DNN_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b83e3b91",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-23 11:49:16.637988: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-03-23 11:49:16.691313: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-03-23 11:49:16.693016: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/user/.mujoco/mujoco200/bin:/home/user/.mujoco/mujoco200/bin\n",
      "2023-03-23 11:49:16.693023: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-03-23 11:49:17.001559: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/user/.mujoco/mujoco200/bin:/home/user/.mujoco/mujoco200/bin\n",
      "2023-03-23 11:49:17.001591: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/user/.mujoco/mujoco200/bin:/home/user/.mujoco/mujoco200/bin\n",
      "2023-03-23 11:49:17.001593: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import classification_report, precision_recall_curve, roc_curve, roc_auc_score, f1_score\n",
    "from sklearn.metrics import average_precision_score\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from copy import deepcopy\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import classification_report,average_precision_score, precision_recall_curve, roc_curve, roc_auc_score, f1_score, confusion_matrix, auc\n",
    "from sklearn.model_selection import GroupKFold, StratifiedKFold, GridSearchCV, train_test_split\n",
    "from sklearn.metrics import classification_report,average_precision_score, precision_recall_curve, roc_curve, roc_auc_score, f1_score, confusion_matrix, auc, precision_score, recall_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d186f0ad",
   "metadata": {},
   "source": [
    "### Loading Data + Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91ee91a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv('./robot_data/merged_robot_data.csv')\n",
    "# df = pd.read_csv('./robotdatacollection3/merged_robot_data_100episodes.csv')\n",
    "# df = pd.read_csv('./sim_data_collection_feb7/100_sim_feb7.csv')\n",
    "# df = pd.read_csv('./data_collection4/merged_sim_data.csv')\n",
    "df = pd.read_csv('./Thesis/classifier_90.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0435a96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "undersample = False\n",
    "use_weights = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fb08b2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0.1', 'Unnamed: 0', 't', 'x', 'y', 'z', 'rx', 'ry', 'rz',\n",
       "       'vx', 'vy', 'vz', 'Fx', 'Fy', 'Fz', 'Mx', 'My', 'Mz', 'Case'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c04035b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(372031, 19)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b2a7946",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df.drop(['Unnamed: 0.2', 'Unnamed: 0.1','Unnamed: 0','t'], axis=1)\n",
    "df = df.drop(['Unnamed: 0.1','Unnamed: 0','t'], axis=1)\n",
    "# df = df.drop(['Unnamed: 0','t'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40445194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['x', 'y', 'z', 'rx', 'ry', 'rz', 'vx', 'vy', 'vz', 'Fx', 'Fy', 'Fz',\n",
       "       'Mx', 'My', 'Mz', 'Case'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee7a1a9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data:  372031\n",
      "Label: 0 366801 | 98.5942031712411%\n",
      "Label: 1 5230 | 1.4057968287588938%\n"
     ]
    }
   ],
   "source": [
    "tot = len(df.Case)\n",
    "pos = df.Case.sum()\n",
    "neg = tot-pos\n",
    "print('Total data: ', tot)\n",
    "print(f'Label: 0 {neg} | {(neg/tot)*100}%')\n",
    "print(f'Label: 1 {pos} | {(pos/tot)*100}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "23c880f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>rx</th>\n",
       "      <th>ry</th>\n",
       "      <th>rz</th>\n",
       "      <th>vx</th>\n",
       "      <th>vy</th>\n",
       "      <th>vz</th>\n",
       "      <th>Fx</th>\n",
       "      <th>Fy</th>\n",
       "      <th>Fz</th>\n",
       "      <th>Mx</th>\n",
       "      <th>My</th>\n",
       "      <th>Mz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.138157</td>\n",
       "      <td>-0.509673</td>\n",
       "      <td>0.072302</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>-0.000311</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>-0.000023</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>-0.354220</td>\n",
       "      <td>-0.144643</td>\n",
       "      <td>4.989051</td>\n",
       "      <td>0.005260</td>\n",
       "      <td>-0.018391</td>\n",
       "      <td>0.006327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.003047</td>\n",
       "      <td>0.002955</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.001924</td>\n",
       "      <td>0.002005</td>\n",
       "      <td>0.005790</td>\n",
       "      <td>0.001140</td>\n",
       "      <td>0.001214</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>1.053794</td>\n",
       "      <td>1.266742</td>\n",
       "      <td>0.879046</td>\n",
       "      <td>0.138463</td>\n",
       "      <td>0.144498</td>\n",
       "      <td>0.113629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.144842</td>\n",
       "      <td>-0.516395</td>\n",
       "      <td>0.068149</td>\n",
       "      <td>-0.015699</td>\n",
       "      <td>-0.013300</td>\n",
       "      <td>-0.014431</td>\n",
       "      <td>-0.007146</td>\n",
       "      <td>-0.006603</td>\n",
       "      <td>-0.023657</td>\n",
       "      <td>-5.893762</td>\n",
       "      <td>-9.714105</td>\n",
       "      <td>-0.537690</td>\n",
       "      <td>-1.217870</td>\n",
       "      <td>-1.444511</td>\n",
       "      <td>-0.294400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.140399</td>\n",
       "      <td>-0.511933</td>\n",
       "      <td>0.072288</td>\n",
       "      <td>-0.001272</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>-0.004872</td>\n",
       "      <td>-0.001091</td>\n",
       "      <td>-0.001033</td>\n",
       "      <td>-0.000150</td>\n",
       "      <td>-1.196908</td>\n",
       "      <td>-1.094954</td>\n",
       "      <td>4.608072</td>\n",
       "      <td>-0.099464</td>\n",
       "      <td>-0.132466</td>\n",
       "      <td>-0.055399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.138189</td>\n",
       "      <td>-0.509794</td>\n",
       "      <td>0.072305</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>-0.000251</td>\n",
       "      <td>0.001839</td>\n",
       "      <td>-0.000058</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-0.368335</td>\n",
       "      <td>-0.052817</td>\n",
       "      <td>4.978326</td>\n",
       "      <td>0.011537</td>\n",
       "      <td>-0.017958</td>\n",
       "      <td>0.003410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.135929</td>\n",
       "      <td>-0.507547</td>\n",
       "      <td>0.072320</td>\n",
       "      <td>0.001797</td>\n",
       "      <td>0.001417</td>\n",
       "      <td>0.005477</td>\n",
       "      <td>0.001022</td>\n",
       "      <td>0.001104</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.485567</td>\n",
       "      <td>0.929513</td>\n",
       "      <td>5.353946</td>\n",
       "      <td>0.126140</td>\n",
       "      <td>0.099284</td>\n",
       "      <td>0.074197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-0.131389</td>\n",
       "      <td>-0.502661</td>\n",
       "      <td>0.072926</td>\n",
       "      <td>0.003593</td>\n",
       "      <td>0.007917</td>\n",
       "      <td>0.015166</td>\n",
       "      <td>0.004523</td>\n",
       "      <td>0.006503</td>\n",
       "      <td>0.005934</td>\n",
       "      <td>8.236035</td>\n",
       "      <td>6.991087</td>\n",
       "      <td>14.483513</td>\n",
       "      <td>0.352819</td>\n",
       "      <td>0.867845</td>\n",
       "      <td>0.296962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   x              y              z             rx  \\\n",
       "count  372031.000000  372031.000000  372031.000000  372031.000000   \n",
       "mean       -0.138157      -0.509673       0.072302       0.000131   \n",
       "std         0.003047       0.002955       0.000079       0.001924   \n",
       "min        -0.144842      -0.516395       0.068149      -0.015699   \n",
       "25%        -0.140399      -0.511933       0.072288      -0.001272   \n",
       "50%        -0.138189      -0.509794       0.072305       0.000127   \n",
       "75%        -0.135929      -0.507547       0.072320       0.001797   \n",
       "max        -0.131389      -0.502661       0.072926       0.003593   \n",
       "\n",
       "                  ry             rz             vx             vy  \\\n",
       "count  372031.000000  372031.000000  372031.000000  372031.000000   \n",
       "mean       -0.000311       0.000474      -0.000023       0.000018   \n",
       "std         0.002005       0.005790       0.001140       0.001214   \n",
       "min        -0.013300      -0.014431      -0.007146      -0.006603   \n",
       "25%        -0.001982      -0.004872      -0.001091      -0.001033   \n",
       "50%        -0.000251       0.001839      -0.000058       0.000038   \n",
       "75%         0.001417       0.005477       0.001022       0.001104   \n",
       "max         0.007917       0.015166       0.004523       0.006503   \n",
       "\n",
       "                  vz             Fx             Fy             Fz  \\\n",
       "count  372031.000000  372031.000000  372031.000000  372031.000000   \n",
       "mean       -0.000017      -0.354220      -0.144643       4.989051   \n",
       "std         0.000602       1.053794       1.266742       0.879046   \n",
       "min        -0.023657      -5.893762      -9.714105      -0.537690   \n",
       "25%        -0.000150      -1.196908      -1.094954       4.608072   \n",
       "50%        -0.000002      -0.368335      -0.052817       4.978326   \n",
       "75%         0.000143       0.485567       0.929513       5.353946   \n",
       "max         0.005934       8.236035       6.991087      14.483513   \n",
       "\n",
       "                  Mx             My             Mz  \n",
       "count  372031.000000  372031.000000  372031.000000  \n",
       "mean        0.005260      -0.018391       0.006327  \n",
       "std         0.138463       0.144498       0.113629  \n",
       "min        -1.217870      -1.444511      -0.294400  \n",
       "25%        -0.099464      -0.132466      -0.055399  \n",
       "50%         0.011537      -0.017958       0.003410  \n",
       "75%         0.126140       0.099284       0.074197  \n",
       "max         0.352819       0.867845       0.296962  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4c23e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# corr = df.corr()\n",
    "# plt.figure(figsize=(15,10))\n",
    "# sns.heatmap(corr,annot=True)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3a4d60c2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# corr_with_case = corr['Case']\n",
    "# # delete .abs() if we do not want abs values\n",
    "# sorted_corr = corr_with_case.abs().sort_values(ascending=False)\n",
    "\n",
    "# corr_df = pd.DataFrame(sorted_corr)\n",
    "\n",
    "# # rename the column\n",
    "# corr_df = corr_df.rename(columns={'case': 'correlation'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7cc3babd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# corr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "531d8694",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_count_0, class_count_1 = df['Case'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "96012784",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_0 = df[df['Case'] == False]\n",
    "class_1 = df[df['Case'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb5155b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class 0: (366801, 16)\n",
      "class 1: (5230, 16)\n"
     ]
    }
   ],
   "source": [
    "print('class 0:', class_0.shape)\n",
    "print('class 1:', class_1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db1fcbcf",
   "metadata": {},
   "source": [
    "Undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4ad77bda",
   "metadata": {},
   "outputs": [],
   "source": [
    "if undersample:\n",
    "    class_0_under = class_0.sample(class_count_1)\n",
    "\n",
    "    test_under = pd.concat([class_0_under, class_1], axis=0)\n",
    "    test_under['Case'].value_counts()\n",
    "    df = test_under\n",
    "    print('Undersampling!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15e7bc34",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "42730d29",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = ['Fx', 'Fy', 'Fz', 'Mx', 'My']\n",
    "# feature_names = ['fz']\n",
    "# \n",
    "# feature_names = ['fx', 'fy', 'fz', 'mx', 'my']\n",
    "\n",
    "X_df = df[feature_names]\n",
    "y = df.Case.to_numpy()\n",
    "\n",
    "# moving average\n",
    "# roll = 30\n",
    "# X_df = X_df.rolling(roll).mean()\n",
    "# X_df = X_df.loc[roll-1:]\n",
    "# y = y[roll-1:]\n",
    "\n",
    "\n",
    "X = X_df.to_numpy()\n",
    "\n",
    "window_len = 100#300\n",
    "\n",
    "if window_len:\n",
    "    n_features = len(feature_names)  # 3\n",
    "\n",
    "    row = X.shape[0] + 1 - window_len\n",
    "    col_len = n_features * window_len\n",
    "    new_x = np.zeros((row, col_len))\n",
    "    new_y = np.zeros((row, 1))\n",
    "\n",
    "    for i in range(len(new_x)):\n",
    "        new_x[i] = X[i:i + window_len].reshape(1, col_len)[0][::-1]\n",
    "        new_y[i] = y[i + (window_len - 1)]\n",
    "\n",
    "    y = deepcopy(new_y)\n",
    "    X = deepcopy(new_x)\n",
    "\n",
    "total = len(y)\n",
    "pos = int(y.sum())\n",
    "neg = total - pos\n",
    "# weight = neg/pos # 13.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e8ec31b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight for class 0: 0.51\n",
      "Weight for class 1: 35.56\n"
     ]
    }
   ],
   "source": [
    "# Scaling by total/2 helps keep the loss to a similar magnitude.\n",
    "# The sum of the weights of all examples stays the same.\n",
    "\n",
    "weight_for_0 = (1 / neg) * (total / 2.0)\n",
    "weight_for_1 = (1 / pos) * (total / 2.0)\n",
    "class_weight = {0: weight_for_0, 1: weight_for_1}\n",
    "\n",
    "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n",
    "print('Weight for class 1: {:.2f}'.format(weight_for_1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e1b96dd7",
   "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>Fx</th>\n",
       "      <th>Fy</th>\n",
       "      <th>Fz</th>\n",
       "      <th>Mx</th>\n",
       "      <th>My</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "      <td>372031.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.354220</td>\n",
       "      <td>-0.144643</td>\n",
       "      <td>4.989051</td>\n",
       "      <td>0.005260</td>\n",
       "      <td>-0.018391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.053794</td>\n",
       "      <td>1.266742</td>\n",
       "      <td>0.879046</td>\n",
       "      <td>0.138463</td>\n",
       "      <td>0.144498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.893762</td>\n",
       "      <td>-9.714105</td>\n",
       "      <td>-0.537690</td>\n",
       "      <td>-1.217870</td>\n",
       "      <td>-1.444511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.196908</td>\n",
       "      <td>-1.094954</td>\n",
       "      <td>4.608072</td>\n",
       "      <td>-0.099464</td>\n",
       "      <td>-0.132466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.368335</td>\n",
       "      <td>-0.052817</td>\n",
       "      <td>4.978326</td>\n",
       "      <td>0.011537</td>\n",
       "      <td>-0.017958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.485567</td>\n",
       "      <td>0.929513</td>\n",
       "      <td>5.353946</td>\n",
       "      <td>0.126140</td>\n",
       "      <td>0.099284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.236035</td>\n",
       "      <td>6.991087</td>\n",
       "      <td>14.483513</td>\n",
       "      <td>0.352819</td>\n",
       "      <td>0.867845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Fx             Fy             Fz             Mx  \\\n",
       "count  372031.000000  372031.000000  372031.000000  372031.000000   \n",
       "mean       -0.354220      -0.144643       4.989051       0.005260   \n",
       "std         1.053794       1.266742       0.879046       0.138463   \n",
       "min        -5.893762      -9.714105      -0.537690      -1.217870   \n",
       "25%        -1.196908      -1.094954       4.608072      -0.099464   \n",
       "50%        -0.368335      -0.052817       4.978326       0.011537   \n",
       "75%         0.485567       0.929513       5.353946       0.126140   \n",
       "max         8.236035       6.991087      14.483513       0.352819   \n",
       "\n",
       "                  My  \n",
       "count  372031.000000  \n",
       "mean       -0.018391  \n",
       "std         0.144498  \n",
       "min        -1.444511  \n",
       "25%        -0.132466  \n",
       "50%        -0.017958  \n",
       "75%         0.099284  \n",
       "max         0.867845  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "296fa75c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.Fy.loc[:1000].plot()\n",
    "# X_df.Fy.loc[:1000].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b92c209e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(371932, 500)\n",
      "(371932, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "be7b5283",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Dataset size: (371932, 500)\n",
      "Ratio of 1's in dataset 1.4061710205091253% and count is: 5230\n",
      "Ratio of 0's in dataset 98.59382897949087% and count is: 366702\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total Dataset size: {X.shape}\")\n",
    "print(f\"Ratio of 1's in dataset {(pos/total) * 100}% and count is: {pos}\")\n",
    "print(f\"Ratio of 0's in dataset {(neg/total) * 100}% and count is: {neg}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5609509",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "11f0eaa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_prc_and_roc(y_true, y_pred):\n",
    "\n",
    "    precisions, recalls, thresholds = precision_recall_curve(y_true, y_pred)\n",
    "    average_precision = average_precision_score(y_true, y_pred)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.step(recalls, precisions, color='b', alpha=1.0, where='post')\n",
    "    plt.fill_between(recalls, precisions, step='post', alpha=0.1, color='b')\n",
    "\n",
    "    plt.xlabel('Recall')\n",
    "    plt.ylabel('Precision')\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.grid()\n",
    "\n",
    "    plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n",
    "              average_precision))\n",
    "    plt.show()\n",
    "    \n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "    areaUnderROC = auc(fpr, tpr)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "    plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic: \\\n",
    "            Area under the curve = {0:0.2f}'.format(areaUnderROC))\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.grid()\n",
    "    plt.show()       \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "952cc050",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_precision_recall_vs_threshold(y_true, y_pred):\n",
    "    precisions, recalls, thresholds = precision_recall_curve(y_true, y_pred)\n",
    "    f1_vec = 2 * (precisions * recalls) / (precisions + recalls)\n",
    "    plt.figure()\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label='Precision')\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label='Recall')\n",
    "    plt.plot(thresholds, f1_vec[:-1], \"k-\", label='F1')\n",
    "    plt.xlabel('Threshold')\n",
    "    plt.title('Precision/ Recall/ F1 Score/ Threshold')\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3ef8aed6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dnn_stratified_kfold_cross_val(X, y, epoch, batch_size, callback, print_info, metrics):\n",
    "    \"\"\"\n",
    "    StratifiedKFold cross-validation. \n",
    "    \"\"\"\n",
    "    skf = StratifiedKFold(n_splits=10, shuffle=False)\n",
    "    aucpr_scores = []\n",
    "    roc_scores = []\n",
    "    prec_scores =  []\n",
    "    recall_scores = []\n",
    "\n",
    "    for fold_id, (train_index, valid_index) in enumerate(skf.split(X, y)):\n",
    "        print(f\"Fold {fold_id}:\")\n",
    "        X_train, X_valid = X[train_index], X[valid_index]\n",
    "        y_train, y_valid = y[train_index], y[valid_index]\n",
    "        \n",
    "        if print_info:\n",
    "            print(f\"Ratio of 1's in train set {np.round((y_train.sum())/len(y_train),4) * 100}%\")\n",
    "            print(f\"Ratio of 1's in validation set {np.round((y_valid.sum()/len(y_valid)),4) * 100}%\")\n",
    "        \n",
    "        n_inputs = X_train.shape[1]  \n",
    "        # make model\n",
    "        model = make_model(n_inputs, metrics=metrics)\n",
    "        # fit model\n",
    "        model_history = model.fit(X_train, y_train,\n",
    "                          validation_data = (X_valid, y_valid),\n",
    "                          epochs=epoch,\n",
    "                          batch_size=batch_size,\n",
    "                          verbose=0, # 1 \n",
    "                          callbacks=[callback],\n",
    "                          shuffle=True) \n",
    "        \n",
    "        y_val_pred = model.predict(X_valid)\n",
    "        roc = roc_auc_score(y_valid, y_val_pred)\n",
    "        aucpr = average_precision_score(y_valid, y_val_pred)\n",
    "        prec = precision_score(y_valid, y_val_pred > 0.5)\n",
    "        recall = recall_score(y_valid, y_val_pred > 0.5)\n",
    "        if print_info:\n",
    "            print('--------------------------------------')\n",
    "            print(f\"Aucpr score for fold {fold_id} is : {aucpr}\")\n",
    "            print(f\"ROC score for fold {fold_id} is : {roc}\")\n",
    "            print(f\"Precision  : {prec}\")\n",
    "            print(f\"Recall : {recall}\")\n",
    "            print('--------------------------------------')\n",
    "\n",
    "        aucpr_scores.append(aucpr)\n",
    "        roc_scores.append(roc)\n",
    "        prec_scores.append(prec)\n",
    "        recall_scores.append(recall)\n",
    "\n",
    "\n",
    "    print()\n",
    "    print(f\"Mean across all folds:\\nAUCPR: {np.mean(aucpr_scores)}\\nROC {np.mean(roc_scores)}\\nPrecision: {np.mean(prec_scores)}\\nRecall: {np.mean(recall_scores)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ffd83f9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scores_thresholds(X_test, y_test, model, algo=None):\n",
    "    thresholds = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
    "    for thresh in thresholds:\n",
    "        if algo == \"XGB\":\n",
    "            y_pred = model.predict_proba(X_test)[:, 1]\n",
    "        else:\n",
    "            y_pred = model.predict(X_test)\n",
    "            \n",
    "        print('threshold:', thresh)\n",
    "        print(confusion_matrix(y_test, y_pred > thresh))\n",
    "        print(classification_report(y_test, y_pred > thresh))\n",
    "        print('--------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f3c5ad9",
   "metadata": {},
   "source": [
    "### Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "341dde7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a2beac1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_model(n_inputs, metrics=None, output_bias=None):\n",
    "    if output_bias is not None:\n",
    "        output_bias = tf.keras.initializers.Constant(output_bias)\n",
    "        \n",
    "    nn = keras.Sequential()\n",
    "    nn.add(keras.layers.Dense(32, input_shape=(n_inputs,), activation='relu'))\n",
    "    nn.add(keras.layers.Dense(64, activation='relu'))\n",
    "    nn.add(keras.layers.Dense(256, activation='relu'))\n",
    "\n",
    "    nn.add(keras.layers.Dense(128, activation='relu'))\n",
    "#     nn.add(keras.layers.Dense(256, activation='relu'))\n",
    "#     nn.add(keras.layers.Dense(64, activation='relu'))\n",
    "#     nn.add(keras.layers.Dense(16, activation='relu'))\n",
    "    nn.add(keras.layers.Dense(1, activation='sigmoid', bias_initializer=output_bias))\n",
    "    # testing to reduce overfit\n",
    "#     nn.add(keras.layers.Dropout(0.1))\n",
    "    \n",
    "    nn.compile(optimizer=keras.optimizers.Adam(learning_rate=1e-3),\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=metrics)\n",
    "    # metrics simply tell us what we will be able to see in the log and on plot\n",
    "    # they do are NOT used for optimization!\n",
    "\n",
    "    \n",
    "    return nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9006b4f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-23 11:50:51.318191: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/user/.mujoco/mujoco200/bin:/home/user/.mujoco/mujoco200/bin\n",
      "2023-03-23 11:50:51.318220: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2023-03-23 11:50:51.318243: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (linux-n8-room20): /proc/driver/nvidia/version does not exist\n",
      "2023-03-23 11:50:51.318796: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "METRICS = [\n",
    "    keras.metrics.TruePositives(name='tp'),\n",
    "    keras.metrics.FalsePositives(name='fp'),\n",
    "    keras.metrics.TrueNegatives(name='tn'),\n",
    "    keras.metrics.FalseNegatives(name='fn'),\n",
    "    keras.metrics.Precision(name='precision'),\n",
    "    keras.metrics.Recall(name='recall'),\n",
    "    keras.metrics.AUC(name='auc'),\n",
    "    keras.metrics.AUC(name='prc', curve='PR'),  # precision-recall curve\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b3b031ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://machinelearningmastery.com/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/ \n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_prc', # validation precision \n",
    "    mode='max', # specify whether we seek to min/max given metrics\n",
    "    verbose=1,  # print training epoch on which we stopped\n",
    "    patience=20, #after how many epochs of no improvement do we stop\n",
    "    restore_best_weights=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ba0677",
   "metadata": {},
   "source": [
    "##### Stratified 10 fold cross validation of different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2833ec66",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # create and compile network\n",
    "# n_inputs = X.shape[1]  # (77862, 3)\n",
    "# dnn = make_model(n_inputs, metrics=METRICS)\n",
    "# batch_size_list = [512, 1024]\n",
    "# for bs in batch_size_list:\n",
    "# # perform statified k fold validation\n",
    "#     print('Batch Size', bs)\n",
    "#     dnn_stratified_kfold_cross_val(X, y, epoch=300, \n",
    "#                                batch_size=bs, callback=early_stopping, \n",
    "#                                print_info=True, metrics=METRICS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c07191",
   "metadata": {},
   "source": [
    "### Chosing one of the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f222f0b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3b610988",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of 1's in train set 1.4061738560553867\n",
      "% of 1's in test set 1.406159678438437\n"
     ]
    }
   ],
   "source": [
    "print(f\"% of 1's in train set {y_train.sum()/len(y_train)*100}\")\n",
    "print(f\"% of 1's in test set {y_test.sum()/len(y_test)*100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f1617a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(297545, 500)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "050cd99e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74387, 500)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ac02ffac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape 500\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 32)                16032     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                2112      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 256)               16640     \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 128)               32896     \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 1)                 129       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 67,809\n",
      "Trainable params: 67,809\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "n_inputs = X_train.shape[1]  # (77862, 3)\n",
    "print('Input shape',n_inputs)\n",
    "model = make_model(n_inputs, metrics=METRICS)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "daa503ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sc = StandardScaler()\n",
    "# X[num_cols] = sc.fit_transform(X[num_cols])\n",
    "# test[num_cols] = sc.transform(test[num_cols])\n",
    "# X = X.values\n",
    "# y = y.values\n",
    "# test = test.values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4966743a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using weights!\n",
      "Epoch 1/300\n",
      "146/146 [==============================] - 4s 25ms/step - loss: 0.4496 - tp: 3279.0000 - fp: 71353.0000 - tn: 222008.0000 - fn: 905.0000 - precision: 0.0439 - recall: 0.7837 - auc: 0.8625 - prc: 0.0935 - val_loss: 0.4766 - val_tp: 821.0000 - val_fp: 18419.0000 - val_tn: 54922.0000 - val_fn: 225.0000 - val_precision: 0.0427 - val_recall: 0.7849 - val_auc: 0.8573 - val_prc: 0.0843\n",
      "Epoch 2/300\n",
      "146/146 [==============================] - 3s 24ms/step - loss: 0.4439 - tp: 3277.0000 - fp: 68639.0000 - tn: 224722.0000 - fn: 907.0000 - precision: 0.0456 - recall: 0.7832 - auc: 0.8657 - prc: 0.0945 - val_loss: 0.4644 - val_tp: 809.0000 - val_fp: 17746.0000 - val_tn: 55595.0000 - val_fn: 237.0000 - val_precision: 0.0436 - val_recall: 0.7734 - val_auc: 0.8584 - val_prc: 0.0875\n",
      "Epoch 3/300\n",
      "146/146 [==============================] - 3s 24ms/step - loss: 0.4421 - tp: 3304.0000 - fp: 70401.0000 - tn: 222960.0000 - fn: 880.0000 - precision: 0.0448 - recall: 0.7897 - auc: 0.8664 - prc: 0.0949 - val_loss: 0.4887 - val_tp: 851.0000 - val_fp: 19485.0000 - val_tn: 53856.0000 - val_fn: 195.0000 - val_precision: 0.0418 - val_recall: 0.8136 - val_auc: 0.8598 - val_prc: 0.0865\n",
      "Epoch 4/300\n",
      "146/146 [==============================] - 4s 26ms/step - loss: 0.4411 - tp: 3333.0000 - fp: 71462.0000 - tn: 221899.0000 - fn: 851.0000 - precision: 0.0446 - recall: 0.7966 - auc: 0.8673 - prc: 0.0955 - val_loss: 0.4446 - val_tp: 809.0000 - val_fp: 17507.0000 - val_tn: 55834.0000 - val_fn: 237.0000 - val_precision: 0.0442 - val_recall: 0.7734 - val_auc: 0.8594 - val_prc: 0.0903\n",
      "Epoch 5/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.4387 - tp: 3355.0000 - fp: 72444.0000 - tn: 220917.0000 - fn: 829.0000 - precision: 0.0443 - recall: 0.8019 - auc: 0.8684 - prc: 0.0985 - val_loss: 0.4518 - val_tp: 829.0000 - val_fp: 18391.0000 - val_tn: 54950.0000 - val_fn: 217.0000 - val_precision: 0.0431 - val_recall: 0.7925 - val_auc: 0.8609 - val_prc: 0.0912\n",
      "Epoch 6/300\n",
      "146/146 [==============================] - 3s 24ms/step - loss: 0.4368 - tp: 3398.0000 - fp: 73752.0000 - tn: 219609.0000 - fn: 786.0000 - precision: 0.0440 - recall: 0.8121 - auc: 0.8699 - prc: 0.1015 - val_loss: 0.4327 - val_tp: 816.0000 - val_fp: 17301.0000 - val_tn: 56040.0000 - val_fn: 230.0000 - val_precision: 0.0450 - val_recall: 0.7801 - val_auc: 0.8633 - val_prc: 0.0947\n",
      "Epoch 7/300\n",
      "146/146 [==============================] - 3s 24ms/step - loss: 0.4356 - tp: 3396.0000 - fp: 74801.0000 - tn: 218560.0000 - fn: 788.0000 - precision: 0.0434 - recall: 0.8117 - auc: 0.8702 - prc: 0.1041 - val_loss: 0.4621 - val_tp: 851.0000 - val_fp: 19297.0000 - val_tn: 54044.0000 - val_fn: 195.0000 - val_precision: 0.0422 - val_recall: 0.8136 - val_auc: 0.8650 - val_prc: 0.0977\n",
      "Epoch 8/300\n",
      "146/146 [==============================] - 4s 26ms/step - loss: 0.4339 - tp: 3427.0000 - fp: 75420.0000 - tn: 217941.0000 - fn: 757.0000 - precision: 0.0435 - recall: 0.8191 - auc: 0.8712 - prc: 0.1050 - val_loss: 0.4624 - val_tp: 852.0000 - val_fp: 19349.0000 - val_tn: 53992.0000 - val_fn: 194.0000 - val_precision: 0.0422 - val_recall: 0.8145 - val_auc: 0.8653 - val_prc: 0.0970\n",
      "Epoch 9/300\n",
      "146/146 [==============================] - 4s 25ms/step - loss: 0.4336 - tp: 3419.0000 - fp: 74773.0000 - tn: 218588.0000 - fn: 765.0000 - precision: 0.0437 - recall: 0.8172 - auc: 0.8714 - prc: 0.1068 - val_loss: 0.4857 - val_tp: 868.0000 - val_fp: 20428.0000 - val_tn: 52913.0000 - val_fn: 178.0000 - val_precision: 0.0408 - val_recall: 0.8298 - val_auc: 0.8656 - val_prc: 0.0970\n",
      "Epoch 10/300\n",
      "146/146 [==============================] - 4s 25ms/step - loss: 0.4336 - tp: 3445.0000 - fp: 76686.0000 - tn: 216675.0000 - fn: 739.0000 - precision: 0.0430 - recall: 0.8234 - auc: 0.8717 - prc: 0.1091 - val_loss: 0.4577 - val_tp: 847.0000 - val_fp: 19075.0000 - val_tn: 54266.0000 - val_fn: 199.0000 - val_precision: 0.0425 - val_recall: 0.8098 - val_auc: 0.8666 - val_prc: 0.1017\n",
      "Epoch 11/300\n",
      "146/146 [==============================] - 4s 25ms/step - loss: 0.4301 - tp: 3441.0000 - fp: 76394.0000 - tn: 216967.0000 - fn: 743.0000 - precision: 0.0431 - recall: 0.8224 - auc: 0.8736 - prc: 0.1102 - val_loss: 0.4780 - val_tp: 889.0000 - val_fp: 20903.0000 - val_tn: 52438.0000 - val_fn: 157.0000 - val_precision: 0.0408 - val_recall: 0.8499 - val_auc: 0.8688 - val_prc: 0.1002\n",
      "Epoch 12/300\n",
      "146/146 [==============================] - 4s 25ms/step - loss: 0.4278 - tp: 3460.0000 - fp: 76046.0000 - tn: 217315.0000 - fn: 724.0000 - precision: 0.0435 - recall: 0.8270 - auc: 0.8753 - prc: 0.1110 - val_loss: 0.4503 - val_tp: 856.0000 - val_fp: 19347.0000 - val_tn: 53994.0000 - val_fn: 190.0000 - val_precision: 0.0424 - val_recall: 0.8184 - val_auc: 0.8685 - val_prc: 0.1024\n",
      "Epoch 13/300\n",
      "146/146 [==============================] - 4s 25ms/step - loss: 0.4284 - tp: 3464.0000 - fp: 75760.0000 - tn: 217601.0000 - fn: 720.0000 - precision: 0.0437 - recall: 0.8279 - auc: 0.8752 - prc: 0.1127 - val_loss: 0.4728 - val_tp: 864.0000 - val_fp: 19933.0000 - val_tn: 53408.0000 - val_fn: 182.0000 - val_precision: 0.0415 - val_recall: 0.8260 - val_auc: 0.8676 - val_prc: 0.1022\n",
      "Epoch 14/300\n",
      "146/146 [==============================] - 3s 23ms/step - loss: 0.4282 - tp: 3461.0000 - fp: 75477.0000 - tn: 217884.0000 - fn: 723.0000 - precision: 0.0438 - recall: 0.8272 - auc: 0.8750 - prc: 0.1099 - val_loss: 0.4301 - val_tp: 839.0000 - val_fp: 18090.0000 - val_tn: 55251.0000 - val_fn: 207.0000 - val_precision: 0.0443 - val_recall: 0.8021 - val_auc: 0.8690 - val_prc: 0.1008\n",
      "Epoch 15/300\n",
      "146/146 [==============================] - 3s 18ms/step - loss: 0.4254 - tp: 3460.0000 - fp: 75545.0000 - tn: 217816.0000 - fn: 724.0000 - precision: 0.0438 - recall: 0.8270 - auc: 0.8765 - prc: 0.1115 - val_loss: 0.4469 - val_tp: 844.0000 - val_fp: 18100.0000 - val_tn: 55241.0000 - val_fn: 202.0000 - val_precision: 0.0446 - val_recall: 0.8069 - val_auc: 0.8687 - val_prc: 0.1012\n",
      "Epoch 16/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.4227 - tp: 3465.0000 - fp: 75880.0000 - tn: 217481.0000 - fn: 719.0000 - precision: 0.0437 - recall: 0.8282 - auc: 0.8777 - prc: 0.1118 - val_loss: 0.4505 - val_tp: 865.0000 - val_fp: 19424.0000 - val_tn: 53917.0000 - val_fn: 181.0000 - val_precision: 0.0426 - val_recall: 0.8270 - val_auc: 0.8710 - val_prc: 0.0991\n",
      "Epoch 17/300\n",
      "146/146 [==============================] - 4s 26ms/step - loss: 0.4277 - tp: 3453.0000 - fp: 75409.0000 - tn: 217952.0000 - fn: 731.0000 - precision: 0.0438 - recall: 0.8253 - auc: 0.8759 - prc: 0.1101 - val_loss: 0.4417 - val_tp: 832.0000 - val_fp: 17902.0000 - val_tn: 55439.0000 - val_fn: 214.0000 - val_precision: 0.0444 - val_recall: 0.7954 - val_auc: 0.8695 - val_prc: 0.0964\n",
      "Epoch 18/300\n",
      "146/146 [==============================] - 4s 24ms/step - loss: 0.4260 - tp: 3468.0000 - fp: 75629.0000 - tn: 217732.0000 - fn: 716.0000 - precision: 0.0438 - recall: 0.8289 - auc: 0.8762 - prc: 0.1084 - val_loss: 0.4177 - val_tp: 826.0000 - val_fp: 17228.0000 - val_tn: 56113.0000 - val_fn: 220.0000 - val_precision: 0.0458 - val_recall: 0.7897 - val_auc: 0.8668 - val_prc: 0.1004\n",
      "Epoch 19/300\n",
      "146/146 [==============================] - 4s 25ms/step - loss: 0.4210 - tp: 3451.0000 - fp: 72987.0000 - tn: 220374.0000 - fn: 733.0000 - precision: 0.0451 - recall: 0.8248 - auc: 0.8798 - prc: 0.1116 - val_loss: 0.4810 - val_tp: 895.0000 - val_fp: 21361.0000 - val_tn: 51980.0000 - val_fn: 151.0000 - val_precision: 0.0402 - val_recall: 0.8556 - val_auc: 0.8717 - val_prc: 0.1012\n",
      "Epoch 20/300\n",
      "146/146 [==============================] - 3s 24ms/step - loss: 0.4219 - tp: 3481.0000 - fp: 75368.0000 - tn: 217993.0000 - fn: 703.0000 - precision: 0.0441 - recall: 0.8320 - auc: 0.8793 - prc: 0.1133 - val_loss: 0.4346 - val_tp: 851.0000 - val_fp: 18163.0000 - val_tn: 55178.0000 - val_fn: 195.0000 - val_precision: 0.0448 - val_recall: 0.8136 - val_auc: 0.8702 - val_prc: 0.0988\n",
      "Epoch 21/300\n",
      "146/146 [==============================] - 4s 25ms/step - loss: 0.4206 - tp: 3460.0000 - fp: 73941.0000 - tn: 219420.0000 - fn: 724.0000 - precision: 0.0447 - recall: 0.8270 - auc: 0.8797 - prc: 0.1114 - val_loss: 0.3896 - val_tp: 804.0000 - val_fp: 15214.0000 - val_tn: 58127.0000 - val_fn: 242.0000 - val_precision: 0.0502 - val_recall: 0.7686 - val_auc: 0.8723 - val_prc: 0.1030\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22/300\n",
      "146/146 [==============================] - 4s 24ms/step - loss: 0.4230 - tp: 3449.0000 - fp: 73532.0000 - tn: 219829.0000 - fn: 735.0000 - precision: 0.0448 - recall: 0.8243 - auc: 0.8793 - prc: 0.1116 - val_loss: 0.4381 - val_tp: 847.0000 - val_fp: 17813.0000 - val_tn: 55528.0000 - val_fn: 199.0000 - val_precision: 0.0454 - val_recall: 0.8098 - val_auc: 0.8741 - val_prc: 0.1011\n",
      "Epoch 23/300\n",
      "146/146 [==============================] - 3s 22ms/step - loss: 0.4217 - tp: 3461.0000 - fp: 73550.0000 - tn: 219811.0000 - fn: 723.0000 - precision: 0.0449 - recall: 0.8272 - auc: 0.8797 - prc: 0.1141 - val_loss: 0.4844 - val_tp: 893.0000 - val_fp: 21013.0000 - val_tn: 52328.0000 - val_fn: 153.0000 - val_precision: 0.0408 - val_recall: 0.8537 - val_auc: 0.8725 - val_prc: 0.0978\n",
      "Epoch 24/300\n",
      "146/146 [==============================] - 3s 24ms/step - loss: 0.4194 - tp: 3476.0000 - fp: 73474.0000 - tn: 219887.0000 - fn: 708.0000 - precision: 0.0452 - recall: 0.8308 - auc: 0.8811 - prc: 0.1131 - val_loss: 0.4959 - val_tp: 913.0000 - val_fp: 21778.0000 - val_tn: 51563.0000 - val_fn: 133.0000 - val_precision: 0.0402 - val_recall: 0.8728 - val_auc: 0.8740 - val_prc: 0.1013\n",
      "Epoch 25/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.4239 - tp: 3469.0000 - fp: 74651.0000 - tn: 218710.0000 - fn: 715.0000 - precision: 0.0444 - recall: 0.8291 - auc: 0.8786 - prc: 0.1095 - val_loss: 0.4946 - val_tp: 903.0000 - val_fp: 21576.0000 - val_tn: 51765.0000 - val_fn: 143.0000 - val_precision: 0.0402 - val_recall: 0.8633 - val_auc: 0.8744 - val_prc: 0.0984\n",
      "Epoch 26/300\n",
      "146/146 [==============================] - 2s 16ms/step - loss: 0.4229 - tp: 3406.0000 - fp: 70971.0000 - tn: 222390.0000 - fn: 778.0000 - precision: 0.0458 - recall: 0.8141 - auc: 0.8795 - prc: 0.1110 - val_loss: 0.4546 - val_tp: 874.0000 - val_fp: 19195.0000 - val_tn: 54146.0000 - val_fn: 172.0000 - val_precision: 0.0435 - val_recall: 0.8356 - val_auc: 0.8730 - val_prc: 0.0975\n",
      "Epoch 27/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.4199 - tp: 3465.0000 - fp: 73206.0000 - tn: 220155.0000 - fn: 719.0000 - precision: 0.0452 - recall: 0.8282 - auc: 0.8809 - prc: 0.1079 - val_loss: 0.4645 - val_tp: 878.0000 - val_fp: 19622.0000 - val_tn: 53719.0000 - val_fn: 168.0000 - val_precision: 0.0428 - val_recall: 0.8394 - val_auc: 0.8741 - val_prc: 0.0947\n",
      "Epoch 28/300\n",
      "146/146 [==============================] - 2s 15ms/step - loss: 0.4177 - tp: 3496.0000 - fp: 73187.0000 - tn: 220174.0000 - fn: 688.0000 - precision: 0.0456 - recall: 0.8356 - auc: 0.8821 - prc: 0.1107 - val_loss: 0.4077 - val_tp: 833.0000 - val_fp: 16327.0000 - val_tn: 57014.0000 - val_fn: 213.0000 - val_precision: 0.0485 - val_recall: 0.7964 - val_auc: 0.8730 - val_prc: 0.1032\n",
      "Epoch 29/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.4168 - tp: 3467.0000 - fp: 72275.0000 - tn: 221086.0000 - fn: 717.0000 - precision: 0.0458 - recall: 0.8286 - auc: 0.8829 - prc: 0.1146 - val_loss: 0.4849 - val_tp: 895.0000 - val_fp: 20096.0000 - val_tn: 53245.0000 - val_fn: 151.0000 - val_precision: 0.0426 - val_recall: 0.8556 - val_auc: 0.8761 - val_prc: 0.0974\n",
      "Epoch 30/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.4137 - tp: 3474.0000 - fp: 71754.0000 - tn: 221607.0000 - fn: 710.0000 - precision: 0.0462 - recall: 0.8303 - auc: 0.8844 - prc: 0.1125 - val_loss: 0.4425 - val_tp: 874.0000 - val_fp: 18086.0000 - val_tn: 55255.0000 - val_fn: 172.0000 - val_precision: 0.0461 - val_recall: 0.8356 - val_auc: 0.8815 - val_prc: 0.1040\n",
      "Epoch 31/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.4140 - tp: 3496.0000 - fp: 72528.0000 - tn: 220833.0000 - fn: 688.0000 - precision: 0.0460 - recall: 0.8356 - auc: 0.8841 - prc: 0.1136 - val_loss: 0.4510 - val_tp: 891.0000 - val_fp: 19112.0000 - val_tn: 54229.0000 - val_fn: 155.0000 - val_precision: 0.0445 - val_recall: 0.8518 - val_auc: 0.8821 - val_prc: 0.1046\n",
      "Epoch 32/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.4146 - tp: 3518.0000 - fp: 73999.0000 - tn: 219362.0000 - fn: 666.0000 - precision: 0.0454 - recall: 0.8408 - auc: 0.8842 - prc: 0.1122 - val_loss: 0.4528 - val_tp: 867.0000 - val_fp: 18829.0000 - val_tn: 54512.0000 - val_fn: 179.0000 - val_precision: 0.0440 - val_recall: 0.8289 - val_auc: 0.8757 - val_prc: 0.0989\n",
      "Epoch 33/300\n",
      "146/146 [==============================] - 3s 17ms/step - loss: 0.4177 - tp: 3490.0000 - fp: 72175.0000 - tn: 221186.0000 - fn: 694.0000 - precision: 0.0461 - recall: 0.8341 - auc: 0.8821 - prc: 0.1071 - val_loss: 0.4434 - val_tp: 865.0000 - val_fp: 18142.0000 - val_tn: 55199.0000 - val_fn: 181.0000 - val_precision: 0.0455 - val_recall: 0.8270 - val_auc: 0.8788 - val_prc: 0.1023\n",
      "Epoch 34/300\n",
      "146/146 [==============================] - 3s 17ms/step - loss: 0.4115 - tp: 3501.0000 - fp: 71430.0000 - tn: 221931.0000 - fn: 683.0000 - precision: 0.0467 - recall: 0.8368 - auc: 0.8857 - prc: 0.1135 - val_loss: 0.4443 - val_tp: 869.0000 - val_fp: 17993.0000 - val_tn: 55348.0000 - val_fn: 177.0000 - val_precision: 0.0461 - val_recall: 0.8308 - val_auc: 0.8793 - val_prc: 0.1066\n",
      "Epoch 35/300\n",
      "146/146 [==============================] - 3s 18ms/step - loss: 0.4098 - tp: 3534.0000 - fp: 72387.0000 - tn: 220974.0000 - fn: 650.0000 - precision: 0.0465 - recall: 0.8446 - auc: 0.8872 - prc: 0.1162 - val_loss: 0.4653 - val_tp: 858.0000 - val_fp: 18796.0000 - val_tn: 54545.0000 - val_fn: 188.0000 - val_precision: 0.0437 - val_recall: 0.8203 - val_auc: 0.8723 - val_prc: 0.0991\n",
      "Epoch 36/300\n",
      "146/146 [==============================] - 3s 22ms/step - loss: 0.4097 - tp: 3516.0000 - fp: 72302.0000 - tn: 221059.0000 - fn: 668.0000 - precision: 0.0464 - recall: 0.8403 - auc: 0.8872 - prc: 0.1156 - val_loss: 0.4277 - val_tp: 859.0000 - val_fp: 16687.0000 - val_tn: 56654.0000 - val_fn: 187.0000 - val_precision: 0.0490 - val_recall: 0.8212 - val_auc: 0.8856 - val_prc: 0.1056\n",
      "Epoch 37/300\n",
      "146/146 [==============================] - 2s 15ms/step - loss: 0.4073 - tp: 3494.0000 - fp: 70077.0000 - tn: 223284.0000 - fn: 690.0000 - precision: 0.0475 - recall: 0.8351 - auc: 0.8885 - prc: 0.1133 - val_loss: 0.4320 - val_tp: 852.0000 - val_fp: 17735.0000 - val_tn: 55606.0000 - val_fn: 194.0000 - val_precision: 0.0458 - val_recall: 0.8145 - val_auc: 0.8785 - val_prc: 0.1028\n",
      "Epoch 38/300\n",
      "146/146 [==============================] - 2s 14ms/step - loss: 0.4146 - tp: 3519.0000 - fp: 72468.0000 - tn: 220893.0000 - fn: 665.0000 - precision: 0.0463 - recall: 0.8411 - auc: 0.8853 - prc: 0.1101 - val_loss: 0.4535 - val_tp: 883.0000 - val_fp: 18406.0000 - val_tn: 54935.0000 - val_fn: 163.0000 - val_precision: 0.0458 - val_recall: 0.8442 - val_auc: 0.8792 - val_prc: 0.0989\n",
      "Epoch 39/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.4056 - tp: 3509.0000 - fp: 69134.0000 - tn: 224227.0000 - fn: 675.0000 - precision: 0.0483 - recall: 0.8387 - auc: 0.8897 - prc: 0.1148 - val_loss: 0.4288 - val_tp: 863.0000 - val_fp: 16598.0000 - val_tn: 56743.0000 - val_fn: 183.0000 - val_precision: 0.0494 - val_recall: 0.8250 - val_auc: 0.8856 - val_prc: 0.1001\n",
      "Epoch 40/300\n",
      "146/146 [==============================] - 3s 23ms/step - loss: 0.4045 - tp: 3533.0000 - fp: 71367.0000 - tn: 221994.0000 - fn: 651.0000 - precision: 0.0472 - recall: 0.8444 - auc: 0.8899 - prc: 0.1144 - val_loss: 0.3940 - val_tp: 841.0000 - val_fp: 15505.0000 - val_tn: 57836.0000 - val_fn: 205.0000 - val_precision: 0.0514 - val_recall: 0.8040 - val_auc: 0.8851 - val_prc: 0.1064\n",
      "Epoch 41/300\n",
      "146/146 [==============================] - 2s 16ms/step - loss: 0.4062 - tp: 3518.0000 - fp: 70161.0000 - tn: 223200.0000 - fn: 666.0000 - precision: 0.0477 - recall: 0.8408 - auc: 0.8894 - prc: 0.1123 - val_loss: 0.4584 - val_tp: 876.0000 - val_fp: 19389.0000 - val_tn: 53952.0000 - val_fn: 170.0000 - val_precision: 0.0432 - val_recall: 0.8375 - val_auc: 0.8725 - val_prc: 0.0935\n",
      "Epoch 42/300\n",
      "146/146 [==============================] - 3s 22ms/step - loss: 0.4075 - tp: 3542.0000 - fp: 71241.0000 - tn: 222120.0000 - fn: 642.0000 - precision: 0.0474 - recall: 0.8466 - auc: 0.8888 - prc: 0.1143 - val_loss: 0.4007 - val_tp: 844.0000 - val_fp: 15201.0000 - val_tn: 58140.0000 - val_fn: 202.0000 - val_precision: 0.0526 - val_recall: 0.8069 - val_auc: 0.8877 - val_prc: 0.1038\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 43/300\n",
      "146/146 [==============================] - 4s 25ms/step - loss: 0.4057 - tp: 3518.0000 - fp: 69284.0000 - tn: 224077.0000 - fn: 666.0000 - precision: 0.0483 - recall: 0.8408 - auc: 0.8899 - prc: 0.1156 - val_loss: 0.5056 - val_tp: 939.0000 - val_fp: 21663.0000 - val_tn: 51678.0000 - val_fn: 107.0000 - val_precision: 0.0415 - val_recall: 0.8977 - val_auc: 0.8869 - val_prc: 0.1025\n",
      "Epoch 44/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.4076 - tp: 3529.0000 - fp: 69856.0000 - tn: 223505.0000 - fn: 655.0000 - precision: 0.0481 - recall: 0.8435 - auc: 0.8893 - prc: 0.1119 - val_loss: 0.4063 - val_tp: 868.0000 - val_fp: 16377.0000 - val_tn: 56964.0000 - val_fn: 178.0000 - val_precision: 0.0503 - val_recall: 0.8298 - val_auc: 0.8896 - val_prc: 0.1061\n",
      "Epoch 45/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.4003 - tp: 3558.0000 - fp: 69933.0000 - tn: 223428.0000 - fn: 626.0000 - precision: 0.0484 - recall: 0.8504 - auc: 0.8922 - prc: 0.1174 - val_loss: 0.4574 - val_tp: 896.0000 - val_fp: 19400.0000 - val_tn: 53941.0000 - val_fn: 150.0000 - val_precision: 0.0441 - val_recall: 0.8566 - val_auc: 0.8861 - val_prc: 0.1036\n",
      "Epoch 46/300\n",
      "146/146 [==============================] - 3s 22ms/step - loss: 0.4005 - tp: 3536.0000 - fp: 70437.0000 - tn: 222924.0000 - fn: 648.0000 - precision: 0.0478 - recall: 0.8451 - auc: 0.8923 - prc: 0.1164 - val_loss: 0.4514 - val_tp: 891.0000 - val_fp: 19192.0000 - val_tn: 54149.0000 - val_fn: 155.0000 - val_precision: 0.0444 - val_recall: 0.8518 - val_auc: 0.8862 - val_prc: 0.1010\n",
      "Epoch 47/300\n",
      "146/146 [==============================] - 3s 24ms/step - loss: 0.3999 - tp: 3572.0000 - fp: 70530.0000 - tn: 222831.0000 - fn: 612.0000 - precision: 0.0482 - recall: 0.8537 - auc: 0.8933 - prc: 0.1192 - val_loss: 0.4386 - val_tp: 910.0000 - val_fp: 18723.0000 - val_tn: 54618.0000 - val_fn: 136.0000 - val_precision: 0.0464 - val_recall: 0.8700 - val_auc: 0.8918 - val_prc: 0.1096\n",
      "Epoch 48/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.4033 - tp: 3560.0000 - fp: 70549.0000 - tn: 222812.0000 - fn: 624.0000 - precision: 0.0480 - recall: 0.8509 - auc: 0.8915 - prc: 0.1129 - val_loss: 0.3922 - val_tp: 837.0000 - val_fp: 14913.0000 - val_tn: 58428.0000 - val_fn: 209.0000 - val_precision: 0.0531 - val_recall: 0.8002 - val_auc: 0.8892 - val_prc: 0.1074\n",
      "Epoch 49/300\n",
      "146/146 [==============================] - 3s 22ms/step - loss: 0.4002 - tp: 3533.0000 - fp: 69195.0000 - tn: 224166.0000 - fn: 651.0000 - precision: 0.0486 - recall: 0.8444 - auc: 0.8931 - prc: 0.1178 - val_loss: 0.4282 - val_tp: 881.0000 - val_fp: 17026.0000 - val_tn: 56315.0000 - val_fn: 165.0000 - val_precision: 0.0492 - val_recall: 0.8423 - val_auc: 0.8925 - val_prc: 0.1061\n",
      "Epoch 50/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3991 - tp: 3558.0000 - fp: 70042.0000 - tn: 223319.0000 - fn: 626.0000 - precision: 0.0483 - recall: 0.8504 - auc: 0.8935 - prc: 0.1163 - val_loss: 0.4442 - val_tp: 900.0000 - val_fp: 18425.0000 - val_tn: 54916.0000 - val_fn: 146.0000 - val_precision: 0.0466 - val_recall: 0.8604 - val_auc: 0.8923 - val_prc: 0.1069\n",
      "Epoch 51/300\n",
      "146/146 [==============================] - 3s 23ms/step - loss: 0.4026 - tp: 3534.0000 - fp: 70152.0000 - tn: 223209.0000 - fn: 650.0000 - precision: 0.0480 - recall: 0.8446 - auc: 0.8914 - prc: 0.1155 - val_loss: 0.4341 - val_tp: 873.0000 - val_fp: 17248.0000 - val_tn: 56093.0000 - val_fn: 173.0000 - val_precision: 0.0482 - val_recall: 0.8346 - val_auc: 0.8901 - val_prc: 0.1100\n",
      "Epoch 52/300\n",
      "146/146 [==============================] - 4s 25ms/step - loss: 0.4004 - tp: 3540.0000 - fp: 69848.0000 - tn: 223513.0000 - fn: 644.0000 - precision: 0.0482 - recall: 0.8461 - auc: 0.8931 - prc: 0.1156 - val_loss: 0.3757 - val_tp: 821.0000 - val_fp: 14779.0000 - val_tn: 58562.0000 - val_fn: 225.0000 - val_precision: 0.0526 - val_recall: 0.7849 - val_auc: 0.8855 - val_prc: 0.1059\n",
      "Epoch 53/300\n",
      "146/146 [==============================] - 3s 24ms/step - loss: 0.4019 - tp: 3550.0000 - fp: 70298.0000 - tn: 223063.0000 - fn: 634.0000 - precision: 0.0481 - recall: 0.8485 - auc: 0.8916 - prc: 0.1130 - val_loss: 0.4760 - val_tp: 898.0000 - val_fp: 19590.0000 - val_tn: 53751.0000 - val_fn: 148.0000 - val_precision: 0.0438 - val_recall: 0.8585 - val_auc: 0.8847 - val_prc: 0.1064\n",
      "Epoch 54/300\n",
      "146/146 [==============================] - 3s 22ms/step - loss: 0.4037 - tp: 3564.0000 - fp: 70407.0000 - tn: 222954.0000 - fn: 620.0000 - precision: 0.0482 - recall: 0.8518 - auc: 0.8908 - prc: 0.1151 - val_loss: 0.4375 - val_tp: 896.0000 - val_fp: 17900.0000 - val_tn: 55441.0000 - val_fn: 150.0000 - val_precision: 0.0477 - val_recall: 0.8566 - val_auc: 0.8915 - val_prc: 0.1032\n",
      "Epoch 55/300\n",
      "146/146 [==============================] - 3s 23ms/step - loss: 0.3962 - tp: 3573.0000 - fp: 69950.0000 - tn: 223411.0000 - fn: 611.0000 - precision: 0.0486 - recall: 0.8540 - auc: 0.8949 - prc: 0.1187 - val_loss: 0.5431 - val_tp: 954.0000 - val_fp: 24569.0000 - val_tn: 48772.0000 - val_fn: 92.0000 - val_precision: 0.0374 - val_recall: 0.9120 - val_auc: 0.8818 - val_prc: 0.1039\n",
      "Epoch 56/300\n",
      "146/146 [==============================] - 3s 23ms/step - loss: 0.3992 - tp: 3565.0000 - fp: 71355.0000 - tn: 222006.0000 - fn: 619.0000 - precision: 0.0476 - recall: 0.8521 - auc: 0.8932 - prc: 0.1148 - val_loss: 0.4054 - val_tp: 851.0000 - val_fp: 16114.0000 - val_tn: 57227.0000 - val_fn: 195.0000 - val_precision: 0.0502 - val_recall: 0.8136 - val_auc: 0.8854 - val_prc: 0.1008\n",
      "Epoch 57/300\n",
      "146/146 [==============================] - 4s 24ms/step - loss: 0.3974 - tp: 3598.0000 - fp: 72096.0000 - tn: 221265.0000 - fn: 586.0000 - precision: 0.0475 - recall: 0.8599 - auc: 0.8940 - prc: 0.1120 - val_loss: 0.4463 - val_tp: 899.0000 - val_fp: 18921.0000 - val_tn: 54420.0000 - val_fn: 147.0000 - val_precision: 0.0454 - val_recall: 0.8595 - val_auc: 0.8867 - val_prc: 0.1034\n",
      "Epoch 58/300\n",
      "146/146 [==============================] - 4s 26ms/step - loss: 0.3963 - tp: 3580.0000 - fp: 70277.0000 - tn: 223084.0000 - fn: 604.0000 - precision: 0.0485 - recall: 0.8556 - auc: 0.8952 - prc: 0.1198 - val_loss: 0.4353 - val_tp: 906.0000 - val_fp: 18049.0000 - val_tn: 55292.0000 - val_fn: 140.0000 - val_precision: 0.0478 - val_recall: 0.8662 - val_auc: 0.8941 - val_prc: 0.1120\n",
      "Epoch 59/300\n",
      "146/146 [==============================] - 3s 23ms/step - loss: 0.3922 - tp: 3587.0000 - fp: 69584.0000 - tn: 223777.0000 - fn: 597.0000 - precision: 0.0490 - recall: 0.8573 - auc: 0.8975 - prc: 0.1216 - val_loss: 0.4397 - val_tp: 928.0000 - val_fp: 18903.0000 - val_tn: 54438.0000 - val_fn: 118.0000 - val_precision: 0.0468 - val_recall: 0.8872 - val_auc: 0.8960 - val_prc: 0.1129\n",
      "Epoch 60/300\n",
      "146/146 [==============================] - 3s 23ms/step - loss: 0.3940 - tp: 3579.0000 - fp: 70407.0000 - tn: 222954.0000 - fn: 605.0000 - precision: 0.0484 - recall: 0.8554 - auc: 0.8961 - prc: 0.1213 - val_loss: 0.4458 - val_tp: 892.0000 - val_fp: 18321.0000 - val_tn: 55020.0000 - val_fn: 154.0000 - val_precision: 0.0464 - val_recall: 0.8528 - val_auc: 0.8890 - val_prc: 0.1108\n",
      "Epoch 61/300\n",
      "146/146 [==============================] - 2s 15ms/step - loss: 0.3952 - tp: 3590.0000 - fp: 71638.0000 - tn: 221723.0000 - fn: 594.0000 - precision: 0.0477 - recall: 0.8580 - auc: 0.8955 - prc: 0.1220 - val_loss: 0.4088 - val_tp: 858.0000 - val_fp: 16110.0000 - val_tn: 57231.0000 - val_fn: 188.0000 - val_precision: 0.0506 - val_recall: 0.8203 - val_auc: 0.8892 - val_prc: 0.1120\n",
      "Epoch 62/300\n",
      "146/146 [==============================] - 3s 18ms/step - loss: 0.3919 - tp: 3565.0000 - fp: 68786.0000 - tn: 224575.0000 - fn: 619.0000 - precision: 0.0493 - recall: 0.8521 - auc: 0.8975 - prc: 0.1224 - val_loss: 0.3870 - val_tp: 859.0000 - val_fp: 15246.0000 - val_tn: 58095.0000 - val_fn: 187.0000 - val_precision: 0.0533 - val_recall: 0.8212 - val_auc: 0.8941 - val_prc: 0.1155\n",
      "Epoch 63/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.3915 - tp: 3606.0000 - fp: 70775.0000 - tn: 222586.0000 - fn: 578.0000 - precision: 0.0485 - recall: 0.8619 - auc: 0.8979 - prc: 0.1211 - val_loss: 0.4063 - val_tp: 873.0000 - val_fp: 16897.0000 - val_tn: 56444.0000 - val_fn: 173.0000 - val_precision: 0.0491 - val_recall: 0.8346 - val_auc: 0.8903 - val_prc: 0.1074\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 64/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.3895 - tp: 3574.0000 - fp: 69516.0000 - tn: 223845.0000 - fn: 610.0000 - precision: 0.0489 - recall: 0.8542 - auc: 0.8985 - prc: 0.1253 - val_loss: 0.4615 - val_tp: 914.0000 - val_fp: 20121.0000 - val_tn: 53220.0000 - val_fn: 132.0000 - val_precision: 0.0435 - val_recall: 0.8738 - val_auc: 0.8884 - val_prc: 0.1124\n",
      "Epoch 65/300\n",
      "146/146 [==============================] - 3s 23ms/step - loss: 0.3919 - tp: 3578.0000 - fp: 69528.0000 - tn: 223833.0000 - fn: 606.0000 - precision: 0.0489 - recall: 0.8552 - auc: 0.8975 - prc: 0.1213 - val_loss: 0.4289 - val_tp: 870.0000 - val_fp: 16756.0000 - val_tn: 56585.0000 - val_fn: 176.0000 - val_precision: 0.0494 - val_recall: 0.8317 - val_auc: 0.8894 - val_prc: 0.1099\n",
      "Epoch 66/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.3919 - tp: 3534.0000 - fp: 67408.0000 - tn: 225953.0000 - fn: 650.0000 - precision: 0.0498 - recall: 0.8446 - auc: 0.8969 - prc: 0.1208 - val_loss: 0.4411 - val_tp: 920.0000 - val_fp: 19266.0000 - val_tn: 54075.0000 - val_fn: 126.0000 - val_precision: 0.0456 - val_recall: 0.8795 - val_auc: 0.8931 - val_prc: 0.1149\n",
      "Epoch 67/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.3920 - tp: 3614.0000 - fp: 70804.0000 - tn: 222557.0000 - fn: 570.0000 - precision: 0.0486 - recall: 0.8638 - auc: 0.8984 - prc: 0.1253 - val_loss: 0.4419 - val_tp: 910.0000 - val_fp: 18422.0000 - val_tn: 54919.0000 - val_fn: 136.0000 - val_precision: 0.0471 - val_recall: 0.8700 - val_auc: 0.8965 - val_prc: 0.1206\n",
      "Epoch 68/300\n",
      "146/146 [==============================] - 3s 22ms/step - loss: 0.3887 - tp: 3588.0000 - fp: 68520.0000 - tn: 224841.0000 - fn: 596.0000 - precision: 0.0498 - recall: 0.8576 - auc: 0.8997 - prc: 0.1266 - val_loss: 0.4175 - val_tp: 906.0000 - val_fp: 17629.0000 - val_tn: 55712.0000 - val_fn: 140.0000 - val_precision: 0.0489 - val_recall: 0.8662 - val_auc: 0.8959 - val_prc: 0.1206\n",
      "Epoch 69/300\n",
      "146/146 [==============================] - 3s 18ms/step - loss: 0.3918 - tp: 3630.0000 - fp: 71880.0000 - tn: 221481.0000 - fn: 554.0000 - precision: 0.0481 - recall: 0.8676 - auc: 0.8978 - prc: 0.1251 - val_loss: 0.4276 - val_tp: 888.0000 - val_fp: 17924.0000 - val_tn: 55417.0000 - val_fn: 158.0000 - val_precision: 0.0472 - val_recall: 0.8489 - val_auc: 0.8893 - val_prc: 0.1098\n",
      "Epoch 70/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3907 - tp: 3596.0000 - fp: 69812.0000 - tn: 223549.0000 - fn: 588.0000 - precision: 0.0490 - recall: 0.8595 - auc: 0.8980 - prc: 0.1220 - val_loss: 0.4372 - val_tp: 912.0000 - val_fp: 18565.0000 - val_tn: 54776.0000 - val_fn: 134.0000 - val_precision: 0.0468 - val_recall: 0.8719 - val_auc: 0.8954 - val_prc: 0.1135\n",
      "Epoch 71/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.3906 - tp: 3627.0000 - fp: 71990.0000 - tn: 221371.0000 - fn: 557.0000 - precision: 0.0480 - recall: 0.8669 - auc: 0.8981 - prc: 0.1219 - val_loss: 0.4325 - val_tp: 902.0000 - val_fp: 18090.0000 - val_tn: 55251.0000 - val_fn: 144.0000 - val_precision: 0.0475 - val_recall: 0.8623 - val_auc: 0.8954 - val_prc: 0.1135\n",
      "Epoch 72/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3896 - tp: 3579.0000 - fp: 70087.0000 - tn: 223274.0000 - fn: 605.0000 - precision: 0.0486 - recall: 0.8554 - auc: 0.8982 - prc: 0.1245 - val_loss: 0.3814 - val_tp: 859.0000 - val_fp: 15565.0000 - val_tn: 57776.0000 - val_fn: 187.0000 - val_precision: 0.0523 - val_recall: 0.8212 - val_auc: 0.8940 - val_prc: 0.1165\n",
      "Epoch 73/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.3873 - tp: 3604.0000 - fp: 70032.0000 - tn: 223329.0000 - fn: 580.0000 - precision: 0.0489 - recall: 0.8614 - auc: 0.8996 - prc: 0.1266 - val_loss: 0.3890 - val_tp: 868.0000 - val_fp: 16019.0000 - val_tn: 57322.0000 - val_fn: 178.0000 - val_precision: 0.0514 - val_recall: 0.8298 - val_auc: 0.8970 - val_prc: 0.1147\n",
      "Epoch 74/300\n",
      "146/146 [==============================] - 2s 17ms/step - loss: 0.3838 - tp: 3617.0000 - fp: 69006.0000 - tn: 224355.0000 - fn: 567.0000 - precision: 0.0498 - recall: 0.8645 - auc: 0.9017 - prc: 0.1293 - val_loss: 0.4061 - val_tp: 878.0000 - val_fp: 17169.0000 - val_tn: 56172.0000 - val_fn: 168.0000 - val_precision: 0.0487 - val_recall: 0.8394 - val_auc: 0.8910 - val_prc: 0.1038\n",
      "Epoch 75/300\n",
      "146/146 [==============================] - 3s 18ms/step - loss: 0.3929 - tp: 3586.0000 - fp: 70307.0000 - tn: 223054.0000 - fn: 598.0000 - precision: 0.0485 - recall: 0.8571 - auc: 0.8966 - prc: 0.1205 - val_loss: 0.4490 - val_tp: 915.0000 - val_fp: 18795.0000 - val_tn: 54546.0000 - val_fn: 131.0000 - val_precision: 0.0464 - val_recall: 0.8748 - val_auc: 0.8922 - val_prc: 0.1060\n",
      "Epoch 76/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.3872 - tp: 3574.0000 - fp: 68963.0000 - tn: 224398.0000 - fn: 610.0000 - precision: 0.0493 - recall: 0.8542 - auc: 0.8993 - prc: 0.1255 - val_loss: 0.4186 - val_tp: 897.0000 - val_fp: 17678.0000 - val_tn: 55663.0000 - val_fn: 149.0000 - val_precision: 0.0483 - val_recall: 0.8576 - val_auc: 0.8956 - val_prc: 0.1187\n",
      "Epoch 77/300\n",
      "146/146 [==============================] - 3s 18ms/step - loss: 0.3865 - tp: 3606.0000 - fp: 71443.0000 - tn: 221918.0000 - fn: 578.0000 - precision: 0.0480 - recall: 0.8619 - auc: 0.8997 - prc: 0.1264 - val_loss: 0.4297 - val_tp: 893.0000 - val_fp: 17941.0000 - val_tn: 55400.0000 - val_fn: 153.0000 - val_precision: 0.0474 - val_recall: 0.8537 - val_auc: 0.8905 - val_prc: 0.1121\n",
      "Epoch 78/300\n",
      "146/146 [==============================] - 3s 17ms/step - loss: 0.3886 - tp: 3622.0000 - fp: 71334.0000 - tn: 222027.0000 - fn: 562.0000 - precision: 0.0483 - recall: 0.8657 - auc: 0.8988 - prc: 0.1210 - val_loss: 0.4298 - val_tp: 914.0000 - val_fp: 18716.0000 - val_tn: 54625.0000 - val_fn: 132.0000 - val_precision: 0.0466 - val_recall: 0.8738 - val_auc: 0.8960 - val_prc: 0.1134\n",
      "Epoch 79/300\n",
      "146/146 [==============================] - 3s 17ms/step - loss: 0.3830 - tp: 3571.0000 - fp: 68803.0000 - tn: 224558.0000 - fn: 613.0000 - precision: 0.0493 - recall: 0.8535 - auc: 0.9013 - prc: 0.1286 - val_loss: 0.4468 - val_tp: 908.0000 - val_fp: 19085.0000 - val_tn: 54256.0000 - val_fn: 138.0000 - val_precision: 0.0454 - val_recall: 0.8681 - val_auc: 0.8912 - val_prc: 0.1285\n",
      "Epoch 80/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3908 - tp: 3574.0000 - fp: 70153.0000 - tn: 223208.0000 - fn: 610.0000 - precision: 0.0485 - recall: 0.8542 - auc: 0.8981 - prc: 0.1275 - val_loss: 0.4322 - val_tp: 875.0000 - val_fp: 16334.0000 - val_tn: 57007.0000 - val_fn: 171.0000 - val_precision: 0.0508 - val_recall: 0.8365 - val_auc: 0.8914 - val_prc: 0.1149\n",
      "Epoch 81/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3864 - tp: 3552.0000 - fp: 65391.0000 - tn: 227970.0000 - fn: 632.0000 - precision: 0.0515 - recall: 0.8489 - auc: 0.9011 - prc: 0.1313 - val_loss: 0.4849 - val_tp: 949.0000 - val_fp: 21100.0000 - val_tn: 52241.0000 - val_fn: 97.0000 - val_precision: 0.0430 - val_recall: 0.9073 - val_auc: 0.8994 - val_prc: 0.1214\n",
      "Epoch 82/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3901 - tp: 3566.0000 - fp: 70145.0000 - tn: 223216.0000 - fn: 618.0000 - precision: 0.0484 - recall: 0.8523 - auc: 0.8978 - prc: 0.1231 - val_loss: 0.3783 - val_tp: 861.0000 - val_fp: 14866.0000 - val_tn: 58475.0000 - val_fn: 185.0000 - val_precision: 0.0547 - val_recall: 0.8231 - val_auc: 0.8988 - val_prc: 0.1190\n",
      "Epoch 83/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.3877 - tp: 3622.0000 - fp: 71404.0000 - tn: 221957.0000 - fn: 562.0000 - precision: 0.0483 - recall: 0.8657 - auc: 0.8998 - prc: 0.1277 - val_loss: 0.4026 - val_tp: 877.0000 - val_fp: 16093.0000 - val_tn: 57248.0000 - val_fn: 169.0000 - val_precision: 0.0517 - val_recall: 0.8384 - val_auc: 0.8964 - val_prc: 0.1126\n",
      "Epoch 84/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3830 - tp: 3583.0000 - fp: 67050.0000 - tn: 226311.0000 - fn: 601.0000 - precision: 0.0507 - recall: 0.8564 - auc: 0.9020 - prc: 0.1277 - val_loss: 0.3985 - val_tp: 882.0000 - val_fp: 16837.0000 - val_tn: 56504.0000 - val_fn: 164.0000 - val_precision: 0.0498 - val_recall: 0.8432 - val_auc: 0.8977 - val_prc: 0.1135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 85/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3858 - tp: 3584.0000 - fp: 69298.0000 - tn: 224063.0000 - fn: 600.0000 - precision: 0.0492 - recall: 0.8566 - auc: 0.9001 - prc: 0.1272 - val_loss: 0.4393 - val_tp: 920.0000 - val_fp: 18654.0000 - val_tn: 54687.0000 - val_fn: 126.0000 - val_precision: 0.0470 - val_recall: 0.8795 - val_auc: 0.8968 - val_prc: 0.1183\n",
      "Epoch 86/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.3792 - tp: 3613.0000 - fp: 68669.0000 - tn: 224692.0000 - fn: 571.0000 - precision: 0.0500 - recall: 0.8635 - auc: 0.9034 - prc: 0.1336 - val_loss: 0.4437 - val_tp: 937.0000 - val_fp: 19763.0000 - val_tn: 53578.0000 - val_fn: 109.0000 - val_precision: 0.0453 - val_recall: 0.8958 - val_auc: 0.8999 - val_prc: 0.1290\n",
      "Epoch 87/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.3796 - tp: 3654.0000 - fp: 72132.0000 - tn: 221229.0000 - fn: 530.0000 - precision: 0.0482 - recall: 0.8733 - auc: 0.9033 - prc: 0.1340 - val_loss: 0.4341 - val_tp: 912.0000 - val_fp: 19010.0000 - val_tn: 54331.0000 - val_fn: 134.0000 - val_precision: 0.0458 - val_recall: 0.8719 - val_auc: 0.8926 - val_prc: 0.1143\n",
      "Epoch 88/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.3843 - tp: 3600.0000 - fp: 69478.0000 - tn: 223883.0000 - fn: 584.0000 - precision: 0.0493 - recall: 0.8604 - auc: 0.9014 - prc: 0.1300 - val_loss: 0.4117 - val_tp: 877.0000 - val_fp: 16963.0000 - val_tn: 56378.0000 - val_fn: 169.0000 - val_precision: 0.0492 - val_recall: 0.8384 - val_auc: 0.8934 - val_prc: 0.1158\n",
      "Epoch 89/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3799 - tp: 3596.0000 - fp: 69334.0000 - tn: 224027.0000 - fn: 588.0000 - precision: 0.0493 - recall: 0.8595 - auc: 0.9029 - prc: 0.1322 - val_loss: 0.4317 - val_tp: 930.0000 - val_fp: 19213.0000 - val_tn: 54128.0000 - val_fn: 116.0000 - val_precision: 0.0462 - val_recall: 0.8891 - val_auc: 0.8991 - val_prc: 0.1189\n",
      "Epoch 90/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3815 - tp: 3655.0000 - fp: 72362.0000 - tn: 220999.0000 - fn: 529.0000 - precision: 0.0481 - recall: 0.8736 - auc: 0.9024 - prc: 0.1293 - val_loss: 0.4560 - val_tp: 886.0000 - val_fp: 18150.0000 - val_tn: 55191.0000 - val_fn: 160.0000 - val_precision: 0.0465 - val_recall: 0.8470 - val_auc: 0.8823 - val_prc: 0.1022\n",
      "Epoch 91/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.3841 - tp: 3575.0000 - fp: 67642.0000 - tn: 225719.0000 - fn: 609.0000 - precision: 0.0502 - recall: 0.8544 - auc: 0.9018 - prc: 0.1280 - val_loss: 0.4337 - val_tp: 896.0000 - val_fp: 17434.0000 - val_tn: 55907.0000 - val_fn: 150.0000 - val_precision: 0.0489 - val_recall: 0.8566 - val_auc: 0.8969 - val_prc: 0.1219\n",
      "Epoch 92/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3802 - tp: 3590.0000 - fp: 67374.0000 - tn: 225987.0000 - fn: 594.0000 - precision: 0.0506 - recall: 0.8580 - auc: 0.9031 - prc: 0.1337 - val_loss: 0.4241 - val_tp: 897.0000 - val_fp: 17281.0000 - val_tn: 56060.0000 - val_fn: 149.0000 - val_precision: 0.0493 - val_recall: 0.8576 - val_auc: 0.8993 - val_prc: 0.1239\n",
      "Epoch 93/300\n",
      "146/146 [==============================] - 3s 18ms/step - loss: 0.3779 - tp: 3612.0000 - fp: 68475.0000 - tn: 224886.0000 - fn: 572.0000 - precision: 0.0501 - recall: 0.8633 - auc: 0.9043 - prc: 0.1323 - val_loss: 0.4320 - val_tp: 915.0000 - val_fp: 18530.0000 - val_tn: 54811.0000 - val_fn: 131.0000 - val_precision: 0.0471 - val_recall: 0.8748 - val_auc: 0.8990 - val_prc: 0.1222\n",
      "Epoch 94/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3812 - tp: 3648.0000 - fp: 71028.0000 - tn: 222333.0000 - fn: 536.0000 - precision: 0.0489 - recall: 0.8719 - auc: 0.9031 - prc: 0.1329 - val_loss: 0.3997 - val_tp: 878.0000 - val_fp: 16488.0000 - val_tn: 56853.0000 - val_fn: 168.0000 - val_precision: 0.0506 - val_recall: 0.8394 - val_auc: 0.8987 - val_prc: 0.1159\n",
      "Epoch 95/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3834 - tp: 3624.0000 - fp: 69755.0000 - tn: 223606.0000 - fn: 560.0000 - precision: 0.0494 - recall: 0.8662 - auc: 0.9026 - prc: 0.1324 - val_loss: 0.4201 - val_tp: 892.0000 - val_fp: 17105.0000 - val_tn: 56236.0000 - val_fn: 154.0000 - val_precision: 0.0496 - val_recall: 0.8528 - val_auc: 0.9003 - val_prc: 0.1223\n",
      "Epoch 96/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3788 - tp: 3575.0000 - fp: 67049.0000 - tn: 226312.0000 - fn: 609.0000 - precision: 0.0506 - recall: 0.8544 - auc: 0.9035 - prc: 0.1308 - val_loss: 0.4528 - val_tp: 932.0000 - val_fp: 19332.0000 - val_tn: 54009.0000 - val_fn: 114.0000 - val_precision: 0.0460 - val_recall: 0.8910 - val_auc: 0.8991 - val_prc: 0.1217\n",
      "Epoch 97/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.3811 - tp: 3630.0000 - fp: 70607.0000 - tn: 222754.0000 - fn: 554.0000 - precision: 0.0489 - recall: 0.8676 - auc: 0.9028 - prc: 0.1335 - val_loss: 0.3727 - val_tp: 870.0000 - val_fp: 15537.0000 - val_tn: 57804.0000 - val_fn: 176.0000 - val_precision: 0.0530 - val_recall: 0.8317 - val_auc: 0.8975 - val_prc: 0.1152\n",
      "Epoch 98/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3764 - tp: 3652.0000 - fp: 69869.0000 - tn: 223492.0000 - fn: 532.0000 - precision: 0.0497 - recall: 0.8728 - auc: 0.9052 - prc: 0.1350 - val_loss: 0.4183 - val_tp: 908.0000 - val_fp: 17736.0000 - val_tn: 55605.0000 - val_fn: 138.0000 - val_precision: 0.0487 - val_recall: 0.8681 - val_auc: 0.8998 - val_prc: 0.1219\n",
      "Epoch 99/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3809 - tp: 3626.0000 - fp: 70025.0000 - tn: 223336.0000 - fn: 558.0000 - precision: 0.0492 - recall: 0.8666 - auc: 0.9032 - prc: 0.1337 - val_loss: 0.3950 - val_tp: 877.0000 - val_fp: 16516.0000 - val_tn: 56825.0000 - val_fn: 169.0000 - val_precision: 0.0504 - val_recall: 0.8384 - val_auc: 0.8917 - val_prc: 0.1022\n",
      "Epoch 100/300\n",
      "146/146 [==============================] - 3s 18ms/step - loss: 0.3861 - tp: 3610.0000 - fp: 69768.0000 - tn: 223593.0000 - fn: 574.0000 - precision: 0.0492 - recall: 0.8628 - auc: 0.9004 - prc: 0.1280 - val_loss: 0.3758 - val_tp: 850.0000 - val_fp: 15136.0000 - val_tn: 58205.0000 - val_fn: 196.0000 - val_precision: 0.0532 - val_recall: 0.8126 - val_auc: 0.8905 - val_prc: 0.1065\n",
      "Epoch 101/300\n",
      "146/146 [==============================] - 3s 18ms/step - loss: 0.3843 - tp: 3618.0000 - fp: 70312.0000 - tn: 223049.0000 - fn: 566.0000 - precision: 0.0489 - recall: 0.8647 - auc: 0.9010 - prc: 0.1291 - val_loss: 0.4220 - val_tp: 891.0000 - val_fp: 16772.0000 - val_tn: 56569.0000 - val_fn: 155.0000 - val_precision: 0.0504 - val_recall: 0.8518 - val_auc: 0.8975 - val_prc: 0.1192\n",
      "Epoch 102/300\n",
      "146/146 [==============================] - 3s 21ms/step - loss: 0.3769 - tp: 3618.0000 - fp: 68823.0000 - tn: 224538.0000 - fn: 566.0000 - precision: 0.0499 - recall: 0.8647 - auc: 0.9043 - prc: 0.1320 - val_loss: 0.3858 - val_tp: 881.0000 - val_fp: 16266.0000 - val_tn: 57075.0000 - val_fn: 165.0000 - val_precision: 0.0514 - val_recall: 0.8423 - val_auc: 0.8967 - val_prc: 0.1285\n",
      "Epoch 103/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.3855 - tp: 3635.0000 - fp: 70567.0000 - tn: 222794.0000 - fn: 549.0000 - precision: 0.0490 - recall: 0.8688 - auc: 0.9011 - prc: 0.1276 - val_loss: 0.4198 - val_tp: 894.0000 - val_fp: 17140.0000 - val_tn: 56201.0000 - val_fn: 152.0000 - val_precision: 0.0496 - val_recall: 0.8547 - val_auc: 0.8988 - val_prc: 0.1186\n",
      "Epoch 104/300\n",
      "146/146 [==============================] - 3s 19ms/step - loss: 0.3779 - tp: 3611.0000 - fp: 68576.0000 - tn: 224785.0000 - fn: 573.0000 - precision: 0.0500 - recall: 0.8630 - auc: 0.9045 - prc: 0.1350 - val_loss: 0.4042 - val_tp: 902.0000 - val_fp: 16925.0000 - val_tn: 56416.0000 - val_fn: 144.0000 - val_precision: 0.0506 - val_recall: 0.8623 - val_auc: 0.9021 - val_prc: 0.1199\n",
      "Epoch 105/300\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3734 - tp: 3612.0000 - fp: 68827.0000 - tn: 224534.0000 - fn: 572.0000 - precision: 0.0499 - recall: 0.8633 - auc: 0.9063 - prc: 0.1374 - val_loss: 0.3820 - val_tp: 885.0000 - val_fp: 15916.0000 - val_tn: 57425.0000 - val_fn: 161.0000 - val_precision: 0.0527 - val_recall: 0.8461 - val_auc: 0.9019 - val_prc: 0.1172\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 106/300\n",
      "141/146 [===========================>..] - ETA: 0s - loss: 0.3813 - tp: 3555.0000 - fp: 70100.0000 - tn: 214599.0000 - fn: 514.0000 - precision: 0.0483 - recall: 0.8737 - auc: 0.9028 - prc: 0.1297Restoring model weights from the end of the best epoch: 86.\n",
      "146/146 [==============================] - 3s 20ms/step - loss: 0.3827 - tp: 3651.0000 - fp: 72383.0000 - tn: 220978.0000 - fn: 533.0000 - precision: 0.0480 - recall: 0.8726 - auc: 0.9021 - prc: 0.1288 - val_loss: 0.4465 - val_tp: 892.0000 - val_fp: 18288.0000 - val_tn: 55053.0000 - val_fn: 154.0000 - val_precision: 0.0465 - val_recall: 0.8528 - val_auc: 0.8960 - val_prc: 0.1126\n",
      "Epoch 106: early stopping\n"
     ]
    }
   ],
   "source": [
    "EPOCHS=300 #200\n",
    "BATCH_SIZE=2048#,2048# 1024\n",
    "if use_weights:\n",
    "    print('Using weights!')\n",
    "    model_history = model.fit(X_train, y_train,\n",
    "                          validation_data = (X_test, y_test),\n",
    "                          epochs=EPOCHS,\n",
    "                          batch_size=BATCH_SIZE,\n",
    "                          verbose=1,\n",
    "                          callbacks=[early_stopping],\n",
    "                          shuffle=True,\n",
    "                          class_weight=class_weight) \n",
    "else:\n",
    "    model_history = model.fit(X_train, y_train,\n",
    "                              validation_data = (X_test, y_test),\n",
    "                              epochs=EPOCHS,\n",
    "                              batch_size=BATCH_SIZE,\n",
    "                              verbose=1,\n",
    "                              callbacks=[early_stopping],\n",
    "                              shuffle=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "02d52eee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot training history\n",
    "plt.plot(model_history.history['loss'], label='Training Loss')\n",
    "plt.plot(model_history.history['val_loss'], label='Validation Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e1b84cc9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bm3G7MyMvqVAoFOmgs89Dv9sHwHnTygAoyLYzvkhc2A+lKS10qr3fuL+7roNNh5tjrJ0YTo+XHSdFCv9yk3cllAnFOQCcNh3DQGgyIiyOsOdKMmyekBIsGczXv/51rFYr8+bNo7y8HJfLxbvvvovX6+Xqq69mwYIFfO1rX6O4uBiLxUJhYSGbNm1izZo1zJo1i//7f/8vP/rRj7j22msBuOuuu5g9ezbLly+nvLycd999d5jfoUKhUKSPM53iIl6cayfbbjWWz6wQzdfSlRY61SYiLHariKr8ZMPhiF4Wl8eX8DY/qGnH6fFRXpDF9PL8qOuNNwRLX9R1ksHochspwpKXWRGW0ZkSGiHMmjWLzZs3hy1//vnnI64/d+5c1q1bF3V75eXlvPbaa2k7PoVCocgkpGCpDEmVzKrI561DTRxu6E7LfqRY+NyqKn79/km2n2xj87EWzp8+BhCpqUdeO8gvNh3jk8sm8e3r5gUJqEhsPtoCiMhQrPSSFCyn0iRYmrrEOQvtwQKBTrftfW68Pn9YFdFQoyIsCoVCoTgraOgQF9+KEMGS7giLFCxLJpdw8/JJAPzXG2Kissfr49/+vJsn3jyK2+vn91truPGn71HT0htzm5uPCcFy/vSymOtNKBbvLV2CJVaEpVj3+/j90J4BaSElWBQKhUJxVhA9wiIFS3ojLOOLs/nypdOxWzXeO9rCu0ea+fJvdvLH7XVYNPiHi6dRkmtn7+lOPvqTt9mwvyHi9vrdXqpr2gFYNS22YEl3SijaHCEAu9VCYbZIxGRC8zglWBQKhUJxVmAIlqKQCMtY4Qlp7nYO2I/h9vqM/UwozmFCcQ43Lp0IwOee2srr+xtw2Cz89DPLuG/NXP7+jxexZHIxnf0evvDMdp7cdCxsmztOtuHy+hhXlM2UstitLyaUpE+wOD1eOvpE8UXoHCFJaQY1j1OCRaFQKBRnBWc6IguWvCybUV0TKS20s6aN944kVunT0NmPzw8Oq8W4yN996XSsFg2Pz09Bto3//fy5rJ4vOoqPL87h2S+t4o7zqwB4+LWDtOilxJL3jop9r4rjX5HbA2jrddPr8iR0zNFo0dNBdqtGUU7kcu9MmiekBItCoVAozgoMwRKhP8msChFlOdQYnBbq6ndz25NbuP2prTR0xi8VluXE44qzjU60U8ry+NfVs1k+pYRnv7SKlSFpHYfNwrevm8eiiUW4PD5+t6Um6HnDcBvHvwJQmG2nIMumH0tiUZZdte0RPTRGSXN+VlBXXTNlslJIpYSGlnS2UB6NqPOnUCgyGSk4Qk23ALMqhY/lcEiE5e3DzfS5vXh8gaZzsTjVLi7844tygpb/wyXT+dPd5zNvfGHE12maxp0XiI7j//v+SaPkucfpYXddBxDfvyIJVArFF1gnmnu48afvccfTW8Oea+4OCJZoyEohFWEZIqxWUU7mcg3/CR/J9PaK/6h2+8A7RSoUCkU6cXq8tOgX1dCUEMCssZErhV7fFzDCbj8Rf/aajLBIL0kyrFkwjrEFWTR2OXllj+hqu+1EKx6fn4klOUwqTWx0SzI+lveOtuDx+TnW1ENnf7APJZbhVpJJvVhGRR8Wm81Gbm4uTU1N2O32pGYC+Xw+XC4X/f396ZklNALx+/309vbS2NhIcXGxIQAVCoUiU2jsFBdfh81CSYT2+7JSyNyLxeP18ebBRuPx9pOJRFhkhVDygsVhs/CZ86bw6PpDPPXuCT6+eIJRzpxodEXsWwiyRASL+T0da+ph8aRi43EgwhLe5VaSSd1uR4Vg0TSNcePGcfz4cU6ePJnUa/1+P319feTk5KRlVsRIpri4mMrKyuE+DIVCoQgjkA7KivhdPWNsPpoGLT0umrudjMnPYmdNO229bnLsVvrcXvad7qTb6SE/K/qlUYoE2Q8lWT69cjL/9eYRdtW2s7Omjfd1/8qqBPwrEiMl1BZfsMh2/wBHG7uDBEtCEZYMmic0KgQLiEGCM2fOTDot5Ha72bRpExdffPGoToXY7XYVWVEoFGnD7fXx1LsnIHY/tYSJ1oNFkuOwMqkkl5rWXg41dDEmP8voi7J6fgXbT7ZR19bHBzVtXDSzPOp+pEhIJcICwi/y8UXjeW5HHT/ZcJgPT+n+lSQEy4QEu902dzs5aTLbHmvuDnneZRxTNDKpSmjUCBYAi8VCdnZyqthqteLxeMjOzh7VgkWhUCjSycsf1vPgukPMK7bw+TRs70yULrdmZlXkU9Pay+GGbs6fPob1umC5cl4FAHVtfWw/EV2w+P1+U4QlNcECcOcFU3luRx1vHmwCoKosl3FFiW/PGIDYEVuw7DjZHvT4aGNP0OPEPCziupcJKaHRacpQKBQKxbDyoV4Z0+ZKT6q9IU6EBYJb9B9v7uFYUw82i8bFs8pZXlUKxPaxdPZ56HF5gdQjLADzxheycmqp8TiZ6Ip532c6+vH6oldv7tC7507WzbyhEZYm3cMSqS2/pMRICanGcQqFQqEYhew/0wlAuq6D9VGaxpmRvVgON3Qb6aCV00opzLazQhcsH9S04/FGnrJcp5c0l+U54g4zjIcscQYx8DAZxhZkYbVouL1+wzgbiR01wr9y03LRifdEc2+QwGmWfVgSqBLqdnpwerxJHWe6UYJFoVAoFEOK3+9nf70oL+52gy9GlCBRGqK05TczU5Y2N3axXi9nvnJuhf5cPoXZNnpdXuPYQhlISXMoV82rYO64Qopz7TE9M5GwWS1GJKkuivHW5YV9p8X7+NiiCWTZLLi8PurahOjqd3vpcopOubFSQoXZdmNKc3vv8EZZUhIsTzzxBFVVVWRnZ7Ny5Uq2bg1vSCPZu3cvN954I1VVVWiaxmOPPRa2zk9/+lMWLlxIYWEhhYWFrFq1ildeeSWVQ1MoFApFhtPU5TRMnD40OvsH1mIe4ptuQVQKWTRx4d2qN4mTgsVi0Vg2pQQgagM5Y+hhEn6TaFgtGn++exVvfeMyI4qRDPFKm2u6wePzU1GYxaTSHKaOyQPgaJNIC0n/isNmMTrnRsJi0Ywy8eE23iYtWJ599lnWrl3Lt7/9bXbu3MmiRYtYvXo1jY2NEdfv7e1l2rRpPPTQQ1FLYidOnMhDDz3Ejh072L59O5dffjkf//jH2bt3b7KHp1AoFIoMZ/+Z4AjGQC+Efr+fBr0PSyzTbbbdavg5/H6YXVEQ1Kwtno/l9AB6sEQi12GLOsMnHhPiTG0+1iWiIsunlKJpGtPLRTrsWJMw3pr9K/FadpRkSGlz0oLl0Ucf5a677uLOO+9k3rx5/OxnPyM3N5ennnoq4vorVqzg4Ycf5pZbbiErK3LY6brrrmPNmjXMnDmTWbNm8R//8R/k5+fz/vvvJ3t4CoVCochw9td3Bj0eaAVKW6/baHUfS7BAoIEcwBVzxwY9t1yPsGw/0RZxFEmdIVhS68GSTsYnKliqxHuaVi4jLEKwJOJfkWRK87ikyppdLhc7duzgvvvuM5ZZLBauvPJKNm/enJYD8nq9PPfcc/T09LBq1aqo6zmdTpzOgNmos1P8B3C73bjd6cuzyW2lc5sKdV4HC3VeBwd1XtPLPr33iKSps29A57auRaQ5SvPsaH4vbnd0c+j08kBE5dJZZUH7nVeZh92q0djl5FhjpxGNkZzS/R+VBY5h/1uoKBAiora1N+xYnC4XJ3TBsnhCIW63mymlQuAcaezC7XZzpkO8lzF59rjvpThHSIXmAX5O0Uh0m0kJlubmZrxeLxUVFUHLKyoqOHDgQDKbCuPDDz9k1apV9Pf3k5+fzwsvvMC8efOirv/ggw/ywAMPhC1/7bXXyM1NbB5DMqxfvz7t21So8zpYqPM6OKjzmh62HbYCGnaLH7dP453tu/DXVqe8vb1tGmAlx+/i5ZdfjrluT7NYN9/u59Tu96j/MPj5CTlWTnRrPPW3tzi3PDjKcvyMOO7je3fwcnJN09POKf09H6xtDHvPp3uhz2vDYfFzvPodanZBfTeAjQOnWnn55Zd5r1a8vq+1Ie456262ABa2VO+lpGVP2t+LnFMXj4xpHDd79myqq6vp6OjgT3/6E5/73Od46623ooqW++67j7Vr1xqPOzs7mTRpEldffTWFhZGnZaaC2+1m/fr1XHXVVapxXBpR53VwUOd1cFDnNX04PT7+ZcsGwM+5VaW8e6yNyskzWHPFzJS32bW9Dg7sY+bEctasWRpz3cvdXjr+uo/LZpfzkQXhvsrdloP86t2TeIqnsGZN4Prj8vj42vuvA/CpNVdQFqN3yVAws6GbXxx4j26/nTVrVgc995v3T8CuQyyZXMJ1HzkXgK5+Dz/68A263BoXXnYVW14/DHV1LJk3gzVXzIi5rwPrD/Ne43HGTKhizZq5aX8vMkMSj6QEy5gxY7BarTQ0NAQtb2hoGPCMGYfDwYwZ4qQtW7aMbdu28fjjj/Pzn/884vpZWVkRPTF2u31QvlAGa7ujHXVeBwd1XgcHdV4HzqGmDjw+P4XZNs6ZUMy7x9po7/cO6Lw2dYuUwrji3LjbsdvtPH5rdFGzctoYfvXuSXbWtAdtq76zF78fsmwWKorzhn223KQxwkTb0efB6dOC5h/tqhOm5uVVpcZ7KLXbqSjMoqHTSU27kxa9AU5lUU7cc1ZWIDw7A/2copHoNpMy3TocDpYtW8aGDRuMZT6fjw0bNsT0m6SCz+cL8qgoFAqFIv14fX5e3XtmyCpADug9TuaMK6QsPz1zahLpcpsosrT5cGM37SaT6SlTS/7hFisABdl2CrOFSKkPMd5u1zvcLptSHLR8mi5yjjb1JDRHSCLLrkdcldDatWt58skneeaZZ9i/fz933303PT093HnnnQDcfvvtQaZcl8tFdXU11dXVuFwuTp06RXV1NUeOHDHWue+++9i0aRMnTpzgww8/5L777mPjxo3cdtttaXiLCoVCoYjGn3fW8Q//u4OHXhmYDzFRZIXQvHGFlKapv4fscjsuRtO4RCnLzzIqat4/1mIsT3dJczqQx1JnEiyNnf3UtfWh4WfxxOKg9aePFe/rWFN3QnOEJJkyADFpD8vNN99MU1MT999/P2fOnGHx4sWsW7fOMOLW1NRgsQR00OnTp1myZInx+JFHHuGRRx7hkksuYePGjQA0NjZy++23U19fT1FREQsXLuTVV1/lqquuGuDbUygUCkUs3jokBvDVtKZpbHIcDug9WOZUFgQG66UpwlJRlA09zdDdABXzU97e5bPHcqzpOD/fdIzV8yvRNM2IsGRCSbNkYkkOB850BZU2bzsh2vGPz4WC7OBLfCDC0m209E8kwlImIywjqaxZcu+993LvvfdGfE6KEElVVVXEenYzv/rVr1I5DIVCoVAMAL/fzxY9ijAUFyPRkl9EWOaOK8TvE+XHrQNs+R7U5fZ318OpHfC1D6F4Ukrb+9LF0/jNlpN8UNPOmwcbuXxOhWlKc/qrUFMltBeL1+fnp2+J7MXMovDr7vSxQrDsOdVJrz7EMaEIS24gwuL3+4ctJaZmCSkUCsUoRfzSFkJlKARLU7eTlh4XFk00cCs1/XKP98M2Gv1urzHjprIwG1qOAH7oqE35OMcWZvO5VVUA/Oi1Q/j9/oyMsAQEixBsv9taw55TnRRk27hyQvgAx2l6e375XnIdVvJitOWXyM/J6fHRF6PHzWCjBItCoVCMUjYfC7Sgb+txpywaIvHe/hr++m510DblUMGqMXnkOKzGhdDt9ac8T0img7LtFgqzLdCvN6Vz9Qzg6OEfLplOnsPK3tOdvLr3TJDpNlOQguVUWx8t3U4eefUgAP98xQwKzIU3+mcwoTiHLFvgsp9IOgiEsHHorxtOH4sSLAqFQjFKMZtKXV4fPa70/XouefbjXPHaav62NWDmPSDTQZWiV1aWzUKWVVxMW7pTqwo90xFIB2mubkAXSK7uFI9cUJrn4PMXTgXg0fWHAimhNExqThcT9GjPqfY+/nPdQTr63MwdV8itKyYGVnL1wI+XwJ++gMWiGUMQIbF0EICmafz+rvNY/88XM7Zg+CJMSrAoFArFKMTsX5Gkq2y1z+Vlmr+GfK2f/133jmHwDPhXAvN88vWMRKq/3KV/paIwOxBdgQFHWAC+eNE0CrNtHGropt8tUiyVaahEShfST3O6o49nt4sU2Pc+Ph+b1XRpbzwAbcfh4CsAxhBEgDH5iU+JXjalhJkVBUakZThQgkWhUChGIUcahX8l225JexVIc0c3WZpI8fj6u/ju3/YB5gqhQDfyfD110ZKiYDF6sBSlX7AU5dj50sXTjMflBVlk2awD3m66KC/IwmbRZMaHG5dONCZOG/TpaT93D3hcTC9PPsKSKSjBolAoFKMQmQ5aNqWEsXrDtbYBVutIWtvbjPsFWj8v7jrNuj1nONIo0jRzxwcES4FdXG1TjrB0iOhN5SBEWADuuGCq4bXJpB4sAFaLZkR8CrJtfPPaOeEr9QU+C/rbmWaKsJTnZ060KBGUYFEoFIpRyPu64fa8qWWU6A3c0pUSam9vN+5fN1ekf/7lj9VGS/7xprRKnp4SStTD4vMFG4PPdApvSUVhNvQF9psuwZKfZeOey8TYmPnj0zenLl3MqdTP71WzIkdMegPGavrag1NCBYmnhDKBjBl+qFAoFIqhwe/3GxGW86aXcaBBpGrSVQHS2RmIdFw3p4jH63ONxnRzxhUG9fGIlRL6/dYafrLhMH1uL06PD6fHh0WD21ZO4f+smYvDZjFMt+MGISUk+fwFVcwbV8j8CZknWP7jEwv4zHmdXDKrPPIKfWbB0sbUClOKa5gHOCaLirAoFArFKONIYzctPcK/smhisRFhaU+Th6W7KyAcHN5eHrxhgfF43rjgi35+jJTQ0++e4HRHP229bnpdXrw+P26vn6ffO8HNv9hMfUcfDZ0iMlMRJlgGViVkRtM0Vk0vozA78wZfVhRmc+nssdGbufUGC5b8LBsT9UqnTEtxxUNFWBQKhWKUIaMry6eU4rBZKJWdTNMkWHp7OgMPXF1cMGMMnz1vCv/7/kkumR0cCZARllDB4vP5Od4ioiS//eJKJpfmkmWzUF3bztef28UHNe185Mfv0NFnahp3dHAiLCMac4Slvx2AH31qEfvrOzMyxRULJVgUCoVilGH4V6aJipISo0ooPabb/h6TcHCKSMd3Pz6fr14xI6yPR4HhYQkWLKc7+nB5fNitGiunlhqlulfPr+SlykK+/Jsd7NPLpDVNr3jRL8iAEiwSs+lWv79yWhkrp5UN0wGljkoJKRQKxSgiyL+iX7TkrJh0mW77e03pGD01o2laxKZjMiXU0hNsuj3RLDwvk0tzg/uKAJPLcnn+K+fzqWWiQdrUsjzsVktwSsg9NMMcM54Q0+1IRkVYFAqFYhRh9q8snFgMBCIs6TLdevq6Ag/iRDryTCkh82C9481C6Ewdkx/xddl2K//5yYV8YukExhXpXoxB8rCMaEJMtyMZJVgUCoUiQ2nVhUWuI31f1aH+FcBkuk1PSsjb3wPSA+rsirmu7HTr9vrpcnoMY+uxZiF0ppkanYWiaRrnTx8TWDBIVUIjmt7gPiwjGZUSUigUigzkWFM3F/3wDW76+eaw3iMD4b2jMh0U6IhaYjLdDnQAYr/bi9VjSsfEiXQ4rJDnEN1jW00+lhO6YKkqiy5YwhiEPiwjGq8LXCbBOMIjLEqwKBQKRQbyo9cO0ePysudUJ28ebEzLNvvdXjYdagLgopmBah3ZydXl8dHnjj8Asa6tlwdf3m9MMDbT3O0kR+sPLHDGT83IlJS5F8txXbCYh/XFRaWEggn1rIxwD4sSLAqFQpFhfFjXwd8/rDceP/Xu8bRsd/PRFnpcXioLs1k4schYnuuw4tCNrfF8LH0uL194ejs/33SMX70dflzN3S7yMBloE4h0lOpGFtnt1u31UdsmxFCslFAYQYJFmW7DIioqwqJQKBSKdPKfrx4A4MIZY7BaNN490mJMOh4Ir+07A8DV8yuCGo1pmkZJnmzPH9vH8p0X93JQ74x7oiVcjDR3OcnBFGFJINJh9IHRxVJtay9en59ch5WxiQ7o83qC0x8+N3jSYyIeqWhmwy0owaJQKBSK9PHe0WbePtyMzaLxg08s4Jr5lQD8zwCjLF6fn/X7GgC4el5l2PNGaXOM5nEvfFDHs9trjce1reFRjOZuJ3maKcISx3QLgZSUTAkdN/lXonZwDcUZQdCN9rSQFCiFE8RtfzsM0KM0nCjBolAoFBmC3+/nP9cdBODTKyczuSyXz184FYC/VJ+mOcEBgZH4oKaN5m4XBdk2VpoMt5J4guVIYzffemEPANcvHg9AXVtfmEm3udtJblBKqDvuRbIspKza8K8klQ5qF7f2PLDqUZnRbryVEZZSfX6QzzOiRZwSLAqFQpEhrN/XQHVtOzl2K/deLiYEL51czKJJxbg8Pn7z/smUt/2aHl25Ys5Y0WQtBBnliNQ8rt/t5d7f7aTX5WXVtDIeunEhmgZ9bm/Y0MLmbhe55pSQzwOe2ELLiLDogswoaU7FcJtdBA79daNcsBgpocLxYNUnM49g460SLAqFQpEBeH1+Hn5VRFc+f2GV0RVW0zS+oEdZfvP+SZye+FU8ofj9fl7dK/0r4ekggGK9F0trhF4sP1x3gANnuhiTn8Xjty4m224Vs3sITws1dTvJ1UIEShzhYJhudfGTUkmzFCw5xeDIT2i/Zz1SnOSUQk6Jvmzk+liUYFEoFIo009jZzy2/2MyLu04n/JpX9tRzuLGbohw7X7p4etBz155TybiibJq7XbxYnfg2JYcbuznZ0ovDZuGSWeUR14kVYXl1jxA737/+HENIyYm/dW3Bpc3NXc7gCAsEm2Fj7HtgKSFzhCVX3HePbsFiRFhyTYJlBDePU4JFoVAo0sy6vWd4/1grT246lvBrtp8Qv3xvXDqRohx70HN2q4XbV1UB8NS7J5Ju7vaaHl25aMYY8rIid82N5mFxerzUdwoBsryqxFg+qUSIgtq2CBEWQiIscXqxmKuE+lxe6jvE/pJKCclogkoJBZDRlJwSyC4OXjYCUYJFoVAo0szhBnGBPtLYnXCXWhlVmFUReXbOredOIsduZX99J3tPJ1fiLP0rV8+viLqOUdYcIlhqW/vw+0U3WmmOBZhYmms8b6a5y0muFhphiS1YyvIDVULyPJTk2inOdcR6WTDKwxJOpAiLEiwKhUKhkBxuFCmQPrc3LGUSDaOUN0pUoTjXYVT3bD/RGnGdSJxu72N3XQeaBlfMjSFYjInNwR6WmlZxXJNDSowDKaFAhMXp8dLZ7wlEWLL15nQJRlhcHh97TgnhEe08RMUQLMUmD8vIrYhJB5oRYSkV3h5QpluFQqFQBDjSGPhlf6ghfh8Sp8drXPhjpUGWTha/knfUtCd8LK/vF9GV5VNKGJMfvQmb4WEJibCcbBHHNUWPqEhkSsgsyFq6XVjwkaPp28jXBVIc4ZDjsJJjF/OEdpwUF9mkWvKDirBEQgoWFWFRKBQKRShtPa6gfimHGuMLltrWXnx62qU8RmdXKVh2nkz8ovPa3ujN4syUhHSblRiCpSxEsJSKCMuptj4j7dXc7STH7F9JULBAQDBtPymiR0n5VyCKYBnF7fn9/sgeFmW6VSgUCgXAkabgi7P0s8TiWFOgKiZWZ9dFk4qwaHCqvY/Gzv6o60m6nR7ePyamM181L3o6CAIDCJ0eH32uQOm0LFueFBJhqSzMxmrRcHl9NHYJkSKaxsnj0iC3TNxNYACi9LEcbYqdGouKvBBnF4nmcTCqU0JWnxPNq4tPVdasUCgUilCkQHHYxNdrIikhOZNn6pjIhltJQbadWRUFAOysiX/h2Xq8BY/Pz5Sy3LgCIM9hxW4VYqnVlBY62Ro5wmKzWhhfrPdi0dNZzV2uQA8WRx5kiWONV9YMBBl6QaWEBorDq4s1q0OcD+VhUSgUCoWZI43iQnHxzHLjsTdOpZDRdySBi/TSKXpaKAEfyzuHRXTl/Olj4q6raZrJeCsEi8/np0YKltLwYwv4WMQ6Td1O8mSEJUiwJDKxOTgVllTTOAhpHKcEi8OjC5acUtA0FWFRKBQKRTCyQuiyOeVk2Sw4PT7joh8NmRJKxLdhGG8T8LG8d7QZgAtmlMVdF8KNtw1d/bg8PmwWzYimmJGVQrK0OcjDYs8NVOskkRICkW6K1i8mKhEjLIOUEvI44Tc3wl++krHDBA3BkqvPjTIES/uwHE86UIJFoVAo0oiMsMypLGCm3lMlXlooqQjL5GIAPjzVgcvji7peU5eTA2fEfldNS0ywGO359QiLNNxOKMnBFmH+kNE8Thdkzd0u8mQPFkd+UsKh1JQSqhqTG2PNKAQ1jhvk1vxHXhf/qn8Lp3cOzj4GiF2mhKRQUaZbhUKhUEi6+t1Gl9YZ5QXMGitSIodjCJZup8cwrSZiNJ06Jo+SXDsuj4+9pzuiriejK/PGFVIWo5zZjBQN7fo8oRpdsEwujSwgJpYGt+dv7jJFWBy5kCUjLPE9LGbBEs/LE4bHCR69vDqoNf8gVQnteT5wf8czg7OPARJICZUE3zo7wesZnoMaIEqwKBQKRZqQFS7lBVkU5dqZqRtkD8WoFJKD/sbkO8Ja8kdC07RAeXMMH8t7R4R/JdF0EGB0ljUiLHrTuFDDrSS0PX9zqIfFIT0s8SMsY0wpoeRLmmXnXw2yBjkl5OqFg68EHu/5c0Ipr6HGMN3KlJBs4geB9NkIQwkWhUKhSBMykjJzrIgQzEogJXQsiXSQJGC8jexj8fv9vHNERFjOnxHfcCuRHWfbe4NTQpEMtxAoda7v6Mfj9YmyZs3kYclKPDVjNt2m3OU2qxAslsFNCR1+TQxVLJoMZTOEKNrz5/TvZ4AEmW4BrDZxfmDEGm+VYFEoFIo0If0rAcEiIgzHmnrweCP7TY7LviNJVMUs0X0sH0Qx3ta09nKqvQ+bRePcqtKEtyt7sbTqKaFoPVgk5flZOGwWvD4/tW19tPW6A31YHPnJmW6DUkID6MECg1sltFdPB53zCVh6u7i/M/PSQmGmWxjxPhYlWBQKhSJNSMEyQxcsE4pzyLFbcXl9nGiJ7Kc43ixeM7U88Yv0oonFWC0apzv6qe8In1X0rp4OWjq5JKlqmxLddNtmpIQi92CRWCwaE4uFj2VXbTsAeRa9h4sjN6mZPuUFWeRn2SjKsUf1zERlqASLsxsOvSbuz78BFn0aLHY4tQPO7EnvvgaI3au/d+ldAVMvFhVhUSgUilHNYUOwiMiKxaIZlULRjLeyQigZ30Zelo05lXoDuZPtYc+/e1SmgxL3r0AgwtLW66Kjz22Yb2MJCDm1+QM9PVVm14cnOvKSMt1m26385Z7zeeEr5xtN9xLGXNIMpk63PektOz60Tph7S6fBuEWQXw5z1ojnMizKEpYSAiVYFAqFQgH9bq9hPpUiBQJpoUjGW7/fb/KwJFcZEzDeBl98fD4/m49Kw23i/hUIeFjaelxGhdCY/KyYURrZi+UDPcJSYtMFiz0vOMKSgHCYMbaAaeVJVghBcNM4CERY8IM7sWnZCSGrg+bfIJqxASz9nLjd9WxGzS4KM93CiO/FogSLQqFQJIHf72fToSbePNgYtPxoUzd+v0irmP0YhvE2whDElh4XXf0eNC162iUaS6cUA+GCZf+ZTlp7XOQ6rCyaWJzUNo1Ot73uuBVCElkptO+0qNQptsmUkCnC4vOAnGszGIRFWEzHnK60UH8HHFkv7p9zQ2D5tMugeDI4O2DfX2Nvw9kFvui9c9JJ5AjLyO52qwSLQqFQJMj2k2188mebuf2prdz5P9uMSAYE+1fMAwxlaXOklJAsaR5flEO23ZrUscgIy95TnfS7A8MKZTnzuVNLk06tlOQJD0uf28shvenclDh+Ejm12aOPHyiwRvCwwOCW/pqbxoGoFEr3AMQDLwvRNWY2jJ0XWG6xwJIEzLed9fDILPj5xeL+YOLzYvfq0R5lulUoFIrRw+GGbp48YOHWX24Laon/0LoD+PVUhxx6KP0rEpkSOt7cgzukUkimg6YlYbiVTC7NZUy+A5fXx55Tgb4a0r9yYZLpIID8LBs2ixBb1XVim5PjRFgmlgQ/X2AMP8wHixVsQtAkMgAxZUIjLJB+461RHWRKB0mW3AaaBWo2Q/ORyK+vrxaN7Bo+hKeuhpaj6TmuSPR3oKGn4KRIARVhUSgUirOZ1/c18LH/3syeNgtWi8at507mpa9eSK7Dyq7adtbtOQMEZgjJkmbJ+KJs8rNsuL1+I6IiSaYlfyiaprFEj7J8+skt3PSzzTzy6kG2Hm8FEht4GGmb0ngrq37iVexM0j0skhxzHxYwGW8HMcJiCJbiwLJ0CpbeVjj6hrg//4bw5wvHw4Rl4n5DlGqhztOB++018NRqqN8Vf9+eFFJpfeJvwO/IB5tpCvYIn9isBItCoVBEYduJVu753U48Pj9zi328fO/5PHjDAs6ZUMQXL5oGwMOvHsTj9QV6sFQECxZN04wy54MhaSHZgyUVwQLwuVVVVBRm4fL62Hqilf968wi9Li+leQ6jiihZpPG2o0+YZ+N5WErzHOSY0lk5ft3kKgXDYM/1gdgRFnca9ntkg/DhVJwD5bMir1M0UdyahYkZuXzux6ByAfQ0wdMfhRPvRt9v4354aDJs+F5Sh6vJCEpOSA8eFWFRKBSKkUttay+vfFhPn8sbtHx/fSeff3obTo+Py2aP4a7ZvqDUzV0XTaU0z8Gx5h5+u6XG6LMyY2x4lUug421wlGEgERaAC2eO4f37ruDNr1/Kf964kBuXTmROZQFfvXwGFosWfwMRkAMQJZOjdLmVaJpm+FgAHD5Ta34wdbsdzJRQu7gdrJRQT5O4LZ8dfZ2C8eK281Tk57t038q4hXDH32HKBWKuz+9uih7xOPmuKKM++V5yxysjLOYeLBCIQI1QwZLk/G6FQqE4e/D7/dzxP1s52tRDSa6dz543hc+uqqLP5eX2p7bS1e9hRVUJj9+0iDdfPxP02oJsO1+9fAYP/G0fD71yAK/PT36WjcrC7LD9zIpgvPX5/BxvkT1YUijl1dE0jalj8pg6Jo+bVkxKeTsS8xDCXIc1aMZPNCaV5BpizO6NEmGJkBLS6rZi86RBUAy2h0UOVrTlRF+nUAqWOBGWgvHiOD/zZ3h8EXQ3QPMhmHRu+Gu6zgTvP1GkIMmNEmEZoaZbJVgUCsWoZfvJNmNgYVuvmx+/cYSfbTpGYbad5m4ncyoL+OXnVpAT5Zvy0ysn89S7x6ltFReU0AohSaAXS0CwnO7ow+XxYbdqjC8OFznDRYlJsEwuzY34fkKZaPKxWDx6dYr0sETrdnt8E7ZnrmNJ0TLgUwM55DiCJQ3eGbceNbLH+JykYOmKUgEklxeO07eVAyVVQrBEEznyNXL/CaLpEZYgTw8Ep4T8/nDzcIajUkIKhWLU8tz2WgBuWDqBn962lMWTinF5xBC/SaU5/Prz58acoJxls/IvVwXSBKGGW8ls3U9yrLmHJ948IqIrejpocmkuNmvmfBWXmFJCifaGkbOGLJofza0LFilUoplu63cDUNG5a2BREL8/vHGcef9pibDogsEWS7BMELfRUkKylFmmjgAKdPHSdSZ8ffPypCMs7QD4wzwsxeLW60pvQ70hQkVYFArFqKTX5eHvu8VF5JYVkzl3ainXnFPJjpNtbDzYxC3nTmJshPROKB9bNJ6fbzrG/vpO5owrjLhORWE2n1s1hWc2n+ThVw+y7UQrSyaJX7vJdrgdbGTzOIApCQ5klKXN43JB8+rltI44ERY9emD1e/CffBfmfSS1A3b3BZrSmSMsMsKTju6zCQkWmRKqF83hLCYR6uoRjeUgEGEBk2CJFmGRgsWZ3PHKCEuoh8WRDxabMBD3tQU+oxGCEiwKhWJUsm7PGXpcXqaU5bKiSnyxa5rG8qpSlicx4dhi0fj5Z5bxwgenuCWGh+Q7H5vP/PFF/Ptf97DxYBMbDwojZyo9WAYTs2CJNqU5lCWTi8lzWLlgsg2O6wuNsma9WimKYAHQjm5IXbDI6IpmCW5Ul9aUkB6NiJUSKqgENPC5obcZ8scGnpPRFXseZJlErRQv0RrJGSmh5KIhWjQPi6aJNFFvs/CxFE1IarvDTebEIRUKhWIIeW57HQCfXDoxIZ9GLCaX5fJPV86MOXNH0zRuWjGJv9xzQVBVUFiFkMcJXQ3QeABqtiQ0ODCdmE238brcSioKs9n2f6/koY9OFwtsOaJpHASEQ2hKyHSRthzdkPqQQrN/xfw5DkpKKIbp1mqH/ApxPzQtJCMoheODj9GIsEQQLB4n9LYE7z9RolUJwYgubU5JsDzxxBNUVVWRnZ3NypUr2bp1a9R19+7dy4033khVVRWapvHYY4+FrfPggw+yYsUKCgoKGDt2LNdffz0HDx5M5dAUCoUiLrWtvWw+1oKmwQ3LJg7pvueOK+TFey/ghiUTmFiSw8WzyvWD2gYPz4Dvj4UfzYL/Xik6ov76+iE9PrPpNpn5RrkOW8Bw6zCJsKgpoUAaRGs/Aa3Hkj1UQaSmceZjSIdgSSTCAtEjJp0hhltJLMHS3RC47+lPStBpvboYyY4kWIrF7QhsHpe0YHn22WdZu3Yt3/72t9m5cyeLFi1i9erVNDY2Rly/t7eXadOm8dBDD1FZWRlxnbfeeot77rmH999/n/Xr1+N2u7n66qvp6RnERkMKhWLU8vxO8Qv4/OllTCiO8at5kCjItvPozYt5598uD+z/wEuBfh9okKX7MRr3D+mxycGNonopyXMj/SJmb4RhujVFivx+w5/R49BTJ4fXp3K4kSuEIM1lzQlEWCC68bbLVNIctL7J9xIqSEKNuMlEWfqjNI6D0RVhefTRR7nrrru48847mTdvHj/72c/Izc3lqaeeirj+ihUrePjhh7nlllvIysqKuM66deu44447mD9/PosWLeLpp5+mpqaGHTt2JHt4CoVCEROfz8+fdorqoE8tG3jfkrTRIVJUXP7vcH8r/FO1eOzuAa97yA5jUmku9142g/uvm4892eolGUUJ8pJID4tJOPS1GRfgmrKLxLIjqQqWdnEbJliiRHZSQZpe40ZYovRiiRph0X/Eu3vCU3+hUZdkfCy6GPHnRoiwjODmcUn9NbpcLnbs2MGVV14Z2IDFwpVXXsnmzZvTdlAdHUIxl5YmbnxTKBSKRNh6opXa1j7ys2ysnh856jssdAgRRdl0UWFiNmf2dw7poXx99Ww+e96U5F/oDunBAqZOtybhoF+M/Tml1BfpM3hOvJNaqW3UCEtu8DENBHlccSMsUQRLtAiLIy8QSQsVKKlGWNz9gdLyWBGWEdg8LqkqoebmZrxeLxUVFUHLKyoqOHDgQFoOyOfz8bWvfY0LLriAc845J+p6TqcTpzNQ6tXZKf5Du91u3O70/RqR20rnNhXqvA4W6rzG54/bagBYc04FNs2H2+2L84qhOa+29lo0wJM3Dr++H5sjD83Vg7u7GRyRS6YzCa2vAxvgs+fi1d+DZsnGBvidXXjksrY6sSy/gq7sCfgKxmPpOo3nyEb8M66Muv1IWHpasQK+rEJjn8H77Tb2myo2d5/4bDSb8dlEQsurEO+/oy7oWKwdp7AAntyxYa+3FVSiOTvwtNXhL54WeF8dp7Ca1nP3d0NOAu+jsxE74MOC25IDIfuzZBViBbw9Lfjkcx11WHb+D75z74a85IdmDpRE/19lXFnzPffcw549e3jnnXdirvfggw/ywAMPhC1/7bXXyM1Nf235+vUphisVMVHndXBQ5zUyLi+8tMsKaIzrP8nLL59M6vWDdV41v4fr9F/Yr28/iHOXMFxe7c8ihx7ee+Nl2nOnxdpERjCleSuLgYa2Lra+/DIAxT1HuQToa29ivb5scstbLAGa+u2gadQ4ZlHFaU6+8RR7DiU3nXjeqQ+YCRw93cI+ffsAJT1HuRjo7WjiddPyVLiio5l8YPP2aloPRI8ClXXVciHQe+YIG0z7vLrpODnAux8eo/1Y8LGsctoYC+x+Zx21+wNRqKUntmNOWL79xnq6cuIbxAv7argMcNvyWP/662HPT2s8zQKg/th+drz8Mvj9XHD4B4zpOcjxwwfZO/HTcfeRbnp7E4uCJSVYxowZg9VqpaGhIWh5Q0NDVENtMtx777289NJLbNq0iYkTY38w9913H2vXrjUed3Z2MmnSJK6++moKC9P3S8TtdrN+/Xquuuoq7PboHS8VyaHO6+CgzmtsDjd049r6HoXZNu65+aqEy5kH/by216BV+/FbHVzxsVtETxHAVvcgNLVywbIF+Kdekv79phnLlhNQCxUTp7JmzRqxsPkQHHqAHJvPWGZ5ex/UQFmViKKPu+iz8JeNTPMeYbJ8XYJYX3oVGmHa3CVUXWh6bdMBOPQAuTZ/4FhSxHbkm+CEVRdein/8kugrts6BIz8gz9fJmmuvFSXMPi+2apEBOH/1J8N8LNa/vQy797Jo2lgWXBA4Tutvfwkmm8nFq5bjH7807rFqJ96GA+Cy5kf8e9U+7IZTv2V8SQ4Va9agHXkdW7Woyp2u1TFlgOcqFWSGJB5JCRaHw8GyZcvYsGED119/PSBSOBs2bODee+9N+iAlfr+fr371q7zwwgts3LiRqVOnxn1NVlZWRBOv3W4flC+UwdruaEed18FBndfItPeLicxjC7NxOOIP9Qtl0M5rj4iuaEUTsTtM32u638Dm7oaR8Hl6RZreklWARR5vbjEAmrM7cO56xI9erWg89IBlxuVgsaG1HsPeVQulSUST9CnQ1rxSrOZzlCu8IZqrZ+Cfme4fseUUxP4cSieLfbp7sXt7xOfX2Qx+L2gW7MXjwRpy2dWbt1l7G4OPvzs4MGDzexL7G3CL8+Gy5VMY6e81rwwAi7MDi9UKb/3AeEpr3IPd3RXecG6QSfTzSbpKaO3atTz55JM888wz7N+/n7vvvpuenh7uvPNOAG6//Xbuu+8+Y32Xy0V1dTXV1dW4XC5OnTpFdXU1R44cMda55557+M1vfsPvfvc7CgoKOHPmDGfOnKGvb+TNOlAoFJlLc49INyQygXhIkRVCRSGRZWkkHSkGSaNKyNyHRb/vcweqbaShVFbJZBXApPPE/cPhaYyYRDPd2vX9evrB501um6HI447Vmh/EQENpdJXGW2m4za8IFysQ6MUSZtQ9E7zPROcJ9YqmcS5rlJEP5rLmfX+BM7tFJVexbrI+8XZi+xkGkhYsN998M4888gj3338/ixcvprq6mnXr1hlG3JqaGurrA27n06dPs2TJEpYsWUJ9fT2PPPIIS5Ys4Ytf/KKxzk9/+lM6Ojq49NJLGTdunPHv2WefTcNbVCgU6eRwQxeLv/sa/73xSPyVM4zmLnHhKcuP3GJh2GjXK4SKJgcvNwRLx9AeT6q4YjSOg0C3W/0i7i8wpUdm6mbbI6kKluLg5eZjGOhwRSkW7An0pTF6segCxBh6OC7y+pGax5lnD5VUidtEJzbr5cpuW5SRD7JxXG8rvPkf4v7598Ks1eL+8cwVLCmZbu+9996oKaCNGzcGPa6qqsIfp0NfvOcVCkXm8JfqU7T3unl1bwNfuXTGcB9OUrT0CMEyJi/TIixSsESLsIwQwRKprNlqE+XAnj6RvskrMy7iQrDo0aUZV8Hr34Hjm8DriRyNiES0CIstCzSrSMe4eiA7hrdx65PiQn/Jv4Y/53WD3xfYZjwKx0PDh6YIS31gecT1I0xslvfteZBXLvw4iZY16/1c3NYoxScywuLsFP9yy2DVPXD0Tdj6i7MrwqJQKEY3W4+LkPOZjuFP2Xq8Ptze+GXJkpZumRLKsAiLFCzFIY3sRppgidQ4Dky9WPQmeLKjrznqMHaemCTs6Qvzb8QkWuM4TUtsnlBfO7z8DRFt6G4Kf96cionXhwXCe7HI22iCpcAkWGTqypwyk1GdRAWL/hl4LFHSV6GRqIv+RaTkqi4ENCGOupI4/0OIEiwKhSJh+t1edtWKi2dTlxNPEmIh3fh8fq7/73e56tG36Hcn5lFo7s7QlFBcD8swC5Y3/gN+vBR6WmKvF6k1PwSEg7NbFyN+sNjFr3uJxQL5uqcl0mydSPj90SMskNjE5lM7xPGAYeANwkjFaIlHWCDQnr8rTkoob6yoCvN7oac5/DVyn4k21dMjLB5rFHFlcwT8PYUTYfkXxP3cUqjUe59laJRFCRaFQpEwH9S049JFis8PTd3OOK8YPGrbetlzqpMTLb0cakhsonFzdwaabv1+k4clQyMse5+H1qNQE6ejuYxkOEL8E0ako8vk6ag0yrcNCqMYUKPurzuQrpHejKD95gYfVyROmUbAuCL0A5ERFlt28KTlaCQbYbHahGiBgEHXHGGxJRlhkYIlWoQFoEBv/nrpvwWPG6i6WNwqwaJQKEY6Mh0kqe9Icux9Gtl7OtC74cCZRAVLBkZYelsDF0Vp2JRkimCRc27iRT7cujCwhwiWLFOExWhTHyHiEGt6cSTkebE6IlfwSOEUqz1/3bbA/UhRjETnCElCBUu8CAuET3nuMok6ud9ETbfxIiwAH/1/sPoHsPi24OVTdcFyfFNi+xpiMq7TrUKhyFy2nghOCTQMo2DZZxIsBxMULC2ZGGGR/pX8ivCL4kgTLHEjLN2BdUIHAUL0Et9oyBRKdnHk6Ee8AYh+P9RtDzyOJGwSnSMkkaKzK8EIC+gzhj4InF8jwjIuMPgy0bJmw3Qb43inXSr+hTJllYh6tR6DjlNGj5hMQUVYFApFQrg8PnacFCWTsyrEhWB4IyyBi3gigqXX5aFP97pklOk2WoUQZIZg8XkDF/LQgXyhRPOwmE23nTEiLJEqZmLRdlzcytLfUAwPS5SUUOsx6DNFDSMJFpmKScS/AgFh0t8h3ocUS7EiLAUh3p0g063sw5Jg+jWe6TYW2UUwbrG4n4FpISVYFApFQnx4qoN+t4/SPAcXzigH4ExnZMHS5/JysmUAvS8SYF+9KcKSgIeluUtEV7LtFnId1jhrDyGG4XZS+HOZIFicpnObcIQlpErIMN12xU6RyGnGXQlGWFqPidtonXHjCRazfwViR1gS6cECouJGTtqW0ZuswoBoi0TUlNC4QGQnXabbeBhpISVYFArFCEX6V86tKmVckfj1diZKhOWbz+/mskc28vbhCGWiaaC520lDp9PIAjR1OWntiT00r1n2YMnPSniG0JDQHiPCIo2k7l7wJDcUMG2YBUtnDMHi95s8LFGqhFzdsfuShF6449GiC5ay6ZGfj1clZE4HQRTTrYywJBGxkO/tlL79WNEVMAm1enEezR4WGdlJ1nSbsmC5SNxmoI9FCRaFQpEQW44L/8q5U0upjCNYth1vxeeH/7f+0KA0hpT+lalleUwuFRfHA2diD1DL2C63Rg+WyeHPZZmanTkTGxCXdswX+1gRFq8LfB5xP9TDYjbdmquEQjFfuBMhXoRFmn8jCREIGG4t+iybSFGMZCMsEBAoUhBF8usErW9KCTk7A5GegnGB/SYSYfH5BpYSAjEiwWKDjhpoO5HaNgYJJVgUCkUQf60+xX+9cRivLyA0vD4/208I/8q5UwMRlvrO8C/RfreXej1VtLOmnS0hlUXpQFYIzR1fyOzKAiC+j6VFj8CUZ5LhFmJ7WCzWgGjpax+yQwrCHGHpb49+4TSnXaKabntM0YMYERZXN/QnINBaj4rbVFJC7n4486G4P+lcfVmkCEuCc4TMSOPt6WpxG+m9Bq1vqiyS/pXsIuEFMmYJJRBhMYnLmKbbWGTlw4Rl4n6GpYWUYFEoFAY+n59v/vlDHnntEE+9c9xYvr++k26nh4JsG3PHFVJRKL5EGzqcYRGUurZezIueeDP9M4ekf2X++ELmJCpYZElzXqZFWGJ4WGD4fSyhkZ1ohlgpCqwOsIZM35URlq76wEU1UtTBkQdZRYF1Yx5Xd6AjbunUyOvESgmd2S0GMuaVQ/lssSyiYEkhwiIFiGxEF6tCCAIRmf52aD0evCyZCIv+Pv2aFZ82gAnVGVrerASLQqEwaO52GpU0D7960BAB7x8T6aAVVaVYLZohWFxeX5h35GSL+NKvKMzCatF4+3Azu+va03qcskJo/vgiZlUIwRKvF4tsGleWSREWd1+gTX2kCAsM/8RmZ8h5jSYkIs0RksgIS/NhcZtVFB6Fkcj0SLzSZpkOyikNzMeJtt9IERaZrpm4InDMEU23SVYJQbhAiZcSyi4KmGtPfyBu5XmwJVElJD+rrILEmtxFY/IqcRtqSh5mlGBRKBQGtW2BX3Eur49/frYal8cXMNxOLQXAYbMYvUxCK4VO6IJl2ZQSPr5IfHH/95tH03aMvS4Px5vFBWjeuECE5VBDFz5fdL+MbBqXWSXNevt2R370i+6wR1hCohPRBEu0OULmZZ16NCmSf0WSaGlzaxzDLcROCUn/yoRlpgZzkRrHJdmHBcIbAMZLCWla4H0bgiUkwpJIHxazYBkIMuLUflIMoswQlGBRKBQGdW1CbMyqyKck186++k4ee/0Q204IwbJSFyxAVONtjV7OPLk0j7svFReTdXvPcDjB9vnx2F/fhd8PYwuyKC/IompMHg6rhV6Xl7q26F/qgS63GRRh6agRt0UTo/8iHnbBEhphiZYSitKDBcJLemNFHBItbY7nXzEfSyTBIit4Ji4PiIJI5lwZYUm00y0kH2GBwPs+vVN/LCMscpZQAh4W+VlFEo3JUDBeRHZ8HiFaMgQlWBQKhYG84C+YUMwPPrEAgP/eeJS2Xjc5divnTAgMmKssFF/yoc3jZISlqiyXmRUFrJ4v5pb8dGN6oiz79HTQvPHCjGq3Wpg+VnxBx6oUkl1uyzMqwhJl6KGZTBMs0VI10brcAjhCfvHHijgkWtpsVAjFirDoF253iGDpboT2GkCD8Utjp4TMs4QSJVSwxIuwQECghE6ytiUfYfEPNMJisQTOa0v6oqMDRQkWhUJhIAXLxJIcrl0wjk8sCYS2l00pwW4NfGVUFokLf0NISqimVXzpTy4TF4GvXDoDgL/uOk1ta4yZLgliNtxKzGmhaMgqoYwqa4429NDMQAXLi1+FP9wWaPGeLNI4Kkt/o0VYos0RgiQjLAnOE2qJU9IM0VNC0r9SPgeyC+MIFjlLKImUUE5JQOBYbMLYG4/QcyIFTDKzhGKl5ZJFptpa0m+aTxUlWBSKIWDbiVZ+u+VkUKlwJiJTQhNLxJfzdz42n0rdYGtOBwGMKwqPsHi8PkOUVJWJi8WiScVcNHMMXp+fB/62L6bPJBFkSfO8cYFojyxtjma89Xh9tPVm4hyhQY6wtNfCzl/DgZfg0KvJvx4CERYpDOJVCUWMsIQsizkIMGR4YDQMD0sqgkX3r0xcLm5jVeK4U4iwaFrgfeRXiohFPELPyQAiLAP2sACUiR8aRuotA1CCRaEYZHw+P3f/ZiffemEPa/9YjcfrG+5DisopI8IifnEW5dj5nztX8IULp3L7+VVB60ohY46w1Hf04/H5cdgsxvMA31g9G4fVwuv7G/h/rx9K+fg8Xp8hSswRlni9WFp7Xfj9YNGgODeTBEuMpnGSgQiW2i2B+zueTv71ELgIls8St9G8JbE8LKG/+GPO1UkgwuLshm5dOMWMsEQZfmj2r0CcCEsKnW4hYLxNxL8CEQRLChEWWYKuIiwKhSIVDjd2G4bPv1af5qu//wCXJ/NEi8/np649kBKSzB1XyL9/dB5FOcF9HaTp1hxhOaEbbieV5GCxBEykCycW84MbhCfmJ28c4cVdCc6KCeFoUw8uj4/8LJvR4RYCKaFjzT04Pd6w18k5QqV5DqyWDGrLH6tpnCS7WNymIlhqNgfuH3k9kIJKBlklVDZT3HadgUjdi410RKQISxIpIRmZ6G6IXqEihx7GKmk2H4s5wuLzwim9EmfiCn09ac6NNUsoWcGiv494bflD15fkC++XEWHxOiOfdzP6Z+WPNbcoUWSERXlYFIrRw1a9pf3EkhwcVguv7DnD3b/ZQb87/MI6nDR3O3F5fFgtmtHJNhaRqoROtgSng8x8ctlEvnSx+DX8jed2pdSbZV+9uGjPHVcQJIgqC7MpzLbh9fk52hheEdJimiOUMfh8gbLmwfKw1OgRFnse4IcPfpP8NmSEZYweYXH3Rh4TYPRhiSBYrLbgsuBYJtS8ctCs4PcFDKihtCRQIQSByInPE5jF1HRA+HIc+cLDYl4vZoQlyc6x8tiiNbULxVzqnVsWqA4y93+J1+3WqBJKY0qoozbxwYuDjBIsCsUg877ew+Tm5ZN48nPLybJZ2HCgkbt+vX3IRYvX5+e9o80RoxCyB0tlYTY2a/yvBpny6XZ66OoXhk45oVkabkP5t2vmcNnscpweH3f9ejvvHW3mj9treeBve7n1F+/z5f+NLeT2npL+lcKg5ZqmMadSLDvYEH4xzciS5u4G0WlVs8b+FZ6qYOnvgMa94v7l3xK3H/yviDAkg7wI5o8NHEukCp5YHhYIGG81S2wTqsUaiC5ESz8l0oMl9FhkBGjXH8Tt5PPEviCxxnHJRljO/RKsfhBWfTWx9c1/A+b7ZrNvPOEg32M6PCy5ZYGuw7L77jCjBItCMYj4/X62HNN7mEwr45JZ5Tx957nkOqy8fbiZX70ztF8E33tpH59+ckvE/YYabuORl2WjINsGBHwssSIsAFaLxo9vXcLMsfk0dDr59JNb+Nc/7eZ/3j3B5mMtrNt7hrcPN0fdZ6BCqCjsuVmVsrQ53MciS5rTFmE58DJs+cXAtiENt4XjRQQiGqkKlrptIkpRPAVWfFGkTzpPidRQMrhMRs5Y/hJDsEQWq4Z4yK+I/X4hfmlzIj1YQIwIsGYFjs/ZBTueEY9X3BVYL5bpNtUIS24prPoK5CdQIQQikpJbJu6bBYvVLkSt+ViiIcua0+Fh0bSM87EowaJQDCLHmnto7nbisFlYOFFceFZNL+P/rJkLwGv7GobsWI439/Cb90UTKCmizNSFGG4TYVyIj0UKlmgRFoCCbDu//NxyJhTnUJrn4PzpZXzhwqmcP118Wb93NLJg8fv9gQqh8YVhz8+WEZYIgsVoy5+OOUJeD/z5i/DKNwY2zdbcNC4Wqbbml+mgyavExXDxp8XjZM23zkiCJUKlkCFYolwsZZoiEU9HPOOt/MUfqweLsV+Tj+WD34CzQ/hxZl4dWMccYQn1iXhSjLCkgnzfoZ2AE50nlM4qITD5WJRgUSjOeqQwWDKpmGy71Vh+1TwR8t5d126kKwabR149iEcvKZaRCjPmHiyJImcKnenox+/3c7JVXLSiRVgkU8ryePebl7Pz36/id3edx79/dB63rZwCwOajLRFfc7qjn44+NzaLxsyK8Iui0YslomBJY0qo6UCg50i85maxiDf0UCIFi6c/sUoRSe374nbySnG79HZxe+jV+CXDZiIKlgivjzVLCAIpoUQES7zS5kQ9LBAQUM5OeP+/xf1VXwkuNZZRIb8vfGZPKmXNqWIIlpBzlOjE5nR1upVkWGmzEiwKxSAiDbcrp5UFLa8ozOacCYX4/bDxYBRjYRqprm3n7x/Wo2ki0tvU5aSxK/jLL9mUEAQiLGc6+mnsctLv9mHRYEJx8qPtz5sm+rwcONMVUcTtPNkGwMyKArJs1rDn5RBEKWzMyEnNaelyW18duN8bPX0VF6NpXJwIS1YhoBuMI5ldI+F1Q50+uG7SeeK2fDZMPh/8Xvjgt4ltx+MEr25WdeQHfvnHjLBEEavyIppQm/o4qSdZ0hyrB4uxX12M7H5WdLfNKYWFtwSvYxZZoT6WVMuaU2HK+eJ20srg5cY8oQQFS9oiLJnV7VYJFoVikPD7/Ww5Hj6DR3L57LEAvHEgsbTQu0ea+cfff0B1bXvSx/Hgy/sBuGHJRKbq0Y/99cGRiNAeLIlQqTePO9PZb6SDJpTk4LAl/9VSlp9lREnkdGgzr+4VF6lLZkX2BBTl2BmvC6jQjrdpndR8ujpwP1oVSyLICEtxnAiLxaKLFhL3sZz5UESBsosClTAAyz4nbnf+WlQpxcM8+DCrIBD5iOlhiWO6TSbCEmk/Mh2UUxK7pFkij2fnr8Xtii+E+2ys9kAn39C0i1HWnLwIT5qL1sK/nYCZVwYvl2IpXoTNJcua0y1YVEpIoRh2fD6/UeGSbmpb+6jv6Mdu1Vg6OfyL9bI5QrC8fagZdwLN5H684TAv7jrNJ/77Xb71wod09CZ23BsPNrHleCsOm4W1V89iru7/2G9KC0XrwRKPSlNKSPZgmVIaOx0Ui1WGjyVYsPS7vbx5oBGAa8+JPul3rl49tLsu+MLeks5JzeYIS0/k9FVCyKFyRTGaxkmSNd7KhnGTVganPuZ9XGyrowZObIq/HRnRseeJihoZYUmlSmjJZ2DKhTD/E/H3G2s/huE2Af+K+Xi8LrA6gs22ZqJVCg1lhAUiizAjJZSghyVdKSF5jnuahm+WlQklWBSjmp++dZRFD7zGpkPpT8u8r6eDFk4sJscRnsJYNLGYsjwHXU6PMQ05FseaxQXB74ffbqnh8h9t5E876vDHaCbl9fl56JUDANx5fhUTinOMkuB9pwOCJdkeLBKz6bZGj7BMiWG4jcf508cA8H6IYHnncDM9Li/jirIN83IkllWJL/ttxwPn0+/309yTpgiLzwNn9gQepxph8fsDht1E+nQka7yt0f0rkVILVReJ+82H428nNMUQy3Qbqw8LwIwr4c6/xy9FBtPE5kiCJYEZQmbMF+8FN0FBReT1DGOrSbD4/SbT7RBEWKKRSLdbnzdw7OmKsGQXQp74YZUJaSElWBSjmtf2NeDzw/++n/4R6ltjpIMALBaNS2VaaH9jzG11Oz00dYkowS8+u4yZY/Np6XHx9ed28fiG6Bee53fWcbChi8JsG3dfKi4UssLGbLxNtgeLRDaPa+g0RVgGIFjOnVqKRRPirL4j8GtynZ4OWj2/Ek2L3qlWnuutJ1oNIdfl9BidhQccYWk+FPwrN1UPS3ejuLholvimW0guwuL3BwTL5FXhz+cUJ74to69HSDqn+0x4SilWp9tkkT4XZ2dwWgoCF85EhE/o8az6Soz1InS79bqFEReGLsISiURMt+ap2umKsEBGdbxVgkUxavH5/BzWvQ5vHWqi2xmlDXiKbNEjLOdGESwAl+tpoTcOxhYsJ/ToSmmeg6vnV/L3f7yIey8TXyS/31oTcaCg3+/npxvFl8w9l80wZujM1yMsx5q6jSZtqRhuIZASaulxcbhBXFimxKkQikVRjp0FE8TFWVYLub0+Xt8vfD6x0kEACyYUk2Wz0Nrj4miTOB7ZgyU/yxZUqZUKWv2u4AWpRlhka/nCiWBLIOqTjMhoPykEhcUOE5aGP59Mq//QCEv+WEATkabekHRYrFlCyZJVECiDDo2yGCXNSUZYpl8OFfOjrxcpJWQWp8MpWBIpa5afldUR3B13oGSQj0UJFkVGUt/Rx12/3s5bg5CqkdS29dLrEhdsl8fHhv3p64lyur2P2tY+rBaN5VXRBctFs8Zgs2gca+oxREkkZPSiSo9eOGwWvnrFDPKzbDR0OqmO0OZ+X30nx5p7yLJZuO28Kcby8oIsyvIc+PyBniWp9GABKM61k6UbbA/q4m8gERaAVXpaSPpYthxrpb3XTVmeI+a5BHFepF/ofb2kPJ0lzdoZXbCMnSduU/WwGOmgqsTWTybCIvuvjFsUOY2RzLZCBYvVHuhSay5t9nrErBtI3697o3lcSGlzsh6WpZ8VPVdW/yD2eoZgMYkCIwWjpVcEJEsiEZZ0drk1k0GlzUqwKDKSp987wfp9DXzlNztiXsgHQmhH1Jc/HEBPjRBkdOWc8YXkZ0Xv6lmYbWeFfhF+40D0KIs8B1VjAtGLLJvVMO7KChozf98t3s9ls8cGHYOmaWFpoVR6sMhtVYZ4XsxDCVNBNpDbfLQFv9/Pur3ifVw9vyKhwYUr9fJomZJLp+HWiLDM0Ks4Uo2wyChBSVVi6ycjMoz+K+cNfFuRZtMURvCxuE3/R6P1YUmWSKXNrp7A40Rn9ExYBrc9B2Pnxl4vkodFRlhs2aIfwHCRSFlzug23EhVhUShi8/Yh4Q3ocXm59/c7I86+GSgyuiBLaTcebKInTWkh2TAuVjpIcsVcITrejJEWOt4svkSnhqRbVs8XBsJX95wJMt/6/X7+rguwNQvDy0hDjbeppoQgkBYCGFuQRa4jTtv1OCyvKsFu1TjV3seJll5e3SsiX6vnx04HSeQ533pc+FgCXW4HFmHR/F60Bt1wKwVLb0ti5cGhyJRQSYIX3aQiLIMgWMy/2qMJCRAt5NMViYhU2mwuac6N/38rKSKlhFKdI5Ru5DmNZbo1PqvwLtADwuxhiTctepBRgkWRcTR1OY1f/kU5dvac6jQqXdKJTGF8YskEppTl4vT4wqIcPU4PH/uvd7jlF5vxJFB6LAkYbsvirBkob37/WEtUH42REhoTLFgunT0Wh83CiZZeDjUEzIn76rs42dJLls3CFfr2zYRGWFLpwSIxVxUNNB0EkOuwsXhSMQBPvHmEpi4nBdk2o4IoHksmCcFzprOf2tY+IyU0pmBgF9L8/no0T5/4BSurb/ze5FvmQ3IVQhBbZLj7RRO6UzvFjKNG0XMnrEIodFuJNKGLKFgilBwb/pW89EUiIu0n2QqhZHBESAmlOkco3cj9xyprTnfTOEnJVEATfy8D6TuUBpRgUQwJLXrZbCK8fVj8pzhnQiGP3rQIgP95V6SI0omMsMyuLGDNAvGr8ZU9wWmhH712iN11Hbx/rJVX9kQo5YxAY1c/x5p70DSMdE8spo3JY0pZLm6vn3eiDP6TKaGpIYIlP8vGRTPEhXyd6fhe2SPO1eVzxpIXISUlIywH6jvxptiDRVIRJFjSUCFCwMfypx2iudqVcysSbkaX47CycGIxIErLjcGHA4ywFPfqv+4rF4pf3PLCn8qXeLpSQrv/CA9OgMfOgScvgz/cCviFvyM/XKjG3FYkQquEIHLJcTorhML2Y/KwHPi7uJW/+tOJTLu4TOmtoZwjFItEypoNwZLmlJA9O9DccJgrhZRgUQw61bXtnPfgBr7xp13xVwajJ8pFM8u5Ym4FX7hQ/Ar9xp92cbo9TuOkBHF6vBzXRcCcykI+oguWNw400usSUY4P6zp4+r3AVOOfbzoas+eJ5FiTbKCWS1GuPe76mqYFqoUidL3t7HfTovcRCY2wAKzWK2ekj8Xvh5d18fKRCOkgEMLHYbPQ4/Ky42RbSj1YJONMKaEpA/SvSKSPRZJoOkiy0pQWCphuBxZhKe47Ie6MXyxupfm0J8nSZmc39OiRvGRTQn3twcv3/FlU7Fgd4gJfuUBUw1zzYPxtJZQS0qMwkSIsZg/L6Q/EbSKdZxMldGLz3r/A7j8AGiy7M337kcj+MUGm2yGcIxSLRCIsg2W6hYDBeZh9LEqwKAadX71zHLfXz0u76w0DZDR8Pj9v61GGi2eKC8K/XTOHhROLaO91869/2p2WYzrS2I3X56cox05FYRbzxxcyqTSHfrePNw804fH6uO+F3fj8IkqRY7ey51Qn7x6JXxVyRp9cHGpGjYXsx/J+hCnKMroyJj8rooH3yrkVWDSR3qlt66WuR/RVybZbDCEUis1qMbw7r+lCJ9keLBLZnh9gSgRBlQpLJhcb1Uc5dmvUdvzRMPtYjAjLAAVLUe8JcWfcYnGbq6eoko2wyA63OSWBcuV4RBMZsond7S/Cv+yHL78Dn30BZq2Ovi1zm/94AjySL6IwJPLhccLbPxL3l3wm9vaSwRzJ6TwNL31NPL7wn2FKhP4yAyWi6XaIu9xGQ0ZYQgczmhks0y1kzNRmJVgUg0pLt5N1eprF6/Mbv/yjsa++k5YeF3kOK8umiF9rDpuFx29ZAsA7R5rpTEMrfXM6SNM0NE0z0kIv76nn6fdOsOdUJ4XZNn5440JuXiFCoj/fFD8keqZTfMmNK0o8vbJkcjGaBjWtvWGi7riRDoocvSjNcxgX6PX7GvmgRfy3vnzO2JgGWJkWek1PtaWSDoJgYZauCEuWzWqk0y6dXR6xU3Aslk0pwaKfzwNnRJQgallzxyk49lbsDfq8FPXpQsOIsOiCJdnmccmmgyCyYOlthU59HlGs/iLRtuV1JTBMT6Z6zCmhkAjLzl9DR60w4y7/fOLHEQ9zNdJf7oa+NiEWL70vffswE9F0O4RzhGIhIyyJ9GEZjAhLhpQ2K8GiGFT+tKMOt9ePrEb9267YY+1l35VV08uCPAtTx+QZg+0OhAztS4XQCiEgkBba38ij6w8BcN+auZQXZPGFC6ditWi8fbiZPadih9JlhKWiMPFfZYXZdqaXi4tC6HDDE3qFUFUMf8g1esrktX2NVLdo+vsZH3Of0nhb0yorhFITG+k23Uq+cOFUppXn8aWLkzdYFmTbmT9eXJg7+0WKL2KEpacZfnkF/PpjcOLd6BtsOYLN58Jvzwt8eUvBkmxKKNkKIYgsWGTFUkmVaKGeKI580WE3dHuRiFUl1NME/Z2w6RHx+KJ/Se+FPW+sOE6/F45tFBftG55MrNFeKsQ03Q53Skj/202krDndVUKQMVOblWAZRew73cmy7603up8ONj6fn99vrQHgq5fPBGDbidagluuhSP/KxRFSAEZly+mBD+E6YIqwSBZMKGJiSQ59bi+9Li/Lp5Rw83IRWZlUmst1uh/kZ2/FPn9GSqgwuRTEEr0y5oOa9qDl0SqEzFytC5YdNe20ODVy7BYumxM7jSIjLJJUIywVhdnccX4Vd1863eimmw4umzOWN/7lUpZEGByZCKEjEcaERlh8PnjhHwLm0Q+fi7ot2TDOX7lADAGE1D0syVYIgSkq4gwYL898KG4rzklu/8lMf44kWHLLApONNz4kuuoWTYKltyd3HPGw2iDfNPdn9fehfFZ692Empul2mCMsyXS6TbfpFoIFSypl/GlCCZZRxC82HaWlx8Vvt6R/bk4kNh9r4URLL/lZNr508TTOrSrF74eXdkVu0Nbt9LDjZBsQ8K+YMXqHmGbgpEqkCIs5LWS3ajx4wwIspkZlX7pY/Kd9+cN6Y9BfJGRKqDKJlBBgXJg/qG0LWn48SoWQmfHFOSwyDQW8bHZ53H4oc9IkWAC+87H5/Ns1c1J+/WBg7oFjs2gU5YQYoN/7MRx5PfB4/9/EALkIyIZx/spFgYXxPCwdpyJ7DlJJCTkKwqMi0r9SuTDx7UgSNd5GMnJqWiDKsuWn4vbibwxOJ1jpl5m5GpZ/If3bNxOr0+2wR1iGsdMtiIniFrsQzDINOQwowTJKaO91Gf6RurY+TqWp2iYWUhh9YskE8rJsXLdYfPm8GCUttPloCx6fn8mluRGjCZGG9qVCR6/bEBWzKoL/c3/2vCnMH1/I/dfNZ2bIc/PGF3LJrHJ8fnjy7WNRt5+K6RYweo/squ3Aa5oNFGjLH9vQerWpkuaa+VEm0prIz7IFpXBSTQllKuaS8rJ8R/DQxJotsOG74v6aR8R8nd5mOPlexG0ZEZZxJsFieFgiGLHrdsD/mwev/Gv4c6mkhCJFRWSEpTLJCAskLlgiVQlBwMfi9wnhtfjTyR9DIlzyTVh8G1z/34PfaTbWLKHhFizJRFgGw3RrtcGV34aP//fgpJwSRAmWUcILH5wK6oOy9XiKM1ASpLGrn9f0DqWfXjkZgDXnVGK1aHx4qsO4CJsJpIMiNwiTnoRDZ7pxJ9HELRRpwpxQnENBdvCv7kmlufz9Hy/is6bZO2b+4RLhp/jj9tqIFU8er48mfXmyJcKzKvLJdVjpdnqMwX3tvS7ae4XJuCqK6VZy7TmVWDTIsfq5NMGqGnNaaCARlkykJM/BbF10luWZfv33tsKfvyC8EefcCCu+CHM+Kp7b99fwDfm8aLo4CIqw5MWIsJx4W9zueUHM2TFti3aRJk0qJQTBIsPjgia9mWLlguS2E7qtaPj90Y2c0hALwgRrjV++nxKzrhZiJS/yd0JayehOt0lMax4sQXH+V2HJbYlXtg0CSrCMAvx+P3/YWguI1ukQaB0/WDy3vQ6Pz8+SycXM1S+KZflZXKA3OXtpd3i10Ca9YVykdBCIC2pBlg2X18eRxu6I6ySC7HBrTgclyqppZcypLMDp8fHu0XDR19ztwuvzY7VoSZfR2qwWFuppnQ9qRFpIpoMqCuO3vJ9Wns/Tdyzj3vnehKtqpGBJtQdLpiPnCgV1uX3xq6KqpXQafPQx8ct9/vXiuf0vhufoG/ehuXvwWLKCG5YZHpYIgkX6VJwdgR4lAB11pr4pkXvkRMUsMpoPgs8tlhVNSm47oduKhrtPRFAgQoRFP/Yxs2DBp5LffyYS0XSbYRGWhATLIKSEMgQlWEYB1bXtHGzoIstm4b41wmew5Xh8weLx+nhx12nWPlttRCUSwefz84dt4lfkp8+dHPTcxxaJtNBLH54JagFxsqWHky292Cwaq0Kahkk0TWOuYbxNPS0UyXCbKJqmGZGemghRIplqGluQldCgvlAWTxI+FlkplGg6SLJqWhkTk2iFItNs44pS68GS6Xx04XhsFi3QiK6jDg68JGbefPJ/AtU1Uy+BrCLoboDaLcEbees/AWgqmB8w3ELAw9LbGu59kWkfEBUuocuLpwRvKxEMkdEe8K9ULEgtVZJdrG8rhmCRF0DNEj7QcNGtMHkVXPfj5N9HpmKYbiNFWIa7rDmZWUKDkBLKEM6+byhFGDK68pEF47h8TgWaJn65N3ZG/uPvcXp46p3jXPLwRv7x9x/w/AeneODFfQnv7+0jzdS29lGYbeOjC4NLa1fPFy3Wjzb1cNr0vSDTQUunlISlacykw3h7cACCBQKlu7Ic2EwqJc1mlkwuBgKVQsbQwzQ1ZAvlopnl3Lx8Et9YPXtQtj/cnDu1lL3fXc2XL9GrHLr0TsIF4wL9VECUys5ZI+6b00I178P+F/FrFvaP+2TwxnOlsPYL0WKm1SxY3gzcl5GXZAy3EnNUZCD+FQgItUQEi6MgXBSNXwyfXzc4DdyGi4gelkwx3Q5zp9sMQQmWs5xup4e/7RYm11vOnUxRjt246EeKsvx9dz3nP/QG331pH6fa+yjLc6BpouLnZISIQiRe0k21NyydGJaaKMi2c7ne1XVzo4U/7zzFZ3+1he/8TQiieB1N5w0wwuL3+zlkVAilluuVguVkhEqhM3rJdmWqgkU33h5s6KLb6TFSQrFKmgeCw2bhh59cyMcXTxiU7WcCWTbT36A0yOZFiOLN+7i43fdXkRby++HVbwHgW/wZunImBq9vtUGObuw1N4/zukUkR1K7NdCATQqZZP0rEBwVaZCCJQX/CiSWEopmuD1biVQllDFlzXFmCXndgWM9iz8vJVjOcl6sPk2vy8u08jxWVIl0gyz33BJivO13e/nWXz6ko8/N1DF5/McnzuHdb17OhTOCB9HFY1ddO4DxulCu09NCb5+x8M0X9vL24Wa8ut/lk8smRnyNxBxhSWSuTyin2vvocnqwWbSUoxaTSmNEWDqF4TbZCiHJ2MJsJhTn4PfD7rp2oy1/oikhRRyksMiNIFimXSaiCV2n4dR22Pu8uLXn4bv43yJvL5LxtqNWGHpt2VA8WXhNZPVRKhVCkqCUUIo9WMK2FUOwRBp8eDYjBYunL+BjMmYJDULJdjLICIvXGbkPioyGgfgbPktRguUsR3pJblkxySjrXDlVfFmHGm9f3HWa9l43E0tyWP/PF3Pbyilk261GW/o/7agLKreNRI/TYxhiF04qirjOFXPHUq438ZpRnse/XDWLN79+KS985YK4qZSZFfnYLBodfW5Od8RpKx4BmQ6aXp6f8PTfUGT7+TOd/fS7g70LRoRlAAbWxaa0ULQpzYoUkRGWSILFng2zrxH3dz8Lr39H3L/wa8ENzMxEah5n7rMy7TJxX/pY0pESatwv2tRbbFCeYu+bhCIsZ7+JMwiHyacjUy9GSihDIiwQ2XgrPytbjoj8naUowXIWs/d0B7vrOrBbNW5cGohcyAjL4cZuozTX7/fz680nAPjMeVOCDJhXzaugONdOfUc/bx+OPehtz6kOfH5h4hxbEPminW238tevrOL/LPbw8lfP56tXzEz4gpxlsxr9UfaGtMj3eH28fbgJpydy8y8IVAil6l8BMbsnz2HF7xc9bcwE5gilLlhkWmjD/ga6nB40Lb0t70c1hmCJUiYr00LbfinKjwvGwap7om9PCh+zYDFHUaZdKu4fe1OkmFpPiMcppYR0kXFys7gdMyv1clslWMIxixJpvM2YsmbTscUSLGd5NEwJlrMUn8/PTzaIyZpXz6ukzFRiW2rqT7FV97F8UNvOnlOdZNksRjt6SZbNyvW6x+GP22tj7nd3nfgCXDgxcnRFUl6QRUUOwc28EiSa8faH6w7w2V9t5ZdvH4/0MmDghlsQxzxZT9HUhqSFBmq6hYDxdqduvB1flEO2/SypxBhuemKkhABmXAl2k3i+/N/BEUNMywhLb4QIS+lUUX2EBo37RN8Upy4QiiP3+YmJFBku/eKUqn/FvC1nDC/YYDYiy0QsloC5VhpvjbLmYY6wWG2isg0iC5ZRYLgFJVjOWn647gDr9p7BZtH44kXhv+YCPhYhWH793glA+EtK8sLnwdyki5j1+xpo7XFF3a/0ryycWDyAo49NJONtW4+L37wv0l+xokCRWvKnwuRS8QVmNiL7/f60RFjmjy/Cbg0IuXgN4xRJIKt5cksjP2/PEc3KQJQML7ol9vYieVjMaZ+8Mhint87f8Yy4za8MTj8kSnbIj4BU/SvmbSUUYRm+zqZDTqjxVo5WGO4IC8TudjtKomFKsJyFPPXOcX6+SbSO/89PLow4PE421NpyvJWmLicvfygaud2+KvIvv3njC1kwoQi3188LH5yKum8ZYVk0mIIlQoTlmc0n6NP9JLvrOvBE6ITb6wr4a0Ln6CTLFD3CUtMa+PLo7PPQ7xb7HUiEJdtuNZrtgTLcphWjSihG59TLvgXzrocbfh6/x0ik5nGGYNF/KMi00K7fidtU0kEQ3mE0HREWlRIKxhAs+g8Rd4ZEWCB2t1tzCfpZTEqC5YknnqCqqors7GxWrlzJ1q1bo667d+9ebrzxRqqqqtA0jcceeyxsnU2bNnHdddcxfvx4NE3jL3/5SyqHpQBe2n2a7/1dlAj/6zWzuWFp5KobGWE5cKaTX2w6isvrY/Gk4piRkZuWi209t702YoVOW4/LqJxZECclNBCkYKlr66Ojz02vy8MzeoQIoNflNbwqZj6oacfj8zO+KJvxA+zqOtmoFApEWOo7xZdbca59wCkc6WMBZbhNK7FMt5IxM+GmZ6BifvztGR4Wfbt+f3jpsjTeSnGQSoUQhEdY0iFYPP3RS2VHSZohiNBut4bpdpirhMAUYYnlYTm7P6ukBcuzzz7L2rVr+fa3v83OnTtZtGgRq1evprGxMeL6vb29TJs2jYceeojKysqI6/T09LBo0SKeeOKJZA9HYeL9Yy2sfXYXfj98btUU7pbNsiIwtiCbaWPy8PvhV++IL9ho0RXJxxZPIMtm4cCZLiOSYma3boKdOiYvfDJuGinKtTOhWPzn3V/fyR+31dLW62ZKWS6rpokLiGy8Zkb6dVZMLU3JO2NmcoTSZmPo4QCiKxJzVExFWNJIrLLmVAiNsPQ06b/ONVHSDDD5PLCaLnipVAhBsGApGDew+TqOAkD/PxDNxzJKjJxBhHa7lcJluPuwgCnCEisldHZ/VkkLlkcffZS77rqLO++8k3nz5vGzn/2M3NxcnnrqqYjrr1ixgocffphbbrmFrKzIKvXaa6/l+9//Pp/4xCeSPRyFTnO3k7t/swOX18c18yu5/7r5cS/KMi3k80NZnoM1C2LPNinKsXPNOUJ0RjLf7tbbyccz3KYD6WPZXdfOk7rJ9q6LprFc7zUTSbBsO6ELlqoo/oUkMHe7ldGmVKc0R2KxKcIyWE3jhh1ntxjiN1R4PdDXLu5HqxJKFmNisy6EZDqocELgV7k9J7gjbKopIbNgGYh/BSJPfw5llPxqDyK0222mdLqFgI9mFJtukyrYdrlc7Nixg/vuu89YZrFYuPLKK9m8eXPaDy4WTqcTpzMwLbezU/xKcLvduN3utO1Hbiud2xwMvvPXPbT1uplTWcDDN87H5/WEjTcJZdnkYn6vt+3/1LIJWPHhdseegnzjknH8tfo0f60+zb9ePSNoIF91rRjYN39cQdzzNdDzOqcij/X74BebjtHc7aIsz8HHF1bwvqx6qmkN2rbL42OnPlBw6aTCAX+e5Xk2rBaNfreP0209jC3I4nSb+JKrKHAMePvjC+1cNnsMfS4vEwrtCW9vpPy90t2I7RcX4h+3CO+tzw3NPnuasSPEpdueD0mco6jn1VGMHaCvDXd/L1rTYWyAr2QKXtO6likXY9V7sXgKJ+FP5fPRsrBpFjS/D+/Y+fgG+BnbsgvRnB14ulvwF1WFPW/t78QCeGy5qR1vAmTa36vVliPec383fpcLm7sPDXBjS+rvZVCOzZqlH1tP2Odh6WvHCnjtefhM18BMOa/xSPQ4kxIszc3NeL1eKiqCmyhVVFRw4MCBZDY1YB588EEeeOCBsOWvvfYaubnpr6pYv3592reZLva1abx0wIqGn4+Ut/HG+lcTel2PEzSsaEBF92Fefvlw3Nf4/FCWZaXF6eGh363nvLEBL8u2o1ZAo6dmLy+3703oGFI9r32tGmCluVv8Qj+vtI831r9KjxvAxrHmXp7768vk6ZmpE13Q77aRZ/NzaNsmDg8sIwRAsd1Ki1Pj2b9vYHohbDtqASx0nKnh5ZdPDnj71+uBoNdeXZf0azP57xVgQutmlve1wrGNrHvpL/gs4ZVp6Sa//xRXAC5rHq+sey2lbYSdV7+Pj6Gh4WfDS89R1fwmc4DabhvVL79srFbUa+VSuY3tR3DtipxCj8e1lhwc3h52nnZz2rT9VLjUpVEEbH17PU2FDWHPX9xYRwmwffcBGk4ObF/xyJS/1xUtHYwH9n6wlZM1OXxMF7ivvfk2HtvwRjrP7+ihHKjetplTR4OfW1Sznyrg0InTHDL9XWTKeY1Hb2941/BIjNiWePfddx9r1641Hnd2djJp0iSuvvpqCgvTV4bndrtZv349V111FXb74PkyUqXH6eGHP3kP6OfO86v48rXJDbEbO6cRh83CxTMTD5GfKjjOI+sPs99VynfXrAREw7TOzZuwWjS+cMPqsBlCoQz0vC5q7+OXB98GIM9h5YHPXmb4Zn5x/B1OtPRSMe9c43394u3jsOcw58+s4CMfWZz0/iLxbMN23jvWyoTZi1izZALP/3onNDZz8fIFrIkzYmCwGJK/194WrC98Ed+Sz+Gfd31Km7C89g6cBA0/1yyfPjADaYJoNe/BfrAXVbBmzZqkXhvzvB4qg95mrjhvEdYt78IZmLjgIsZfYNqH34fvb3vx27K58tqbU5uwDFjbl+Ov3crij32ZxQWxU7hxt9XyM6ip4dyFc/DPCz8ftprvQi8sv+Ay/JPPH9C+opFp36/Wv/4NOnZwzuxpzFt8KewSy69e87FhN95a//Br6N7P4nPmsGhx8OdlfeF5aIFZC5YxY8WajDuv8ZAZkngkJVjGjBmD1WqloSFYjTc0NEQ11A4WWVlZET0xdrt9UD6gwdruQHl83SFOd/QzsSSHr18zB7s9OQ167cLkh97ddO5kHttwhOraDo409zF3XCH7zogqiZlj8ynMSzzfm+p5nTLGRmG2jc5+D7eeO5kxhYGo2tLJJZxo6WX3qS6umCe+1HfWiDz9ymllafscp4zJ571jrZzqcGG322noEinK8SV5w/63Mqh/r0dehRNvY2mvgYWfTO3ie3qHcdfedhQmLU3jAUZBb9qm5ZWnfG4inte8cuhtxu5qh3YRWbOOmYY1dL0bnxTPpbRnndv+BK5u7NH6yCRDjvB72dzdEOl8uEQFnC23JPLzaSRjvl+zRBTF6nNhxaMv1LBn56UsMtOGXsFk87vDPw/9s7LmFAf93WXMeY1DoseYlOnW4XCwbNkyNmzYYCzz+Xxs2LCBVavOojHjI4QPatp4Wi/n/cEnFgT5SQaTsQXZXDVPpAX/sFU0a9utN4wbzP4rZjRN444LpnLOhEK+dPG0oOdkp9gPdBOwz+dPq+FWYlQK6c3jZNO4dJhuMxpZGtx+EpoOJv96dz/U7w48bhqidHIiJc2pYDSPax7YcMNEsDmiN71Llni9WEZJ5UkQ0nTr6jF1uc0efrECsRvHjZJBlUlXCa1du5Ynn3ySZ555hv3793P33XfT09PDnXfeCcDtt98eZMp1uVxUV1dTXV2Ny+Xi1KlTVFdXc+TIEWOd7u5uYx2A48ePU11dTU1NzQDf3tmL2+vjvuc/xO+HG5ZM4OJZ5UO6/1vPFSWbz39wij6XN9CSP8rAw8Fg7VWzeOmrFzE2pIxYlgRX17Th8/k52NBFZ7+HXIeV+ePTly40Vwr1u7209wrj2LjCDCiBHEzkhR/gUPL+Gup3iQnGkiETLLKkOX2iFQgIlvaT0K1Hn1OtBBpKYgkWn890ERylnW4zZY6QxChrdoY/N0oqupL+SX7zzTfT1NTE/fffz5kzZ1i8eDHr1q0zjLg1NTVYLAEddPr0aZYsWWI8fuSRR3jkkUe45JJL2LhxIwDbt2/nsssuM9aR3pTPfe5zPP3006m8r7OeNw80cuBMFyW5dv7vR+cN+f4vnDGGiSU51LX18dLu00PS4TZRZlcWkG230Nnv4VhztxFdWTalJGio40Ax92KRJc3ZdguFOSPWGpYYsr09wKFXxTTjZKjbJm6zi6G/fQgFi2zLn6aSZoncXp2e5souMtItGU0swSLFCpz1F8EgjChGT+bMEZIk0oflLO90m9I367333su9994b8TkpQiRVVVURu6KaufTSS+OuowjmjQOiyuDjiydQGmH2z2BjsWjceu5kHn71II+9fpiOPjcOm2VAQwXThd1qYeHEYrYeb2VnTbsxL+ncNKaDACbrEZbmbhfHmsUX/LiinAE3pct4zBGW2i1CCCQTtZCCZeHNsPXn0HpM/GocbFPjoKWE9OimfF+DlQ5KN9kx+rDIC6DFPuxm0yFFDrp092XWHCEIHIfqdKsYSfj9fkOwXD5n7LAdx6eWTcRq0TjVLhT/vHGF2NMYwRgIho+lpo1tpg636aQw205JrjCLbT0uerxUFI6CL3fzZGK/F46+kdzr67aL27kfhawi8Pug5Ujs16SDQRMssj2/Xqo8EtJBEDvCMkougGGYfSKZNEcIAscRKcIyShrHZcbVRZEUe0930tjlJNdhNbrVDgdjC7O5cm5AMC0agg63ibJkkgjJv7q3gcYuJ3arFtQ9Nl3ItNDW4+JiOK4oQ77cBhN54Zelrsn4WDpPQ2cdaBYYvxTK9TL8oUgL9ehCayAt7SORF+IfGzERFiVYwggy3WbQHCGIHmHxOMGrd4xWpltFprFhv/gld+GMMWTZBjZkb6BI8y0Qc3DiUCMjLK094j/yoonFAx5IGInJ+pyfD/U5SgOZ0jxikF6QJbeJ28PrRdv7RJBpk4r54svVECwpVBsli+FhSXOEJdQTk+qsoKFGCpZIs4RcUrCMIsMthJhuM2iOEESPsEhxCeBQgkWRYbxxcPjTQZKLZpYzY2w+DpvFmACdCVQUZhsDEiH96SDJ5FKxD7dXeLAqz/aUkMcVuMDNukaYS/vboS76xPYgpGCZuELcjp0rbociwmKkhNJdJRQSYTmrUkJn9wUwDCMl1JtZc4QgeoRFflb2PLAM7w/YwUYJlhFGU5eTXXp/kcsyQLBYLRp//IdVvPa1i5lUmv6RCANhsR5lgfQbbiVTSoPbdVee7SmhPj1KoVkgpxRmXCUeJ5oWkv4VKVhkhKVxkAWLu0+foszg9WGRqJTQyMU8/FAKloyJsEQZfjiKPislWEYYG/XoyjkTCjMm/VCa58jIicJLdM+KpsGyqsEpM5WVQpJR0zQup1RM/J21Wjw+lMD8Kq8bTn8g7huCZY64bT06uJOb5XFb7OlPc2QXg6b/srU6oHB8erc/WEjB4u4NP/fO0WHiDMMRoQ9LpkRYogmWUWK4BSVYRhyB6qCKOGsqLplVjs2isWpaGYXZg9OeenJIVGnc2S5YpHFVRilmXCEu1k0HoO1E7Nc27BFfttnFUDpdLCucIHpH+DyivHmwMFcIpbvs3GIJRFmKJ4+csLxZuIX6WIy+HqMtJSRNt70Br0jGlDVH6XQ7itJ3SrCMIFweH28fFheMTPCvZDozKwpYv/YSfnrbskHbR2VhNg69lNtq0RiTf5Z7WEJLg3NKYLI+luNQnAnIRjpoubjIgxAPQ1EpNFglzRJpvB0p6SAQwko2GgtNC0kBMwp+tQdhTgkZERaVEsoUlGAZQWw70Uq308OYfAcLJ2ROCXEmM3VMHkW5gzf8y2LRmKgbb8cWZGG1jJKmcXmmC7+RForjY6nVjbkyHSSRaaHBrBTqiXDc6URGWEaK4VZi+Fjag5ePxrb8EIhi+NyBc5AxZc3xIixn/2elBMsIQpYzXzp7LJaz/cI4gpiip4UyxVM0qEQqDZ51jbg9vgnaa6O/1qgQWh683Iiw7E/PMUZisCMsxXp5vxRfI4VoxttRlGYIwm5K8cq/9ZFiuh0F6TslWEYQb+qG2ytUOiijkD6WylEhWCJc+MfMhCkXil+lb/5H5NeZJxlPCBUsQxBhGWzBctn/gY88CotuHZztDxaGYIniYRkFaYYgbFmiAg4CFXHKdJsxKMEyQjjW1M3x5h7sVo0LZ6a5U6diQJw/Q3wemdSHZtCIdOHXNLj6u+L+rj9A/e7w10n/ypjZkFMc/NxYXbA0H068AV2yGJOaB+n/TuF4WPGFQJXJSCFqhGX0XASD0LRAlCXTIizx+rCMgmiYEiwjBFkddO7UUgoGqeJFkRqr51ey+ztX8/kLR5h/IRV6Q6qEJBOWwTk3An5Y/+9gHmbq7II3vifuTz4vfJuFE0XTK587EIVJN4MdYRmpRBUsesTlLJ/+GxFDsOh/MxkTYdGFk9cJPl9g+SgySCvBMkL4S/UpAK6aq8qZM5GgsmmfF178Kmx/avgOaLCIdeG/4n7Rh+TYRjiyQSzzeuC5O0VJc95YuPgb4a+zWKB8lrg/WJVChvdmFETBkiGuh+XsvwiGISMqfRkaYYHgtJBz9BiklWAZARw808WeU53YLBrXLRohTalGM7VbYeev4Y3vD/6+zHNEhoJY83hKquDcL4n76/9dCLd134Qj68Wvw0//AYonRd6u9LGYO956XFC/K/jXZMrHLauEVDo1iGiCZRT5IsKQERZ5TjKlSshcXh0kWJTpVpFB/HlnHSBa8Zed7X0+zgaaD4nb3hbR3XWwOL4JHpoMmx4evH2Y8fvjp1Yu+hfRGK5xH/z2U7DtSUCDG34h0kbRMIy3umBpPgy/vBx+fjHsfHrgxx7a8E4hUBGWcEJ9SJnSh8VqA4tN3DeXNvcIu8BoiB4qwZLheLw+XvhApINuXDpxmI9GkRAthwP35YVyMDi5Gfy+gKE1VdprwqtEImGerxLtwp9bGkj7HNXTQlc9APM+FnvbZsHywW+EUDnzoVh2bGP8Y4tFIkJrtJKtpxHMgsXrDnzOo8DIGYY9RLBkSqdbCK8U8vmg+Yi4XzZjeI5pCFGCJcN5+0gzTV1OSnLtqrvtSKHZLFgaB28/nULIGjnsVGg+Aj9ZBs9+Jv668qJvzQJHjNlR594FxVPE/aWfg/P/Mf62ZS+Whj3w13uEOCqbKZad2hn/9bHo7wC/V9xXgiWYSBEWc5pxNJtuJZkSYYFwwdJRK0YIWB2B/3NnMUqwZDh/3iHSQR9fPAGHTX1cIwKZEgLobhq8/XSeFrehc2CS4cQm8Lrg+FvQHUdcmdMqsebx2LLgtj/Bx/4LPvKjxGb3FE8JXBg0qzDw3rUB0MSXcldDQm8nIlJoOQoyx4+QKUjBYv4bkjOdsotEGmK0EWqyzaQIi9HtVhcs8rumbMao+KzUFTCD6ehz89o+8UWt0kEjBI8zeAhgtAhLfwfsfSG8p0IySMHiGkCExdwz5eibsdeNZbgNpXwWLP0sWBMswbdYRB+TyoXw+XW6F6YokCo6PYAoi5EOOvtz/EkTKcJy8GVxO/3yoT+eTGBERFh0D4sULGNmDc/xDDFKsGQwf99dj8vjY1ZFPudMOPtL1s4KWo8LX4kkWtRi08Pw3B2w9eep78tICQ2gUuiMWbC8EXvdSHOE0snq/4Avvw2Tzg0sk0bdUztS367yr0QnkmA58HdxO+ejQ388mUCY6TaDonKhzeNkd2glWBTDjawOunHpRLREwuqK4cecDgLoiZISkka5VA2lrp7AwLpUPSxeDzTsDTw++kbsEuLhuPBPWCpu0yFYVElzONnF4tbVLf4eWo4K47PFBjOuHNZDGzbCUkKZFGHRj8WIsOh+OekBO8tRgiVDOd7cw46TbVg0+MSSCcN9OIpECRUs0SIsXfXitnZrau3oO+sD9z19qW2j5Ygw79nzxL+eRmF6jcawCBZThMXcPTcZVElzdMzNxpydgehK1UXhIxRGC/YQQ3mmdLqFQLTH4xS3zTLCMnN4jmeIUYIlQ3lej65cNLOcsaNhqN7ZgvzFI0O00TwsUrC4uoPTMoki00ESVwppIbnfynNg6kXifqy00HAIlor5oiqpvyNgBk0WlRKKjtUWaDjW325KB31k2A5p2MnkCIthuu2DnpbA33aZEiyKYaKurZdfbz4JwI3LlNl2RCF7sEw5X9xGqhLyeoIjLzWbk9+PNNxKUvGx1O8St5ULAwZL2TslEsNx4bfaYdwicT/VtFAyZuHRiPSxNB+B2i3i/uxrh+94hpsg060mSoYzBXNZs4zmFk0eeUM3U0QJlgzD6fFyz2930tHnZtGkYq49p3K4D0nRWR+/5BdEykJGWKZcKG4jRVi6GwBTeuPke8kfU1eoYEnBx2JEWBbA9CvE/Zr3hT8mEsNVbTNQ4220gY0KgUwL7X4W8MP4JVA0in8omS/+9pzEyvKHCnOERaaDykeH4RaUYMk4fvD3/eyq66Aox84Tn16C3ao+omFl/0vw+CLRedXnjb1ud4PwAWgWmLxSLOttCX9d15ngxzXvJ+/PGGiExe8PdJIdtxDKpkPxZNGT5cQ7kV9jCJYhNq8OWLColFBMZITlwEvidvYoTgdBcAookyqEICTCItPPo8NwC0qwZBR/23WaZ/RU0P+7eRETS0ZHmC9j2fE0/PGzYpx7V3240AhFfoEUT4GC8YAmSpzlBVMioyOVC8UXUG9zcHfcRAgVLMl6WDrqoK9NVIOMnSd+RRppoSg+luG68MtKofrdYiBisijBEhspWGT31NHsX4Fg020m9WCB4AhL0+gy3IISLBnD0aZuvvlnEaK/57LpXD6nYpiPaBTj98NbD8Pf/im4p0pHXezXmZs4WW2BC2RoOkkKn5IpMGG5uH/y3eSOMdR0m2yERaaDyucEfkXKtNCRCD4Wn2/4vCCl00T5rdcJjXvjrh6GKmuOjRQsACVTYezc4TuWTMAcYcmkLrdgqhLqN6WEVIRFMYS4vT6+8pud9Li8nDetlH++cvTkJDMOnw9e/ga8+X3x+KKvw2TdQNtRG/u1RohW/8WTr89+CvWxyOhIwTiYskrcT9Z4a2xjvLhN1sMiO9xWLgwsm3aJaIvfclgMRDTjNM/jGWIPi6alnhbyugNN0VSEJTJmwTLnI5nl2RgOzKbbTIuwyOPpa4N2/ftolDSNAyVYMoIdJ9s42NBFUY6dH9+6BJvyrQwfR16HbU8CGlz7n3DFvwtvByQgWELaZOeVi9vQSiEZYSkYB5N1wXIyCcHicQYa0knDXaoRlnEmwZJdBBNXiPuhaSEZXRmueTyGYAlp0d9eK5qdRUMet2YJNElTBBMqWEY7QabbDIuwyONp2AP4Iad0VEUO1ZUxA3jviKhiuGRWOWMLMuw/yGij6YC4nX89rPwHcV9WTMRLCbUkGGHpMkVYJp0rohodNfG3b7xe7+FizQpMaI0mWI6+AW/8R6DRlKTeVCFkRvpYQtNCRvO1YZrHEynCcuJdeOJc+Pklwa3lzch0UE6pmFekCEcKltwymLRyeI8lEwgy3WZohKVxv7gdRekgUIIlI3j3qPhSvWCGClkPO1IMFE0KLCvW77fHiLC4esNDtHm6YInmYSkcB1kFgShHolEWmQ4qHC9eD9FNt69+Czb9J2x+IrCstxU6dXEUKlhm6D6WY28Fd88dbh+INN42HYT+TqjZAr/9FLh7xXuv2x75daqkOT5SDC75LFisw3ssmUBQSijDqoRkhMWn/98cRYZbUIJl2Ol2ethV2w7A+dNHT2gvY5GCpWBcYFkiEZbWo4gQbUng4pivp4RC5wmF7kN6ZGoS7MdiCJYJgR4a0SIsct/vPBZIj8h0UElVcDoARA+O7GLhWTn9QWD5cFfa5I8VDbLww/ZfwW8/Ce4eEZ0CqNsW+XXDfdwjgaoL4OuH4Yr7h/tIMgN7SB+WTCI04jOKSppBCZZhZ+vxFjw+P5NLc5lUqsqYhx1z9ENSlICHxexfkaZFw8NiirC4egPpCylYpPE20QZyQREWva16JNOt3w997frzHbDpEXE/kuFWYrEG2vQf3xhYngkXfhllef07ot9N1UVw5bfFsmiCRZ6rwZowfbaQP1ZFVyRBEZYMS9GHRnxGkeEWlGAZdt49otJBQ8qr34Lf3xq9CVzECIs+fNLZGd0rIacvm2d65EXwsMjt2/MC6RxpvG06EIiCxCJSSihShMXdCz534PG2J6HtZGTDrZmpl4jbY28FlmWEYFkWuD/pPLj1DzD1YvG4blvkSdNSBI5fOvjHpzg7sNoC7fgzznQbEmEZRV1uQQmWYedd3XA72tJB2uFXGde2dWh36vfDlp/BwZcDprXQ5+UU5ALTSARHXuBCHc3HYkRYTIIlP0KVkBQsheNMkZgxgV9KiZQ3yx4shRMCg+tcESIsMrpisYkLu9cFb3zfFGFZFHn70y4Tt7VbREQITD1Yhsl0CzDzavFeJp0Htz0noksV54gweX+HmD5txueFE2+L+1KEKRSJIIVBxplus4Pvm712owAlWIaR5m4nB86IX8bnTx9FERZXL9Y/38m5J/4rUFkzFPS3B8xqkfwofW2iORkER1ggvo8ltKQZTBGWpsCvf3NJs5nJSaSFgiIs0sPSGb5ef7u4zS6Gq74r7n/4x8CxRouwlE0XYsjrgtr3xbJMMK+OnQPfOAJ3vgLZ+vu22oXvBqAuRACf2S2ETFZhYICiQpEIMi2UyRGWspmjLo2nBMswslmvDppTWUBZfoa50QeT5kNoXtFi3fLhc0O3X3O6JZIfRYqJnNLwXLH8JRPpdT5f4Nd9kGDRIyx+rxBDENw0zoyc7lybQNQpUQ+LjLDkFIuL+jk36k/4xbHlR+mmrGmmtNBGcTtcc4RCySkJL0+epPeOCfWxyJTWlAtEmF+hSBQpWDLOw2I6nlGWDgIlWIaV946KX60XzBhd6SBjBgZg+fCPkb0Hg4HsJQJRBEsUMQGxBUvXaeEXsdhEu32JzRFoViZ9LEaEJWQK97jF4rZxX+zz4fVAtzQGx/GwmCMsAJf/O1js4n7lwtgdTaeF+FgywcMSDdnsrjZEsBzfJG6nqXSQIkkyVbCYIyyjzHALSrAMK4NuuN31rJg0/Ltb4N0fQ90O0ap8uJHN2QCtsy75OTqpYh5CGCm1E01MQCAlFMnDIlMspdNEisKMbB4nK4W6TNERM2XThZhwdYsmctHobhDzjSw2ESVJxMOSU6wf39RAMzxpVo2GjLDU7xKRqZEgWBr3BYSbxxXwA8V7rwpFKLLbbcaVNZsiv0qwKIaK2tZealp7sVk0zp06SBeBt38EbSfg0Cuw/t/hl5fDD6tg1x8GZ3+JokdYPBb918vuITqeXnOEJZJgMRliQ5HN4yK9zpghFOELxOxjgeiiyGoPdK1s2Be+HYk5pWSxBiIsnv5wMRoaYQG46nvw+dfgvK9E3weIczBmNuCHY29m9jyegspAjxbZCffUDhH1yh0D5aN8mJ8ieQzTbYZFWGwqwqIYBmQ6aNGkYvKzBiG/3nJUTPO02ODKB2D2GnHhcnWL4X7y1/dwoEdYjoy9Rjze+9dANcpgEi/C0hmhpFlimG4jRFhk1U2kKbf5Ib1YonlYAMbOE7eNsQSLrBDSIzRSsEB4Wig0wgLC/zF5pUhXxUOmUva+oC/QgreVSYT6WI7rqaypF6uW/IrkqThH3Gba5OrsQhFVzSqEshnDfTRDjvqfPEwY6aDBqg468HdxW3URXPg1uPX38K/HxK9NZ6c+4G8Y8Dih7TgAJ8suxV80WbRWP/jy4O/b7GHpqg+PSMRMCU0OrONxBT93Wh/IF6nXh7kXi98fvUoIoCIBwWJEgXTBYrUHfgWGCpZIEZZkkGmhw+vFbU5J5lYlhPpYpH9FpYMUqXDV9+BfDgbM8JmCLQs+v078y7QKpiFACZZhwO/3855eIXR+qoZbZzf8+np49/HIz0vBYp6+arHCRWvF/c3/Da6e1PY9EFqOgN+HP7uIfnsJvgWfEsuHIk1lrhLy+wLRDonRNC7EXwKiV4otG/AHohwgzqH05MjyWjPmXixBZdMRRJGMsMRMCZl6sEiMeUIhPpZIEZZkqLpQTDn29IvHmTwVduK54rZum/hMZLWVEiyKVLBYIv8fzQQqF0DF/OE+imFBCZZh4FBDN83dTrLtFpZMLk5tI8c2Cm/Bhu8GUhmS7ibR9Atg9rXBz82/AUqmQl8r7Hg6tX0PBP3i7h8zGzQN34KbxPKjG6CrYXD3bfawQHhaKFaERdMi92Kp3y3ET8G4yN4Xc4RFCqLcsshD1aRgaTkcHsWRdEYw7UrjbbojLLIcWpKJ/hVJ5QIxvbqvFap/Jzr8Fk0SRmiFQnFWoATLMLBZ96+sqColy5ZiiL31mLj1ecLTO4deAfyiVFZeZCVWG1z4z+L+ez8RKZqhRJY0yxb2pdNFON/vgz1/Gtx9mz0sECw8fF5RgQOR0zUQWbDIAYGRoisQXCUUyyMjt59VJD5TWXkUSiTBEq20eaARFgjuEJvJgsXmgPGLxX0ZdZx6cezSbYVCMaJQgmUY2FHTDsDKqQNoc956NHB/+/8Em1YP6H4QczrIzKJbRUqhqx6qfxv8XHejKGUdCD4fNB4Qno1QZIRFVsQALLpF3O76ffRtuvvhycvhuTtSPy7pYZFmNXP5cE+TaPCmWQIiI5RIxttY/hUIrhKKNKfIjKYFTH6RRgdA7JRQuiMsENzDZDjb8ieC9LHIz0elgxSKswolWIaBnSdF19Olk0tS30iLSbD0tYqW6yDy98feFPejCRabA87/R3H/ncdEMzJnl5gz89hC+PnFwYPvksHngz/dCf+9Ej74TfjzeoTFbx6LPv8G0YPkzIfQsDfydk/tEP/2vhBI3SSL9LDIJm3mSIkUE/kV0Y2lkaY2x42w6B6WniZThVCM3LhhvI1wHny+QJRmqCIsk84LmHozOcICAcEiUYJFoTirUIJliDnT0c+p9j4smihpTplWUWnDvOvF7fs/FRGNo28Ik2TxlIAnIhJLbxc9KtpPwov3wo+XwKaHwdMnno8V7YjFxh/Avr+I+3v+HPyc1220sA8SLLmlMP1ycf/om5G3a4761Lyf/HF5nKIaCQKpA7NgiTT0MJTQ5nF97YGW/NEEi4yweF0Bc25o0zgzsYy3vc3Cm6FZgtvqR2oe5/enJ8Jiz4ZJK8X9vCiRp0zBLFjKZsY+zwqFYsShBMsQs7NGRFfmjiskL9X+K+4+6NQvtld+W1ywmg6IyIq5OihW/t6RC6vuEfd3/V5EAEqnwyX/JpYdeDl5f8vu54TokZx8N3jGTesx4c9w5AenNAAm6VUeMmIRypndgfvSUJwM0r+iWQMO+0gRlmjpGghvHidFVPFkyIsSfbBnBwYUnvlQ30cMURSrF4tMB+WNDe6oGynC4ukTIgkG3jvlim/Dks+aZhFlKEUTAn9XKrqiUJx1KMEyxOxIRzqo7YS4zSoSFT9LPiMev/cTOLRO3I+WDjKz4ovi9TmlcO3DcM8WuOSbkF8Jzo7o0Y5I1G6Dv+oC6IJ/EhEerwtOvB1YRxpux8wKF1MyQhFNsAw0wmJuLS9TO+21AZ9NrP4oErPp1u+P71+RyCGIMhoTqWxaIlNCHbXQHzKBOZLhFkwDEE2CpU/vTKtZAxGYVJm4DD7+X1AQZVhiJjHno4AG8z8x3EeiUCjSTEqC5YknnqCqqors7GxWrlzJ1q3RJ8zu3buXG2+8kaqqKjRN47HHHhvwNkcyMsKybEoa/Ctl08SF/9wvAZpIB/W1iQZfk86Lv53sQrhnK3zjKKz8kvjVbrHAvI+L52VqJx7ttfCHT4seI7M/Ald8B2ZeJZ47/FpgPSlYyueEb0MKltaj4V143X1BAxM5szv5HjLScJtbJn6JA7h7AlOUE4mwFE4ANBG96G2J71+RGCZeXRzFirDklAQETajxNqpg0SM4ZsFipIOKRlelzNXfh3/eA1MvGu4jUSgUaSZpwfLss8+ydu1avv3tb7Nz504WLVrE6tWraWxsjLh+b28v06ZN46GHHqKyMvIXdbLbHKn0u73sOSV++Q4owiJLmkuni9uy6cH9VmZdI8qXE8HmCG9dPv96cZtoWugvd4s+IxUL4IZfiO3NvFo8d3h9IIohPRzmCiFJbimUVIn79dXBzzXsExU8uWPExdznCcyMSRQZYckbI+aEyKiHTO90JeBhsWUFvCPtNXBKFywTEoywSOJ5K4xKoRDjrSFYQtJpERrHaVKwZGor/cHC5ggv5VcoFGcFSQuWRx99lLvuuos777yTefPm8bOf/Yzc3FyeeuqpiOuvWLGChx9+mFtuuYWsrAjNslLY5khlz6kO3F4/Y/KzmFQ6gCmgsqTZ3BTLPMwukXRQLCadl3hayOOEk++J+5/6n0B6ouoi0cirozYQHTEiLBEEC0RPC53R00HjFok5OAA1SfpYjJSQXpob2lNFpoQiNX8zI30s9dWBsuhxi2K/xlwmbbEJ4RWLiijG22gRlkiN49JhuFUoFIoMIinXp8vlYseOHdx3333GMovFwpVXXsnmzZtTOoBUt+l0OnE6A7/+OztFvt/tduN2u6O9LGnkttKxzW3HxUVzyaQiPB5PytuxthzFAniKq/DL45qwEuuc66CrHu+Ui2GAx2uZcx3W7U/i2/M83mlXRF+xYT92vxd/djGewimB/Wp2rFMuwHLsDbwHX8FXVIWt+RAa4C6ZHvG8WioWYt37Ar66nXjNy099gBXwVpwD+ZVinZObg9aJ+366GsU2skvxud1YCyZg4QO8rSfwud3YuurFseWUxzx31oLxWADfvhexAP6yGXisuTFfY8kuRRZK+/Mr8Hi94PVGXV8rm4MN8DXsDbzH/k5sR9ajAZ4i0+cOaLZcsX5/p3E+vT2tYll2UVLnSRGZdH4PKAKo8zo4jLTzmuhxJiVYmpub8Xq9VFQEm+8qKio4cOBAMpsa8DYffPBBHnjggbDlr732Grm5uSkdSyzWr18/4G28ctACWMjpqefll0/HXT8aV53aSy7w3oF62mpNQwNzPgU5wPqNAzxSKO0ey0WAd9/fWGe5Gp/FHnG9Ca3vsRxosVbw7iuvBD03zTmeBUDrlmfZVZ/HlV4nXs3Oy+/tBU34M8zndUyXkwuA/mPvsf7lwPu6+OA7lAA7T3vpyXJzKeA9uZmX//6SKPFNgIW1O5kKHDndyoGXX+acVjfTgWPVmzjQMJbr9AjM+vc/xG07EXU781pczAQ4Jobr1fnGstN0rJGoam5ExmDaPNm8HWf9ot4WLgU8p3bxyt//DprG3NPPMau3ha6scbx51If/WGAb5Z37OB/oaj7NRv18Htq1hQXA6dZedsTZnyJx0vE9oAhHndfBYaSc197e3vgrkaRgySTuu+8+1q5dazzu7Oxk0qRJXH311RQWFqZtP263m/Xr13PVVVdht0e+aCeC3+/nex++Bbj49NXnpW66dfdh/0A0QFv1kc8MXjMv/zX4f/wr7N1nuHZ2Nv6ZqyOuZnlzJ5yEktnns+baNcFPts6Bn/6GMb2HuXRmPuwDy9g5rPnIRyOfV+dF8MhD5LqaWXPJucJv4nVj230XAIuvvR2KJ+N/5IfY3T2sWT414SFg1uf/DM0wY+F5TFuxBsvWGlj/KtPLHFRduAR2gd+axVXX3RTTpGrZdhpeewULIkIyfvlHqDx3TdT1AbQDfqh9GoDiSXNZsyb2+nj68f/nd3B4e1hz8VLwg+2n/wBAznU/5NrZwa/X6srh6CMUZlm46qqrWL9+PXOmVMIpGDdtbvjnokiadH0PKIJR53VwGGnnVWZI4pGUYBkzZgxWq5WGhuAhdQ0NDVENtYO1zaysrIieGLvdPigf0EC3W9vaS3O3C7tVY/GUMuz2FGcItemlsVlF2AsrBrcCZP71sOVn2A6+BPM+GnmdZuFLsVbOxxp6fipmQ+l0tNaj2Lb/EgBt7Jyg8xh0Xu2louFXy2HsTXuh+EpoPSSqj7IKsZfPFIbeSSvg2Ebs9dth4uLE3kufEHnWgrHiOEumAGDpOo2lT1QQaQWV2B2O2NspnRL00Dppefj7DqUo4IuxFI3HEm99u10YqlsOY289BHv/IiqTJp2Hbf7Hwj/z3GJx/K4u41xa9CZ51tzS+MenSJjB+n4Z7ajzOjiMlPOa6DEmZbp1OBwsW7aMDRs2GMt8Ph8bNmxg1apVyR3hIG4zE5H9V+aPLyI7VbEC4SXNg4nsohurWkg2OIvWVVeWN8uqnmiGW4lhvNV7nMj+K5ULAtVMsmQ7GeOtuQ8LBHet7ZIt8+MYbiFgugWRjqpcGP81sj0/JD6yXhpv9zwfmPd09fcif+ZG4zhVJaRQKM5ekq4SWrt2LU8++STPPPMM+/fv5+6776anp4c777wTgNtvvz3IQOtyuaiurqa6uhqXy8WpU6eorq7myJEjCW/zbEAKlgH1XwFTSfO02Oulg0krxUU8WrWQs1u09odAKW4oUrBIIvVgMSNLhGWlUL3e4dZciWNUCiXRQM7chwVEd1qA7jPQpr+HRMSEuWS2fK7oGBwPc0v7WE3jzIzVU13VvxWTrOd+LNANOBRZmeV1BoSlqhJSKBRnGUl7WG6++Waampq4//77OXPmDIsXL2bdunWGabampgaLqa/H6dOnWbIk0FjrkUce4ZFHHuGSSy5h48aNCW3zbCAtDePAVNI8fYBHlACyidyWn8GBl2D2NcHPyzLl/Irok3ynXAi2nMCMoniCJbS02YiwmCIZE1eI6EZHjSj1jdfXxO8P7sMCQrjYssXcJRnNSWT2THYxOArEXKIJcRrGSbLywZ4L7t7kIywgutVe8e3o6zoKAvdlLxbZJVdFWBQKxVlCSqbbe++9l3vvvTfic1KESKqqqvDLxmEpbnOk0+P0sL9eXEAG1DAOAhGWsiEQLADTrxCC5cQ74c8Z6aAo0RUQs3SmXgyHXxUTmUumxt5f5QIhRrrqhRiR83fMEZasAqg4R3S8rXkfzrkh9jb720XjORBjCECkVoominb5ddvFskTEhHxd0/74HW7NTFwu9lNxTmLrm1Nsy++EMTOir2u1BUShLlg0FWFRKBRnGWqW0BCwq64dnx8mFOdQWZQ9sI21DGFKCGDyeUJAtB0PNC6TyNbxsaZCQyAtVDYjfgdeR14gCrPneRHJsGWL+UOhxwWJDULsbdW3nS8ElESmd+RQwUQ8LADL7hARnznXJbY+wG1/hn/eG+xniUVJlYii5Y0NDKSMRegAROVhUSgUZxlKsAwBO+XAw4Gmg8xTmociJQRi3pCMbpx4N/i5RCIsAIs/DUtvh6vC++ZERA4T3PG0uK2YHy50JkkfSwINC0P9K5KiScGPE03XnPdl+PLbyQ0DtDmip80iYbGKfdy7LbhTbjR0waIZKSF9+KGKsCgUirMEJViGgMCE5uKBbcg8pTmZi99AmXKBuDVPXobEIyyOPPjYT2BW5F4uYYxfLG5bDovbSJU4k/UKsjN7gqpjIhJaISQJEywJGmKHCkde4hES08Rmi8+F5ukXj1WERaFQnCUowTLI+P1+PqhtB9LpXxmCkmYzVReK25OmCEtvq6iwgfilyskiIyySSLN6iiaISh+/F7b+PPb2evUIS17IDJ/QIXnJREwyDTmx2dWN3at3jdQswYZchUKhGMEowTLIHGvuob3XTZbNwtxxA+zAK3uwDJV/RTJ5FaAJg6ocEiijK8WTA/6JdFExXwwJlIyL0uvk4n8Vt2/8R3i6yky0CIu5p4qjIP3vYygxDUB0eHrE/eyi8EncCoVCMUJR32aDjPSvLJpYjMM2wNNt9GAZIv+KJKdYVO9AoFooXsO4gWDPDrTc16yBniShLPkMLLxFRFn+9Hnoboq8XtSUkCnCEm9Kc6Zj8rDYvVKwFA/f8SgUCkWaUYJlkJH9V5ZMKY694vanYN+LsddpHaYIC4SnhQz/ShzDbarIkuHyOcGVPWY0DT76KIyZLdJTz38RfBGmIPdEESyFEwL3EzXcZiomD4shWJR/RaFQnEUowTLI7DzZDsTxrzQegJf+Gf74WdjxTPT1Woa4B4sZw3gbKlgGIcICIIctzrgi9nqOPLjpGdGY7dhG2PRI+DqhTeMktizR9A4SL2nOVGQ6y+xhUREWhUJxFqEEyyDS2e/mUKPoixFTsJhLc//2T6L/SCjDUdJsZsr5gCaGHXY3Jl7SnCpz1sA92+Dy/xt/3bFz4SM/Evc3Pggn3wt+vjdKWTMEKoVGeoRFN9dqZg+LirAoFIqzCCVYBpFdte34/TCpNIfygvDJ0gZ128RtXjngh+e/BIfXB68zXCXNktzSgK9kz59FYzLNKqYrDxbls0QUJBEWfxoW3gz4A/1bJIaHZUzoq0QzO4jfgTfTCYqwKA+LQqE4+1CCZRBJKB0EAcFy3Y/hnBvB54ZnPxNc+TJcJc1mZFpo2y/1Y5ke3V8yHCy6RdyGRliieVgALv8WXPMQLLxpcI9tsDE8LN3Kw6JQKM5KUpolpEiMHYkMPOxtheZD4v6klaKNvbNbzN55eo3wZjjyAmbS4TDcSqouFD1PWvRJ24OVDkqVSStFOXRHrZjAXDJFTC926e3qI0WmiifDeXcP7XEOBkERFqu4ryIsCoXiLEJFWAYJn8/PBzWyw20MwXJqh7gtnQ55ZWC1CxPpDH3+jrsXepqgT5+HM/HcQTzqOMgIi2SwDLep4siDcYvFfRllkekgzXp2X8CzAh4Wu0c33aoIi0KhOItQEZZB4mhTN139HnLsVuZUxmhIVrtV3E4yCRF7DnzmT2IGjrNLiBZXj+hcmsyE4HSTVwblc8WkYsi8CAtA1QVwajucfAcW32ryr5Se3U3UHIEIi8Or+37OZoGmUChGHUqwDBKy/8rCiUXYrDEulHW6YJm4Ivy5vDHhpbjDTdUFJsGSYREWEFGgdx8PRFiMwYcZdh7TjWlas93vEfdVhEWhUJxFnMU/OYcXw3Aby7/i80KdnhKaNIypnmSQDeSsWZlZWTP5PEATJuXO+uhdbs82ghrHqT4sCoXi7ENFWAaJnYn4V5oOCkOoPU+kWkYCM64UPpqJy8GagX8+2UVijMCZ3aIrr9E07mwXLLqHxecmy9cplqkIi0KhOIvIwCvOyKej183hxm4Alkwujr6iTAdNWJqZF/9IZBXAF9fHX284mXKBLljeC6TUzvYIixx+CFjQK8pUhEWhUJxFqJTQIPBBrYiuVJXlMiY/RuOzWr3/ykhJB40UqvRqppP/v727D4rqPvcA/l2WfQEVEIFdQBZJYkSFWAUlaKbeXokvsYkkGZt6bUVr0kmDI5Z7bdVUHa9j8E4nadPEMTHT2HtbExPvNZqkJqkXNQkTBEEwEqOSYuMbu2i4sCjK2z73jw2rG3nZXXY5u8v3M8Ps8eyPM88+OvD1d37nnM+GzhqWELV9pu5bAhWgG+DTwYmI/EiA/Lc+sBw/3wTAlRvGdS+4ZWDxKtN0++uVL4fODAtgX8fS0X2X28jgviqKiIYc/kTzgSrHE5pdvGFcT1cIkee6L78Gbl0t5G9XW/mC7rbL53k6iIiCDAOLl5282IzKr7sX3Eb1PvC7N4wj70r+dpZFvl3PocTzlwbbbetYRB+pYCFERN7HwOJFH9aY8aNXS9Ha3oX7Rkci1djHGoKebhhH3jPmO3flHRKnhG6fYWFgIaLgwjUsXiAi2PFJHbZ+eBoiwPfvjcXL/zIZ6pA+HlLY/cDD0ZmDU+RQ893HCAT7olvAeZEtTwkRUZBhYBmg1vZObHr3FN6quAAAWJKdjA0/nND33W1ttlunhLjg1jdGGO2n2xr/bv/zkJhhuXVKiDMsRBRsGFg8ZLMJ9p+4hP/44AzM1psIUQEbfjgBS2e4cPfXK6eBNqv9MlR/vL19sEiebg8s2uGARq90Nb532ykh4U3jiCjIMLB4oOr8/2HTe6dQfaEJADB6ZBi2PJqOmffGunaA7tNBgXTDuECUPAOo+vPQWHALOC265SkhIgo2/G3ppv/87B/Y+O4XAIBwrRr5P7gHyx9IgV6jdv0g9dX218Qp3i+Qbkl9CEi6Hxg3T+lKBgcX3RJREGNgccNZSwu2/NX+pOLHJidizbxUxEV4cKqh/nP7a/wkL1ZHd9BHAss/UrqKwXP7KSHOsBBRkGFgcVFHlw3/+vYJtHfZMCs1Ds//aBJUqj6uAupNVydgqbFvGxlYyIt44zgiCmK8D4uLXv3kHE5eakZkmAZFj6V7FlYA4JtaoPOmfb1B9F3eLZKGNi2vEiKi4MUZFhdcvA5sK6sDAPz7gomenQbqVn/C/mpM57NeyLt4lRARBTH+xuxHe6cNu75So9MmmDPRgEcmJQzsgFy/Qr7CU0JEFMQYWPqx7UgdLreqMDJcgy2PDuBUUDfHDMt9Ay+O6Ha3BxZdH4+FICIKQDwl1AeL9SZeKzkHANj08HjEDNcN7IA2G2DmDAv5SORoSPgoNMsIDAtx4zJ7IqIAwBmWPhgi9PivZZmYlWDDvDTjwA/Y9A/7HW7VOiB23MCPR3Q77TB0PlOBT+9dr3QlRERexxmWfmQmj0RDss07B+tev2KYAKg13jkm0e10I2AL4b8tIgo+nGEZTFy/QkRE5BEGlsHE9StEREQeYWAZLCK3ZlgYWIiIiNzCwDJYWszA9SuASg0YJipdDRERUUBhYBks3bMrMfcCmjBlayEiIgowDCyDhetXiIiIPMbAMlgc61d4hRAREZG7GFgGC58hRERE5DEGlsHQ2gg0n7dvG9OVrYWIiCgAMbAMhu71KyNTAH2ksrUQEREFIAaWwcD1K0RERAPCwOJrIsC5T+zbXL9CRETkEQYWXyt/Dfjqf+03jBs7W+lqiIiIAhIDiy99/Rnw0Vr79uzNXHBLRETkIQYWX7FeBt7OA2ydQNrjwP3PKF0RERFRwPIosGzbtg1jxoyBXq9HVlYWysvL+xy/Z88epKamQq/XIz09HQcOHHB632KxYOnSpUhISEB4eDjmzp2L2tpaT0rzD51twNtLgOsNgCENeOQlQKVSuioiIqKA5XZgeeutt1BYWIiNGzfi+PHjmDRpEubMmYOGhoYex3/22WdYtGgRli9fjqqqKuTm5iI3Nxc1NTUAABFBbm4u6urqsH//flRVVSE5ORk5OTm4fv36wD6dUj5aB1w8Zr+E+Yk/A9phSldEREQU0NwOLC+88AKeeuopLFu2DBMmTMArr7yC8PBwvP766z2Of/HFFzF37lysXr0a48ePx+bNmzFlyhS8/PLLAIDa2locPXoU27dvx9SpUzFu3Dhs374dN27cwJtvvjmwT6eEm83AsT/atx//IxB9l7L1EBERBYFQdwa3t7ejsrISa9eudewLCQlBTk4OSktLe/ye0tJSFBYWOu2bM2cO9u3bBwBoa2sDAOj1eqdj6nQ6lJSU4Mknn+zxuG1tbY7vBQCr1QoA6OjoQEdHhzsfq0/dx3L1mKrLJxEKgYxIQOeYfwK8WEswcbev5Br21TfYV99gX30j0Prqap1uBZarV6+iq6sLBoPBab/BYMDp06d7/B6z2dzjeLPZDABITU2FyWTC2rVr8eqrr2LYsGH43e9+h4sXL6K+vr7XWoqKirBp06Y79v/tb39DeHi4Ox/LJQcPHnRp3JirhzAJgEUVi7LvrNWhO7naV3IP++ob7KtvsK++ESh9bW1tdWmcW4HFFzQaDfbu3Yvly5cjOjoaarUaOTk5mDdvHkSk1+9bu3at08yN1WpFUlISZs+ejYiICK/V19HRgYMHD+LBBx+ERqPpd3zIB4eBC0DsxJl46J8f8lodwcbdvpJr2FffYF99g331jUDra/cZkv64FVhiYmKgVqthsVic9lssFhiNxh6/x2g09js+IyMD1dXVaG5uRnt7O2JjY5GVlYXMzMxea9HpdNDpdHfs12g0PvkLcvm4V+0zTer4dKgD4B+K0nz19zXUsa++wb76BvvqG4HSV1drdGvRrVarRUZGBoqLix37bDYbiouLkZ2d3eP3ZGdnO40H7NNUPY2PjIxEbGwsamtrUVFRgQULFrhTnvJEAMsp+7ZhorK1EBERBRG3TwkVFhYiLy8PmZmZmDZtGn7/+9/j+vXrWLZsGQBgyZIlSExMRFFREQCgoKAAM2fOxPPPP4/58+dj9+7dqKiowI4dOxzH3LNnD2JjY2EymXDy5EkUFBQgNzcXs2cH2K3smy8Cbc1ASCgwaqzS1RAREQUNtwPLE088gStXrmDDhg0wm8343ve+hw8//NCxsPb8+fMICbk1cTN9+nS88cYb+M1vfoN169Zh7Nix2LdvH9LS0hxj6uvrUVhYCIvFgvj4eCxZsgTr16/3wscbZJYv7K8x44BQrbK1EBERBRGPFt2uWLECK1as6PG9I0eO3LFv4cKFWLhwYa/HW7lyJVauXOlJKf6l4dvAYpigbB1ERERBhs8S8qbuGZY4BhYiIiJvYmDxJseC27S+xxEREZFbGFi8pbMN+ObbBzbylBAREZFXMbB4y9WzgK3T/sDDiESlqyEiIgoqDCze0n06KG4ioFIpWwsREVGQYWDxFkuN/ZWng4iIiLyOgcVbGniHWyIiIl9hYPFEV+ed+xyXNDOwEBEReRsDi7sqdgLPxQMn//vWvtZGoKXevh03Xpm6iIiIghgDi7tq/gfoagf+Wgi0mO37uk8HRZkAfYRytREREQUpBhZ32GzA5Wr79s1m4MC/2bd5OoiIiMinPHqW0JD1TS3Q3gKodYB0AV++B5zafyuwcMEtERGRT3CGxR2XKu2viRnAjFX27b/+G3ChzL7NS5qJiIh8goHFHY7AMgX4/mog5l7gegNw5bR9P58hRERE5BMMLO64dNz+mjgF0OiBR14C8O1dbdU6IPpuxUojIiIKZgwsrupsA8wn7duJGfZX0/3A1Cft23HjATWXBBEREfkCf8O6SNXwBWDrAMKigajkW288uAkIjwbunqVccUREREGOgcVFqstV9o3EDOeHG2qHAT9Yp0xRREREQwRPCblIVd8dWKYoWwgREdEQxMDiItXl7gW3GcoWQkRENAQxsLggtOsGcLXW/ocEzrAQERENNgYWF0S1noMKAkSagOGxSpdDREQ05DCwuCCqtc6+kThZ2UKIiIiGKAYWF4xsPWff4PoVIiIiRTCwuCDq+t/tGwwsREREimBg6c81C8I7GiFQAfGTlK6GiIhoSGJg6YfjhnGx4wDdCGWLISIiGqIYWPrRHVgkngtuiYiIlMLA0o/uO9xKAgMLERGRUhhY+iJyK7BwhoWIiEgxDCx9sXXCNnMdvo7+PsQwUelqiIiIhiw+rbkvag1sGctQbTEgQa1VuhoiIqIhizMsRERE5PcYWIiIiMjvMbAQERGR32NgISIiIr/HwEJERER+j4GFiIiI/B4DCxEREfk9BhYiIiLyewwsRERE5PcYWIiIiMjvMbAQERGR32NgISIiIr/HwEJERER+L2ie1iwiAACr1erV43Z0dKC1tRVWqxUajcarxx7K2FffYF99g331DfbVNwKtr92/t7t/j/cmaAJLS0sLACApKUnhSoiIiMhdLS0tiIyM7PV9lfQXaQKEzWbD5cuXMWLECKhUKq8d12q1IikpCRcuXEBERITXjjvUsa++wb76BvvqG+yrbwRaX0UELS0tSEhIQEhI7ytVgmaGJSQkBKNHj/bZ8SMiIgLiLz7QsK++wb76BvvqG+yrbwRSX/uaWenGRbdERETk9xhYiIiIyO8xsPRDp9Nh48aN0Ol0SpcSVNhX32BffYN99Q321TeCta9Bs+iWiIiIghdnWIiIiMjvMbAQERGR32NgISIiIr/HwEJERER+j4GlH9u2bcOYMWOg1+uRlZWF8vJypUsKGEVFRZg6dSpGjBiBuLg45Obm4syZM05jbt68ifz8fIwaNQrDhw/H448/DovFolDFgWnr1q1QqVRYtWqVYx/76plLly7hJz/5CUaNGoWwsDCkp6ejoqLC8b6IYMOGDYiPj0dYWBhycnJQW1urYMX+r6urC+vXr0dKSgrCwsJw9913Y/PmzU7PjWFf+/fJJ5/g4YcfRkJCAlQqFfbt2+f0vis9bGxsxOLFixEREYGoqCgsX74c165dG8RPMUBCvdq9e7dotVp5/fXX5YsvvpCnnnpKoqKixGKxKF1aQJgzZ47s3LlTampqpLq6Wh566CExmUxy7do1x5inn35akpKSpLi4WCoqKuT++++X6dOnK1h1YCkvL5cxY8bIfffdJwUFBY797Kv7GhsbJTk5WZYuXSplZWVSV1cnH330kXz11VeOMVu3bpXIyEjZt2+fnDhxQh555BFJSUmRGzduKFi5f9uyZYuMGjVK3n//fTl37pzs2bNHhg8fLi+++KJjDPvavwMHDsizzz4re/fuFQDyzjvvOL3vSg/nzp0rkyZNkqNHj8qnn34q99xzjyxatGiQP4nnGFj6MG3aNMnPz3f8uaurSxISEqSoqEjBqgJXQ0ODAJCPP/5YRESamppEo9HInj17HGO+/PJLASClpaVKlRkwWlpaZOzYsXLw4EGZOXOmI7Cwr5759a9/LQ888ECv79tsNjEajfLb3/7Wsa+pqUl0Op28+eabg1FiQJo/f7787Gc/c9r32GOPyeLFi0WEffXEdwOLKz08deqUAJBjx445xnzwwQeiUqnk0qVLg1b7QPCUUC/a29tRWVmJnJwcx76QkBDk5OSgtLRUwcoCV3NzMwAgOjoaAFBZWYmOjg6nHqempsJkMrHHLsjPz8f8+fOd+gewr5569913kZmZiYULFyIuLg6TJ0/Ga6+95nj/3LlzMJvNTn2NjIxEVlYW+9qH6dOno7i4GGfPngUAnDhxAiUlJZg3bx4A9tUbXOlhaWkpoqKikJmZ6RiTk5ODkJAQlJWVDXrNngiahx9629WrV9HV1QWDweC032Aw4PTp0wpVFbhsNhtWrVqFGTNmIC0tDQBgNpuh1WoRFRXlNNZgMMBsNitQZeDYvXs3jh8/jmPHjt3xHvvqmbq6Omzfvh2FhYVYt24djh07hpUrV0Kr1SIvL8/Ru55+JrCvvVuzZg2sVitSU1OhVqvR1dWFLVu2YPHixQDAvnqBKz00m82Ii4tzej80NBTR0dEB02cGFhoU+fn5qKmpQUlJidKlBLwLFy6goKAABw8ehF6vV7qcoGGz2ZCZmYnnnnsOADB58mTU1NTglVdeQV5ensLVBa63334bu3btwhtvvIGJEyeiuroaq1atQkJCAvtKbuEpoV7ExMRArVbfcWWFxWKB0WhUqKrAtGLFCrz//vs4fPgwRo8e7dhvNBrR3t6OpqYmp/Hscd8qKyvR0NCAKVOmIDQ0FKGhofj444/xhz/8AaGhoTAYDOyrB+Lj4zFhwgSnfePHj8f58+cBwNE7/kxwz+rVq7FmzRr8+Mc/Rnp6On7605/il7/8JYqKigCwr97gSg+NRiMaGhqc3u/s7ERjY2PA9JmBpRdarRYZGRkoLi527LPZbCguLkZ2draClQUOEcGKFSvwzjvv4NChQ0hJSXF6PyMjAxqNxqnHZ86cwfnz59njPsyaNQsnT55EdXW14yszMxOLFy92bLOv7psxY8Ydl92fPXsWycnJAICUlBQYjUanvlqtVpSVlbGvfWhtbUVIiPOvGrVaDZvNBoB99QZXepidnY2mpiZUVlY6xhw6dAg2mw1ZWVmDXrNHlF716892794tOp1O/vSnP8mpU6fk5z//uURFRYnZbFa6tIDwi1/8QiIjI+XIkSNSX1/v+GptbXWMefrpp8VkMsmhQ4ekoqJCsrOzJTs7W8GqA9PtVwmJsK+eKC8vl9DQUNmyZYvU1tbKrl27JDw8XP7yl784xmzdulWioqJk//798vnnn8uCBQt4+W0/8vLyJDEx0XFZ8969eyUmJkZ+9atfOcawr/1raWmRqqoqqaqqEgDywgsvSFVVlXz99dci4loP586dK5MnT5aysjIpKSmRsWPH8rLmYPLSSy+JyWQSrVYr06ZNk6NHjypdUsAA0OPXzp07HWNu3LghzzzzjIwcOVLCw8Pl0Ucflfr6euWKDlDfDSzsq2fee+89SUtLE51OJ6mpqbJjxw6n9202m6xfv14MBoPodDqZNWuWnDlzRqFqA4PVapWCggIxmUyi1+vlrrvukmeffVba2tocY9jX/h0+fLjHn6d5eXki4loPv/nmG1m0aJEMHz5cIiIiZNmyZdLS0qLAp/GMSuS22w0SERER+SGuYSEiIiK/x8BCREREfo+BhYiIiPweAwsRERH5PQYWIiIi8nsMLEREROT3GFiIiIjI7zGwEBERkd9jYCEiIiK/x8BCREREfo+BhYiIiPweAwsRERH5vf8HwAe0BZyt9xkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot training history\n",
    "plt.plot(model_history.history['prc'], label='train')\n",
    "plt.plot(model_history.history['val_prc'], label='test')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e65377c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot training history\n",
    "plt.plot(model_history.history['tp'], label='train')\n",
    "plt.plot(model_history.history['val_tp'], label='test')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "36ad3f42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2325/2325 [==============================] - 1s 512us/step\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5bdcab62",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fancy_confusion(cf_matrix, title):\n",
    "    group_counts = ['{0:0.0f}'.format(value) for value in\n",
    "                    cf_matrix.flatten()]\n",
    "    group_percentages = ['{0:.2%}'.format(value) for value in\n",
    "                         cf_matrix.flatten()/np.sum(cf_matrix)]\n",
    "\n",
    "    labels = [f\"{v1}\\n{v2}\" for v1, v2 in\n",
    "              zip(group_counts,group_percentages)]\n",
    "    labels = np.asarray(labels).reshape(2,2)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    sns.heatmap(cf_matrix, annot=labels,cmap='YlOrRd',fmt=\"\")\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Actual Class')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "da5cef5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perfect confusion matrix\n",
      "[[73341     0]\n",
      " [    0  1046]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Perfect confusion matrix\")\n",
    "perfect_cf_matrix = confusion_matrix(y_test, y_test > 0.5)\n",
    "print(perfect_cf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8bfc2a66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fancy_confusion(perfect_cf_matrix, 'Perfect Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7d60d080",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUPRC 0.12656107853789644\n",
      "AUROC 0.9000208056642505\n",
      "\n",
      "[[53578 19763]\n",
      " [  109   937]]\n"
     ]
    }
   ],
   "source": [
    "print('AUPRC',average_precision_score(y_test, y_test_pred))\n",
    "print('AUROC',roc_auc_score(y_test, y_test_pred))\n",
    "\n",
    "print()\n",
    "valid_cf_mat = confusion_matrix(y_test, y_test_pred > 0.5)\n",
    "print(valid_cf_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "38ac3e4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fancy_confusion(valid_cf_mat, 'Validation Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc1d2f0a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "print(classification_report(y_test, y_test_pred > 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "616cdcc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_precision_recall_vs_threshold(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1bf04bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_prc_and_roc(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3074858",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "get_scores_thresholds(X_test, y_test, model, algo=\"DNN\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f475642",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('thesis1_classifier')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6690cda2",
   "metadata": {},
   "source": [
    "### Re-fit on whole data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6fe022e",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = X.shape[1]  # (77862, 3)\n",
    "print('Input shape',n_inputs)\n",
    "model = make_model(n_inputs, metrics=METRICS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dcd9a83",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "EPOCHS=25 #200\n",
    "BATCH_SIZE=64# 2048\n",
    "final_history = model.fit(X, y,\n",
    "                          epochs=EPOCHS,\n",
    "                          batch_size=BATCH_SIZE,\n",
    "                          verbose=1,\n",
    "                          shuffle=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cf86b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot training history\n",
    "plt.plot(final_history.history['loss'], label='train')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a087e8c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.save('./Thesis/thesis4_classifier')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbaff835",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = keras.models.load_model('./sim_final_full')\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c46b0469",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67876974",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(confusion_matrix(y, y > 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8321728",
   "metadata": {},
   "outputs": [],
   "source": [
    "fancy_confusion(confusion_matrix(y, y > 0.5), 'Perfect Data Confusion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c5e5168",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(confusion_matrix(y, y_predict > 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0398ed4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fancy_confusion(confusion_matrix(y, y_predict > 0.5), ' Data Confusion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33e00aeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(confusion_matrix(y, y_predict > 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7d42a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "y.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef1404f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2565be30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79b49143",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1741545b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cac05f50",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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