oos_evaluate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2aa3b3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import mlflow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import mysql.connector\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report, f1_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6f2c5256",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c55c5026",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to MySQL Server\n"
     ]
    }
   ],
   "source": [
    "with open('db_config.json', 'r') as json_file:\n",
    "    db_config = json.load(json_file)\n",
    "\n",
    "try:\n",
    "    # Attempt to connect to the MySQL server\n",
    "    connection = mysql.connector.connect(**db_config)\n",
    "\n",
    "    # Check if the connection is successful\n",
    "    if connection.is_connected():\n",
    "        print(\"Connected to MySQL Server\")\n",
    "    \n",
    "    df = pd.read_sql(\"SELECT * FROM matured_loan\", connection)\n",
    "\n",
    "    # Close the connection\n",
    "    connection.close()\n",
    "\n",
    "except mysql.connector.Error as err:\n",
    "    print(f\"Error: {err}\")\n",
    "    print(\"Failed to connect to MySQL Server\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f54ba3e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "irrelevant_columns = [\n",
    "    'id', 'initial_list_status', 'out_prncp', 'out_prncp_inv', 'total_pymnt',\n",
    "    'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int', 'last_pymnt_to_income',\n",
    "    'total_rec_late_fee', 'recoveries', 'collection_recovery_fee', 'last_pymnt_amnt',\n",
    "    'is_matured_loan', 'calculated_installment', 'ROI', 'IRR', 'int_rate_bin', 'annual_inc_bin'\n",
    "] + [\n",
    "    'funded_amnt', 'funded_amnt_inv', 'debt_settlement_flag',\n",
    "    'last_pymnt_to_income', 'last_fico_range_high', \n",
    "    'last_fico_range_low', 'FICO_change'\n",
    "] + [\n",
    "    'purpose', 'title', 'zip_code', \"emp_title\"\n",
    "] + ['issue_d']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "af299b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(irrelevant_columns, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5c45731e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['emp_length_int'] = df['emp_length'].replace(\n",
    "    ['4 years', '2 years', '10+ years', \n",
    "     '3 years', '5 years', '6 years',\n",
    "     '1 year', '7 years', '< 1 year', \n",
    "     '9 years', '8 years'], [\n",
    "        4, 2, 10, 3, 5, 6, 1, 7, 1, 9, 8\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "379e70b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = [\n",
    "    \"term\", \"grade\", \"sub_grade\", \"emp_length\", \"home_ownership\",\n",
    "    \"verification_status\", \"addr_state\", \"application_type\", \"hardship_flag\"\n",
    "]\n",
    "\n",
    "df = pd.get_dummies(df, columns=categorical, prefix=categorical) #, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d65047d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"loan_status\"].replace([\"Fully Paid\", \"Charged Off\"], [0, 1], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "14304e11",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_mem_usage(df):\n",
    "    \"\"\" iterate through all the columns of a dataframe and modify the data type\n",
    "        to reduce memory usage.        \n",
    "    \"\"\"\n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n",
    "    \n",
    "    for col in df.columns:\n",
    "        col_type = df[col].dtype\n",
    "        \n",
    "        if col_type != object:\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)  \n",
    "            else:\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "        else:\n",
    "            df[col] = df[col].astype('category')\n",
    "\n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "432734a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 1304.43 MB\n",
      "Memory usage after optimization is: 717.94 MB\n",
      "Decreased by 45.0%\n"
     ]
    }
   ],
   "source": [
    "df = reduce_mem_usage(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cbcd2d58",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X = df.drop('loan_status', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "88270681",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2e92646e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlflow.set_tracking_uri(\"http://127.0.0.1:8080\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "461c9185",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a107629856934c7d8d950cde01d6f687",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading artifacts:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load model as a PyFuncModel.\n",
    "logged_model = 'runs:/d3d273fa1b5b4848a37e68f4d1655d9c/model'\n",
    "model = mlflow.sklearn.load_model(model_uri=logged_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e805eb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_in = X.iloc[:300000]\n",
    "X_out = X.iloc[300000:]\n",
    "\n",
    "y_in = y.iloc[:300000]\n",
    "y_out = y.iloc[300000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "97bf58b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "73dc1742",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_seed = 42\n",
    "\n",
    "_, X_in, _, y_in = train_test_split(\n",
    "        X_in, y_in, test_size=0.3, random_state=random_seed, stratify=y_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f6753e9c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y_prob = model.predict_proba(X_in)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3c7a2da5",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = []\n",
    "f1 = []\n",
    "for i in range(5, 35):\n",
    "    y_pred = np.where(y_prob > i/100, 1, 0)\n",
    "    threshold.append(i / 100)\n",
    "    f1.append(f1_score(y_in, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a5198081",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_threshold = threshold[np.argmax(f1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "aecd05ff",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'max f1 at threshold = 0.21')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uUCgU+P7772vUPNDpdBg6dCimTp2KXbt24erVqzZV7ZSIiIhsm0XF98rKynD48GHMnTvX6HhMTAz27t1b63krVqxAZmYmVq1ahVdffdVkm4ULF6JNmzZ4+OGHzRrOKi0tNSpPr9frUVBQAB8fH+5LQkREZCeEECgqKkLbtm0hlzduqq9FSc2VK1eg0+lqbI7m7+9f694mGRkZmDt3Lnbt2gWl0vSP27NnD5YtW1aj96YuixYtQlxcnNntiYiIyHadP3++xn5rlmrQNgnVe0KEECZ7R3Q6HSZOnIi4uDh069bN5LWKioowadIkLF26tM6N4qqbN28e5syZY/heq9WiQ4cOOH/+vFHpbiIiIrJdhYWFCAoKgqenZ6OvZVFS4+vrC4VCUaNXJi8vr0bvDVCZsBw6dAhHjhzBrFmzAFQOEwkhoFQqsWXLFnh7e+Ps2bMYNWqU4Ty9Xl8ZnFKJkydPIiQkpMa13dzcTO5b4+XlxaSGiIjIzlhj6ohFSY2rqysiIyORnJyMf/3rX4bjycnJuO+++2q09/Lywp9//ml0LD4+Htu2bcO3336L4OBgKBSKGm1eeuklFBUV4f3330dQUJAlIRIREZGTsnj4ac6cOZg8eTL69euHQYMG4bPPPkN2djamT58OoHJYKCcnB19++SXkcjnCw8ONzvfz84NKpTI6Xr1Nq1atTB4nIiIiqo3FSc24ceOQn5+PhQsXQqPRIDw8HJs3b0bHjh0BABqNpt6aNURERETW5jC7dBcWFkKtVkOr1XJODRERkZ2w5uc3934iIiIih8CkhoiIiBwCkxoiIiJyCExqiIiIyCE0qKIwEZEj0OkFUrIKkFdUAj9PFaKCvaGQc+84InvFpIaInFJSmgZxienQaEsMxwLVKsSOCsOI8EAJIyOihuLwExE5naQ0DWasSjVKaAAgV1uCGatSkZSmkSgyImoMJjVE5FR0eoG4xHSYKtBVdSwuMR06vUOU8CJyKkxqiMhu6fQC+zLzseloDvZl5puViKRkFdToobmZAKDRliAlq8CKkRJRc+CcGiKyS5bMiblyrRR/5miRdkGLLem5Zl3/5KVCDArxqbMNJxoT2RZuk0BEdqdqTkz1X15V6UTCpAgM7uKLZ7/+HWk5Wlyso2emLiFtPDCkaxvcH9ke4e3UNWLgRGOixuM2CUTktMydE+OuVGBfZj4uaksgkwGd23jgvj5t8eI/QuHt4Yq6+lNcFDLIAGRevo6Ve8/i1KUiw2uXCkvw6c5MTjQmskEcfiIiu2LunJhD5/7Cov/rhTYt3dCznRot3f73666DdwvMWJUKGWCUHFUlOh9O6ItBIb7Yl3kFv2Vcwa1dfQ1tNqZewJtJJ2v92TJUJlXDwwI4FEXUzNhTQ0R2RXO12Kx2eUUluLd3Wwzo7GOU0ADAiPBAJEyKQIBaZXQ8QK1CwqQIjAgPhNrdBSPCA/H6v3rBz/N/7U7nXavz53KiMZF02FNDRHblt4zLZrW7ORExZUR4IIaHBVg80fe2bm2wITWn3p+fV9SweTxE1HBMaojI5pWU66ByUQAA5o7sgcQ/NLUu35ahssclKti73usq5LJ6VzhVV1+ydHO7DYcvYFioH7w9XGu8zpVTRNbHpIaIJFXXh/uJ3EK88fMJAMDKqVEAKhOWD8f3xcw1qQBMz4mJHRXWZAlCVLA3AtUq5GpLTE5WrkqqPNwUeOab3+GmlOOft7TFg4M7GVZQceUUUdPgkm4ikkxtH+5P3NEFqdlXsSH1AoQAlHIZts4Zik6+HvWe2xyJQdWScsB0UpUwKQK+Ld2wIPEY0nIKDa/369gatwS1wvLdWXUuR2diQ87Emp/fTGqISBK11Zqp7p5egXju7u5GCU0VKYdwzEmqhBBIzf4LK/eew89/alBRT8Xjql6e3S/cwaEochpMakxgUkNkP3R6gVvf3Fbn0mxXhQxrHh2Ifp3qnxsjFUuSqkuFJXgz6QQ2mjHJeO2jAy2e60Nkr6z5+c05NUTU7OqrNQMAZTqBcp1t/81lyURjfy8VhnZrY1ZSw5VTRA3DOjVE1OxyC8370Ha0D3dLVk4RkeWY1BBRs9HrBX74/SIWJ50wq72jfbhXrZyqb7bMhsPnkX+ttFliInIkTGqIqMkJIZCUpsHI93fhybVHoPl7P6bayFA56dacWjP2RCGXIXZUGADUSGxu/v7b1Bzc8fZOrD5wDvp6JhcT0f8wqSGiRtHpBfZl5mPT0Rzsy8yvURSvsKQc9364G9NXpeLkpSJ4qpSYM7wb3nngFshQ+4d7U9aakVJdWzR8MikCG2YMRo9AL2iLy7Hp6MU6kz8iMsaJwkTUYOYsa/ZSuUDt7gIPVwWmRgfj0SGdoW7hAgBwd1XUOD/ACYrQ1bdFQ+KsaHy1/xyiu/hC9ndWc620Ajq9gNq98t6xIjFRTVzSTUQNUl+dmbfu740H+gUBAM5euQ4vdxduF9AICxPT8cPvOXjxHz3g7qLAwh9ZkZgcA+vUmMCkhqj5mFNnxsNNgT9i72aCYgWlFTr888M9OHmpqNY2rEhM9sqan9+cU0NEFjOnzsz1Uh1SsgqaKSLH5qZUIPGJW/Hs3d1qbVP112lcYnqtm30SOTomNURkMXPrxzhanRkpuSrliOxQ92owAUCjLWEySU6LSQ0RWYxF5KTBZJKobkxqiMhsQghcL62ot4ico9aZkZq5SeLv56+irELfxNEQ2R4mNURkFo22GP9ZeRAzVqdCLkO9ReQctc6MlMytSLx8z1kMW7ID6w9mo1zH5IacB5MaIqqTEALrUrIR885v2H7yMvZn5uPkpaI6i8hxBU7TqK8isQzAuH5B8PN0Q87VYryw4U/c9c5ObDh8wWjycH0FE4nsFZd0E1GttWLOF9zAvI1/YvfpKwCAvh1a4a37e6OLn2e951LTqa/oYUm5Dqv2n0PCjkzkXy8DAHRu44GVD0UhXaOtt2AiUXNinRoTmNQQNYypD8gALxVu794GP/x+ETfKdHBTyvHc3d0xNTqYCYuNMCeZvF5agS/3ncOnv2VC7e6C5+/ujllrjtQomMgaNyQlJjUmMKkhslxtVYFl+F/dk6hgb7z5f70R7OvRzNGRtRSVlOPslRt47KtDtdYXkqFy6HD3C3cwcaVmxeJ7RNRoOr1AXGK6yW0Oqo55qZRY/fAAJjR2zlPlgmulFXUWTGSNG3IETGqInJQ5VYELSypw6NxfzRQRNSXWuCFnwKSGyEnxQ865mFvj5qc/NFwGTnaLSQ2Rk2JVYOdibo2bG2UVcFHwo4HsE59cIicVFeyN1i1can2dVYEdizk1bmbf1RWvjOppOH7lWinWH8yuUceGdW7IVimlDoCIpJFdcAPF5TqTr7EqsGOqKphYYwl/LXVqPvw1A1/sO4dlu7Mwd2QohnX3wy/HclnnhmwWl3QTOaHiMh3u+3g3Tl26hs5tPHCjVIfcQn5IOQtzCyau3JOFd7dmQFtcDgDo5t8Spy5dq9GOdW6oMVinxgQmNUTmE0IgYWcmvtp3DptmRsOnpRurApNJ2hvliN95Gst3Z6FcV/vHBevcUEMxqTGBSQ2R5a6XVsDDjaPQVL/E3y/iibVH6m239tGBGBTi0wwRkaNg8T0iapC0HC1ulFUYvmdCQ+bSm/n3L0sAkJSY1BA5ifMFN/Dvzw9gTPxe5NZTdI+oOpYAIHvApIbICRSX6fDYV4ehLS6Hm1KOVnUs5SYyxZw6NwEsAUASY1JD5OCEEJi78Q8c1xTCt6UrEiZFQuWikDossjN11bmp0rqFC0orTJcJIGoOTGqIHNzyPWex6ehFKOQyfDQxAm1buUsdEtmpqjo3AWrjISYfD1e4KuU4rinC5GUphiXgRM2Nq5+IHNjezCuYvCwFOr1A7KgwTI0OljokcgCm6tz8ceEqHlpxENricvQI9MJXD0fBt6Wb1KGSHeDqJyKql14vsDAxHTq9wJi+7fDQ4E5Sh0QOQiGXYVCID+7r0w6DQnygkMvQt0NrrHtsIHxbuuG4phATPtuPsgpujEnNi0kNkYOSy2VYOTUK4/sH4fUxvSCTsSAaNa0egV74ZvogtGvljsdu6wxXJT9iqHlx+InIAZhb9p6oORSX6eDu+r/J6EIIJtVUK2t+frPyFpGdS0rT1NhgsFULF7wxphf34SFJ3JzQXC4qxaNfHkLsqDD07dBawqjIGbBvkMiOJaVpMGNVqlFCAwBXb5Rj+qpUJKVpJIqMqNLbW07i6Pmr+PfnB7A38wp0eoF9mfnYdDQH+zLzodM7xGAB2Qj21BDZKZ1eIC4xHbV9JMgAxCWmY3hYAIeiSDIv3xuGC38VY/fpK5iyLAWeKiX+uvG/Jd/cEZ6siT01RHYqJaugRg/NzQQAjbYEKVkFzRcUUTUebkp8/mA/3NJejQq9MEpoACBXW4IZ7FUkK2FSQ2SnzN04kBsMktRcFHJcKiw1+VpVT2Pc3+UHiBqDSQ2RneIGg2QvUrIKkFvIXkVqekxqiOxUVLB3jXL1N5Ohcr4CNxgkqbFXkZoLkxoiO6WQy7BgVJjJzQWrjsWOCuMkYZIcexWpuTCpIbJDuzIuo6RcZ9hgMLBaj02AWoWESRFcUUI2ISrYG4FqVa27ewPsVSTr4JJuIjvz6/FLeOyrw4jo0Apf/CcKI8IDMTwsgBWFyWYp5DLEjgrDjFWpkAEmyxBMHdyJzyw1GntqiOzI4XMFmLkmFTq9QAdvD7i7VFZuNbXBIJEtqepVrD4PTPX3/lDrDp1HcZlOitDIgbCnhshOZFwqwn9WHkJJuR7DurfBG//HTSrJvpjqVezq1xL3fLgLZy5fx2ub0/Hq6F5Sh0l2jD01RHbg4tViTFmeAm1xOfp2aIWP/x0BFwX/+ZL9qd6r6OvphiUP3AIAWLU/G78evyRxhGTPGvRbMT4+HsHBwVCpVIiMjMSuXbvMOm/Pnj1QKpXo06eP0fGlS5diyJAhaN26NVq3bo277roLKSkpDQmNyOFcvVGGKctToNGWIKSNB5Y/2B8tXNnJSo5jSNc2+E90MADg+W//wOUi04X6iOpjcVKzfv16zJ49G/Pnz8eRI0cwZMgQjBw5EtnZ2XWep9VqMWXKFNx55501XtuxYwcmTJiA7du3Y9++fejQoQNiYmKQk5NjaXhEDifnajHyr5UiwEuFLx8egNYerlKHRGR1z4/ojtAAT+RfL8O2E+ytoYaRCSEsqks9YMAAREREICEhwXCsR48eGD16NBYtWlTreePHj0fXrl2hUCjw/fff4+jRo7W21el0aN26NT766CNMmTLFrLgKCwuhVquh1Wrh5eVl9vshsgen865BpxfoHuApdShETeZkbhEuFZbgtm5tpA6FmpE1P78t6qkpKyvD4cOHERMTY3Q8JiYGe/furfW8FStWIDMzE7GxsWb9nBs3bqC8vBze3rXXLCgtLUVhYaHRF5E90+kF9mXmY9PRHOzLvIKsy9cNr3Xxa8mEhhxe9wBPJjTUKBYNzF+5cgU6nQ7+/v5Gx/39/ZGbm2vynIyMDMydOxe7du2CUmnej5s7dy7atWuHu+66q9Y2ixYtQlxcnPnBE9mwpDQN4hLTjXbdlgF48s6ueHp4N+kCI5LI+YIbSNiZiQWjesJVyUnxZJ4GPSnVl5EKIUwuLdXpdJg4cSLi4uLQrZt5v5gXL16MtWvXYuPGjVCpai+ZPW/ePGi1WsPX+fPnLXsTRDYiKU2DGatSjRIaoLJA2fu/ZiApTSNNYEQSqdDp8e/PD2DNgWy8k3xK6nDIjliU1Pj6+kKhUNTolcnLy6vRewMARUVFOHToEGbNmgWlUgmlUomFCxfi999/h1KpxLZt24zaL1myBK+//jq2bNmC3r171xmLm5sbvLy8jL6I7I1OLxCXmG6ywipQ2VsTl5gOnd6iqW9Edk2pkGP+PT0AAJ/+lol9mfkSR0T2wqKkxtXVFZGRkUhOTjY6npycjMGDB9do7+XlhT///BNHjx41fE2fPh3du3fH0aNHMWDAAEPbt956C//973+RlJSEfv36NfDtENmXlKyCGj00NxMANNoSpGQVNF9QRDbg7p4BGN8/CEIAz3x9FNob5VKHRHbA4mIXc+bMweTJk9GvXz8MGjQIn332GbKzszF9+nQAlcNCOTk5+PLLLyGXyxEeHm50vp+fH1QqldHxxYsX4+WXX8aaNWvQqVMnQ09Qy5Yt0bJly8a8PyKblldUe0LTkHZEjuTle8Ow/0w+zubfwEub0vDB+D6sok11snhOzbhx4/Dee+9h4cKF6NOnD3777Tds3rwZHTt2BABoNJp6a9ZUFx8fj7KyMtx///0IDAw0fC1ZssTS8Ijsip9n7fPGGtKOyJF4uCnx3vi+UMhlSPz9Ir4/ytplVDeL69TYKtapIXuk0wvc+uY25GpLTM6rkQEIUKuw+4U7uEklOa0Pfs3AO8mnENXJG2seHYCDZ//ijvQOxJqf36y1TiQhhVyG2FFhmLEqFTLAKLGp+jUdOyqMv7TJqT1+ewg8VUr4tnTFkMXbjeahBapViB0VhhHhgRJGSLaCi/+JJFJYUo74HadxZw9/JEyKQIDaeIgpQK1CwqQI/rImp6dUyBGoVuHJtUdrTKzP1ZZgxqpUlj4gAOypIZKEEALzNv6Jn/7Q4ISmCB9M6IvhYQFIySpgtzpRNXWVPhD4X+mD4WEB/Dfj5JjUEElg9YFs/PSHBkq5DFOjOwGoHIoaFOIjbWBENsiS0gf8N+TcOPxE1MzSLxZi4Y/pAIAXRoSib4fWEkdEZNtY+oDMxaSGqBldL63ArDWpKKvQ485QPzwyJFjqkIhsHksfkLmY1BA1EyEEXvo+DWeuXEegWoUlD9zCQmJEZogK9kagWoXa/rXIULkKKirYuznDIhvEpIaomVz4qxi/HMuFQi7DBxP6orWHq9QhEdmFqtIHAEwmNgIsfUCVmNQQNZMg7xZIfOJWLBrTC/078S9KIkuMCA80WfoAAKI6tWbpAwLA1U9EzSqkTUuEtOF+ZkQNMSI80Kj0wfXSCmw5lot5/wiTOjSyEUxqiJrY4qQTuK1bGwzszKWmRI1VvfTBxAEdJYyGbA2Hn4ia0KajOYjfkYnJyw4g52qx1OEQObTSCp3UIZDEmNQQNZEzl6/hxY1/AgBm3N4F7Vq5SxwRkWPS3ijH/O/+xKgPd6Ncp5c6HJIQh5+IrESnF4ax/lYtXPDG5hO4XqbDgGBvPHVnV6nDI3JYcjmQlJaL/OtlWJeSjcmDOkkdEkmESQ2RFSSlaRCXmF6jlHtLNyXeH9+XS02JmpCnygWzh3fDy9+n4d2tGbivbzt4qVykDoskwOEnokZKStNgxqpUk3vTXCutwNHzf0kQFZFzGd8/CCFtPFBwvQyf7MiUOhySCJMaokaoa/dg4H+7B+v0tbUgImtwUcgxb2QPAMCy3VmcmO+kmNQQNYIluwcTUdO6s4cfBnb2RmmFHkt+OSl1OCQBJjVEjcDdg4lsh0wmw/y/C/FtO5GHv66XSRwRNTdOFCZqBO4eTGRberVX491xt+D2bn7cX80JsaeGqBGigr3hU8cvTu4eTNT8/tW3PRMaJ8WkhqgRrlwrrbXYV9Uibu4eTCQNIQR2ZVxmQT4nwqSGqIHKKvR4fHUqCksq0Fatgr+Xm9HrAWoVEiZFcPdgIok8ue4oJi9LwbqD56UOhZoJ59QQNdDCH4/h8Lm/4KVSYs2jAxHk3cJQUdjPs3LIiT00RNKJ6tQaib9fxHvJpzC6T1t4siCfw2NPDVEDfH3wPFbtz4ZMBrw/vi86+XoYdg++r087DArxYUJDJLHxUR3QuY0H8q+X4ZOdLMjnDJjUEDVAtwBPBKpVmHNXNwwL9ZM6HCIy4eaCfJ/vysJFFuRzeExqiBqgT1Ar/PzUEMwc1kXqUIioDnf18ENUMAvyOQsmNURmKtfpkXGpyPB9qxaukHOIicimVRbkq+yt2XgkB2sOnMOmoznYl5nP7UscECcKE5np9c3HseZANhbf3xv39WkndThEZKZbglqhf6fWSM2+ihe/SzMcD1SrEDsqjCsUHQh7aojM8N2RC1ix5yxKK/RQuSikDoeILJCUpsGhs3/V6JnJ1ZZgxqpUJKVpJIqMrI1JDVE90nK0mLfxTwDAE3d0wd09AySOiIjMpdMLxCWmw9RAU9WxuMR0DkU5CCY1RHX463oZpq86jJJyPW7v3gaz7+omdUhEZIGUrAJotLVvKCsAaLQlSMkqaL6gqMlwTg3RTXR6YSig5+PhioQdmbjwVzE6+rTA++P6svYMkZ3JK6o9oWlIO7JtTGqI/paUpkFcYnqNv+pcFXJ8OjkS6hasRkpkb/w8VVZtR7aNw09EqExoZqxKNdlNXabT4+yV6xJERUSNFRXsjUC1CnX1sQaqK7c1IfvHpIacXl0TCYHK3bY5kZDIPinkMsSOCgOAWhObqYM7cWjZQTCpIafHiYREjm1EeCASJkUgQG08xKRyqfwI/ObwBZSU66QIjayMc2rI6XEiIZHjGxEeiOFhAYaFAH6eKnTxa4l/fLAL5/Jv4Ej2VQwK8ZE6TGokJjXk9DiRkMg5KOSyGonLB+P7wtvDFd0DPCWKiqyJSQ05vahgbwR4qZBbaLonRgYggBMJiRwSe2ccC+fUkNNTyGUY0Nl0wlI1dTB2VBgnEhI5uN/PX8X7WzOkDoMagT015PTOXrmOpLRcAIDa3QXa4nLDawHc8I7IKWi0xbj/k70o1wn0bOuFu8L8pQ6JGoBJDTm91zYfR2mFHtFdfPDF1CgcPPuXYSJhVLA3e2iInECg2h0PDe6EpbuyMHfjH/ilw23waekmdVhkISY15PRiR4VBIZPhhZGhUCrkHGMnclLPxHTHjpOXkZF3DfO/S0PCpAjIZPyjxp5wTg05vfatW+CTyZEI9vWQOhQikpDKRYF3x/WBUi5D0rFcfH80R+qQyEJMashpZXHrAyKqJrydGk/d2RUA8MqmY9BoiyWOiCzBpIac0uFzBbjj7R147pvfuf0BERmZcXsIbglqhaKSCqw5kC11OGQBzqkhp1Ou0+PFjWkQApDJwInARGREqZDjnbG3YO/pK5g0sKPU4ZAFmNSQ01m66wxOXiqCt4cr5o3sIXU4RGSDQtq0REibllKHQRbi8BM5lez8G/jg18riWvP/0QOtPVwljoiIbN210gp8vP00KnR6qUOherCnhpyGEAIvbUpDSbkegzr7YExEO6lDIiIbp9cLjP9sH9JyCqEXAv06erOOlQ1jUkNO48c/NPjt1GW4KuR49V/hrD9BRPWSy2X4T3Qw5nz9O97ecsrotUBWHLc5HH4ip+GmlMO3pRseHxbCsXIiMpu7i8Lk8VxtCWasSkVSmqaZI6LasKeGnEZMzwAM6OwDlQtzeSIyj04vsPDHdJOvCVRuehuXmI7hYQEcirIB/O1OTkXt7gI3pem/uoiIqkvJKoBGW1Lr6wKARluClKyC5guKasWkhhxauU6PSZ8fwKajORCCRfaIyDJ5RbUnNA1pR02Lw0/k0JbtzsLu01dw7KIWt3fzg7qFi9QhEZEd8fNUWbUdNS0mNeRQdHqBlKwC5BWVQAbg3eSTAIAX/9GDCQ0RWSwq2BuBahVytSUw1dcrAxCgrlzeTdJjUkMOIylNg7jE9Brj3139WuL+yPYSRUVE9kwhlyF2VBhmrEqFDDBKbKqmBceOCuMkYRvBOTXkEJLSNJixKtXkhL6MvGv45ViuBFERkSMYER6IhEkRCFAbDzG5KeW4P7I969TYEPbUkN3T6QXiEtNNdg0DXHJJRI03IjwQw8MCDMPbaTlaLN1VOWevXKeHi4J9BLaA/xXI7nHJJRE1B4VchkEhPrivTzs8E9MdbTzdoNGW4IejF6UOjf7GpIbsHpdcElFzU7ko8J/oYADAp79lQq9nyQhbwKSG7B6XXBKRFCYO6ICWbkqcunQNO07lSR0OgUkNOYCqJZe1kaFy4zkuuSQia1K7u+DfAzoAAD7ZcUbiaAhgUkMOQCGX4YF+ppdsc8klETWl/9waDFeFHClnC3D43F9Sh+P0uPqJ7F5hSTm+PXQBANDCVYEbZTrDawFqFWJHhXHJJRE1CX8vFaYP7YzWHq7oEegpdThOj0kN2b1Xf0zHRW0JOvq0wI9P3Iq0nELkFZXAz7NyyIk9NETUlObEdJc6BPobkxqya9tOXMLXhy5AJgOWPHALPFUuGBTiI3VYREQkgQbNqYmPj0dwcDBUKhUiIyOxa9cus87bs2cPlEol+vTpU+O1DRs2ICwsDG5ubggLC8N3333XkNDIiVy9UYa5G/4EADwcHYz+nTgRmIikIYTAxtQLuPfDXdBoi6UOx2lZnNSsX78es2fPxvz583HkyBEMGTIEI0eORHZ2dp3nabVaTJkyBXfeeWeN1/bt24dx48Zh8uTJ+P333zF58mSMHTsWBw4csDQ8ciKf/XYGeUWl6NzGA8/eze5fIpKOTCbDuoPnkZZTiOW7s6QOx2nJhBAWVQwaMGAAIiIikJCQYDjWo0cPjB49GosWLar1vPHjx6Nr165QKBT4/vvvcfToUcNr48aNQ2FhIX7++WfDsREjRqB169ZYu3atyeuVlpaitLTU8H1hYSGCgoKg1Wrh5eVlyVsiO1VaocP7WzMwPMwffTu0ljocInJy20/kYerKg/BwVWDvvDuhdneROiS7UFhYCLVabZXPb4t6asrKynD48GHExMQYHY+JicHevXtrPW/FihXIzMxEbGysydf37dtX45p33313nddctGgR1Gq14SsoKMiCd0KOwE2pwPMjQpnQEJFNuL17G4QGeOJ6mQ6r9p+TOhynZFFSc+XKFeh0Ovj7+xsd9/f3R26u6V2QMzIyMHfuXKxevRpKpel5ybm5uRZdEwDmzZsHrVZr+Dp//rwlb4XslBACm//UoEKnlzoUIiIjMpkM04Z2BgCs2HMWJeW6es4ga2vQRGGZzHiJrBCixjEA0Ol0mDhxIuLi4tCtWzerXLOKm5sbvLy8jL7I8SX+ocHjq1Mx9tN9TGyIyObc27st2rVyx5VrpdiYmiN1OE7HoqTG19cXCoWiRg9KXl5ejZ4WACgqKsKhQ4cwa9YsKJVKKJVKLFy4EL///juUSiW2bdsGAAgICDD7muS88opK8MqmNADAkK5toFSwIDYR2RYXhRwP31q50eVnv2VCx40um5VFnwqurq6IjIxEcnKy0fHk5GQMHjy4RnsvLy/8+eefOHr0qOFr+vTp6N69O44ePYoBAwYAAAYNGlTjmlu2bDF5TXJOQgi8uDENV2+Uo2dbL8y6o4vUIRERmTQ+KgjDw/yx4J89wdqfzcvi4ntz5szB5MmT0a9fPwwaNAifffYZsrOzMX36dACVc11ycnLw5ZdfQi6XIzw83Oh8Pz8/qFQqo+NPPfUUbrvtNrz55pu47777sGnTJmzduhW7d+9u5NsjR7ExNQdbj1+Ci0KGt8feAhf20hCRjWrhqsTSKf2kDsMpWZzUjBs3Dvn5+Vi4cCE0Gg3Cw8OxefNmdOzYEQCg0WjqrVlT3eDBg7Fu3Tq89NJLePnllxESEoL169cbenLIuWm0xViQeAwAMPuubggN4PwpIiKqyeI6NbbKmuvcybY89uUhbEm/hFuCWmHD9EGcS0NEduGv62VYvicLZ/Nv4MMJfaUOx2ZZ8/Obez+RzdHpBVKyCgybUj4T0x3a4nK89q9wJjREZDeulVYgfkflZOFpt3VGeDu11CE5PCY1ZFOS0jSIS0yHRltiOBaoViF2VBi6+HlKGBkRkWWCvFvgnl6B+OH3i/jstzP4gL01TY5/9pLNSErTYMaqVKOEBgBytSWYsSoVSWkaiSIjImqYqmJ8P/5xEecLbkgcjeNjUkM2QacXiEtMh6kJXlXH4hLTWfOBiOxKz7Zq3NatDfQC+PS3TOzLzMemoznYl5nP32dNgMNPZBNSsgpq9NDcTADQaEuQklWAQSE+zRcYEVEjTb+tM347dRmr9mdj1f7/rQ6uGlofER4oYXSOhT01ZBPyimpPaBrSjojIVmiLy00e59C69TGpIZvg56myajsiIlug0wss/DHd5GscWrc+JjVkE6KCvRHgVXvCIkNlV21UsHfzBUVE1EiWDK1T4zGpIZugkMswuIvpuTJVW6fEjgqDghupEJEd4dB682JSQzYjqpM33F3kULu7GB0PUKuQMCmCk+mIyO5waL15cfUT2YzxUR1wd88AtHRT4tC5vwwVhaOCvdlDQ0R2KSrYG4FqFXK1JSZLVshQ+Ycbh9atg0kN2ZTWHq4AwGXbROQQFHIZYkeFYcaqVMgAk4kNh9ath8NPJCkhBGavO4Kt6ZfgIHurEhEZGREeiIRJEQhQ1xximn1XNw6tWxGTGpLUpqMX8f3Ri3hy3REUXC+TOhwioiYxIjwQu1+4A2sfHYj3x/fB8B7+AIAt6bnQczm31TCpIclcL63Aop+PAwBmDusCn5ZuEkdERNR0FHIZBoX44L4+7fDG//WCp5sSxy4WYuORHKlDcxhMakgyCTsycamwFB28W+DhW4OlDoeIqNn4tHTDrDu6AABWHzjH4Xcr4URhkkR2/g18tusMAGD+PT2gclFIHBERUfN6KLoTlAo5/j2gA2QyThS2BiY1JInXNqejrEKP6C4+iAnzlzocIqJm56ZUsJfayjj8RM3uzwta/HLs0t9LHXvyLxQicno6vcC+zHypw7B77KmhZhfezgvLH+qHk7nX0M3fU+pwiIgkVVKuw5j4vUjXFCJx1q3o1V4tdUh2iz011OxkMhnuCPXHjNtDpA6FiEhyKhcFugdU/oH335/SOWm4EZjUULPR3ihH/rVSqcMgIrI5z93dHW5KOVKyCvDLsUtSh2O3mNRQs3k7+SRuX7IDm46yJgMR0c3atnLHY7d1BgAs+vk4yir0Ekdkn5jUULM4kVuIVfvPoaikgrvREhGZMH1oCNp4uuFc/g18ue+s1OHYJSY11OSEEIj7IR16AfyjVwA3qyQiMsHDTYlnY7oBAN7/NYNbxzQAkxpqcklpudh3Jh9uSjnmjewhdThERDbr/sgg9Aj0Qnd/TxQWl0sdjt3hkm5qUiXlOry2uXJ/p2m3dUaQdwuJIyIisl0KuQxfPRwFHw9X1vBqACY1ZHU6vUBKVgHyikqwJ+MKLvxVjEC1CtO5hJuIqF6+3Ny3wZjUkFUlpWkQl5gOjbbE6PjI8AC0cOXjRkRkrsKScsRvz8RtXX0xuIuv1OHYBc6pIatJStNgxqrUGgkNAKzYcxZJaRoJoiIisk8fbz+NT3ZmYuGP6dDpWZDPHExqyCp0eoG4xHTU9c8uLpH/MImIzDX9thB4qZQ4kVuEbw+flzocu8CkhqwiJavAZA9NFQFAoy1BSlZB8wVFRGTHWnu44sk7uwIAFiedxLYTedh0NAf7MvP5B2ItOMmBrCKvqPaEpiHtiIgImDKoEz7dmYnL18rwn5UHDccD1SrEjgrDiPBACaOzPeypIaswt0owqwkTEZlv24lLuHytZhG+XG0JZqxK5VzFapjUkFVEBXsjUF17wiJD5V8WUcHezRcUEZEdq5qraErV4BPnKhpjUkNWoZDLEDsqzORrVeWjYkeFQSFnMSkiInNwrqLlOKeGrKa2glEBHPslIrIY5ypajkkNWc1H208DAMb1b4/Rfdojr6gEfp6VQ07soSEisgznKlqOSQ1ZhRACg0N8cEJThMdv74KOPh5Sh0REZNeq5irmaktM1gCTobInnHMV/4dzasgqZDIZHrstBLtfGMaEhojICm6eq1hbXzfnKhpjUkNWpVTwkSIispYR4YFImBSBABOrSycN7Mi5itVw+Ika7f2tGejq3xIjegZAzr8YiIisakR4IIaHBSAlqwB5RSVIycrH6gPncSArH0IIyGT8vVuFf1ZTo2Tn38AH2zLw+OpUnMgtkjocIiKHpJDLMCjEB/f1aYfnR/SAh6sCpy5dw+7TV6QOzaYwqaFGSdiZCZ1eYGi3Nghr6yV1OEREDk/t7oIH+gUBAD7flSVxNLaFSQ01WK62BBsOXwAAzLqji8TREBE5j/9EB0MmA3aeuoxTl9hLXoVzaqjBPvvtDMp0ekQFe6N/Jy4pJCJqLh18WuChwZ0Q1LoF2rZylzocm8Gkhhok/1op1qScAwA8wV4aIqJmFzuqp9Qh2BwOP1GDLNudhZJyPW5pr8atXXylDoeIiIhJDTVMVLA3bglqhZnDunA5IRGRRCp0emxMvYDJyw6gpFwndTiS4/ATNcjt3f0wtFsbqcMgInJqAsBbv5yERluCH36/iLF/r4pyVuypoQaTyWTspSEikpCLQo6HBncCACzfnQUhTO0S5TyY1JBF1qVk46NtGdAWl0sdChERARgf1QEtXBU4kVuEPafzpQ5HUkxqyGwl5Tq8k3wKS7acwi9puVKHQ0REqCzGVzXs9PnuMxJHIy0mNWS2bw5fQF5RKdqqVRjdt53U4RAR0d+mRneCTAbsOHkZp/OctxgfkxoyS7lOj092ZAIApg0NgauSjw4Rka3o6OOB4T38AQDLdp+VNhgJcfUTmWXT0YvIuVoM35ZuGNffuWfXExHZokeGdMa10grc3dNf6lAkw6SG6qXTC8TvOA0AeGRIMFQuCokjIiKi6qKCvbHm0YFShyEpjiFQvX5O0+DM5etQu7tg0sCOUodDRERkEntqqF5hgV4YE9EOwT4eaOnGR4aIyJZdLirFV/vPITTAE//oFSh1OM2Kn1BUg04vkJJVgLyiEvh5qhAV7I13xvaROiwiIjLDhtQL+ODXDIQGeGJkeIBTFUllUkNGktI0iEtMh0ZbYjgWqFYhdlQYRoQ7V8ZPRGSPJvTvgPe3ZuBEbhH2ZuYj2ok2HeacGjJIStNgxqpUo4QGADTaEsxYlYqkNI1EkRERkbnULVwwtl97AMDnu5yrGB+TGgJQOeQUl5iOunYNiUtMh07v3PuKEBHZg6nRwZDJgO0nL+N03jWpw2k2TGoIAJCSVVCjh+ZmApU9NilZBc0XFBERNUgnXw/c9XcxvuV7siSOpvkwqSEAQF5R7QlNQ9oREZG0Hr41GACw4fAFFFwvkzia5sGJwgQA8PNUWbUdERFJa0CwN/p1bI2u/p6o0OmlDqdZMKkhAJWVKAPVKuRqS0zOq5EBCFBXLu8mIiLbJ5PJ8PW0QZDLZdDpBfZl5huV6lDIHW+pN5MaAgAo5DLEjgrD9FWpNV6reuxjR4U55D8CIiJHJZfLnKpUR4Pm1MTHxyM4OBgqlQqRkZHYtWtXrW13796N6Oho+Pj4wN3dHaGhoXj33XdrtHvvvffQvXt3uLu7IygoCE8//TRKSjh/ozmNCA/EO2NvqVE1OECtQsKkCId7+ImIHF1tpTpyHbRUh8U9NevXr8fs2bMRHx+P6OhofPrppxg5ciTS09PRoUOHGu09PDwwa9Ys9O7dGx4eHti9ezemTZsGDw8PPPbYYwCA1atXY+7cuVi+fDkGDx6MU6dO4aGHHgIAkwkQNZ0xEe1xX592NSoKs4eGiMi+1FWqQ6CyFz4uMR3DwwIc5ne8TAhhUeGRAQMGICIiAgkJCYZjPXr0wOjRo7Fo0SKzrjFmzBh4eHjgq6++AgDMmjULx48fx6+//mpo88wzzyAlJaXOXqCbFRYWQq1WQ6vVwsvLy4J3RERE5Hj2ZeZjwtL99bZb++hADArxaYaITLPm57dFw09lZWU4fPgwYmJijI7HxMRg7969Zl3jyJEj2Lt3L4YOHWo4duutt+Lw4cNISUkBAJw5cwabN2/GPffcU+t1SktLUVhYaPRFDafRFuOBT/Zia/olWJjnEhGRDXLGUh0WDT9duXIFOp0O/v7+Rsf9/f2Rm5tb57nt27fH5cuXUVFRgQULFuCRRx4xvDZ+/HhcvnwZt956K4QQqKiowIwZMzB37txar7do0SLExcVZEj7V4dOdZ3Dw7F+Qy87grjD/+k8gIiKb5oylOho0Ubj6jp9CiHp3Ad21axcOHTqETz75BO+99x7Wrl1reG3Hjh147bXXEB8fj9TUVGzcuBE//vgj/vvf/9Z6vXnz5kGr1Rq+zp8/35C3Qqjcpn5tSjYA4Ik7ukocDRERWUNVqY7aPp1lqFwF5UilOizqqfH19YVCoajRK5OXl1ej96a64ODKyoa9evXCpUuXsGDBAkyYMAEA8PLLL2Py5MmG3ptevXrh+vXreOyxxzB//nzI5TVzLzc3N7i5uVkSPtVi2e4slFbo0SeoFaK7SDeuSkRE1lNVqmPGqlTIAJMThh2tVIdFPTWurq6IjIxEcnKy0fHk5GQMHjzY7OsIIVBaWmr4/saNGzUSF4VCASEE53c0sas3yvDVvrMAgFnDutTb40ZERPZjRHggEiZFIEBtPMTUqoWLQ5bqsHhJ95w5czB58mT069cPgwYNwmeffYbs7GxMnz4dQOWwUE5ODr788ksAwMcff4wOHTogNDQUQGXdmiVLluCJJ54wXHPUqFF455130LdvXwwYMACnT5/Gyy+/jH/+859QKBTWeJ9UixV7zuJ6mQ49Ar1wZw8/qcMhIiIrGxEeiOFhAUjJKsD5v26gXSt3DOzs41A9NFUsTmrGjRuH/Px8LFy4EBqNBuHh4di8eTM6duwIANBoNMjOzja01+v1mDdvHrKysqBUKhESEoI33ngD06ZNM7R56aWXIJPJ8NJLLyEnJwdt2rTBqFGj8Nprr1nhLVJtrpVWYOXeswDYS0NE5MgUchkGhfhgEBx7ioHFdWpsFevUWE6vF/jlWC5+/EODDyb0dcisnYiIairX6bHtRB7uDPWDUtGgNUNWY83Pb+795MTkchlG9grEyF6ONaZKRES1E0Jg1Ie7cSK3CJ9NjkRMzwCpQ7IaadMzkoyDdNAREZGFZDIZhnZrAwBYk5JdT2v7wqTGCZVW6HDfx3vw6c5MlJTrpA6HiIia2YSoyr0ad566jPMFNySOxnqY1DihDYdz8McFLZbvyQLnBhMROZ9Ovh6I7uIDIYD1Bx2neC2TGidTodMjYedpAMC020LgpuSSeSIiZzQxqnLV8vpD51Gu00scjXUwqXEyP/x+EecLiuHj4WrofiQiIuczPMwfvi1dcbmoFL8evyR1OFbBpMaJ6PUCH2+v7KV5eEgw3F3ZS0NE5KxclXI80C8IAHAgq0DiaKyDS7qdSNKxXGRevg4vlRKTB3aUOhwiIpLYQ4M74d7egejZVi11KFbBpMZJCPG/Xpqp0cHwVLlIHBEREUnN30sFfy9V/Q3tBIefnIRMJsOSB27BmL7tMDW6k9ThEBGRjdHeKLf7CcNMapxIj0AvvDOuD1q1cJU6FCIisiGLfj6OqNe3Yssx+54wzKTGCej1rB5MRES1c5HLUVqhx5qUc1KH0ihMapzAgytS8MK3fyBXWyJ1KEREZIPGRwVBJgP2nM7H2SvXpQ6nwZjUOCidXmBfZj7eSz6FXRlXsCH1PPTc74mIiExo37qFYT+otXa8HxRXPzmgpDQN4hLTobmpZ8ZFqcAfF66ibSt3CSMjIiJbNTGqA3acvIxvDl/AnJhudllxnj01DiYpTYMZq1KNEhoAKC7TYcaqVCSlaSSKjIiIbNkdoX4I8FKh4HqZ3U4YZlLjQHR6gbjEdNQ1yBSXmA4dJw4TEVE1SoUcY/tXVhhed9A+h6A4/ORAUrIKavTQ3EwA0GhLkJJVgEEhPs0XGBER2YXx/YPgppTjgcj2UofSIExqHEhekXmrm8xtR0REzqVtK3fMHNZF6jAajMNPDsTP07xS1+a2IyIisidMahxIVLA3AtUqyGp5XQYgUK1CVLB3c4ZFRER2ZvvJPExcuh+bjuZIHYpFmNQ4EIVchhdGhAJAjcSm6vvYUWFQyGtLe4iIiIDfz1/F3sx8rD5gXxOGmdQ4mB//0KBzGw/4tDTe3ylArULCpAiMCA+UKDIiIrIX4/oHQS6rXIByOq9I6nDMxonCDuTPC1psPX4Jchnw81O3oeB6GfKKSuDnWTnkxB4aIiIyR6DaHXeE+mPr8UtYc+A8XhkVJnVIZmFS40De23oKAPDPW9qie4CnxNEQEZE9+/eADth6/BI2pF7A8yO6Q+Vi+xWGmdQ4iD8uXMWvJ/IglwFP3tlV6nCIiMjO3datDdq1ckfO1WJ88GsGugd42nzPP5MaB/He1gwAwOg+7dC5TUuJoyEiInunkMsQ2aEVcq4WI35HpuF4oFqF2FFhNjlHkxOFHcDR81ex7UQeFHIZnmAvDRERWUFSmgY//FFzv8BcbYnN7iXIpMYBrD94HkBlL02wr4fE0RARkb2r2kvQlKrdA21xL0EOPzmAV0eHY0CwN/p2aCV1KERE5ADsdS9BJjUOQCGXYXTfdlKHQUREDsJe9xLk8JMdy9WWoKRcJ3UYRETkYOx1L0EmNXbsuW9/x+1v7cDe01ekDoWIiByIve4lyKTGTh06W4BdGVdw5VopgrxbSB0OERE5EIVchti/qwjb016CTGrs1Lt/Vw++P7I9kxoiIrK6EeGBSJgUgQC18RCTLe8lyInCdiglqwB7TudDKZdh5rAuUodDREQOakR4IIaHBSAlq8Au9hJkUmOH3k2u7KV5oF8Qe2mIiKhJKeQym1q2XRcOP9mZ/Wfyse9MPlwUMsy6g700REREVZjU2JmqlU5j+wWhXSt3iaMhIiKyHRx+sjNzYrrjrjB/m6sNQEREJDUmNXaod/tWUodARERkczj8ZCcyLhVBoy2WOgwiIiKbxaTGDggh8OJ3f2Lo4h3Y/KftbfVORERkC5jU2IE9p/Nx8OxfkMmAyI6tpQ6HiIjIJnFOjY3S6UVlsaPCEny84zQAYOKADvD34gRhIiIiU5jU2KCkNA3iEtOh0Rpv6R7q7ylRRERERLaPSY2NSUrTYMaqVAgTr83d+CfULVxscr8NIiIiqXFOjQ3R6QXiEtNNJjRV4hLTodPX1YKIiMg5MamxISlZBTWGnG4mAGi0JUjJKmi+oIiIiOwEkxobkldUe0LTkHZERETOhEmNDTF36wNukUBERFQTkxobEhXsjUB17QmLDECgWoWoYO/mC4qIiMhOMKmxIQq5DC/d08Pka7K//zd2VBgUcpnJNkRERM6MSY2NKS7XAwBk1fKWALUKCZMiuJybiIioFqxTY0PKKvR4b+spAMDzd3dHn6DWyCsqgZ9n5ZATe2iIiIhqx6TGhnx96Dwu/FWMNp5ueGhwMNxdFVKHREREZDc4/GQjSsp1+HBbBgBg1rAuTGiIiIgsxKTGRqzafw6XCkvRVq3C+KggqcMhIiKyOxx+shEBahXatXLHE3d0gZuSvTRERESWYlJjI+7t3RYxYQHgXGAiIqKGYVJjQ1yVHA0kIiJqKH6KSuyLvWex/mA2ynV6qUMhIiKya+ypkVDB9TK89ctJXCutgLeHG4aH+UsdEhERkd1iT42EPv0tE9dKK9CzrRfuDPWTOhwiIiK7xqRGInlFJfhi71kAwDMx3SDnDGEiIqJGYVIjkfjtmSgp16Nvh1YY1p29NERERI3FpEYCOVeLseZANgDg2ZjukFXfvZKIiIgsxqRGAh9ty0CZTo9BnX0Q3cVX6nCIiIgcQoOSmvj4eAQHB0OlUiEyMhK7du2qte3u3bsRHR0NHx8fuLu7IzQ0FO+++26NdlevXsXMmTMRGBgIlUqFHj16YPPmzQ0Jz+bdH9kegzr74JmYblKHQkRE5DAsXtK9fv16zJ49G/Hx8YiOjsann36KkSNHIj09HR06dKjR3sPDA7NmzULv3r3h4eGB3bt3Y9q0afDw8MBjjz0GACgrK8Pw4cPh5+eHb7/9Fu3bt8f58+fh6enZ+HdogyI7emPtYwOlDoOIiMihyIQQwpITBgwYgIiICCQkJBiO9ejRA6NHj8aiRYvMusaYMWPg4eGBr776CgDwySef4K233sKJEyfg4uJiSTgGhYWFUKvV0Gq18PLyatA1mpoQgvNniIiIbmLNz2+Lhp/Kyspw+PBhxMTEGB2PiYnB3r17zbrGkSNHsHfvXgwdOtRw7IcffsCgQYMwc+ZM+Pv7Izw8HK+//jp0Ol2t1yktLUVhYaHRl6175uvf8frm4yi4XiZ1KERERA7HoqTmypUr0Ol08Pc3rnzr7++P3NzcOs9t37493Nzc0K9fP8ycOROPPPKI4bUzZ87g22+/hU6nw+bNm/HSSy/h7bffxmuvvVbr9RYtWgS1Wm34CgoKsuStNLu0HC02HsnB0l1nkFdUInU4REREDqdB2yRUH0IxZ1hl165duHbtGvbv34+5c+eiS5cumDBhAgBAr9fDz88Pn332GRQKBSIjI3Hx4kW89dZbeOWVV0xeb968eZgzZ47h+8LCQptObN5NPgUAGNW7LUIDbHN4jIiIyJ5ZlNT4+vpCoVDU6JXJy8ur0XtTXXBwMACgV69euHTpEhYsWGBIagIDA+Hi4gKFQmFo36NHD+Tm5qKsrAyurq41rufm5gY3NzdLwm92Or1ASlYBUs7m49cTeZDLgNl3dZU6LCIiIodk0fCTq6srIiMjkZycbHQ8OTkZgwcPNvs6QgiUlpYavo+Ojsbp06eh1/9vp+pTp04hMDDQZEJjD5LSNLj1zW2YsHQ/3k3OAAC4KRU4dalI4siIiIgck8V1aubMmYPPP/8cy5cvx/Hjx/H0008jOzsb06dPB1A5LDRlyhRD+48//hiJiYnIyMhARkYGVqxYgSVLlmDSpEmGNjNmzEB+fj6eeuopnDp1Cj/99BNef/11zJw50wpvsfklpWkwY1UqNFrjuTPF5TrMWJWKpDSNRJERERE5Lovn1IwbNw75+flYuHAhNBoNwsPDsXnzZnTs2BEAoNFokJ2dbWiv1+sxb948ZGVlQalUIiQkBG+88QamTZtmaBMUFIQtW7bg6aefRu/evdGuXTs89dRTeOGFF6zwFpuXTi8Ql5iOutbJxyWmY3hYABTcxJKIiMhqLK5TY6tspU7Nvsx8TFi6v952ax8diEEhPs0QERERke2SrE4N1c/c5dpc1k1ERGRdTGqszM9TZdV2REREZB4mNVYWFeyNAK/al5rLAASqVYgK9m6+oIiIiJwAkxorU8hluLWLr8nXqqYFx44K4yRhIiIiK2NSY2Xa4nJsPZEHAFC7G2/OGaBWIWFSBEaEB0oRGhERkUNr0DYJVLtPd2bi6o1ydPVriR+fuBWp2VeRV1QCP8/KISf20BARETUNJjVWlKstwfI9WQCA50eEws1FwWXbREREzYTDT1b0/q+nUFKuR7+OrXFXDz+pwyEiInIqTGqspKRchz2n8wEA8/4RWu+u5URERGRdHH6yEpWLAluevg07T11GZEcu1yYiImpu7KmxIpWLAnf3DJA6DCIiIqfEpKaRhBD45VguKnR6qUMhIiJyakxqGunX43mY9tVh/Ct+L3R6h9gblIiIyC4xqWkEnV7gzaQTAIBbu/qyBg0REZGEmNQ0wobUC8jIuwa1uwumDw2ROhwiIiKnxqSmgUrKdXg3+RQAYNawLjW2RCAiIqLmxaSmgb7YexYabQnaqlWYPKij1OEQERE5PSY1DaC9UY6Pt58GAMyJ6Q6Vi0LiiIiIiIjF9xrganEZQgO8oC0ux7/6tpM6HCIiIgKTmgbp6OOB9dMG4q8b5VzxREREZCM4/NRAMpkM3h6uUodBREREf2NSY4FTl4qwaPNxXL1RJnUoREREVA2HnyywOOkkth6/hMtFpXhnXB+pwyEiIqKbMKmpg04vkJJVgLyiEvx1vQxbj1+CQi7DzDu6SB0aERERVcOkphZJaRrEJaZDoy0xOj44xAchbVpKFBURERHVhnNqTEhK02DGqtQaCQ0A7Mq4gqQ0jQRRERERUV2Y1FSj0wvEJaajtv22ZQDiEtO5IzcREZGNYVJTTUpWgckemioCgEZbgpSsguYLioiIiOrFpKaavKLaE5qGtCMiIqLmwaSmGj9PlVXbERERUfNgUlNNVLA3AtUq1Lb5gQxAoFqFqGDv5gyLiIiI6sGkphqFXIbYUWEAUCOxqfo+dlQY93wiIiKyMUxqTBgRHoiESREIUBsPMQWoVUiYFIER4YESRUZERES1YfG9WowID8TwsABDRWE/z8ohJ/bQEBER2SYmNXVQyGUYFOIjdRhERERkBg4/ERERkUNgUkNEREQOgUkNEREROQQmNUREROQQmNQQERGRQ2BSQ0RERA6BSQ0RERE5BCY1RERE5BCY1BAREZFDcJiKwkIIAEBhYaHEkRAREZG5qj63qz7HG8NhkpqioiIAQFBQkMSREBERkaWKioqgVqsbdQ2ZsEZqZAP0ej0uXrwIT09PyGTW23SysLAQQUFBOH/+PLy8vKx2XUfH+9YwvG+W4z1rGN63huF9a5i67psQAkVFRWjbti3k8sbNinGYnhq5XI727ds32fW9vLz4ADcA71vD8L5ZjvesYXjfGob3rWFqu2+N7aGpwonCRERE5BCY1BAREZFDYFJTDzc3N8TGxsLNzU3qUOwK71vD8L5ZjvesYXjfGob3rWGa6745zERhIiIicm7sqSEiIiKHwKSGiIiIHAKTGiIiInIITGqIiIjIITCpISIiIofglElNfHw8goODoVKpEBkZiV27dtXZfufOnYiMjIRKpULnzp3xySefGL2+cuVKyGSyGl8lJSVN+TaalSX3TKPRYOLEiejevTvkcjlmz55tst2GDRsQFhYGNzc3hIWF4bvvvmui6KVj7fvmDM8aYNl927hxI4YPH442bdrAy8sLgwYNwi+//FKjHZ83Y+bcN2d43iy5Z7t370Z0dDR8fHzg7u6O0NBQvPvuuzXa8VkzZs59s9qzJpzMunXrhIuLi1i6dKlIT08XTz31lPDw8BDnzp0z2f7MmTOiRYsW4qmnnhLp6eli6dKlwsXFRXz77beGNitWrBBeXl5Co9EYfTkKS+9ZVlaWePLJJ8UXX3wh+vTpI5566qkabfbu3SsUCoV4/fXXxfHjx8Xrr78ulEql2L9/fxO/m+bTFPfN0Z81ISy/b0899ZR48803RUpKijh16pSYN2+ecHFxEampqYY2fN5qMue+OfrzZuk9S01NFWvWrBFpaWkiKytLfPXVV6JFixbi008/NbThs1aTOffNWs+a0yU1UVFRYvr06UbHQkNDxdy5c022f/7550VoaKjRsWnTpomBAwcavl+xYoVQq9VWj9VWWHrPbjZ06FCTH85jx44VI0aMMDp29913i/HjxzcqVlvSFPfN0Z81IRp336qEhYWJuLg4w/d83sxT/b45+vNmjXv2r3/9S0yaNMnwPZ8181S/b9Z61pxq+KmsrAyHDx9GTEyM0fGYmBjs3bvX5Dn79u2r0f7uu+/GoUOHUF5ebjh27do1dOzYEe3bt8e9996LI0eOWP8NSKAh98wctd3XxlzTljTVfQMc91kDrHPf9Ho9ioqK4O3tbTjG561+pu4b4LjPmzXu2ZEjR7B3714MHTrUcIzPWv1M3TfAOs+aUyU1V65cgU6ng7+/v9Fxf39/5ObmmjwnNzfXZPuKigpcuXIFABAaGoqVK1fihx9+wNq1a6FSqRAdHY2MjIymeSPNqCH3zBy13dfGXNOWNNV9c+RnDbDOfXv77bdx/fp1jB071nCMz1v9TN03R37eGnPP2rdvDzc3N/Tr1w8zZ87EI488YniNz1rt6rpv1nrWlBa1dhAymczoeyFEjWP1tb/5+MCBAzFw4EDD69HR0YiIiMCHH36IDz74wFphS8rSeybVNW2Ntd+jMzxrQMPv29q1a7FgwQJs2rQJfn5+VrmmPbH2fXOG560h92zXrl24du0a9u/fj7lz56JLly6YMGFCo65pb6x936z1rDlVUuPr6wuFQlEjm8zLy6uRdVYJCAgw2V6pVMLHx8fkOXK5HP3793eIv2Yacs/MUdt9bcw1bUlT3bfqHOlZAxp339avX4+HH34Y33zzDe666y6j1/i81a6u+1adIz1vjblnwcHBAIBevXrh0qVLWLBggeHDmc9a7eq6b9U19FlzquEnV1dXREZGIjk52eh4cnIyBg8ebPKcQYMG1Wi/ZcsW9OvXDy4uLibPEULg6NGjCAwMtE7gEmrIPTNHbfe1Mde0JU1136pzpGcNaPh9W7t2LR566CGsWbMG99xzT43X+byZVt99q86Rnjdr/RsVQqC0tNTwPZ8181S/b6Zeb9Cz1uipxnamainasmXLRHp6upg9e7bw8PAQZ8+eFUIIMXfuXDF58mRD+6ol3U8//bRIT08Xy5Ytq7Gke8GCBSIpKUlkZmaKI0eOiKlTpwqlUikOHDjQ7O+vKVh6z4QQ4siRI+LIkSMiMjJSTJw4URw5ckQcO3bM8PqePXuEQqEQb7zxhjh+/Lh44403HHbZozXvm6M/a0JYft/WrFkjlEql+Pjjj42Wgl69etXQhs9bw+6boz9vlt6zjz76SPzwww/i1KlT4tSpU2L58uXCy8tLzJ8/39CGz1rD7pu1njWnS2qEEOLjjz8WHTt2FK6uriIiIkLs3LnT8NqDDz4ohg4datR+x44dom/fvsLV1VV06tRJJCQkGL0+e/Zs0aFDB+Hq6iratGkjYmJixN69e5vjrTQbS+8ZgBpfHTt2NGrzzTffiO7duwsXFxcRGhoqNmzY0AzvpHlZ+745w7MmhGX3bejQoSbv24MPPmh0TT5vlt83Z3jeLLlnH3zwgejZs6do0aKF8PLyEn379hXx8fFCp9MZXZPPmuX3zVrPmkyIv2e9EhEREdkxp5pTQ0RERI6LSQ0RERE5BCY1RERE5BCY1BAREZFDYFJDREREDoFJDRERETkEJjVERETkEJjUEBERkUNgUkNEREQOgUkNEREROQQmNUREROQQ/h8LbEGSI8yy2wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(threshold, f1, '--o')\n",
    "plt.title(f'max f1 at threshold = {best_threshold}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a8b82c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob = model.predict_proba(X_out)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6b50f589",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_pred = np.where(y_prob>best_threshold, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "df1103ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = pd.DataFrame(confusion_matrix(y_out, best_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "26ba21e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat.columns = ['fully paid', 'charged off']\n",
    "mat.index = ['fully paid', 'charged off']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f68b12d6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(50.72222222222221, 0.5, 'true')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(mat, annot=True, fmt='.0f')\n",
    "plt.xlabel('pred')\n",
    "plt.ylabel('true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2e5b19e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mat = confusion_matrix(y_out, best_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7d15c794",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.32133126, 0.67866874])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[1]/mat.sum(axis=1)[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e14707af",
   "metadata": {},
   "source": [
    "Evaluate the performance by comparing the ROI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28a7c2e7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to MySQL Server\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_18508\\2324452014.py:12: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  eval_df_2 = pd.read_sql(\"SELECT ROI, IRR FROM matured_loan\", connection)\n"
     ]
    }
   ],
   "source": [
    "with open('db_config.json', 'r') as json_file:\n",
    "    db_config = json.load(json_file)\n",
    "\n",
    "try:\n",
    "    # Attempt to connect to the MySQL server\n",
    "    connection = mysql.connector.connect(**db_config)\n",
    "\n",
    "    # Check if the connection is successful\n",
    "    if connection.is_connected():\n",
    "        print(\"Connected to MySQL Server\")\n",
    "    \n",
    "    eval_df_2 = pd.read_sql(\"SELECT ROI, IRR FROM matured_loan\", connection)\n",
    "\n",
    "    # Close the connection\n",
    "    connection.close()\n",
    "\n",
    "except mysql.connector.Error as err:\n",
    "    print(f\"Error: {err}\")\n",
    "    print(\"Failed to connect to MySQL Server\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "233698f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_df = X_out.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "09ddeade",
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_df['true'] = y_out\n",
    "eval_df['pred'] = best_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "84cb8b1f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "eval_df = pd.concat([eval_df.iloc[:, -2:], eval_df_2.iloc[300000:]], axis=1)\n",
    "del eval_df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "b697b587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5846498746470772"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(eval_df.loc[eval_df['pred']==0, 'ROI'].mean() - eval_df.loc[:, 'ROI'].mean())/eval_df.loc[:, 'ROI'].mean()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}