n",
    "conf_matrix(ytest,hardpredtst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The true pos rate (Recall of class 1 (frauds)) is too low -- we want to identify more than 72% of frauds as such"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ultimately it is a business decision what threshold to apply when deciding whether to approve a transaction, as it is a trade-off between false approvals and false rejections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot the true positive rate and false positive rate as a function of threshold to manage the trade-off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pKYmJEyfy8ssv+/U5PXv2JCcnh1dffbVhmrWW++67j1tvvfWIZX/zm98cdzszZszg9ddfZ/DgwcyePZuPPvqo2c++6qqreOaZZ0hOTmb48OHExcVhrfW7/ieeeILLL7+cp556iuuvv56lS5fy0Ucf8cEHH/D5558THR3NuHHjjmm3w/t4/fXX88tf/rLZz3GCzllzyICMBDYWlFJefey5ASIiIo0VFxeTlpaGx+Phww8/ZNu2bccss23bNtLS0rjlllu46aabWLZsGaNGjeLTTz9l48aNAJSXl7N+/foTftYDDzzQcO4ZwOTJk3nhhRcazhXbuXMnBQUFnHXWWbz55ptUVlZSWlrKW2+91bBOSUkJnTp1oqamhjlz5jRMj4uLo6SkpMnPHTduHMuWLeP555/nqquuAjjp+l0uF3fddRf19fW89957FBcXk5SURHR0NGvXrj2ix83j8VBTUwPAeeedx2uvvUZBQQEABw4caLKNnaKw5pBBGQnUW1i1S7fwEBGRE7vmmmvIy8sjNzeXOXPm0KdPn2OW+eijj8jJyWHIkCHMmzePu+66i9TUVGbPns306dMZNGgQo0aNYu3atSf8rP79+zN06NCG95MmTeLqq69m9OjRDBw4kCuvvJKSkhKGDx/OlClTGDx4MJdffjm5ubkkJCQA3gsPRo4cycSJE4+oddq0aTzxxBMMGTKETZs2HfG5brebiy++mHfeeYeLL74Y4JTqN8bw4IMP8qtf/Yrzzz+f2tpaBg0axEMPPcSoUaMalps5cyaDBg3immuuoV+/fjzyyCNMmjSJQYMGMXHiRHbv3n3CzwkkY611uoZj5Obm2ry8PKfLaFX7S6sY9sgH3DO5N7eP7+l0OSIi4rNmzRr69u3rdBkhobS0lNjYWMrLyxk7diyzZs06Iui1RU19P4wxS621ua31mTpnzSEpsRH0SI0hb6tGMhARkdA0c+ZMVq9eTWVlJddff32bD2pOUVhz0IjsZP75zW7q6i1ul2l+BRERkSDy0ksvOV1Cu6Bz1hw0vFsyJZW1rNvT9MmWIiIiIgprDhreLRmAvG06FCoiEkyC8XxucZ5T3wuFNQdlJkWRHh/J4i0KayIiwSIyMpL9+/crsMkRrLXs37+fyMjIgH+2zllzkDGG4dnJLN7i/VFoPGyIiIg4IzMzk/z8fPbt2+d0KRJkIiMjyczMDPjnKqw5bGR2Mm9+vYsthWV0T411uhwRkXbP4/GQnZ3tdBkiDXQY1GFn9ewAwKeb9jtciYiIiAQjhTWHdU2JJi0uQvdbExERkSYprDnMGENutyTyth50uhQREREJQgprQSC3azI7iyrYVVThdCkiIiISZBTWgsDQrkkAfL2jyNlCREREJOgorAWBvp3i8LgN3+wsdroUERERCTIKa0EgIsxNv07xfLlZV4SKiIjIkRTWgsTZvVJZvqOIsqpap0sRERGRIKKwFiSGZydTb+Gr7UVOlyIiIiJBRGEtSAzrmoTLwGLdb01EREQaUVgLErERYfTvnMDiLTpvTURERL7lV1gzxpxvjFlnjNlojLm3ifnXGGO+8T0+M8YMbjRvqzFmhTFmuTEmryWLb2tGZCfz1fYiqmrrnC5FREREgkSzYc0Y4waeAS4A+gHTjTH9jlpsC3COtXYQ8HNg1lHzx1trc6y1uS1Qc5s1IjuZqtp6VuTrFh4iIiLi5U/P2ghgo7V2s7W2GngFmNp4AWvtZ9baw+MlfQFktmyZ7cPwbsmAzlsTERGRb/kT1jKAHY3e5/umHc9NwDuN3ltggTFmqTFm5vFWMsbMNMbkGWPy9u3b50dZbU9yTDi90mJZvEVhTURERLz8CWumiWm2yQWNGY83rP240eQx1tqheA+j3m6MGdvUutbaWdbaXGttbmpqqh9ltU0jspPJ23qQuvomm1hERETaGX/CWj7QpdH7TGDX0QsZYwYBfwCmWmsbLmm01u7yPRcA8/EeVpXjGJGdTGlVLWt2H3K6FBEREQkC/oS1JUAvY0y2MSYcmAa80XgBY0wW8HfgOmvt+kbTY4wxcYdfA5OAlS1VfFs0Itt33poOhYqIiAh+hDVrbS1wB/AesAZ41Vq7yhhzmzHmNt9iDwMpwP876hYdHYF/G2O+BhYDb1lr323xvWhDOiVEkZUcrbAmIiIiAIT5s5C19m3g7aOmPdfo9c3AzU2stxkYfPR0ObHh3ZL5aF0B1lqMaeqUQREREWkvNIJBEBrZPZn9ZdWs31vqdCkiIiLiMIW1IHR2rw4AfLKhfd7CRERERL6lsBaEOiVE0Sstlo/XK6yJiIi0dwprQersXqks3nKAyhqNEyoiItKeKawFqbFndKCqtp4vdVWoiIhIu6awFqRGZqcQHuZikQ6FioiItGsKa0EqKtzNiG7JushARESknVNYC2Ljeqeyfm8pO4sqnC5FREREHKKwFsTG9fYOaP/RugKHKxERERGnKKwFsR6psWQkRvHROh0KFRERaa8U1oKYMYZxvVP5bGMh1bX1TpcjIiIiDlBYC3LjeqdRVl1H3lbdwkNERKQ9UlgLcqN7eG/hsWD1XqdLEREREQcorAW52IgwzuyRwqcbC50uRURERBygsBYCBmcmsmlfKSWVNU6XIiIiIgGmsBYCRmQnU29hic5bExERaXcU1kLAsK5JRIe7+dda3W9NRESkvVFYCwGRHjdn9+rAwjUFWGudLkdEREQCSGEtREzo25HdxZWs2nXI6VJEREQkgBTWQsT4PmkYAx+s0S08RERE2hOFtRDRITaCIV0SWbhG562JiIi0JwprIWRCv46s2FnMnuJKp0sRERGRAFFYCyET+3YEdChURESkPVFYCyE902LJ7hDDe6v2OF2KiIiIBIjCWggxxjCxX0e+2Lyfsqpap8sRERGRAFBYCzFn9+pATZ1l8RaNZiAiItIeKKyFmOHdkon0uPhwna4KFRERaQ8U1kJMpMfNWT01moGIiEh7obAWgs4f0ImdRRV8nV/sdCkiIiLSyhTWQtDEvh3xuA1vr9jtdCkiIiLSyhTWQlBCtIcxPTvw9ordOhQqIiLSximshagLB3Yi/2AFK3bqUKiIiEhbprAWoib160iYy/CWDoWKiIi0aQprISoxOpwze3bgnRV7dChURESkDVNYC2EXDUxn+4FyVu065HQpIiIi0koU1kLYpH7puF2Gd1bqUKiIiEhbpbAWwpJiwsntmsS/1u5zuhQRERFpJQprIe7cPmms2X2IHQfKnS5FREREWoHCWoi7YEAnAN0gV0REpI1SWAtxWSnRDMpM4J/fKKyJiIi0RQprbcDUnAxW7Cxm3Z4Sp0sRERGRFqaw1gZcmtOZMJfhtaU7nC5FREREWpjCWhuQEhvBuX3SmP/VLmrq6p0uR0RERFqQwlob8Z3cLhSWVvGvtQVOlyIiIiItSGGtjRjfO5XOCZHM/nSr06WIiIhIC/IrrBljzjfGrDPGbDTG3NvE/GuMMd/4Hp8ZYwb7u660jDC3i2tGdeXzzfvZWFDqdDkiIiLSQpoNa8YYN/AMcAHQD5hujOl31GJbgHOstYOAnwOzTmJdaSFXDe+Cx2146cvtTpciIiIiLcSfnrURwEZr7WZrbTXwCjC18QLW2s+stQd9b78AMv1dV1pOh9gIJvTtyOvLd1JVW+d0OSIiItIC/AlrGUDje0Lk+6Ydz03AOye7rjFmpjEmzxiTt2+fxro8VVePzOJAWTXvrtzjdCkiIiLSAvwJa6aJabbJBY0Zjzes/fhk17XWzrLW5lprc1NTU/0oS5oypkcHuqZE89cvtjldioiIiLQAf8JaPtCl0ftMYNfRCxljBgF/AKZaa/efzLrSclwuwzUjs1iy9SArdxY7XY6IiIicJn/C2hKglzEm2xgTDkwD3mi8gDEmC/g7cJ21dv3JrCstb9qILOIiwpi1aLPTpYiIiMhpCmtuAWttrTHmDuA9wA28YK1dZYy5zTf/OeBhIAX4f8YYgFrfIc0m122lfRGf+EgPVwzL5KUvt1NcXkNCtMfpkkREROQUGWubPIXMUbm5uTYvL8/pMkLayp3FXPy7f/PLywcyfUSW0+WIiIi0WcaYpdba3NbavkYwaKP6d44nPT6SRet1Za2IiEgoU1hro4wxTO7fkYVrCzhYVu10OSIiInKKFNbasOkjs6iurefvX+10uhQRERE5RQprbVif9HiGZCXy8uLtBOO5iSIiItI8hbU2bvqILDYWlLJk68HmFxYREZGgo7DWxl08qBNxEWG8vFiDu4uIiIQihbU2Ljo8jEuHZPDWit0UletCAxERkVCjsNYOTB/hvdBg3jJdaCAiIhJqFNbagX6d4xmalciLn26hpq7e6XJERETkJCistRMzx/Yg/2AFH6ze63QpIiIichIU1tqJif06kpUcze8XbdZtPEREREKIwlo74XYZbjk7m+U7inQbDxERkRCisNaOXDmsC8kx4fz+401OlyIiIiJ+UlhrR6LC3Xx3dFcWri1ga2GZ0+WIiIiIHxTW2plpw7NwGZibt8PpUkRERMQPCmvtTHpCJBP6dmTukh1U1dY5XY6IiIg0Q2GtHbpudFcOlFXz7so9TpciIiIizVBYa4fG9OhAt5Ro/vL5NqdLERERkWYorLVDLpfh2lFdydt2kNW7DjldjoiIiJyAwlo7deWwTCI9Lv7yxVanSxEREZETUFhrpxKjw7lsSAZ/X7aTg2XVTpcjIiIix6Gw1o7NODObqtp6Xlmi23iIiIgEK4W1dqx3ehyjuifz1y+2UVev8UJFRESCkcJaO/fd0d3YWVTBovX7nC5FREREmqCw1s5N6NuR5Jhw/rZUh0JFRESCkcJaOxce5uLSnAzeX72X/aVVTpcjIiIiR1FYE64e2YWaOstfv9judCkiIiJyFIU1oWdaHOf2SePPn2+lskbjhYqIiAQThTUB4Jazu7O/rJq3vtntdCkiIiLSiMKaADCqezIdYsP517oCp0sRERGRRhTWBABjDBcN7MT7q/ZqRAMREZEgorAmDaaPzKK6rp7XluY7XYqIiIj4KKxJgz7p8eR0SeTvX+3EWo1oICIiEgwU1uQI38nNZM3uQyzbXuR0KSIiIoLCmhxlcv90PG7DvGU6FCoiIhIMFNbkCB1iI7hyWBdeXbKDonJdaCAiIuI0hTU5xrWjsqitt7rQQEREJAgorMkx+ndOYGhWIn/6fCtVtRrRQERExEkKa9KkH0w4gx0HKvjL59ucLkVERKRdU1iTJo09I5Wze3Xg6Q83Ul5d63Q5IiIi7ZbCmhzXDyb0oqi8hj99pt41ERERpyisyXEN65rMuN6p/H7RJsqq1LsmIiLiBIU1OaE7z/P2rj3/yWanSxEREWmXFNbkhIZmJXHRwE78/uPN7D1U6XQ5IiIi7Y7CmjTrR+f3pra+nicXrHe6FBERkXbHr7BmjDnfGLPOGLPRGHNvE/P7GGM+N8ZUGWPuPmreVmPMCmPMcmNMXksVLoHTNSWG60d349WlO1iz+5DT5YiIiLQrzYY1Y4wbeAa4AOgHTDfG9DtqsQPAncCvj7OZ8dbaHGtt7ukUK86549yexEWE8ct31jpdioiISLviT8/aCGCjtXaztbYaeAWY2ngBa22BtXYJUNMKNUoQSIwO53vje7Jo/T4+WlfgdDkiIiLthj9hLQPY0eh9vm+avyywwBiz1Bgz82SKk+Byw5huZHeI4Wdvrqa6tt7pckRERNoFf8KaaWKaPYnPGGOtHYr3MOrtxpixTX6IMTONMXnGmLx9+/adxOYlUCLC3Dx8cT82F5bxwqdbnC5HRESkXfAnrOUDXRq9zwR2+fsB1tpdvucCYD7ew6pNLTfLWptrrc1NTU31d/MSYOP7pDGpX0eeXLCelTuLnS5HRESkzfMnrC0Behljso0x4cA04A1/Nm6MiTHGxB1+DUwCVp5qsRIcHr9iECmx4fznnKUUlVc7XY6IiEib1mxYs9bWAncA7wFrgFettauMMbcZY24DMMakG2PygR8CDxpj8o0x8UBH4N/GmK+BxcBb1tp3W2tnJDCSYsJ55pqh7DxYwa/eW+d0OSIiIm1amD8LWWvfBt4+atpzjV7vwXt49GiHgMGnU6AEp6FZScw4M5sXP9vC9OFZDMxMcLokERGRNkkjGMgp+8HEXqTEhPPTN1dh7clccyIiIiL+UliTUxYf6eGeyb3J23aQN772+5oTEREROQkKa3JarhzWhQEZ8Tz2zlpKKnVPZBERkZamsCanxe0y/HTKAPYcquR/NdC7iIhIi1NYk9M2rGsS00dk8ZcvtrF2jwZ6FxERaUkKa9Ii7pnUm/jIMB5+fZXTpYiIiLQpCmvSIpJiwrntnB4s3nqAr7YfdLocERGRNkNhTVrM1SOz6BAbwU/eWEVNnQZ6FxERaQkKa9Ji4iI9PHxJP77JL+b5TzY7XY6IiEiboLAmLeqSQZ24YEA6Ty5Yzzf5RU6XIyIiEvIU1qRFGWP45eUDSY2L4M6Xv6KsqtbpkkREREKawpq0uMTocH5zVQ7bDpTzkzd0daiIiMjpUFiTVjGqewr/eU4PXluar6tDRUREToPCmrSa28f3JCnaw/+8uZqq2jqnyxEREQlJCmvSamIiwnjk0oF8vaOIJ9/XUFQiIiKnQmFNWtVFgzoxbXgXZi3azKcbC50uR0REJOQorEmr+8kl/emRGssP5i6nsLTK6XJERERCSpjTBUjbFxXu5umrhzDl6U/571e/5sUZw3G5jNNliZyeg1th2V/AarQOEWldCmsSEH3S43no4n489PpK5ubtYPqILKdLEjk9y/4Mn/wvuDxOVyIibZzCmgTMtSOzePPrXTzyz9UMykygf+cEp0sSOXXV5RARD/ftcLoSEXHaT1r3aJHOWZOAMcbwu+lDSIjycOPsJewurnC6JJFTV1MOniinqxCRdkBhTQKqY3wkL9wwnLKqOm6cnUephqOSUFVbqbAmIgGhsCYB1yc9nmeuGcr6vSXc8dIyaut0graEoJpyCFNYE5HWp7AmjjjnjFR+PnUAH63bxwPzV2KtdbokkZNTo541EQkMXWAgjrl6ZBa7iyv43b82Eh8Vxv0X9sUY3dJDHFZXC4XrgWb+A1FeCJ6YgJQkIu2bwpo46ocTz+BQRQ3Pf7KFtLhIbhnb3emSpL1b9AR8/Jh/y/a5uHVrERFBYU0cZozhJ5f0Z8+hSp54bx3n9E7ljI5xTpcl7Vn5fgiPhUv/X/PLZg5v/XpEpN1TWBPHuVyGX1w2kEm/WcQdLy1j/vfGEBOhr6Y4xNZBWCT0m+p0JSIigC4wkCDRITaCp6YNYUNBKT+Yu5zqWl0hKg6x9eByO12FiEgDhTUJGmf16sADF/bl/dV7mfmXPCpr6pwuSdqj+jow+mkUkeChXyQJKjef3Z1HLxvIR+v28eDrK6mr1y09JMBsPRj1rIlI8NCJQRJ0Gt/SIynawwMX9XO6JGlPbD249P9YEQkeCmsSlP57Um/2l1Xz/CdbiAoP478m9NI92CQwNv0LwiKcrkJEpIHCmgStn08dQF2d5amFG6isqeO+C/oosEnri0yEioNOVyEi0kBhTYKW22X45eUDifC4mLVoM/tKqnj8ikGEh+kQlbQiY6DraKerEBFpoLAmQc3lMvx0Sn/S4iL49YL1FJVX8+y1w4j06ARwERFpH9RFIUHPGMMd5/bi0csG8uG6fdz216W6rYeIiLQbCmsSMq4emcUvL/fe1uOq339OYWmV0yWJiIi0OoU1CSnTR2Qx67phrN59iJv+lEdRebXTJYmIiLQqnbMmIWdS/3R+N30Id768nMuf/YyXbh5FekKk02VJKKkuh7wXoKbi2HllhZDaO/A1iYgch8KahKTzB3TizzeFc90fv2TGi4v5w/W5ZCZFO12WhIoti2DBA8efn9IrcLWIiDRDYU1C1qjuKfzh+uHc8dIypj79KXNvHU3PtFiny5JQUOc73/HWRZDW/9j5bv00ikjw0DlrEtLOOSOV+d8bQ5213DB7MbuKmjisJXK0+lrvc1ikN5gd/RARCSIKaxLyeqbF8ucbR1BUVsMVz37Gyp3FTpckwa7ed+sXl4KZiAQ/hTVpEwZlJvLyzFG4jGH6rC9Yuu2A0yVJMKur8T67dHNlEQl+CmvSZgzISOBvt42mQ1wE1/5hMZ9uLHS6JAlWhw+DqmdNREKAX2HNGHO+MWadMWajMebeJub3McZ8boypMsbcfTLrirSkzolRzL11FF1TornhxSW8u3K30yVJMKgug/2bvn2U7vVOd3mcrUtExA/N/rfSGOMGngEmAvnAEmPMG9ba1Y0WOwDcCVx6CuuKtKi0uEhemTmKG2Yv4XtzlnHfBX256axsXC7jdGnilBcvhN3Lj5powKP784lI8PPnGMAIYKO1djOAMeYVYCrQEListQVAgTHmopNdV6Q1JEaHM+fmkfzX3OX84u01fLS+gF9/ZzCdEqKcLq1deXzx4wD8eMSPnS2kbB90PQuGfvfbaXHpEJngXE0iIn7yJ6xlADsavc8HRvq5fb/XNcbMBGYCZGVl+bl5keOLDg/juWuHMXfJDn72z9VM/s0iHr18IBcP6ux0ae3G2gNrnS7Bq74OUnrA4KucrkRE5KT5c85aU8eOrJ/b93tda+0sa22utTY3NTXVz82LnJgxhmkjsnj7zrPpnhrLHS99xY9f+4by6lqnS5NAsvVgdD2ViIQmf3698oEujd5nArv83P7prCvSYrp1iOG120Zzx/ievLp0B9NmfUFhaZXTZUmgKKyJSAjz59drCdDLGJNtjAkHpgFv+Ln901lXpEWFuV3cPbk3z1+Xy9o9JUx48mP+vizf6bIkEBTWRCSENfvrZa2tBe4A3gPWAK9aa1cZY24zxtwGYIxJN8bkAz8EHjTG5Btj4o+3bmvtjIg/JvTryD+/fxa90mL54atf89DrK6murXe6LGlNCmsiEsL8uiOktfZt4O2jpj3X6PUevIc4/VpXxGlndIzjlZmj+dW7a/n9os2s21vCk/8xmMykaKdLCy7VZTD3Oqg4xREhPL6hv2aNa7GSTknVIYU1EQlZun23tFtul+G+C/vSr3M8985bwcQnF/HgxX25ekQWxuiebAAUbYdNCyF9IMR1Ovn1a8u9zzEOXzTUaxL0m+JsDSIip0hhTdq9qTkZDOuaxN1/+5oH5q9kwaq9/N+0HBKjw50uzXnWd/H22f8N/S87+fXfvcH7fP6LLVeTiEg7o+MCIkBmUjQv3TyKn03tz+eb9nPJ0/9m9a5DTpclIiKisCZymMtl+O7obsy9dRTVtfVc/uynvLJ4O9b6e1vBtujwvuuwsIiIUxTWRI4yJCuJN79/FkO6JHHv31dw7R+/pOBQpdNlOaNdB1URkeCgsCbShLS4SObcPJJHLh3Asm1FXPjUv/lwbYHTZTlHF1yIiDhGFxiIHIfLZbh2VFeGd0vm+y8v44bZS7hsSAYPXdyP5JggvPhg5Tw41MIDhJTs8b1QWBMRcYrCmkgzeqfH8eb3z+KZDzfx/z7cyCcbCnniykGM75PmdGnfqjgIr93YOtt2hUF8RutsW0REmqWwJuKHiDA3P5x4BhcMSOe/5i7nhtlLuGZkFvdf2JeYiCD4a1Rf532e+HPIvaFlt+0KA09Uy25TRET8FgT/yoiEjr6d4nn99jH874J1/OHfW3h/9V7+Z0p/Lhx4CjeMbQ2eKIiIc7oKERFpQbrAQOQkRXrcPHBRP1677UzS4iP43pxl/Oi1rymuqHG6NBERaYMU1kRO0bCuScz7zzP53rgezFu2k4lPfsx7q/Y0v6KIiMhJUFgTOQ0RYW5+dH4f/nH7GDrERnDrX5byvTlLKShpp/dlExGRFqdz1kRawICMBP5xxxhmfbyJl/61hO+sW8fVI7pyzcguxEZ4Wr+AigOt/xkiIuIIhTWRFuJxu7g94m1uD3vYOyHP9wgkdwCCoYiIBJTCmkhLKtkD7gi44HHyiyp4d+UeNu0rJTkmnMn90xmYkdB6t5d1e6Df1NbauoiIOERhTaSlhUVA7g1kAjdPgA/XFvDYO2t55vMSzu7VgUcvG0iX5GinqxQRkRChCwxEWtn4Pmm8fdfZ/HRKf5ZuO8iEJz/mN++vp7KmzunSREQkBCisiQSA22W4/sxu/Ou/xzG5fzr/t3ADE578mLlLtiu0iYjICSmsiQRQekIkT00fwsu3jCIhysOP561g1C8X8st31pB/sNzp8kREJAgprIk4YHSPFP75/bN4ZeYoRndP4Q+fbGHsrz7k/vkr2FdS5XR5IiISRHSBgcjxrH8PPn0KsP6vc2Cz34saYxjVPYVR3VPYVVTB7z/exF+/3M7fl+UzdXAG/zG8C8O6Jp183SIi0qaoZ03keNa9DTu+BOPy/5HSE3JvOOmP6pwYxU+nDuD9/xrL1MEZvPnNLq549jNu+XMem/eVtsLOiYhIqFDPmsiJRCfDjH8G7OO6p8by+JWDePiSfsz+bCv/t3AD76/ey3l90rhqeBfG90nD49b/sURE2hOFNZEgFBMRxu3je3LZkAzmfLmNv+Xls3BtAalxEVw5LJOrcrvQrUOM02WKiEgAKKyJHI89iXPVWknnxCjumdyH/5pwBh+u28fcJdv5/cebePajTYzunsK0EV2Y3D+dSI/b6VJFRKSVKKyJhIAwt4uJ/ToysV9H9hRXMm9ZPq8s2c5drywnIcrDZUMy+E5uJv06xWNMqw1oJSIiDlBYEzmh4As+6QmR3D6+J/95Tg++2LyfV5bs4KUvtzP7s610S4nm/AGduGZkloa0EhFpIxTWpPXsXQXbP3e6ilO3b53TFZyQy2U4s2cHzuzZgYNl1by1YjcLVu/lD59sZtaiTZxzRio/mHAGg7skOl2qiIicBoU1aT3v/Bi2fuJ0Faen8xCnK/BLUkw4147qyrWjurKnuJIXP93CX7/YxtRnPmVMzxRmju3B2F4ddIhURCQEKaxJ66mthK5nwXdedLqSUxeZ6HQFJy09IZL7LuzL7ef25KUvt/Pip1u4/oXF9EmPY/qILK4clklMhP7qi4iECv1iS+ux9eCJhNg0pytpl+IjPdx2Tg9uHJPNG1/v4oV/b+Enb6zi8XfXMn1EFrec3Z30hEinyxQRkWYorEnrsfXeu/qLo8LDXFw5LJMrh2WybPtB/vzZVmZ/tpUXPt1CTpdEJvTtyHl90+jdMU6HSUVEgpDCmrQehbWgMzQriaFZSfxwYm/mf7WTf63dyxPvreOJ99aRkRjFeX3TOK9vR0Z1TyYiTPduExEJBgpr0npsPcF46wuBrJRo7prQi7sm9KLgUCX/WlvAB2sKeDVvB3/+fBsx4W7O7pXKeX3TGN8njQ6xEU6XLCLSbimsScurrYaacqirVc9aCEiLj2TaiCymjciisqaOzzYVsnBNAQvXFPDuqj0Ygw6Xiog4SGFNWpa18NQQOJTvfZ/a29l65KREetyc26cj5/bpyCOXWlbtOsTCNQU6XCoi4iCFNWlZ9XXeoNZzIvQ4F3pOcLoiOUXGGAZkJDAgI6HZw6Xn9k1jXO9U0uJ0damISEtTWJOWZeu8z1mjYPT3nK1FWlRzh0sBBmYkML53KuP7pDEoM9HZgkVE2giFNWlZ9b6w5tKhsbbs6MOla3aX8OG6Aj5cW8DTH27kqX9tJDkmnOiupcRFhrFqVzF90uNxu3Sum4jIyVJYk5Z1uGfNKKy1F8YY+nWOp1/neG4f35Oi8moWbSjkw7UF/Ku4hv2lVVz01L+JiwjjnN6pnD8gnXG904jVKAoiIn7Rr6W0LPWstXuJ0eFMGdyZKYM7c8O7SVTV1nPVmTl8vmk/H6zZyz+/2U14mIuR2cmM7ZXKsG5J9O8crwsVRESOQ2FNWpat9z6rZ018IsJcTM3JYGpOBnX1lqXbDrJg1R7eX7OXX7y9BoBwt4uBmQkMzUpkWFfvjXvT4nWxgogIKKxJS/vgJ95n9axJE9wuw4jsZEZkJ/Pgxf0oKKlk2bYilm0/yLJtB/nT59t4/pMtAGQmRTE0K4ncbkn075xA305xRIfrJ0tE2h/98knL+mqO9zlrlLN1SEhIi4vk/AHpnD8gHYCq2jpW7TrEsm0HWbb9IJ9v3s8bX+8CwGWgR2psQ4Ab3i2ZrinRukGviLR5foU1Y8z5wP8BbuAP1trHjppvfPMvBMqBGdbaZb55W4ESoA6otdbmtlj1EnyMC8bcBekDna5EQlBEmLth/FIAay27iitZtbOYVbsOsWJnMe+u2sPcvB0AdIgNZ1hXb8/bGR1j6dUxjq7J0YS5NXKGiLQdzYY1Y4wbeAaYCOQDS4wxb1hrVzda7AKgl+8xEnjW93zYeGttYYtVLcGrvhZc6rCVlmGMISMxiozEKCb19/a+1ddbNu0rZcnWg+RtPUDetoO8t2pvwzqRHhcDMxLI6ZLI4C6JDOicoB44EQlp/vyrOgLYaK3dDGCMeQWYCjQOa1OBP1trLfCFMSbRGNPJWru7xSuW4FVfD1iFNWlVLpehV8c4enWM4+qRWQCUVdWysaCUDQWlrNpVzPIdRfzps21U13nPf4uLDKN/53gG+kZkGJiRQLeUGFy675uIhAB//lXNAHY0ep/Pkb1mx1smA9gNWGCBMcYCv7fWzmrqQ4wxM4GZAFlZWX4VL0Gmvtb77NIhKAmsmIgwBvt60q4clglAdW096/eWsGJnMSt9jz99vo3qWu8VyzHhbnqmxdIzLY5eHWPplRZLr7Q4MpOiFOJEJKj4E9aa+tWyJ7HMGGvtLmNMGvC+MWattXbRMQt7Q9wsgNzc3KO3L6Hg8A1x1bMmQSA8zNUwtulhNXX1bNhbysqdxazefYiNBaX8e+M+5i3Lb1gm0uOiR6ovvHWMo2ea93WWzoUTEYf4869qPtCl0ftMYJe/y1hrDz8XGGPm4z2sekxYkwArK4QN73Ns7j4NtZXeZ4U1CVIet6thtIXGiitq2FhQysaCEjbs9R5OXbL1IK8v//anLtztIjMpis6+c+iyU2PomhxN307xZCVHqzdORFqNP/+qLgF6GWOygZ3ANODqo5Z5A7jDdz7bSKDYWrvbGBMDuKy1Jb7Xk4CftVz5cso+ewo+/b/W2XZsx9bZrkgrSYjyMKxrEsO6Jh0xvbSqlk2+c+E2FJSQf6CC/KIKFq4toDCvqmG52Igw+nWOp3/nePp3TmBARjw9U2PVEyciLaLZsGatrTXG3AG8h/fWHS9Ya1cZY27zzX8OeBvvbTs24r11xw2+1TsC831XYYUBL1lr323xvZCTV1MJEfFw2yctu12XBxIyWnabIg6JbXQu3NGKy2vYdqCM1bsOsWrXIVbuKublxduprPGeExfudpHdIYaeabH0SY+jR1os3VNj6JYSQ6RHN40WEf/5dbzKWvs23kDWeNpzjV5b4PYm1tsMDD7NGqW1GBckdXO6CpGQlBDtYVB0IoMyExum1dVbthSWsnLnIdb4zolbvqOIt1Z8e2G8MdA5IYruqTFkd4ihe4cYuqfG0iMtlk7xkTqcKiLH0MlF7Zb1/qshIi3G7TL0TIujZ1oclw75toe5rKqWLYVlbC4sY8u+MjYXlrKlsIy/L9tJaVVtw3KRHhfZHWLpkeoLcKkxdO/g7ZGLidDPtUh7pb/97ZW1NH0Rr4i0tJiIsGOuTAXvCA37SqrY5Atwmwq8z9/kF/P2it3UN7r+Jz0+ku6pMZzRMY4+6XFkJkU3XPAQHqZz40TaMoW1dkt3RxFxmjGGtPhI0uIjGd0j5Yh5lTV1bNtfzuZ9pWwuLGNTQSmbCst4afH2hnvFebcBHeMi6ZIc1RDgMpOi6JIUTWZSNJ0SI/HoQgeRkKawFmh1NWDrm1+utdXX6TCoSBCL9LjpnR5H7/S4I6bX1tWz51Al+QcryD9YwY4D5b7X5SzecoB/LK84okfOZby9cplJ0WQ2Eeg6JUTqqlWRIKewFkg7lsCLF0B9jdOVeMWmO12BiJykMLfLF7iim5xfU1fPnuJKdhwsbwh0+b5A98Wm/ew+tNN7FoSP22V8Yc4b5I7uoUuPV5gTcZrCWiAVbfMGtVG3Q0xK88u3tvRBTlcgIi3M43bRJTmaLslNh7nq2np2F1c09MY17qH7dGMhe0sqjwhzYS5Dp8RIMhOjGwJdZlIUXZK9zx3jI3HrClaRVqWwFkiHx84cfhOk9HC2FhFpl8LDXHRNiaFrSkyT86tq69hd1LhnrrwhzH28fh8FJVVHLB/mMnRO/PawaufEKDolRNIpMZJOCd7XupJV5PTob1Ag1WvsTBEJbhFhbrp1iKFbh6bDXGVNHbuKfL1xjQ+1Hiz3juxQWnXMOvGRYQ0h7ttDrN/20CVFezA6h1bkuJQaAqlhoHPdvVxEQlOkx0331Fi6p8Y2Ob+qto69xVXsLq5gd3Gl7+F9vauogmXbiyiuOPK83ehwNymx4cRHeoiP9NAhLoL0+AjSfT1zHeMj6ZYSTUpsRCB2USToKKwF0uHDoEZhTUTapogwN1kp0WSlNH3OHMChyhp2HjzyvLmDZdUcqqyhuKKGFflFLCiupKr2yCvnE6M9ZCZFkZEYdcR95jonRNEpMZKUmHD10EmbpLAWSPXqWRMRiY/0EN/JQ99O8cddxlpLUXkNew55e+Y27ytjS2EZO4u8rxetL6Sipu6IdcLdLjomRJAaG0FqXAQdfM8psRGkxobTIdY7rUNcBDHhbgU7CRkKa4Hy1V9h6Wzva/WsiYickDGGpJhwkmLC6dspnnP7HDnfWsuBsmp2FVWyq7iC3UUV7D5UyZ7iSgpLq9hSWMbiLQc4WN70rZIiPS56p8czpkcKXZK9F0ZkJEbROTGS6HD90yjBRd/IQFn8PBzcCr0mQVSi09WIiIQ0Ywwpsd5es4GZCcddrqaungNl1ewrqWJ/WTWFJVUUllaxu7iSr7Yf5LmPNx1xE2GApGhPw/ly6QmRdIr3PndOjCI9IZL0eF3hKoGlb1sgdR0D17zqdBUiIu2Gx+2iY7z3IoWm1NbVs7ekil1FFew8WMHOIu9jr+/iiOU7ijhQVn3MerERYSTFeEiO9vb+JceEH/E6KTqclFjvc3JMOAlRHt2PTk6ZwlrAaCxOEZFgE+Z2keE7BDq8W9PLVNbUsfeQN7zt8YW4fSVVHCyv5kCZ97FhbykHy6spr65rchsuA4nR4SRFe5oMc4efDz+SYsJ1Xp00UFgLJP2lExEJOZEe9wlvJNxYZU0dB8ur2V9a3RDmDvoC3YHyag6W1XCgrJpt+8v5akcRB8uqqT36OKxPuNtFfFQY8ZEe4qI8xEd6X8dHhREX6Xsf5SGuYfqRrxX22g6FtUCx6lkTEWnrIj1u38gNUX4tb62lpKqWg2XV7G8U7LxBr4ZDlTUcqqjhUGUtJZU17CqqaHhdWVN/wm27DN5QFxVGXMS3IS82IoyIMBcRYS6iwsOIi2z0iPAQ63sdH+lRD1+QUFgLKH3ZRUTkW8aYhpsB+9Nz11h1bT0lld4gd6iihpLK2oZwd/h1iW/eId9yOw6UU15dR1VtHVW19ZRX11Fde+LQF+lxkRITQYfYcOKjPA29e0f35sVGhBEV7vY+PG6iw93ERISREOXB43adTjO1ewprzamthk0Lobby9LZTWQQJmS1SkoiISHiYq+GK2NNRVVtHSWWt71HT8Hyo0tvjV1haxf7SagrLqjlUUcPOooqGEHj0jYuPJy4ijMQYD4lR4WQkRpEQ9W0PXmyEL+w1eh8X6Wno7YvyqGdPYa0569+FV69rmW11O7tltiMiItJCIsLcRMS66XAKoa+ypu6IkFdZU0d5TR2V1XVU+OYVlddQVFFNUXkNhaVVbCgoobTKGw6Pd0FGYy7DMQEuLtJDYpSn4aKNxBjfxRvR4d5pMR5iIsKI9rgJawO9egprzaku9T5fOw/iM05vW8ndT78eERGRIBHpcRPpcZMad2q9e7V19ZRV1VFS5Q17pVW1lPoO4R4OdKW+6Ycqayj19QDuPVTJuj0lFJVXU9ZM4AsPcxET7iY6PIzocDfRvhAXE+EmKjzsqHluoj3eZWIOTzs8P8JNTLj3UG90uDugh3YV1ppzeDzP1D46jCkiItKCwtwuEqJdJER7TnkbVbV1FJXXcNB3tW1ReTUHy2sor66lrKqO8ppayqvqKKuupaK6jrLqOiqqa9lVVENFTR1lVd4evvLq2mNukHwi4W5XQ4BrbQprzTkc1lxqKhERkWATEeamY7z7uDc+9pe1tuGii7Kq2oYgdzjglVfXfjuvUegrq67jsxbal+NRAmlOw+DraioREZG2yhjTcFg3OSb8pNb931aq6bDQP+uutTX0rGnwdREREQk8hbXmHA5rRmFNREREAk/H9t5/GD773fHnW989ZNynfvKjiIiIyKlSWNu7CmI7wpBrj79MUjfw+Dd0iIiIiEhLUlirr4XELDj3QacrERERETmGzlmrr9OVniIiIhK0FNbqa3Wlp4iIiAQthbX6Wl3pKSIiIkFLYU2HQUVERCSIhX5K+ehx2L/x1Nc/sNl7NaiIiIhIEArtsFZbDR89CpGJEJV0atuISoLu41qyKhEREZEWE9ph7fDoAmf9AM76L0dLEREREWkNoX3OmvUNsq4LBERERKSNCu2w1jDIemh3EIqIiIgcT4iHNd+4nQprIiIi0kaFeFg73LMW2rshIiIicjyh2SVVsAYOboOKA9736lkTERGRNio0U84L50Nl0bfvT/W2HSIiIiJBLjTDWlUJ5FwLw2+CsAhI6+d0RSIiIiKtIvTCmrXeW3YkZEDGUKerEREREWlVoXdmvvVdAap7q4mIiEg74FdYM8acb4xZZ4zZaIy5t4n5xhjzlG/+N8aYof6ue9IargBVWBMREZG2r9mwZoxxA88AFwD9gOnGmKNPErsA6OV7zASePYl1T069b9QChTURERFpB/zpWRsBbLTWbrbWVgOvAFOPWmYq8Gfr9QWQaIzp5Oe6J0ejFoiIiEg74k/iyQB2NHqfD4z0Y5kMP9c91u5v4NGMpudZ6312eZrdjIiIiEio8yesmSamWT+X8Wdd7waMmYn3ECpAlXlg18oTVvXT/wT+84SLtDEdgEKniwhCapemBVW7zGa20yUcFlTtEiTUJk1TuzRN7dK03q25cX/CWj7QpdH7TGCXn8uE+7EuANbaWcAsAGNMnrU214/a2g21SdPULk1TuzRN7XIstUnT1C5NU7s0zRiT15rb9+ectSVAL2NMtjEmHJgGvHHUMm8A3/VdFToKKLbW7vZzXRERERE5jmZ71qy1tcaYO4D3ADfwgrV2lTHmNt/854C3gQuBjUA5cMOJ1m2VPRERERFpg/y6pNJa+zbeQNZ42nONXlvgdn/X9cOsk1y+PVCbNE3t0jS1S9PULsdSmzRN7dI0tUvTWrVdjLVNnu8vIiIiIkEg9IabEhEREWlHWiWstcbwVMaYZGPM+8aYDb7npEbz7vMtv84YM7k19ul0BbJNjDEpxpgPjTGlxpinA7OHpybA7TLRGLPUGLPC93xuYPby5AW4XUYYY5b7Hl8bYy4LzF6evED/tvjmZ/n+Lt3dunt3agL8XelmjKlo9H157ujPCxaB/q4YYwYZYz43xqzy/cZEtv5enrwAf1+uafRdWW6MqTfG5ARkR09SgNvFY4z5k+97ssYYc1+zBVprW/SB90KCTUB3vLfu+Brod9QyFwLv4L0P2yjgy+bWBX4F3Ot7fS/wuO91P99yEUC2b313S+9XiLVJDHAWcBvwtNP7H0TtMgTo7Hs9ANjpdBsESbtEA2G+152AgsPvg+kR6HZptM15wN+Au51uA6fbBOgGrHR6v4OwXcKAb4DBvvcpBNm/Q060y1HbHQhsdroNgqFdgKuBV3yvo4GtQLcT1dgaPWutNTzVVOBPvtd/Ai5tNP0Va22VtXYL3itSR7TCfp2OgLaJtbbMWvtvoLI1d6oFBLpdvrLWHr7P3yog0hgT0Ur7djoC3S7l1lrfOG5EcpwbVweBQP+2YIy5FNiM9/sSjALeJiEi0O0yCfjGWvs1gLV2v7W2rpX27XQ4+X2ZDrzconvTcgLdLhaIMcaEAVFANXDoRAW2Rlg73tBT/ixzonU7Wu+92/A9p53E5zkt0G0SKpxslyuAr6y1VadcfesJeLsYY0YaY1YBK4DbGoW3YBLQdjHGxAA/Bn7aQvW3Bif+DmUbY74yxnxsjDn79HehVQS6Xc4ArDHmPWPMMmPMj1pkL1qek7+5VxG8YS3Q7fIaUAbsBrYDv7bWHjhRga0xGnpAhqc6yc9zWqDbJFQ40i7GmP7A43j/NxyMAt4u1tovgf7GmL7An4wx71hrg61nNtDt8lPgN9baUmOaWj0oBLpNdgNZ1tr9xphhwOvGmP7W2hP2Cjgg0O0ShvfUk+F47zW60Biz1Fq7sLlCA8yp39yRQLm19sTDSDon0O0yAqgDOgNJwCfGmA+stZuPt0Jr9KydzvBUJ1p3r6/LEd9zwUl8ntMC3SahIuDtYozJBOYD37XWbmqBfWgNjn1frLVr8P6Pb8Bp1N9aAt0uI4FfGWO2Aj8A7jfem3wHk4C2ie90k/2+10vxnqtzRovsScty4t+hj621hdbacrz3Fh1K8HHqt2UawdurBoFvl6uBd621NdbaAuBT4MRDeNmWP1EvDO85Htl8e7Jd/6OWuYgjT9Rb3Ny6wBMceaLer3yv+3PkBQabCbITOwPdJo22OYPgvsAg0N+VRN9yVzi970HWLtl8e4FBV7w/NB2cbgen2+Wo7f4PwXmBQaC/K6n4fl/xnlC9E0h2uh2CoF2SgGX4LtYBPgAucrodnG4X33sX3kDT3en9D5Z2wXt6xYu+bcUAq4FBJ6yxlXb8QmA93v91PeCbdhvec2HwFfiMb/4KIPdE6/qmpwALgQ2+5+RG8x7wLb8OuMDpP/ggaZOtwAGg1PcXpV9r72OwtwvwIN5eo+WNHmlOt0EQtMt1eE+gX473H5xLnd7/YGiXoz73fwjCsObAd+UK33fla9935RKn9z8Y2sU371pf26ykicAfLA8H2mUc8IXT+x1M7QLE4r3CfBXeoHZPc/VpBAMRERGRIKYRDERERESCmMKaiIiISBBTWBMREREJYgprIiIiIkFMYU1EREQkiCmsiUhQMMakGGOW+x57jDE7fa+LjDGrW+Hz/scYc/dJrlN6nOmzjTFXtkxlIiJHUlgTkaBgvYNf51hrc4Dn8A71lAPkAPXNre8bFFlEpM1RWBORUOA2xjxvjFlljFlgjIkCMMZ8ZIx51BjzMXCXMWaYb4Dxpb5BtQ8P9XKnMWa1MeYbY8wrjbbbz7eNzcaYOw9PNMb80Biz0vf4wdHFGK+nfdt8i6YHrhYRaRH6n6iIhIJewHRr7S3GmFfx3kn/r755idbac4wxHuBjYKq1dp8x5irgF8CNeId6ybbWVhljEhtttw8wHogD1hljngUGATfgHRvUAF8aYz621n7VaL3LgN7AQKAj3ruQv9AaOy4iorAmIqFgi7V2ue/1UqBbo3lzfc+98Q5A/74xBsAN7PbN+waYY4x5HXi90bpvWWurgCpjTAHe4HUWMN9aWwZgjPk7cDbQOKyNBV621tYBu4wx/zr9XRQRaZrCmoiEgqpGr+uAqEbvy3zPBlhlrR3dxPoX4Q1YU4CHjDH9j7PdMN92/KGx+kQkIHTOmoi0FeuAVGPMaABjjMcY098Y4wK6WGs/BH4EJOIdSPl4FgGXGmOijTExeA95ftLEMtOMMW7feXHjW3hfREQaqGdNRNoEa2217/YZTxljEvD+vv0WWA/81TfN4L3KtMh3qLSp7SwzxswGFvsm/eGo89UA5gPnAit82/+4hXdHRKSBsVY9+SIiIiLBSodBRURERIKYwpqIiIhIEFNYExEREQliCmsiIiIiQUxhTURERCSIKayJiIiIBDGFNREREZEgprAmIiIiEsT+P9j0rS1Lbb8FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predtst=gbc.predict_proba(Xtest)[:,1]\n",
    "fpr, tpr, thresholds = metrics.roc_curve(ytest, predtst)\n",
    "dfplot=pd.DataFrame({'Threshold':thresholds, \n",
    "        'False Positive Rate':fpr, \n",
    "        'False Negative Rate': 1.-tpr})\n",
    "ax=dfplot.plot(x='Threshold', y=['False Positive Rate',\n",
    "        'False Negative Rate'], figsize=(10,6))\n",
    "ax.plot([0.00035,0.00035],[0,0.1]) #mark example thresh.\n",
    "ax.set_xbound(0,0.0008); ax.set_ybound(0,0.3) #zoom in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## choose a threshold of 0.00035 (0.035%) (vertical green line shown) which will give us a false negative (approved fraud) rate of about 9% and a false positive (rejected legitimate) rate of about 6%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred 0(Approve as Legit)</th>\n",
       "      <th>Pred 1(Deny as Fraud)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True 0(Legit)</th>\n",
       "      <td>TN = 80388 (TNR = 94.25%)</td>\n",
       "      <td>FP = 4907 (FPR = 5.75%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True 1(Fraud)</th>\n",
       "      <td>FN = 14 (FNR = 9.46%)</td>\n",
       "      <td>TP = 134 (TPR = 90.54%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Pred 0(Approve as Legit)    Pred 1(Deny as Fraud)\n",
       "True 0(Legit)  TN = 80388 (TNR = 94.25%)  FP = 4907 (FPR = 5.75%)\n",
       "True 1(Fraud)      FN = 14 (FNR = 9.46%)  TP = 134 (TPR = 90.54%)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hardpredtst_tuned_thresh = np.where(predtst >= 0.00035, 1, 0)\n",
    "conf_matrix(ytest, hardpredtst_tuned_thresh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sure enough, False neg rate of 9.46% (i.e. identified/denied 90.54% of our frauds) and false pos rt of 5.75% (i.e. still approved 94.25% of our legitimate transactions)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This second part is for \"Get confidence intervals for model performance, like “92.4% [89.6% to 94.7%]”, for metrics like Accuracy, Recall, Precision, F1, and others\", for any metric such as Accuracy, Recall, Precision, Specificity, F1 and others\" by David Rosen \n",
    "https://towardsdatascience.com/get-confidence-intervals-for-any-model-performance-metrics-in-machine-learning-f9e72a3becb2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Original test set and two generated bootstrap sample datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from boot_conf_intervals_ml import ci, data_args_to_df, make_boot_df \\\n",
    "     , specificity_score, raw_metric_samples, metric_boot_histogram \\\n",
    "     #don't need:#     , metrics_from_df, metrics_from_data_args\n",
    "# boot_conf_intervals_ml.py concatenates all the gists from standalone notebk\n",
    "\n",
    "# gist 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Orig</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Boot0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Boot1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>PredClass</th>\n",
       "      <th>Class</th>\n",
       "      <th>PredClass</th>\n",
       "      <th>Class</th>\n",
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       "      <td>...</td>\n",
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       "      <th>85438</th>\n",
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       "<p>85443 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Orig           Boot0           Boot1          \n",
       "      Class PredClass Class PredClass Class PredClass\n",
       "0         0         0     0         0     0         0\n",
       "1         0         0     0         0     0         0\n",
       "2         0         0     0         1     0         0\n",
       "3         0         0     0         0     0         0\n",
       "4         0         0     0         0     0         0\n",
       "...     ...       ...   ...       ...   ...       ...\n",
       "85438     0         0     0         0     0         0\n",
       "85439     0         1     0         0     0         0\n",
       "85440     0         0     0         0     0         0\n",
       "85441     0         0     0         0     0         0\n",
       "85442     0         0     0         0     0         0\n",
       "\n",
       "[85443 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hardpredtst_tuned_thresh = pd.Series\\\n",
    "  (hardpredtst_tuned_thresh, name='PredClass') #was #Numpy #array\n",
    "dforig=data_args_to_df(ytest, hardpredtst_tuned_thresh)\n",
    "np.random.seed(13)  # to get same #sample #datasets #every #time\n",
    "df0, df1 = make_boot_df(dforig), make_boot_df(dforig)\n",
    "pd.concat([dforig,df0,df1], axis=1, keys=['Orig','Boot0','Boot1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## custom metric specificity_score(), and utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gist 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making each boot sample dataset and calculating metrics for it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gist 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at metrics on 10 boot sample datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  5.63it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>        <th class=\"col_heading level0 col8\" >8</th>        <th class=\"col_heading level0 col9\" >9</th>    </tr>    <tr>        <th class=\"index_name level0\" >Metric (class 1 +ve)</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59level0_row0\" class=\"row_heading level0 row0\" >Recall</th>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row0_col0\" class=\"data row0 col0\" >93.46%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row0_col1\" class=\"data row0 col1\" >89.81%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row0_col2\" class=\"data row0 col2\" >96.62%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row0_col3\" class=\"data row0 col3\" >89.38%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row0_col4\" class=\"data row0 col4\" >89.57%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row0_col5\" class=\"data row0 col5\" >89.68%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row0_col6\" class=\"data row0 col6\" >82.82%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row0_col7\" class=\"data row0 col7\" >89.74%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row0_col8\" class=\"data row0 col8\" >88.07%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row0_col9\" class=\"data row0 col9\" >85.71%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59level0_row1\" class=\"row_heading level0 row1\" >Specificity (Recall of −ve)</th>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row1_col0\" class=\"data row1 col0\" >94.29%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row1_col1\" class=\"data row1 col1\" >94.14%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row1_col2\" class=\"data row1 col2\" >94.29%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row1_col3\" class=\"data row1 col3\" >94.35%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row1_col4\" class=\"data row1 col4\" >94.27%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row1_col5\" class=\"data row1 col5\" >94.09%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row1_col6\" class=\"data row1 col6\" >94.28%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row1_col7\" class=\"data row1 col7\" >94.21%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row1_col8\" class=\"data row1 col8\" >94.14%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row1_col9\" class=\"data row1 col9\" >94.22%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59level0_row2\" class=\"row_heading level0 row2\" >Balanced Accuracy</th>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row2_col0\" class=\"data row2 col0\" >93.88%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row2_col1\" class=\"data row2 col1\" >91.98%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row2_col2\" class=\"data row2 col2\" >95.45%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row2_col3\" class=\"data row2 col3\" >91.86%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row2_col4\" class=\"data row2 col4\" >91.92%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row2_col5\" class=\"data row2 col5\" >91.88%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row2_col6\" class=\"data row2 col6\" >88.55%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row2_col7\" class=\"data row2 col7\" >91.98%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row2_col8\" class=\"data row2 col8\" >91.11%</td>\n",
       "                        <td id=\"T_43c3763e_13f8_11ec_8c8a_e470b8cd6e59row2_col9\" class=\"data row2 col9\" >89.97%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2c6991cd888>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "met=[ metrics.recall_score, specificity_score, \n",
    "      metrics.balanced_accuracy_score\n",
    "    ]\n",
    "np.random.seed(13)\n",
    "raw_metric_samples(met, ytest, hardpredtst_tuned_thresh, \n",
    "         nboots=10).style.format('{:.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram of bootstrap sample datasets showing confidence interval for Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gist 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 500/500 [00:23<00:00, 20.99it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(13)\n",
    "metric_boot_histogram\\\n",
    "  (metrics.recall_score, ytest, hardpredtst_tuned_thresh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to calculate all the confidence intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gist 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 500/500 [01:22<00:00,  6.09it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59\" ><thead>    <tr>        <th class=\"index_name level0\" >500 Boot Samples</th>        <th class=\"col_heading level0 col0\" >Point Estim</th>        <th class=\"col_heading level0 col1\" >2.5%ile</th>        <th class=\"col_heading level0 col2\" >97.5%ile</th>    </tr>    <tr>        <th class=\"index_name level0\" >Metric (class 1 +ve)</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59level0_row0\" class=\"row_heading level0 row0\" >Recall</th>\n",
       "                        <td id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59row0_col0\" class=\"data row0 col0\" >90.54%</td>\n",
       "                        <td id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59row0_col1\" class=\"data row0 col1\" >85.00%</td>\n",
       "                        <td id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59row0_col2\" class=\"data row0 col2\" >95.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59level0_row1\" class=\"row_heading level0 row1\" >Specificity (Recall of −ve)</th>\n",
       "                        <td id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59row1_col0\" class=\"data row1 col0\" >94.25%</td>\n",
       "                        <td id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59row1_col1\" class=\"data row1 col1\" >94.11%</td>\n",
       "                        <td id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59row1_col2\" class=\"data row1 col2\" >94.40%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59level0_row2\" class=\"row_heading level0 row2\" >Balanced Accuracy</th>\n",
       "                        <td id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59row2_col0\" class=\"data row2 col0\" >92.39%</td>\n",
       "                        <td id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59row2_col1\" class=\"data row2 col1\" >89.61%</td>\n",
       "                        <td id=\"T_8368aa06_13f8_11ec_bf4c_e470b8cd6e59row2_col2\" class=\"data row2 col2\" >94.73%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2c69a100d48>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(13)\n",
    "ci(met,ytest, hardpredtst_tuned_thresh).style.format('{:.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-09-12 14:37:41.921443\n"
     ]
    }
   ],
   "source": [
    "print(datetime.datetime.now())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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