22_nn_embed.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8816b366-5be4-4748-b769-4dc6f7d1735f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modify the file 00_setup.py to define input/output file paths on your system\n",
    "# The information in 00_setup.py will be used across notebooks\n",
    "from importlib.machinery import SourceFileLoader\n",
    "setup = SourceFileLoader(\"setup\", \"./00_setup.py\").load_module()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a945de0c-f042-46b2-9d06-ab064af6c05b",
   "metadata": {},
   "source": [
    "## Neural Network With NAICS Embedding Layer\n",
    "Add NAICS embedding to model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47a7ec9-7d7b-47df-a902-b2f1e54f1135",
   "metadata": {},
   "source": [
    "*This script takes about 2 hours on my MacBook Air*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb42f9b4-99a8-4753-aacf-8722469be4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf51170a-4487-4d10-b63d-c88f41b529f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import importlib, pickle\n",
    "from sklearn import model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "09dd973b-60a0-434b-9193-8c5fcec27dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import re\n",
    "\n",
    "import keras\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22b466e7-a419-45aa-83f7-5dafc7a3998b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense, Dropout, Input, Embedding, Concatenate, Reshape\n",
    "import tensorflow.keras.metrics as km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f1e4f2e-0e45-4e28-8e9c-212e0bb51199",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sba_gnn.sba_gnn import sg_plot "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cbc00d2-bc83-42d9-83eb-65fcf306dccd",
   "metadata": {},
   "source": [
    "## Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f0183dc-7ee1-49eb-8723-a9f4cea14b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "business_data = pd.read_parquet(Path(setup.temp_path).joinpath('20_DATA_combined_scaled_all.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "feaedcfe-7722-41c1-be4c-4921f0d11db9",
   "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>target</th>\n",
       "      <th>dset_naics_holdout</th>\n",
       "      <th>mhier_NAICS</th>\n",
       "      <th>menc_NAICS</th>\n",
       "      <th>cenc_NAICS</th>\n",
       "      <th>NS___Accommodation and Food Services</th>\n",
       "      <th>NS___Administrative and Support and Waste Management and Remediation Services</th>\n",
       "      <th>NS___Construction</th>\n",
       "      <th>NS___Health Care and Social Assistance</th>\n",
       "      <th>NS___Manufacturing</th>\n",
       "      <th>...</th>\n",
       "      <th>new_business</th>\n",
       "      <th>urban_flag</th>\n",
       "      <th>franchise_flag</th>\n",
       "      <th>missingindicator_LowDoc</th>\n",
       "      <th>missingindicator_new_business</th>\n",
       "      <th>missingindicator_urban_flag</th>\n",
       "      <th>NAICS</th>\n",
       "      <th>NAICS_alt</th>\n",
       "      <th>NAICS_alt3</th>\n",
       "      <th>NAICS_alt4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.203011</td>\n",
       "      <td>0.094541</td>\n",
       "      <td>0.203906</td>\n",
       "      <td>0.205152</td>\n",
       "      <td>0.007265</td>\n",
       "      <td>0.097958</td>\n",
       "      <td>0.047133</td>\n",
       "      <td>0.095606</td>\n",
       "      <td>0.079398</td>\n",
       "      <td>0.096843</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.444129</td>\n",
       "      <td>0.705279</td>\n",
       "      <td>-0.899352</td>\n",
       "      <td>-0.985316</td>\n",
       "      <td>-0.997529</td>\n",
       "      <td>-0.602849</td>\n",
       "      <td>701.045842</td>\n",
       "      <td>767.921227</td>\n",
       "      <td>769.528095</td>\n",
       "      <td>769.233577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.402241</td>\n",
       "      <td>0.292580</td>\n",
       "      <td>0.095690</td>\n",
       "      <td>0.091362</td>\n",
       "      <td>0.010443</td>\n",
       "      <td>0.297258</td>\n",
       "      <td>0.211922</td>\n",
       "      <td>0.294051</td>\n",
       "      <td>0.270359</td>\n",
       "      <td>0.295744</td>\n",
       "      <td>...</td>\n",
       "      <td>0.895963</td>\n",
       "      <td>0.708930</td>\n",
       "      <td>0.437226</td>\n",
       "      <td>0.170743</td>\n",
       "      <td>0.070251</td>\n",
       "      <td>0.797856</td>\n",
       "      <td>383.284385</td>\n",
       "      <td>326.131581</td>\n",
       "      <td>324.361889</td>\n",
       "      <td>326.453771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012987</td>\n",
       "      <td>0.020495</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.126003</td>\n",
       "      <td>0.137475</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>436.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>644.000000</td>\n",
       "      <td>638.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.205592</td>\n",
       "      <td>0.204655</td>\n",
       "      <td>0.003056</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>844.000000</td>\n",
       "      <td>825.000000</td>\n",
       "      <td>825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.264286</td>\n",
       "      <td>0.254574</td>\n",
       "      <td>0.009309</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1031.000000</td>\n",
       "      <td>1034.000000</td>\n",
       "      <td>1034.000000</td>\n",
       "      <td>1034.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.572148</td>\n",
       "      <td>0.551324</td>\n",
       "      <td>0.044873</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              target  dset_naics_holdout    mhier_NAICS     menc_NAICS  \\\n",
       "count  688081.000000       688081.000000  688081.000000  688081.000000   \n",
       "mean        0.203011            0.094541       0.203906       0.205152   \n",
       "std         0.402241            0.292580       0.095690       0.091362   \n",
       "min         0.000000            0.000000       0.012987       0.020495   \n",
       "25%         0.000000            0.000000       0.126003       0.137475   \n",
       "50%         0.000000            0.000000       0.205592       0.204655   \n",
       "75%         0.000000            0.000000       0.264286       0.254574   \n",
       "max         1.000000            1.000000       0.572148       0.551324   \n",
       "\n",
       "          cenc_NAICS  NS___Accommodation and Food Services  \\\n",
       "count  688081.000000                         688081.000000   \n",
       "mean        0.007265                              0.097958   \n",
       "std         0.010443                              0.297258   \n",
       "min         0.000000                              0.000000   \n",
       "25%         0.000718                              0.000000   \n",
       "50%         0.003056                              0.000000   \n",
       "75%         0.009309                              0.000000   \n",
       "max         0.044873                              1.000000   \n",
       "\n",
       "       NS___Administrative and Support and Waste Management and Remediation Services  \\\n",
       "count                                      688081.000000                               \n",
       "mean                                            0.047133                               \n",
       "std                                             0.211922                               \n",
       "min                                             0.000000                               \n",
       "25%                                             0.000000                               \n",
       "50%                                             0.000000                               \n",
       "75%                                             0.000000                               \n",
       "max                                             1.000000                               \n",
       "\n",
       "       NS___Construction  NS___Health Care and Social Assistance  \\\n",
       "count      688081.000000                           688081.000000   \n",
       "mean            0.095606                                0.079398   \n",
       "std             0.294051                                0.270359   \n",
       "min             0.000000                                0.000000   \n",
       "25%             0.000000                                0.000000   \n",
       "50%             0.000000                                0.000000   \n",
       "75%             0.000000                                0.000000   \n",
       "max             1.000000                                1.000000   \n",
       "\n",
       "       NS___Manufacturing  ...   new_business     urban_flag  franchise_flag  \\\n",
       "count       688081.000000  ...  688081.000000  688081.000000   688081.000000   \n",
       "mean             0.096843  ...      -0.444129       0.705279       -0.899352   \n",
       "std              0.295744  ...       0.895963       0.708930        0.437226   \n",
       "min              0.000000  ...      -1.000000      -1.000000       -1.000000   \n",
       "25%              0.000000  ...      -1.000000       1.000000       -1.000000   \n",
       "50%              0.000000  ...      -1.000000       1.000000       -1.000000   \n",
       "75%              0.000000  ...       1.000000       1.000000       -1.000000   \n",
       "max              1.000000  ...       1.000000       1.000000        1.000000   \n",
       "\n",
       "       missingindicator_LowDoc  missingindicator_new_business  \\\n",
       "count            688081.000000                  688081.000000   \n",
       "mean                 -0.985316                      -0.997529   \n",
       "std                   0.170743                       0.070251   \n",
       "min                  -1.000000                      -1.000000   \n",
       "25%                  -1.000000                      -1.000000   \n",
       "50%                  -1.000000                      -1.000000   \n",
       "75%                  -1.000000                      -1.000000   \n",
       "max                   1.000000                       1.000000   \n",
       "\n",
       "       missingindicator_urban_flag          NAICS      NAICS_alt  \\\n",
       "count                688081.000000  688081.000000  688081.000000   \n",
       "mean                     -0.602849     701.045842     767.921227   \n",
       "std                       0.797856     383.284385     326.131581   \n",
       "min                      -1.000000       0.000000       1.000000   \n",
       "25%                      -1.000000     436.000000     651.000000   \n",
       "50%                      -1.000000     756.000000     844.000000   \n",
       "75%                      -1.000000    1031.000000    1034.000000   \n",
       "max                       1.000000    1166.000000    1166.000000   \n",
       "\n",
       "          NAICS_alt3     NAICS_alt4  \n",
       "count  688081.000000  688081.000000  \n",
       "mean      769.528095     769.233577  \n",
       "std       324.361889     326.453771  \n",
       "min         0.000000       0.000000  \n",
       "25%       644.000000     638.000000  \n",
       "50%       825.000000     825.000000  \n",
       "75%      1034.000000    1034.000000  \n",
       "max      1166.000000    1166.000000  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f0ce28e-a80c-415b-b068-5c4d49370da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1166"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(Path(setup.temp_path).joinpath('20_DATA_naics_max_levels.pkl'), 'rb') as fin:\n",
    "    naics_max_levels= pickle.load(fin)\n",
    "naics_max_levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "555f4031-e66d-4476-a37e-50e2d382ecf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('20_DATA_features.pkl'), 'rb') as fin:\n",
    "    imputer_features = pickle.load(fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3a916e26-2419-4bd0-8fd4-58a959dc4bac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NoEmp', 'CreateJob', 'LowDoc', 'DisbursementGross', 'new_business', 'urban_flag', 'franchise_flag', 'missingindicator_LowDoc', 'missingindicator_new_business', 'missingindicator_urban_flag']\n"
     ]
    }
   ],
   "source": [
    "numeric_features = [f for f in imputer_features if 'NAICS' not in f]\n",
    "print(numeric_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d94006a0-f010-4874-a7f1-0fd80c1e9a35",
   "metadata": {},
   "source": [
    "##### Datasets for train, validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a02a909d-834a-46a8-9cdd-ccfa36b07d69",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = business_data[['dset', 'LoanNr_ChkDgt'] + numeric_features].set_index('LoanNr_ChkDgt').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "42dc5c92-f5a2-457c-be3a-60cb0f6c2ef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training X: (436120, 10), y:(436120,)\n"
     ]
    }
   ],
   "source": [
    "X_train = X[X['dset'] == 'train'].drop(columns='dset')\n",
    "y_train = business_data[business_data['dset'] == 'train'].set_index('LoanNr_ChkDgt').sort_index()['target']\n",
    "print(f'training X: {X_train.shape}, y:{y_train.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "038b0e4f-a84a-4c8b-863e-4c594c1c9f32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val X: (93454, 10), y:(93454,)\n"
     ]
    }
   ],
   "source": [
    "X_val = X[X['dset'] == 'val'].drop(columns='dset')\n",
    "y_val = business_data[business_data['dset'] == 'val'].set_index('LoanNr_ChkDgt').sort_index()['target']\n",
    "print(f'val X: {X_val.shape}, y:{y_val.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bcb86909-e8a7-4f22-b089-6ca61dd86c73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2046546821975603\n"
     ]
    }
   ],
   "source": [
    "base_thresh = y_train.mean()\n",
    "print(base_thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1b8ce4c9-6907-4b70-982a-b92a52b3159d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_naics = business_data[['dset', 'LoanNr_ChkDgt', 'NAICS']].set_index('LoanNr_ChkDgt').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a6f129e0-a4d0-4d93-9886-153c9803d403",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_naics_train = X_naics[X_naics['dset'] == 'train'].drop(columns='dset')\n",
    "X_naics_val = X_naics[X_naics['dset'] == 'val'].drop(columns='dset')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c68623-4599-4472-a13a-9400d101633e",
   "metadata": {},
   "source": [
    "## Function to create model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9f4deffd-2b85-43d0-9cd1-1d9cf707b9e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(n_feat = len(numeric_features), naics_max_levels = naics_max_levels,\n",
    "                 naics_emd_dim = setup.nn_naics_embed_size,\n",
    "                 hidden_size = setup.nn_layer_sizes,\n",
    "                 activation='tanh', lr=setup.nn_learning_rate,\n",
    "                 opt_func = setup.nn_optimizer, dropout = setup.nn_dropout,\n",
    "                embed_layer_name = 'naics_reshape'):\n",
    "    \n",
    "    n_layers = len(hidden_size)\n",
    "    \n",
    "    features_in = Input(shape=(n_feat,))\n",
    "    naics_in = Input(shape=(1,))\n",
    "    embed_layer = Embedding(naics_max_levels, output_dim=naics_emd_dim, input_length=1)(naics_in)\n",
    "    embed_layer = Reshape(target_shape=(naics_emd_dim,), name=embed_layer_name)(embed_layer)\n",
    "    x = Concatenate(axis=-1)([features_in, embed_layer])\n",
    "    \n",
    "    for i in range(n_layers):\n",
    "        x = Dense(hidden_size[i],activation=activation,\n",
    "                  name=f'layer_{i:02d}')(x)\n",
    "        x = Dropout(dropout, name=f'dropout_{i:02d}')(x)\n",
    "    output = Dense(1, activation='sigmoid')(x)\n",
    "    \n",
    "    model = Model(inputs=[features_in, naics_in], outputs=output)\n",
    "    \n",
    "    # Compile model\n",
    "    optimizer = opt_func(learning_rate=lr)\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, \n",
    "                  metrics=[km.AUC(curve='PR'), km.AUC(curve='ROC')])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db4ea1c-77b2-4721-b106-1e66fc502139",
   "metadata": {},
   "source": [
    "## Create, fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4d48ddfa-580c-4321-9917-0bd176b67d87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-29 06:31:01.582383: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
      "2024-03-29 06:31:01.582409: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n",
      "2024-03-29 06:31:01.582415: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n",
      "2024-03-29 06:31:01.582452: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:303] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2024-03-29 06:31:01.582472: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:269] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "this_model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "47036621-ef13-4957-997b-f8ef86016f7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_2 (InputLayer)        [(None, 1)]                  0         []                            \n",
      "                                                                                                  \n",
      " embedding (Embedding)       (None, 1, 8)                 9328      ['input_2[0][0]']             \n",
      "                                                                                                  \n",
      " input_1 (InputLayer)        [(None, 10)]                 0         []                            \n",
      "                                                                                                  \n",
      " naics_reshape (Reshape)     (None, 8)                    0         ['embedding[0][0]']           \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)   (None, 18)                   0         ['input_1[0][0]',             \n",
      "                                                                     'naics_reshape[0][0]']       \n",
      "                                                                                                  \n",
      " layer_00 (Dense)            (None, 128)                  2432      ['concatenate[0][0]']         \n",
      "                                                                                                  \n",
      " dropout_00 (Dropout)        (None, 128)                  0         ['layer_00[0][0]']            \n",
      "                                                                                                  \n",
      " layer_01 (Dense)            (None, 64)                   8256      ['dropout_00[0][0]']          \n",
      "                                                                                                  \n",
      " dropout_01 (Dropout)        (None, 64)                   0         ['layer_01[0][0]']            \n",
      "                                                                                                  \n",
      " dense (Dense)               (None, 1)                    65        ['dropout_01[0][0]']          \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 20081 (78.44 KB)\n",
      "Trainable params: 20081 (78.44 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "this_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "31772016-3643-468b-8d65-c456168cad6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-29 06:31:02.481227: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13629/13629 [==============================] - ETA: 0s - loss: 0.4657 - auc: 0.3377 - auc_1: 0.6984"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-29 06:34:43.642810: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13629/13629 [==============================] - 245s 18ms/step - loss: 0.4657 - auc: 0.3377 - auc_1: 0.6984 - val_loss: 0.4599 - val_auc: 0.3687 - val_auc_1: 0.7176\n",
      "Epoch 2/20\n",
      "13629/13629 [==============================] - 214s 16ms/step - loss: 0.4569 - auc: 0.3612 - auc_1: 0.7169 - val_loss: 0.4592 - val_auc: 0.3740 - val_auc_1: 0.7216\n",
      "Epoch 3/20\n",
      "13629/13629 [==============================] - 213s 16ms/step - loss: 0.4548 - auc: 0.3682 - auc_1: 0.7215 - val_loss: 0.4567 - val_auc: 0.3802 - val_auc_1: 0.7246\n",
      "Epoch 4/20\n",
      "13629/13629 [==============================] - 1161s 85ms/step - loss: 0.4537 - auc: 0.3712 - auc_1: 0.7238 - val_loss: 0.4562 - val_auc: 0.3817 - val_auc_1: 0.7252\n",
      "Epoch 5/20\n",
      "13629/13629 [==============================] - 765s 56ms/step - loss: 0.4526 - auc: 0.3755 - auc_1: 0.7259 - val_loss: 0.4575 - val_auc: 0.3794 - val_auc_1: 0.7231\n",
      "Epoch 6/20\n",
      "13629/13629 [==============================] - 1154s 85ms/step - loss: 0.4518 - auc: 0.3770 - auc_1: 0.7275 - val_loss: 0.4562 - val_auc: 0.3826 - val_auc_1: 0.7253\n",
      "Epoch 7/20\n",
      "13629/13629 [==============================] - 1103s 81ms/step - loss: 0.4514 - auc: 0.3792 - auc_1: 0.7282 - val_loss: 0.4560 - val_auc: 0.3850 - val_auc_1: 0.7267\n",
      "Epoch 8/20\n",
      "13629/13629 [==============================] - 1103s 81ms/step - loss: 0.4510 - auc: 0.3800 - auc_1: 0.7289 - val_loss: 0.4554 - val_auc: 0.3841 - val_auc_1: 0.7265\n",
      "Epoch 9/20\n",
      "13629/13629 [==============================] - 204s 15ms/step - loss: 0.4506 - auc: 0.3819 - auc_1: 0.7297 - val_loss: 0.4554 - val_auc: 0.3851 - val_auc_1: 0.7271\n",
      "Epoch 10/20\n",
      "13629/13629 [==============================] - 205s 15ms/step - loss: 0.4504 - auc: 0.3826 - auc_1: 0.7301 - val_loss: 0.4553 - val_auc: 0.3862 - val_auc_1: 0.7268\n",
      "Epoch 11/20\n",
      "13629/13629 [==============================] - 2004s 147ms/step - loss: 0.4501 - auc: 0.3829 - auc_1: 0.7307 - val_loss: 0.4553 - val_auc: 0.3860 - val_auc_1: 0.7271\n",
      "Epoch 12/20\n",
      "13629/13629 [==============================] - 205s 15ms/step - loss: 0.4497 - auc: 0.3839 - auc_1: 0.7316 - val_loss: 0.4553 - val_auc: 0.3849 - val_auc_1: 0.7263\n",
      "Epoch 13/20\n",
      "13629/13629 [==============================] - 1352s 99ms/step - loss: 0.4496 - auc: 0.3840 - auc_1: 0.7318 - val_loss: 0.4561 - val_auc: 0.3873 - val_auc_1: 0.7275\n",
      "Epoch 14/20\n",
      "13629/13629 [==============================] - 205s 15ms/step - loss: 0.4491 - auc: 0.3861 - auc_1: 0.7325 - val_loss: 0.4561 - val_auc: 0.3874 - val_auc_1: 0.7275\n",
      "Epoch 15/20\n",
      "13629/13629 [==============================] - 1104s 81ms/step - loss: 0.4490 - auc: 0.3866 - auc_1: 0.7330 - val_loss: 0.4554 - val_auc: 0.3871 - val_auc_1: 0.7275\n",
      "Epoch 16/20\n",
      "13629/13629 [==============================] - 206s 15ms/step - loss: 0.4485 - auc: 0.3878 - auc_1: 0.7337 - val_loss: 0.4549 - val_auc: 0.3876 - val_auc_1: 0.7275\n",
      "Epoch 17/20\n",
      "13629/13629 [==============================] - 213s 16ms/step - loss: 0.4485 - auc: 0.3882 - auc_1: 0.7339 - val_loss: 0.4562 - val_auc: 0.3881 - val_auc_1: 0.7269\n",
      "Epoch 18/20\n",
      "13629/13629 [==============================] - 214s 16ms/step - loss: 0.4481 - auc: 0.3896 - auc_1: 0.7346 - val_loss: 0.4546 - val_auc: 0.3888 - val_auc_1: 0.7281\n",
      "Epoch 19/20\n",
      "13629/13629 [==============================] - 189s 14ms/step - loss: 0.4481 - auc: 0.3891 - auc_1: 0.7345 - val_loss: 0.4551 - val_auc: 0.3882 - val_auc_1: 0.7271\n",
      "Epoch 20/20\n",
      "13629/13629 [==============================] - 187s 14ms/step - loss: 0.4480 - auc: 0.3899 - auc_1: 0.7346 - val_loss: 0.4552 - val_auc: 0.3894 - val_auc_1: 0.7277\n"
     ]
    }
   ],
   "source": [
    "this_history = this_model.fit([X_train, X_naics_train], y_train,\n",
    "                              validation_data=([X_val, X_naics_val], y_val),\n",
    "                              batch_size=setup.nn_batch_size,\n",
    "                              epochs=setup.nn_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "460c4b43-c673-4ee1-b070-d2d596ac4561",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_history_df = pd.DataFrame(this_history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c85bd09a-2fa9-4255-958b-4d48b17085ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename columns\n",
    "try:\n",
    "    this_history_df.columns = ['_'.join(c.split('_')[0:-1])  \\\n",
    "                               if re.search(r'_\\d+$', c) else c for c in this_history_df.columns]\n",
    "except:\n",
    "    pass\n",
    "try:\n",
    "    cur_col = list(this_history_df.columns)\n",
    "    this_history_df.columns = [cur_col[0]] + \\\n",
    "        [f'{cur_col[i]}_roc'  if (cur_col[i] == cur_col[i-1]) and 'auc'in cur_col[i] \\\n",
    "         else cur_col[i] for i in range(1, len(cur_col))]\n",
    "except:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8ea25384-9fc0-4916-a577-4bd9bc9b203f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loss', 'auc', 'auc_roc', 'val_loss', 'val_auc', 'val_auc_roc'], dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_history_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ad4783f2-8d0b-4850-a45a-3a4be8e63318",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_history_df.to_csv(Path(setup.temp_path).joinpath('22_REPORT_fit_history.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "73e89efd-75a9-4b27-81a8-13e12d5482be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "this_history_df[['loss', 'val_loss']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2342a31f-4891-4834-851a-b90b8b729c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aQ0w3sx9Ek+cHQ06EOfBZS1omqsvN4yneafa5OPi4ZCdGcQ628nwAbEDPhgWAw7qKGHZfbGE9zdtww3uZt/h2TTz0MyhCrSSdgAKLiIjVKvbCp/fDz/899nY2Bzh8we5r9q+w+xz22Nd87vjFz8bHDc9tNvP22YoiqNxrLkY9VJeay/7tLavZ7mPe0tsYYhoWh58ZSkp2moOWHdjf/OE0/Kw0/Mk1IsmzRUFYPN16JNM7OYWg6D4QFo+tS4z7O7uKx1Fg6cASExO59957uffee60uRUSas/4j+Pj/oKLQDCRn3AsjbjYv0dgdDYHksLDR1pxOqC6Byn2HhZiGnxVF5vom6/ZCTZl5iamiwFyO9ysCwin1j2V7XTfWlIeyvS6CXCOKXUYkFQHdGT4gmbEnd+fMkyKbzhwschgFFhERK1Tug3n3w8/vmc+jUmDiLHN8D3ey2835awK7Qreklr2nrvpQ60yTYFMEdVUQFk+xXwzfFAbywXYHX26ros556BJXXFgA5w+MZfLAGEYmRjSdKFCkGQosIiLutv7jhlaVAnMk0tPvhTEPWtfx1VU+/uadNw2jphqGwb6KGrbvreCH7ftZ8EMeP+4sxjBqGt+SHN2FsQNjGDswlkE9wo4+g7DIMXTOwGIYUFtpze/2DWpRs+7LL7/MI488Qm5uLvbDrt1ecskldOvWjYceeoiMjAyWLVtGRUUF/fv3JzMzk/T09FaV9cwzz/Daa6+xdetWIiIiuOiii3jqqafo0qULAI888gj/+9//WLVqVeN7ZsyYwYwZM9i+fXvjuldffZWnn36azZs3ExERweWXX84LL7zQqppEOpzKffDp72HNu+bzyH5mq0rP4dbW1UIV1XVsK6posmwtqmBbYTmlVXVHbD8kPrwxpCRFdbGgYulIOmdgqa2EP1k0n8IfdoNf8HE3+9WvfsVdd93Fl19+ybnnngvAvn37mD9/PvPmzaO8vJwLLriAJ554An9/f15//XUuuugisrOz6dWrl8tl2e12nnvuOXr37s3WrVu5/fbb+f3vf8+LL77Y4n3MmjWLjIwM/vznPzN+/HhKSkpYsmSJy7WIdEgbPoGP7j3UqnLa3TBmqsfdcltT5yRnX2VDICk3Q0lhBdv3VpBfWt3s+2w2iAsL5KSYLpyTEs35A2KJDfOsYxPv1jkDixfo2rUr48eP5z//+U9jYHnvvfeIjIzk7LPPxm63M2TIkMbtH3vsMd5//30+/PBD7rzzTpd/3+EdcxMTE3n88ce59dZbXQosjz/+OPfddx/33HNP47oRI0a4XIt0cMU5kLPMfOwX3LB0MX/6Bh167OPfMW5VrdwH8x+E1W+bzyP7NrSqpFpWktNpsLvkwKFWkoZAsq2ogp37KnEe447qbsF+9I4MNpeoYPpEBtM7sgsJ3YLUYVbaVecMLL5BZkuHVb+7ha699lpuvvlmXnzxRfz9/XnjjTe46qqrsNvtlJeX88gjj/DJJ5+wZ88e6urqOHDgADk5Oa0q6/PPPyczM5MNGzZQWlpKXV0dVVVVVFZWEhR0/JoLCgrYvXt3Y7gSaVRbBTuWwObPzaVoY8veZ3M0hJegYweboy1B3aDnSPO9Vsr+FD66B8rzzVaVUXfC2Q9Z0qpS7zR4b8VO/vXtDrYWllNd1/xgbMF+DnpHmUGkd+TBUBJMYmQwYYG+bqxa5JDOGVhsthZdlrHaRRddhGEYfPLJJ4wYMYKvv/6av/3tbwD87ne/Y+HChfz1r38lOTmZwMBArrjiCmpqao6z1yNt376dCy+8kNtuu40nnniCiIgIvvnmG2688UZqamoICgrCbrdjGE3/7KqtrW18HBgYeGIHKx3L3i2HAsq2r6HuwKHXbA7zThjfIKipMC/R1pSbj2sqzLtMoGFskBJzaQ2fQHOyuL7jzCUk5sSPq6UO7IdPH4TVb5nPu51ktqrEW9Pi+N3Wvfzx43Ws3V3auM7XYSOhW3BjIEmMPPQ4KsRfnWLF43TOwOIlAgICuOyyy3jjjTfYvHkz/fr1Y9gw85bHJUuWcP3113PppZcCUF5e3qTzqytWrFiB0+nk6aefbuzg+8477zTZJioqiry8PAzDaPwf2eEdcENCQkhMTCQrK4uzzz4b6WRqKsxgcjCk7N/W9PWQOEg+F5LToc8YCAxvfl/1dVBbATWVDSHmsDBTW3Ho8eHrf7ns3waluyB7nrkA9BgO/cZD3/HmBHft9YWcPb+hVSUPsMFpB1tV3B/qd+6rJPPT9cxbkwdASIAPd59zEmMHxhIXHqDbicWrKLB4uGuvvZYLL7yQtWvX8utf/7px/UknncTcuXO56KKLsNlsPPzwwzidx5lvoxnJycnU1tby/PPPc9FFF7FkyRJeeumlJtuMGTOGwsJCnnrqKa644grmz5/Pp59+SmhoaOM2jzzyCLfeeivR0dGMHz+esrIylixZwl133dW6gxfPZRhQmA2bF5oBZce3UH9Y657dFxJGmQElOR2iB7Q8IDh8wBFmzndzIvXlrYGN883AsvtH2LXCXL54HMJ6Qb9xZoBJOAN8/Fr/uw46sB/mT4Wf3jSfd0tuaFUZeeL7dlFFdR2zFm3h5a+3UlPnxG6Dq0b24r7z+tKti5fcOi3yCwosHu6cc84hIiKC7Oxsrrnmmsb1zzzzDDfccAOnnXYakZGRPPDAA5SWlh5jT80bMmQIzzzzDE8++SRTp05l9OjRZGZmct111zVu079/f1588UX+9Kc/8dhjj3H55Zfzu9/9jpdffrlxm8mTJ1NVVcXf/vY3fve73xEZGckVV1zR+oMXz1JVCtsWw6aFsDkLSnObvh7eC5LPMwNK7zPBP8SaOsEMR90Hm8tZv4fSPWZ42Tgfti6CkhxY/rK5+IWYrT/9xsNJ55vz0rhq42fw0d1Qtgewwag74Jz/5/ZWFafTYO6Pu3hq/gYKysw7ek5L6sbDFw6gf/fQ47xbxLPZjF92TPBCpaWlhIWFUVJS0uQvfoCqqiq2bdtG7969CQjQLXaeROfGwx1spTh4mWfnd+Zw7Ac5/CHxDDipIaR0S/aOu3pqKs3Qkj0PNi5oOrS8zQ7xp5rhpd94iDzp2Ps6UAwL/gCr3jCfRyTBxBeh16ntVX2zVuzYxx8/WsdPuWafn14RQTw0oT/nD4hRfxTxWMf6/v4ltbCIyCEVe2Hrl7DlCzOkNMyk26hbcsNlnvMg4TTr78JpDb8gSLnAXJxO2L3SvJtn43zI/xlyvjWXhQ+bx9t3HPS7AOLTzMtVB21aCB/eDWW7ARucervZquLmf5PdxQf486cb+PAn887HLv4+3HlOMlNOT8TfR7cZS8ehwNIJvPHGG/z2t7896msJCQmsXbvWzRV1cIZhzrq74RPzL/Rep5q32Pp74Eif9bWwczlsyTJDyu5VwGGNrr7B0Hv0oQ6zEb2tqrR92O3meCg9U+Hch2H/DrPVJXsebP8G9m6GpS+YS2BX85JR37Hmv9WPc8x9RPSBS140++y40YGael5avIW/f7WFqlonNhtcOTye+8b2JTpELZbS8SiwdAIXX3wxaWlpR33N11djKrSp4p3wSQZs+qzpepsDYgeZrRK9ToVeo6BLtDU17ttq9kHZ8oV5Z09NWdPXY06GpHPMkNJrlPfMb9MWuiZA2i3mUlVqBrns+bBpgdmpdvXbhwaAwwan3gbnPOzWVhXDMPjwp938+dMN7CkxbwEfmRjBtIsGcHKPE+ioLOLhFFg6gZCQEEJCLOwA2Rk46+H7f0LWo+bttg4/SL0RDuyDnKXm6K57VpnLsobRgyOSzECQMMr8GdGnffqAVJXC9q8bQkoW7N/e9PWgSHO8kqRzzZ8hsW1fgzcKCIWBl5pLfR3kLjdbXjYtNMeQGfuEGUDd6KedxTz60VpW5hQD0CM8kD9c0J8LBsWqn4p0eJ0msHSAvsUdToc5JwXrzb4MucvN5/GnwsXPQVS/Q9uU7DKDS85Sc1j6/LWwb4u5rGq4tBAcfaj1JWEUxAxq2meipZxOMxhtyYLNX5h1Hd5Z1u5j1ph8jhlSYgebl0akeQ4fM5wknAbnP+72X59fWsWT8zcwd+UuAIL8HNw+Jombzuyj4fCl0+jwgeXgJY/KykqNxuphKivNGbO99rJUXTV8/Qx8/TQ4a83bY897BIbfcGQACOsBg64wFzDvLtm5/FCI2bXCvFtl/YfmAmb/kfgR0KvhMlLP1OZHaC7dY17i2ZIFW740W3YOF5F06DJP4hnW3nIsLVZVW88/vt7Ki4u2UFlTD8Blw3rwwLgUYkLVT0U6lw4fWBwOB+Hh4RQUmLcuBgUFqenUYoZhUFlZSUFBAeHh4TgcXvgXYs538OFdUJRtPu87Hib8FcJ6tuz9geHQ93xzAXO+nT2rzAHYcpbBzmVQVWLefrt1kbmN3Qe6DzFbYHqNMuej2dJwR0/Buqb79w81O8seDCldE0/4kMV9DMNg3po8/jRvPbuKzWkNhvUKZ9pFAxkaH25tcSIW6fDjsID54c/Ly6O4uNj9xUmzwsPDiY31smvv1WXw+aPw/T8AA4KjYPxTZj+HtjwOpxMK15utLzsaWmFKdx3jDTZzfp6kc82Q0jMVHF7actWBOJ0G9YZBvdOgzmn+NB87cTqhzulsXHdwm/0VNcz4fBPLt5utZN3DAnhwfAoXD4nzrs+KSAu4Mg5LpwgsB9XX1zeZsE+s4+vr630tK9nzzTuADgaHob+G8x9r3ciorVG889AlpB1LzUkDe59pBpQ+Z7uvjk7IMAyy88tYnF3IV5sKyd1/gLr6hqBxMJDUO5s+dxqcyP9dA3zt3HpWEr8dnUSgn5d9VkRaSAPHNcPhcHjfl6RYr7wQ5j9gjq0C5uWVC2eYd9S4U3i8uQy+0r2/t5Mqqazlm81FLN5YwOKNheSXVrfZvm028LHbcNhtOGzmTx+HHbvNho/dxmlJ3fjd2H7EhavfnchBnSqwiLjEMMyJ7Bb8wRyDw2aHUXfCmKneOcKrHJPTabBmVwmLNxayeGMhP+bsx3lYC0mAr51Rfboxum8Ug3qE4euwm4HDbjsUPhqf23/x/LDXbTbsdl3aEXGVAovI0ezfDh/daw5TD+agbxc/D3GnWFmVtLHCsmq+3mQGlK83FbGvoqbJ6ydFd+GsvlGc1S+KEYkRuoVYxEIKLCKHq6+D716CL58w+4j4BMCYB82WFXVi9Xq19U5+zCluvMzz866mM5yH+PtwenIkZ/WLYnTfKHrokoyIx1BgETkob415q/LuH83niWfCRc9CtyRr65ITsqv4AF9tLGRxdiFLNhdRVl3X5PWTe4SarSh9ozmlVzi+Dg2iJ+KJFFhEag/A4qdgybNg1IN/GIx9HE75TfsMlS/twuk0KKuqo+RALdv3VpghZWMhmwrKm2zXNciX0X2jOKtvFGeeFEVUSCeaK0nEiymwSOe2/RtzWP19W8zn/S+GC/6i+XQsUlvvpPRALSUNS2lDACk5UNu4/vDXD3+trLruqLcR221wSq+uDa0oUZzcIwyHOr2KeB0FFumcaipg/lRY+W/zeUh3uOCv0P9Ca+vqwKpq61m/p5TVuSWs31PK3oqaxrBxMIRUNAw/fyICfO1EdvHntKRunNU3mjOSIwkLUv8jEW+nwCKdT3kB/OfKQ31Vhk+B8x6FgDBr6+pAauudbMwvY01uCT/llrA6t5jsvDLqnC0bSa2Lvw9hgb6EBvoSFuhDaIAvYYGHltCjPA4NNN/j76M7eUQ6IgUW6VyKNsOcy6B4BwRGwJX/NufckVZzOg22FlWwOreY1Q3hZO3uUqrrnEds2y3Yj8E9wxjUI4yYsAAzaPwijIQE+OCjjq8i8gsKLNJ55HwHb15lzmTcNRGu/S9EJltdlVcxDIPc/Qf4Kbe4ofWkmJ93lVL+iztvwLxFeFDPMAb3DGdIzzAG9QyjR3ig5sMRkVZRYJHOYd2HMPdmqKuCuGFwzTvQJcrqqjxefmlVY6vJT7klrMktZn/lkfNxBfjaOTmuIZzEmy0oid2CNaKriLQZBRbp+JbNMjvYYkDf8XDFP8Ev2OqqPNba3SW8/NVWlm3de9T5c3wdNvp3D2VwzzAG9whncHwYyVFddBlHRNqVAou4zjBg22JY/gqU5EL6dHPGYE/jdMJn/w+WzTSfp94I458Ch/6zP5o1uSU8m7WJz9fnN66z26BvTAiDeoQxON68tNMvNkQdW0XE7fR/bmm56jL46S1Y/jIUbTy0fvalMOQaGPsEBEVYV9/haqvg/d/Cuv+Zz8+dDmf8nwaCO4pVO4t5LmsTX2woAMyQctGQOK4Z2YtBPcMI8tP/JkTEevo/kRxf4Ub4/hVY9SbUlJnr/LrAkKvNx9//A376D2xeCOOfhIGXWRsMKvfBW9dAzlKw+8LEWTD4V9bV46FW7NjPc1mbWLyxEDCDysShPbjjnGSSorpYXJ2ISFMKLHJ0znrYON9sTdm66ND6bifByFtgyFUQEGquG3wlfHAnFGXDezfA6ndhwtMQ1sP9de/fDnOugL2bzCH2r5qj25Z/4fvt+3j28018s7kIAIfdxmWn9OCOs5NJjFTfHhHxTDbDONpg1t6ltLSUsLAwSkpKCA0Ntboc71a5D1a+Dt//E0pyzHU2u9lZdeTN0GfM0VtP6qrh62fg66fBWQt+IXDeIzD8BrC7qTPmrpXwn0lQUQChPeHadyFmgHt+txdYtnUvz36+iaVb9wLgY7dxxfCe3D4mmV7dgiyuTkQ6I1e+vxVYxLR7ldmJ9uf3zFt/AQK7wrDJkHoDdE1o2X4K1pszHud+bz7vNQoueg6i+rZL2Y02fgbvTobaSog52QwroXHt+zu9gGEYLN2ylxlZm1i+bR9g3uXzq9R4bjsrifgIBRURsY4Ci7RMXQ2s+8C87JO7/ND67kNg5G/h5MvAN9D1/TrrzX4tnz8KtRXg8IOzfg+n3QM+fm1X/0Er/gUfZ5gzLfc5G658/dDlqk7KMAy+3lTEc1mb+GHHfgD8HHYmjYjntjFJxIW34ryKiLQxBRY5ttLd8MNr5hd9hXlnCHZfGHip2T+lZ2rbdJotzoGP/w82f24+jx4IlzwPPYaf+L7BvL36i8fh67+az4dcAxc/B47OO9GdYRgs2ljIc1mb+DGnGAA/HzvXjOzFrWclERsWYG2BIiKHceX7u1WdC2bOnEliYiIBAQGkpaWxfPnyZredO3cuqamphIeHExwczNChQ5k9e3aTbcrLy7nzzjvp2bMngYGBDBgwgJdeeqk1pUlzDAO2L4F3JsPfToavnjLDSkh3OPshyFgHl78C8SPa7g6f8F5w7Xtw2SvmvD0Fa+Ef6TD/D+ZsySeirgbev/VQWDnrAZj4YqcNK4ZhkLU+n4kzlzDlte/5MacYfx87N5zem29+fzaPXDxQYUVEvJrLdwm9/fbbZGRk8NJLL5GWlsaMGTMYO3Ys2dnZREdHH7F9REQEDz30ECkpKfj5+fHxxx8zZcoUoqOjGTt2LAAZGRl88cUXzJkzh8TERD777DNuv/124uLiuPjii0/8KDuzmgpY/Y7ZP6Vg7aH1CaebnWhTLmzfL3mbzbyLKOkcc7TZNe+YA7lt+BgumtG6AeeqSuDt35iD19kc5n6GXdfWlXsFwzBYuC6f577YxM+7SgEI9HXwm1EJ3HRmb6JDFFJEpGNw+ZJQWloaI0aM4IUXXgDA6XQSHx/PXXfdxYMPPtiifQwbNowJEybw2GOPAXDyySczadIkHn744cZthg8fzvjx43n88cePuz9dEmrGli/NjqhVJeZz3yAzPIy4GWJPtqamjZ+Zl4lKc83nrg44V7IL3viVGb58g83Zlk86r/3q9VBOp8Fn6/J4Nmsz6/eYQSXIz8F1oxK56czeRHbxt7hCEZHjc+X726UWlpqaGlasWMHUqVMb19ntdtLT01m6dOlx328YBl988QXZ2dk8+eSTjetPO+00PvzwQ2644Qbi4uJYtGgRGzdu5G9/+9tR91NdXU119aE5TkpLS105jM6huhw+uMMMK117m60pQ68x7/yxUt/z4Y5lkPWY2dm3ccC5p8w+NMe6HJW/1hxjpWw3dIkxJzCMG+q20q1UVVvPml0lrNyxnxU79rMyp5iicvMz0MXfh8mnJXDjGX2ICG6HTs0iIh7ApcBSVFREfX09MTExTdbHxMSwYcOGZt9XUlJCjx49qK6uxuFw8OKLL3LeeYf+Kn7++ee55ZZb6NmzJz4+Ptjtdl555RVGjz76gF+ZmZk8+uijrpTe+Xz9NJTugvAEuH1p6+72aS/+IXDBUzDoisMGnJtiXrpqbsC5rYvMy0DVpRDZz7xtuaW3Wnuh3cUHWJlzKJys211CbX3TxtAQfx+mnJ7IDWf0JjxIQUVEOja3jHQbEhLCqlWrKC8vJysri4yMDPr06cOYMWMAM7AsW7aMDz/8kISEBL766ivuuOMO4uLiSE9PP2J/U6dOJSMjo/F5aWkp8fHx7jgU77B3Cyw1L9kxLtOzwsrh4kfCrV8fGnBu46ew/ZsjB5z76S0z2Dhrzb43V71hfUtRG6qpc7J2dwkrduznx5xiVuzYT15p1RHbRYX4M6xXOMMTujKsV1dO7hFGgK8mIRSRzsGlPiw1NTUEBQXx3nvvMXHixMb1kydPpri4mA8++KBF+7npppvYuXMnCxYs4MCBA4SFhfH+++8zYcKEJtvk5uYyf/784+5PfVgOYxhmH4/NCyE53bxLxxsm/GtuwLn1H8IXZl8nBl4KE18CX+/uSFpQWsXKHLPlZMWO/azZVUJNnbPJNg67jQHdQxnWK5xhDQGlZ9dAbN5wLkVEWqjd+rD4+fkxfPhwsrKyGgOL0+kkKyuLO++8s8X7cTqdjX1Qamtrqa2txf6L4dsdDgdOp/Nob5dj2TjfDCt2Xxj3pHeEFYDo/nDDAvNupqw/mhMXvpgGRsN/A6fdBel/dN8w/22ktt7Jhj1lh13e2U/u/gNHbNc1yJfhCV05pVdXhid0ZbBmSRYRacLl/yNmZGQwefJkUlNTGTlyJDNmzKCiooIpU6YAcN1119GjRw8yMzMBs79JamoqSUlJVFdXM2/ePGbPns2sWbMACA0N5ayzzuL+++8nMDCQhIQEFi9ezOuvv84zzzzThofaCdRWwacPmI9PuxMik62tx1V2B5x6K6RccNiAczazQ27aLVZX12KGYTBvTR6zl23np50lHKitb/K6zQb9YkIYltCV4b26MiyhK4ndgtR6IiJyDC4HlkmTJlFYWMi0adPIy8tj6NChzJ8/v7Ejbk5OTpPWkoqKCm6//XZyc3MJDAwkJSWFOXPmMGnSpMZt3nrrLaZOncq1117Lvn37SEhI4IknnuDWW29tg0PsRL59Dop3QEgcnPk7q6tpvYMDzm3OMofYjx9pdUUttim/jOkfruXbLXsb14UG+DS2nAzr1ZUh8WGEBHTOAe5ERFpLQ/N3FMU58MIIc+LCK16Fky+3uqJOpby6jmc/38hrS7ZT5zTw87Fz6+g+XDQkjqSoLtjtaj0REfmlduvDIh5swUNmWEk8EwZeZnU1nYZhGHz4026e+GQ9BWVmv6z0/jFMu3AAvbppJmQRkbaiwNIRbPnCvJvG5oDxXtTR1sttyCtl2gdrWb5tHwAJ3YJ45KKBnJ1y5BQVIiJyYhRYvF1dzaGOtiNvgZiB1tbTCZRW1fK3hRt5fekO6p0GAb527jw7mZvO7KNxUURE2okCi7f77iUo2gjBUTCmZXM5Ses4nQZzf9zFnz9dT1F5DQDjT47loQn96dlVl39ERNqTAos3K90DixvmZEp/FALDLS2nI1u7u4RpH6xlxY79APSJCuaRiwYyum+UxZWJiHQOCizebOE0qCmHniNgyNVWV9MhlVTW8vTCbOYs24HTMGdEvuuck7jxjN74+XjXIHYiIt5MgcVb7fgW1rwD2OCCv3jdCLCezuk0eHfFTp6cn82+CvPyz4WDu/PQhP50D/PQuZlERDowBRZvVF8H8+43Hw+fDHGnWFtPB7M6t5iHP1jLTzuLATgpuguPXjKQ05IirS1MRKQTU2DxRiteg/yfISAczplmdTUdxv6KGp5akM1b3+dgGNDF34d7009i8mmJ+DrUgiUiYiUFFm9TUXRo9uJzH4bgbtbW0wHUOw3eXJ7DXz/LpriyFoBLT+nB1PEpRId698zQIiIdhQKLt8l6FKpKIHYQDJ9idTVeb2XOfqZ/sJY1u0oASIkN4Y+XnMzI3hEWVyYiIodTYPEmuStg5Wzz8QV/NWc3llbZXFDO3xdv4d0VuQCEBPhw33l9+fWpCfjo8o+IiMdRYPEWTifM+x1gmLcw9zrV6oq8Tk2dk4Xr8pmzbAdLtx6aTfmK4T15YFwKUSH+FlYnIiLHosDiLVbNgd0rwS/EHCROWix3fyVvLd/JW9/vpKjcnKDQboNzUmK4bUwSwxO6WlyhiIgcjwKLNziwHz5vCCljHoSQGGvr8QL1ToOvNhYyZ9kOvswuwGmY66NC/LlqRDxXjexFj3CNpyIi4i0UWLzBl5lQWQSR/SDtt1ZX49EKy6p554edvLk8h9z9BxrXn57cjWvTEjhvQIxuURYR8UIKLJ4u72f4/hXz8QVPgcPX2no8kGEYfLdtH3OW7WDB2jxq683mlLBAX64Y3pNr0nqRFNXF4ipFROREKLB4MsMwR7Q1nDBgIvQZY3VFHqXkQC1zV+byxnc5bC4ob1w/ND6cX5+awIWDuxPgqzupREQ6AgUWT7bmPcj5FnyD4PzHra7GY6zOLWbOsh18+NNuqmqdgDkp4SVDe3BtWi9O7hFmcYUiItLWFFg8VXUZfPb/zMdn3gfh8dbWY7HKmjo++mk3c5blNA7yBtAvJoRfn9qLiaf0ICRAl8tERDoqBRZPtfgpKM+Drr3htLusrsYym/LLeOO7HP67MpeyqjoA/Bx2LhgUy7WnJpCa0BWbzWZxlSIi0t4UWDxR4UZYNst8PP5J8Ol8A5pV1tRx139+JGtDQeO6XhFBXJvWiyuG96Rbl873byIi0pkpsHgaw4BPfw/OWug7DvqOtboit6t3Gtz95iqyNhRgt0F6/xiuPTWBM5MjsdvVmiIi0hkpsHiaDR/D1i/B4QfjMq2uxhKPf7KOz9fn4+dj5z83pZGaqIkIRUQ6O42g5UlqKmH+H8zHp98DEX2srccCry3ZxmtLtgPwzJVDFFZERARQYPEsS2ZASQ6ExcMZGVZX43YL1+Xzx4/XAfDAuBQuHBxncUUiIuIpFFg8xb5t8M0M8/HYJ8AvyNJy3G11bjF3v/kjhgFXj4zn1rM6X+uSiIg0T4HFUyz4A9RXQ++zoP/FVlfjVrn7K7nhXz9woLae0X2j+OMlJ+tWZRERaUKBxRNsWgjZ88DuA+Ofgk70ZV1aVcsN//qeovJqUmJDmHnNKZqcUEREjqBvBqvVVcOnD5iP026F6BRr63Gjmjont81Zwcb8cmJC/Xn1+hEarVZERI5KgcVqS2fCvi3QJQbOesDqatzGMAween8NSzbvJcjPwT8njyAuPNDqskRExEMpsFipeCd89Rfz8XmPQUCotfW40cwvN/PuilzsNph5zTBNWCgiIsekwGIVZz28fyvUVkKvUTD4SqsrcpsPVu3ir59tBODRiwdydkq0xRWJiIinU2CxyrfPw45vwDcYLpnZaTraLt+2j/vfXQ3AzWf25jejEq0tSEREvIICixX2/ARfPG4+Hv9n6JZkbT1usqWwnFtm/0BNvZPxJ8cydXx/q0sSEREvocDibjWV8N+bzckNUy6EU35jdUVusbe8mimvfU9xZS1D48P526ShmshQRERaTIHF3T6fDkXZ0CUWLnquU1wKqqqt5+bXfyBnXyXxEYH8Y3IqAb4Oq8sSEREvosDiTpsWwvKXzccTZ0JwN2vrcQOn0+C+d35iZU4xoQE+vHb9SCK7+FtdloiIeBkFFnepKIL/3W4+TrsVktOtrcdNnlywgU/W7MHXYePl61JJju5idUkiIuKFFFjcwTDgw7uhogCi+kP6I1ZX5Bb/+S6Hvy/eCsBTVwzm1D4dv0VJRETahwKLO6z8N2R/Ag4/uPwV8O34I7ouyi7g4Q9+BuD/0vty6Sk9La5IRES8mQJLe9u7BeZPNR+fOw1iB1lbjxus213KHW+spN5pcPmwntx9brLVJYmIiJdTYGlP9bXw35vM0WwTz4RT77C6onaXV1LFDf/6noqaekb16UbmZYOwdYI7oUREpH0psLSnxU/B7pUQEAaXvgT2jv3PXV5dxw3/+p680iqSo7vw0m+G4+fTsY9ZRETcQ98m7SVnGXz9V/PxhTMgrGP34aird3Lnf1aybk8pkV38eO36EYQF+lpdloiIdBAKLO2hqhTm3gKGEwZfBSdfZnVF7cowDKZ/uJZF2YUE+Nr55+QRxEcEWV2WiIh0IAos7eHTB6B4B4T3ggv+YnU17e6Vr7fyxnc52Gzw7FWnMCQ+3OqSRESkg1FgaWtr34ef/gM2O1z6MgSEWl1Ru/p0zR7+NG8DAP9vwgDGDoy1uCIREemIFFjaUulu+Ohe8/EZGZAwytJy2tvKnP3c+/YqACaPSuCG0xMtrUdERDouBZa24nTC+7dCVTHEnQJjHrS6onb12do8fv2P76iuc5LeP5ppFw3U7csiItJufKwuoMP4bhZsWwy+QXDZK+DomHfIGIbBzC8389fPNgJwWlI3nr3qFBx2hRUREWk/CixtIe9n+PwR8/HYJyDyJEvLaS8Haur5/X9X89FPuwHzMtD/u3AAvg411ImISPtSYDlRtVUw92aor4G+42H4FKsrahd7Sg5wy+srWLOrBB+7jT9ecjLXpPWyuiwREekkWvWn8cyZM0lMTCQgIIC0tDSWL1/e7LZz584lNTWV8PBwgoODGTp0KLNnz26yjc1mO+ryl794wS3BWY9CwToIjoKLn4cO2I9jZc5+Ln5hCWt2ldA1yJc5N6UprIiIiFu5HFjefvttMjIymD59OitXrmTIkCGMHTuWgoKCo24fERHBQw89xNKlS1m9ejVTpkxhypQpLFiwoHGbPXv2NFleffVVbDYbl19+eeuPzB22fAHLXjQfX/IidImytp52MHdlLle9vIzCsmpSYkP48M4zOLVPN6vLEhGRTsZmGIbhyhvS0tIYMWIEL7zwAgBOp5P4+HjuuusuHnywZXfGDBs2jAkTJvDYY48d9fWJEydSVlZGVlZWi/ZXWlpKWFgYJSUlhIa6adyTyn0w6zQo2wMjboIJT7vn97pJvdPgqfkb+PtXWwE4b0AMf5s0lC7+uoooIiJtw5Xvb5daWGpqalixYgXp6emHdmC3k56eztKlS4/7fsMwyMrKIjs7m9GjRx91m/z8fD755BNuvPFGV0pzL8OAj+4xw0pkXzjv6MHLW5VW1XLTv79vDCt3np3M3389XGFFREQs49I3UFFREfX19cTExDRZHxMTw4YNG5p9X0lJCT169KC6uhqHw8GLL77Ieeedd9Rt//3vfxMSEsJllzU//051dTXV1dWNz0tLS105jBO36j+w/kOw+5i3MPt1nHlzthdVcNPrP7C5oBx/Hzt/+dUQLh4SZ3VZIiLSybnlT+aQkBBWrVpFeXk5WVlZZGRk0KdPH8aMGXPEtq+++irXXnstAQEBze4vMzOTRx99tB0rPoZ9W+HT35uPz34I4oZaU0c7WLK5iNvfWEnJgVpiQwN4+brhDO4ZbnVZIiIirgWWyMhIHA4H+fn5Tdbn5+cTG9v8HDJ2u53k5GQAhg4dyvr168nMzDwisHz99ddkZ2fz9ttvH7OOqVOnkpGR0fi8tLSU+Ph4Vw6lderrYO5voaYcEk6H0+9p/9/pBoZh8O9vt/PYJ+updxoMjQ/n5d8MJzq0+dAoIiLiTi71YfHz82P48OFNOsM6nU6ysrIYNarl8+Y4nc4ml3QO+uc//8nw4cMZMmTIMd/v7+9PaGhok8Utvn4acpeDfyhc+hLYHe75ve2ops7JH95fwyMfraPeaXDZKT1465ZTFVZERMSjuHxJKCMjg8mTJ5OamsrIkSOZMWMGFRUVTJliDph23XXX0aNHDzIzMwHz8k1qaipJSUlUV1czb948Zs+ezaxZs5rst7S0lHfffZenn/bQu21yf4DFT5qPJzwN4d4/Dsne8mpue2Mly7ftw2aDB8elcMvoPpoTSEREPI7LgWXSpEkUFhYybdo08vLyGDp0KPPnz2/siJuTk4PdfqjhpqKigttvv53c3FwCAwNJSUlhzpw5TJo0qcl+33rrLQzD4Oqrrz7BQ2oH1eXmaLZGPZx8BQy+0uqKTtj6PaXc/PoP5O4/QIi/D89dfQpnp0RbXZaIiMhRuTwOiydq93FYPrwLVr4OoT3htiUQGN72v8ONFqzN4//eXkVlTT0J3YL45+RUkqNDrC5LREQ6GVe+vzWwxvGs/9gMK9jMfiteHFYMw+CFLzbz9EJzpuXTk7sx85phhAf5WVyZiIjIsSmwHEtZntm6AnD63dD7TGvrOQEHauq5/72f+Hj1HgCuPy2Rhyb010zLIiLiFRRYjsVwQvQAqC4xx1zxUr+cafmxiSdz9Ujv7zQsIiKdhwLLsYTGweQPzXmDfPytrqZVVubs55bXV1BUXk1EsB+zrh1GmiYvFBERL6PAcjx2h9fOwvzVxkJu+vcP1NQ7SYkN4ZXrUomP6DjTCIiISOehwNJB1dY7mfbBz9TUO0nvH8OzVw0lWJMXioiIl1KPyw7qnR92sn1vJd2C/RRWRETE6ymwdEAHaup59vNNANx5TrLCioiIeD0Flg7oX99up6Csmh7hgVyTpruBRETE+ymwdDAllbXMWrQZgIzz+uLv4/0TNIqIiCiwdDB//2oLpVV19I3pwsRTelhdjoiISJtQYOlACkqreHXJNgDuH5uCw65Zl0VEpGNQYOlAnvtiE1W1Tob1Cie9v2ZeFhGRjkOBpYPYXlTBW8t3AvDAuBRsNrWuiIhIx6HA0kE8s3AjdU6DMf2iNPS+iIh0OAosHcDa3SV8+NNuAO4f28/iakRERNqeAksH8JcF2QBcNCSOgXFhFlcjIiLS9hRYvNx3W/eyKLsQH7uN+87ra3U5IiIi7UKBxYsZhsFTDa0rk0bEkxgZbHFFIiIi7UOBxYtlrS9gxY79BPjaufvck6wuR0REpN0osHipeqfR2Hdlyum9iQkNsLgiERGR9qPA4qU+WLWL7PwyQgN8uHV0ktXliIiItCsFFi9UXVfPMws3AnDbmGTCgnwtrkhERKR9KbB4oTe/yyF3/wGiQ/y5/rREq8sRERFpdwosXqaiuo7nv9gMwD3pJxHo57C4IhERkfanwOJlXv1mG3srakjsFsSVqfFWlyMiIuIWCixeZF9FDS9/tRWAjPP74evQ6RMRkc5B33heZNaizZRV1zGgeygXDupudTkiIiJuo8DiJXYXH+DfS3cA8Ptx/bDbbRZXJCIi4j4KLF7i2c83UVPnJK13BGf1jbK6HBEREbdSYPECmwvKeXfFTgB+Py4Fm02tKyIi0rkosHiBpz/LxmnAeQNiGJ7Q1epyRERE3E6BxcP9tLOYT3/Ow2aD+8f2s7ocERERSyiweLiDExxedkpP+saEWFyNiIiINRRYPNg3m4r4ZnMRvg4b96afZHU5IiIillFg8VCGYfDUgg0AXJuWQHxEkMUViYiIWEeBxUPN/zmP1bklBPk5uPOcZKvLERERsZQCiweqq3fyl8/Mvis3ndmHyC7+FlckIiJiLQUWD/TflblsLayga5AvN5/Z2+pyRERELKfA4mGqauuZ8fkmAO44O5mQAF+LKxIREbGeAouHmbNsB3tKqogLC+DXpyZYXY6IiIhHUGDxIKVVtcz8cjMA957XlwBfh8UViYiIeAYFFg/yj6+2sr+yluToLlx2Sg+ryxEREfEYCiweorCsmn98sw2A353fFx+HTo2IiMhB+lb0EDO/3ExlTT1DeoYxdmCs1eWIiIh4FAUWD7BzXyVvfLcDgAfGpWCz2SyuSERExLMosHiAvy3cSG29wZknRXJacqTV5YiIiHgcBRaLbcgr5f1VuwC4f2w/i6sRERHxTAosFvvrgo0YBkwY1J3BPcOtLkdERMQjKbBYaN3uUj5fn4/DbiPj/L5WlyMiIuKxFFgs9OPO/QCcnhxJUlQXi6sRERHxXAosFtpSUAFA32iFFRERkWNRYLHQ5sJyAJIVWERERI5JgcVCWwrMwJKkwCIiInJMrQosM2fOJDExkYCAANLS0li+fHmz286dO5fU1FTCw8MJDg5m6NChzJ49+4jt1q9fz8UXX0xYWBjBwcGMGDGCnJyc1pTnFQ7U1LOr+ACA+q+IiIgch8uB5e233yYjI4Pp06ezcuVKhgwZwtixYykoKDjq9hERETz00EMsXbqU1atXM2XKFKZMmcKCBQsat9myZQtnnHEGKSkpLFq0iNWrV/Pwww8TEBDQ+iPzcFuLzNaVrkG+RAT7WVyNiIiIZ7MZhmG48oa0tDRGjBjBCy+8AIDT6SQ+Pp677rqLBx98sEX7GDZsGBMmTOCxxx4D4KqrrsLX1/eoLS8tUVpaSlhYGCUlJYSGhrZqH+724U+7ufvNH0lN6Mp7t51mdTkiIiJu58r3t0stLDU1NaxYsYL09PRDO7DbSU9PZ+nSpcd9v2EYZGVlkZ2dzejRowEz8HzyySf07duXsWPHEh0dTVpaGv/73/+a3U91dTWlpaVNFm/T2H9Fl4NERESOy6XAUlRURH19PTExMU3Wx8TEkJeX1+z7SkpK6NKlC35+fkyYMIHnn3+e8847D4CCggLKy8v585//zLhx4/jss8+49NJLueyyy1i8ePFR95eZmUlYWFjjEh8f78pheIQthQc73AZbXImIiIjn83HHLwkJCWHVqlWUl5eTlZVFRkYGffr0YcyYMTidTgAuueQS/u///g+AoUOH8u233/LSSy9x1llnHbG/qVOnkpGR0fi8tLTU60LLlkJzDBa1sIiIiByfS4ElMjISh8NBfn5+k/X5+fnExsY2+z673U5ycjJghpH169eTmZnJmDFjiIyMxMfHhwEDBjR5T//+/fnmm2+Ouj9/f3/8/f1dKd2jOJ0GWwt1SUhERKSlXLok5Ofnx/Dhw8nKympc53Q6ycrKYtSoUS3ej9PppLq6unGfI0aMIDs7u8k2GzduJCEhwZXyvMau4gNU1znxc9jp2TXQ6nJEREQ8nsuXhDIyMpg8eTKpqamMHDmSGTNmUFFRwZQpUwC47rrr6NGjB5mZmYDZ3yQ1NZWkpCSqq6uZN28es2fPZtasWY37vP/++5k0aRKjR4/m7LPPZv78+Xz00UcsWrSobY7Swxzsv5IYGYSPQ2P3iYiIHI/LgWXSpEkUFhYybdo08vLyGDp0KPPnz2/siJuTk4PdfuhLuKKigttvv53c3FwCAwNJSUlhzpw5TJo0qXGbSy+9lJdeeonMzEzuvvtu+vXrx3//+1/OOOOMNjhEz6P+KyIiIq5xeRwWT+Rt47D84f01/Oe7HO48O5nfje1ndTkiIiKWaLdxWKRtHByDRZMeioiItIwCiwW26A4hERERlyiwuFlxZQ1F5TUA9InSoHEiIiItocDiZgc73HYPCyDY3y3j9omIiHg9BRY30+UgERER1ymwuNmhwKLLQSIiIi2lwOJmWwoaxmDRHUIiIiItpsDiZppDSERExHUKLG5UU+dkx75KQIFFRETEFQosbpSzr4J6p0Gwn4OYUO+dbVpERMTdFFjcaPNh/VdsNpvF1YiIiHgPBRY30i3NIiIiraPA4ka6pVlERKR1FFjc6OAot5r0UERExDUKLG5iGEbjLM26JCQiIuIaBRY3KSirpry6DofdRq9uQVaXIyIi4lUUWNzkYOtKr4gg/H0cFlcjIiLiXRRY3EQdbkVERFpPgcVNDna4Vf8VERER1ymwuInGYBEREWk9BRY3abxDKFqXhERERFylwOIGFdV17C6pAqBPpFpYREREXKXA4gbbisz+K92C/ega7GdxNSIiIt5HgcUN1H9FRETkxCiwuIH6r4iIiJwYBRY30C3NIiIiJ0aBxQ10SUhEROTEKLC0s3qnwdYizdIsIiJyIhRY2lnu/kpq6pz4+9iJCw+0uhwRERGvpMDSzg5eDuodGYzDbrO4GhEREe+kwNLOthQ0dLjV5SAREZFWU2BpZ+pwKyIicuIUWNrZocCiMVhERERaS4GlnWkMFhERkROnwNKO9lXUsK+iBoA+amERERFpNQWWdrS14XJQj/BAgvx8LK5GRETEeymwtKOD/VfUuiIiInJiFFjakfqviIiItA0FlnZ0aJZmBRYREZETocDSjnRLs4iISNtQYGkn1XX15OyrBDTpoYiIyIlSYGkn24sqcRoQEuBDVBd/q8sRERHxagos7eTwIfltNk16KCIiciIUWNpJY4db3SEkIiJywhRY2kljC0u0OtyKiIicKAWWdqIxWERERNqOAks7MAyjSR8WEREROTEKLO0gr7SKypp6fOw2EroFWV2OiIiI11NgaQdbCszLQb26BeHr0D+xiIjIidK3aTvQ5SAREZG2pcDSDhRYRERE2pYCSzvQHEIiIiJtS4GlHRzsw6JZmkVERNpGqwLLzJkzSUxMJCAggLS0NJYvX97stnPnziU1NZXw8HCCg4MZOnQos2fPbrLN9ddfj81ma7KMGzeuNaVZrry6jrzSKkCXhERERNqKj6tvePvtt8nIyOCll14iLS2NGTNmMHbsWLKzs4mOjj5i+4iICB566CFSUlLw8/Pj448/ZsqUKURHRzN27NjG7caNG8drr73W+Nzf3zsnDNzacDkoKsSfsEBfi6sRERHpGFxuYXnmmWe4+eabmTJlCgMGDOCll14iKCiIV1999ajbjxkzhksvvZT+/fuTlJTEPffcw+DBg/nmm2+abOfv709sbGzj0rVr19YdkcU2F6j/ioiISFtzKbDU1NSwYsUK0tPTD+3Abic9PZ2lS5ce9/2GYZCVlUV2djajR49u8tqiRYuIjo6mX79+3Hbbbezdu7fZ/VRXV1NaWtpk8RS6Q0hERKTtuXRJqKioiPr6emJiYpqsj4mJYcOGDc2+r6SkhB49elBdXY3D4eDFF1/kvPPOa3x93LhxXHbZZfTu3ZstW7bwhz/8gfHjx7N06VIcDscR+8vMzOTRRx91pXS3aexwq8AiIiLSZlzuw9IaISEhrFq1ivLycrKyssjIyKBPnz6MGTMGgKuuuqpx20GDBjF48GCSkpJYtGgR55577hH7mzp1KhkZGY3PS0tLiY+Pb/fjaIlDszQrsIiIiLQVlwJLZGQkDoeD/Pz8Juvz8/OJjY1t9n12u53k5GQAhg4dyvr168nMzGwMLL/Up08fIiMj2bx581EDi7+/v0d2yq2rd7J978EWFvVhERERaSsu9WHx8/Nj+PDhZGVlNa5zOp1kZWUxatSoFu/H6XRSXV3d7Ou5ubns3buX7t27u1Ke5XbuP0BtvUGAr524sECryxEREekwXL4klJGRweTJk0lNTWXkyJHMmDGDiooKpkyZAsB1111Hjx49yMzMBMz+JqmpqSQlJVFdXc28efOYPXs2s2bNAqC8vJxHH32Uyy+/nNjYWLZs2cLvf/97kpOTm9z27A22NNwh1CeyC3a7zeJqREREOg6XA8ukSZMoLCxk2rRp5OXlMXToUObPn9/YETcnJwe7/VDDTUVFBbfffju5ubkEBgaSkpLCnDlzmDRpEgAOh4PVq1fz73//m+LiYuLi4jj//PN57LHHPPKyz7Go/4qIiEj7sBmGYVhdxIkqLS0lLCyMkpISQkNDLavj9+/9xDs/5HJv+kncm97XsjpERES8gSvf35pLqA1tKdQtzSIiIu1BgaWNGIZx2Ci3CiwiIiJtSYGljeyrqKHkQC02G/TRLc0iIiJtSoGljRy8HNSzayABvkeOzisiIiKtp8DSRnQ5SEREpP0osLQRTXooIiLSfhRY2ogCi4iISPtRYGkjhwKLOtyKiIi0NQWWNlBVW0/u/gOARrkVERFpDwosbWBbUQWGAWGBvnQL9rO6HBERkQ5HgaUNHH45yGbTpIciIiJtTYGlDWwp0JD8IiIi7UmBpQ1olmYREZH2pcDSBnRLs4iISPtSYDlBTqfB1sZZmnVLs4iISHtQYDlBe0qrOFBbj6/DRnxEkNXliIiIdEgKLCdoS8McQgndgvF16J9TRESkPegb9gQd7L+SrP4rIiIi7UaB5QQ1ztIcrf4rIiIi7UWB5QTpDiEREZH2p8BygrYUatA4ERGR9qbAcgJKDtRSWFYNQB/d0iwiItJuFFhOwNaGy0Exof6EBPhaXI2IiEjHpcByAnQ5SERExD0UWE6AOtyKiIi4hwLLCTg4aJyG5BcREWlfCiwnQLM0i4iIuIcCSyvV1jvZsbcS0CUhERGR9qbA0ko5+yqpcxoE+TmIDQ2wuhwREZEOTYGllQ72X+kTFYzdbrO4GhERkY5NgaWVDt7SrEkPRURE2p8CSys1TnqowCIiItLuFFhaSXcIiYiIuI8CSysYhqFB40RERNxIgaUVCsurKauqw26DhG5BVpcjIiLS4SmwtMKWArPDbXxEEAG+DourERER6fgUWFpBl4NERETcS4GlFQ4FFs0hJCIi4g4KLK1wcAwWtbCIiIi4hwJLKzTO0qxbmkVERNxCgcVFB2rq2VV8AFALi4iIiLsosLhoa5HZutI1yJeIYD+LqxEREekcFFhcpP4rIiIi7qfA4qItmkNIRETE7RRYXHTwluZkdbgVERFxGwUWFzXO0hytMVhERETcRYHFBfVOg21F6sMiIiLibgosLthdfIDqOid+Djs9u2rSQxEREXdRYHHB5ob+K70jg3HYbRZXIyIi0nkosLhgi/qviIiIWEKBxQUag0VERMQaCiwuODRLswKLiIiIOymwuGCrAouIiIglWhVYZs6cSWJiIgEBAaSlpbF8+fJmt507dy6pqamEh4cTHBzM0KFDmT17drPb33rrrdhsNmbMmNGa0tpNcWUNReU1APSJUh8WERERd3I5sLz99ttkZGQwffp0Vq5cyZAhQxg7diwFBQVH3T4iIoKHHnqIpUuXsnr1aqZMmcKUKVNYsGDBEdu+//77LFu2jLi4ONePpJ0d7L/SPSyAYH8fi6sRERHpXFwOLM888ww333wzU6ZMYcCAAbz00ksEBQXx6quvHnX7MWPGcOmll9K/f3+SkpK45557GDx4MN98802T7Xbt2sVdd93FG2+8ga+vb+uOph2p/4qIiIh1XAosNTU1rFixgvT09EM7sNtJT09n6dKlx32/YRhkZWWRnZ3N6NGjG9c7nU5+85vfcP/99zNw4MDj7qe6uprS0tImS3s7FFh0OUhERMTdXAosRUVF1NfXExMT02R9TEwMeXl5zb6vpKSELl264Ofnx4QJE3j++ec577zzGl9/8skn8fHx4e67725RHZmZmYSFhTUu8fHxrhxGq2wpMC8JadJDERER93NLZ4yQkBBWrVpFeXk5WVlZZGRk0KdPH8aMGcOKFSt49tlnWblyJTZby0aPnTp1KhkZGY3PS0tL2z206JKQiIiIdVwKLJGRkTgcDvLz85usz8/PJzY2ttn32e12kpOTARg6dCjr168nMzOTMWPG8PXXX1NQUECvXr0at6+vr+e+++5jxowZbN++/Yj9+fv74+/v70rpJ6S6rp6cfZUAJKmFRURExO1cuiTk5+fH8OHDycrKalzndDrJyspi1KhRLd6P0+mkuroagN/85jesXr2aVatWNS5xcXHcf//9R72TyAo5eyupdxp08fchOsR9QUlERERMLl8SysjIYPLkyaSmpjJy5EhmzJhBRUUFU6ZMAeC6666jR48eZGZmAmZ/k9TUVJKSkqiurmbevHnMnj2bWbNmAdCtWze6devW5Hf4+voSGxtLv379TvT42sThHW5betlKRERE2o7LgWXSpEkUFhYybdo08vLyGDp0KPPnz2/siJuTk4PdfqjhpqKigttvv53c3FwCAwNJSUlhzpw5TJo0qe2Oop1pDiERERFr2QzDMKwu4kSVlpYSFhZGSUkJoaGhbb7/jLdXMffHXdw/th93nJ3c5vsXERHpjFz5/tZcQi2gMVhERESspcByHIZh6JKQiIiIxRRYjqOgrJry6jocdhu9ugVZXY6IiEinpMByHFsKzMtBvSKC8PdxWFyNiIhI56TAchzqvyIiImI9BZbjUP8VERER6ymwHEdjC4uG5BcREbGMAstxHOzDohYWERER6yiwHENFdR27S6oA9WERERGxkstD83cmTsPgoQv6s6v4AOFBflaXIyIi0mkpsBxDSIAvN4/uY3UZIiIinZ4uCYmIiIjHU2ARERERj6fAIiIiIh5PgUVEREQ8ngKLiIiIeDwFFhEREfF4CiwiIiLi8RRYRERExOMpsIiIiIjHU2ARERERj6fAIiIiIh5PgUVEREQ8ngKLiIiIeLwOMVuzYRgAlJaWWlyJiIiItNTB7+2D3+PH0iECS1lZGQDx8fEWVyIiIiKuKisrIyws7Jjb2IyWxBoP53Q62b17NyEhIdhstjbdd2lpKfHx8ezcuZPQ0NA23ben6UzHCp3reHWsHVdnOl4da8djGAZlZWXExcVhtx+7l0qHaGGx2+307NmzXX9HaGhoh/6P5nCd6Vihcx2vjrXj6kzHq2PtWI7XsnKQOt2KiIiIx1NgEREREY+nwHIc/v7+TJ8+HX9/f6tLaXed6Vihcx2vjrXj6kzHq2Pt3DpEp1sRERHp2NTCIiIiIh5PgUVEREQ8ngKLiIiIeDwFFhEREfF4CizAzJkzSUxMJCAggLS0NJYvX37M7d99911SUlIICAhg0KBBzJs3z02Vtl5mZiYjRowgJCSE6OhoJk6cSHZ29jHf869//QubzdZkCQgIcFPFJ+aRRx45ovaUlJRjvscbzytAYmLiEcdqs9m44447jrq9t53Xr776iosuuoi4uDhsNhv/+9//mrxuGAbTpk2je/fuBAYGkp6ezqZNm467X1c/9+5wrGOtra3lgQceYNCgQQQHBxMXF8d1113H7t27j7nP1nwW3OF45/X6668/ou5x48Ydd7+eeF7h+Md7tM+wzWbjL3/5S7P79NRz2146fWB5++23ycjIYPr06axcuZIhQ4YwduxYCgoKjrr9t99+y9VXX82NN97Ijz/+yMSJE5k4cSI///yzmyt3zeLFi7njjjtYtmwZCxcupLa2lvPPP5+Kiopjvi80NJQ9e/Y0Ljt27HBTxSdu4MCBTWr/5ptvmt3WW88rwPfff9/kOBcuXAjAr371q2bf403ntaKigiFDhjBz5syjvv7UU0/x3HPP8dJLL/Hdd98RHBzM2LFjqaqqanafrn7u3eVYx1pZWcnKlSt5+OGHWblyJXPnziU7O5uLL774uPt15bPgLsc7rwDjxo1rUvebb755zH166nmF4x/v4ce5Z88eXn31VWw2G5dffvkx9+uJ57bdGJ3cyJEjjTvuuKPxeX19vREXF2dkZmYedfsrr7zSmDBhQpN1aWlpxm9/+9t2rbOtFRQUGICxePHiZrd57bXXjLCwMPcV1YamT59uDBkypMXbd5TzahiGcc899xhJSUmG0+k86uvefF4B4/3332987nQ6jdjYWOMvf/lL47ri4mLD39/fePPNN5vdj6ufeyv88liPZvny5QZg7Nixo9ltXP0sWOFoxzp58mTjkksucWk/3nBeDaNl5/aSSy4xzjnnnGNu4w3nti116haWmpoaVqxYQXp6euM6u91Oeno6S5cuPep7li5d2mR7gLFjxza7vacqKSkBICIi4pjblZeXk5CQQHx8PJdccglr1651R3ltYtOmTcTFxdGnTx+uvfZacnJymt22o5zXmpoa5syZww033HDMiUC9+bwebtu2beTl5TU5d2FhYaSlpTV77lrzufdUJSUl2Gw2wsPDj7mdK58FT7Jo0SKio6Pp168ft912G3v37m122450XvPz8/nkk0+48cYbj7utt57b1ujUgaWoqIj6+npiYmKarI+JiSEvL++o78nLy3Npe0/kdDq59957Of300zn55JOb3a5fv368+uqrfPDBB8yZMwen08lpp51Gbm6uG6ttnbS0NP71r38xf/58Zs2axbZt2zjzzDMpKys76vYd4bwC/O9//6O4uJjrr7++2W28+bz+0sHz48q5a83n3hNVVVXxwAMPcPXVVx9zcjxXPwueYty4cbz++utkZWXx5JNPsnjxYsaPH099ff1Rt+8o5xXg3//+NyEhIVx22WXH3M5bz21rdYjZmsU1d9xxBz///PNxr3WOGjWKUaNGNT4/7bTT6N+/P3//+9957LHH2rvMEzJ+/PjGx4MHDyYtLY2EhATeeeedFv3V4q3++c9/Mn78eOLi4prdxpvPq5hqa2u58sorMQyDWbNmHXNbb/0sXHXVVY2PBw0axODBg0lKSmLRokWce+65FlbW/l599VWuvfba43aG99Zz21qduoUlMjISh8NBfn5+k/X5+fnExsYe9T2xsbEube9p7rzzTj7++GO+/PJLevbs6dJ7fX19OeWUU9i8eXM7Vdd+wsPD6du3b7O1e/t5BdixYweff/45N910k0vv8+bzevD8uHLuWvO59yQHw8qOHTtYuHDhMVtXjuZ4nwVP1adPHyIjI5ut29vP60Fff/012dnZLn+OwXvPbUt16sDi5+fH8OHDycrKalzndDrJyspq8hfo4UaNGtVke4CFCxc2u72nMAyDO++8k/fff58vvviC3r17u7yP+vp61qxZQ/fu3duhwvZVXl7Oli1bmq3dW8/r4V577TWio6OZMGGCS+/z5vPau3dvYmNjm5y70tJSvvvuu2bPXWs+957iYFjZtGkTn3/+Od26dXN5H8f7LHiq3Nxc9u7d22zd3nxeD/fPf/6T4cOHM2TIEJff663ntsWs7vVrtbfeesvw9/c3/vWvfxnr1q0zbrnlFiM8PNzIy8szDMMwfvOb3xgPPvhg4/ZLliwxfHx8jL/+9a/G+vXrjenTpxu+vr7GmjVrrDqEFrntttuMsLAwY9GiRcaePXsal8rKysZtfnmsjz76qLFgwQJjy5YtxooVK4yrrrrKCAgIMNauXWvFIbjkvvvuMxYtWmRs27bNWLJkiZGenm5ERkYaBQUFhmF0nPN6UH19vdGrVy/jgQceOOI1bz+vZWVlxo8//mj8+OOPBmA888wzxo8//th4Z8yf//xnIzw83Pjggw+M1atXG5dcconRu3dv48CBA437OOecc4znn3++8fnxPvdWOdax1tTUGBdffLHRs2dPY9WqVU0+x9XV1Y37+OWxHu+zYJVjHWtZWZnxu9/9zli6dKmxbds24/PPPzeGDRtmnHTSSUZVVVXjPrzlvBrG8f87NgzDKCkpMYKCgoxZs2YddR/ecm7bS6cPLIZhGM8//7zRq1cvw8/Pzxg5cqSxbNmyxtfOOussY/LkyU22f+edd4y+ffsafn5+xsCBA41PPvnEzRW7Djjq8tprrzVu88tjvffeexv/XWJiYowLLrjAWLlypfuLb4VJkyYZ3bt3N/z8/IwePXoYkyZNMjZv3tz4ekc5rwctWLDAAIzs7OwjXvP28/rll18e9b/dg8fkdDqNhx9+2IiJiTH8/f2Nc88994h/h4SEBGP69OlN1h3rc2+VYx3rtm3bmv0cf/nll437+OWxHu+zYJVjHWtlZaVx/vnnG1FRUYavr6+RkJBg3HzzzUcED285r4Zx/P+ODcMw/v73vxuBgYFGcXHxUffhLee2vdgMwzDatQlHRERE5AR16j4sIiIi4h0UWERERMTjKbCIiIiIx1NgEREREY+nwCIiIiIeT4FFREREPJ4Ci4iIiHg8BRYRERHxeAosIiIi4vEUWERERMTjKbCIiIiIx1NgEREREY/3/wHWeOE4yjb3qAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "this_history_df[['auc', 'val_auc']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cee80637-cabf-4974-b476-08c5fba165ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/7XTcwYMHufXWW4mOjiYwMJAzzzyTjz76yPUfgEgLtetAOa9/k8PNr33HwEeXcv0/V/PSl7/wc0EpVgsM7tKO31/Yiw+mn8u3D6XwzISzuPHcrgzq3E6BRloUtdQcT3U5/MVDSwH8cQ/4B590t3HjxnHXXXexfPlyRo0aBUBRURFLlizhk08+obS0lEsvvZTHH3+cgIAA/vWvf3H55ZeTnZ3dqHWbrFYrzz77LF27duWXX37hjjvu4IEHHuCFF15o8DlefPFFUlNTeeKJJ7jkkksoLi5m1apVDT5+zpw5PPHEE8ybNw9fX1/ef/997rnnHubNm0dKSgofffQRU6ZMoVOnTowcORK73c4ll1zCoUOHeP311+nevTs//fQTPj76j1par8oaG1nbi1iRvY8vft7H1r3OrTFRbQP4Ta/2jOjdnvN6RhEe5O+hmoo0LYWaFqxdu3ZccsklvPnmm45Q83//939ERUUxcuRIrFYriYmJjv3//Oc/8/777/PBBx80uHXkWMd2Jk5ISOCxxx7jtttucynUPPbYY/z+97/nnnvucWwbOnRog4+//vrrmTJliuP5ddddx4033sgdd9wBQGpqKt988w1/+9vfGDlyJJ999hlZWVls2rSJXr16AdCtW7cGlyfSUuTuL2fFz3v5InsfX2/bz+Fqm+M1H6uFQZ3DGdG7A7/p1Z6+saEalSStkkLN8fgFmS0mniq7gW644QamTZvGCy+8QEBAAG+88QYTJkzAarVSWlrKnDlz+Pjjj8nLy6OmpobDhw+Tm5vbqGp99tlnpKWlsXnzZkpKSqipqaGiooLy8nKCgk5e571797Jnzx5HAGuMIUOGOD3ftGkTt9xyi9O2c889l2eeeQaAdevW0alTJ0egEWktKqptrN5exBfZe1mRvY9fCsucXu8QEsCI3u0Z0bsD5/aIIqyN+p9J66dQczwWS4NuAXna5ZdfjmEYfPzxxwwdOpSvvvqKp59+GoD777+fZcuW8be//Y0ePXrQpk0brrnmGqqqqlwuZ8eOHfz2t7/l9ttv5/HHHyciIoKVK1cydepUqqqqCAoKwmq1YvyqP1B19dH5LNq0aXNqFwsEB7v2njRFmSLuYhgGlTV2yqtslFXWUFpZc8xXG2VV5nNzm83x/b7SSr7dUURF9dH+dL5WC4O7tOM3vdszolcH+sSGaJI7Oe0o1LRwgYGBXH311bzxxhts3bqV3r17M2jQIABWrVrFjTfeyFVXXQVAaWkpO3bsaFQ5a9aswW638/e//x2r1exf/s477zjt0759e/Lz8zEMw/Gf6bp16xyvh4SEkJCQQEZGBiNHjmxUPX6tT58+rFq1ismTJzu2rVq1ir59+wIwYMAAdu3axc8//6zWGmkWBSUVfLoxn7ziCqeg4hxSbI7tNfaGDQyoT0xo4JHWmPac0yOK0EC1xsjpTaGmFbjhhhv47W9/y8aNG5k4caJje8+ePVm8eDGXX345FouFhx9+uM5IqYbq0aMH1dXVPPfcc1x++eWsWrWKBQsWOO0zYsQI9u3bx5NPPsk111zDkiVL+N///kdoaKhjnzlz5nDbbbfRoUMHRwfeVatWcddddzWqXn/4wx+49tprOeuss0hJSeHDDz9k8eLFjlFev/nNbzj//PMZO3Ysc+fOpUePHmzevBmLxcLFF1/cqDJFfq34cDWfbsjnP+t2k/nL/oYOYHQS6GelbYAvwQG+BPv7Hvneh6AAX9r6m9vbBvgQHOBLSKAfg7qE0ztarTEix1KoaQUuuOACIiIiyM7O5vrrr3dsnzt3LjfddBPnnHMOUVFRzJgxg5KSkkaVkZiYyNy5c/nrX//KzJkzOf/880lLS2PSpEmOffr06cMLL7zAX/7yF/785z8zduxY7r//fl566SXHPpMnT6aiooKnn36a+++/n6ioKK655ppGX/uYMWN45pln+Nvf/sY999xD165deeWVVxgxYoRjn/fee4/777+f6667jrKyMnr06METTzzR6DJFwOzTsnzzXv67bg+fZ++l6pjFGwd3aceATmFHQ8qRQBLkCCtHA0ptiPFRx12RU2Yxft0JopUqKSkhLCyM4uJip5YDgIqKCrZv307Xrl0JDAz0UA3FW+jfgxyPzW6QuW0//123myUb8jl0zOy7PTu0ZcxZHbkiMY74iIZ39heREzvR5/evqaVGROQEDMPgx93F/Of7PXy4fg/7Dh1dCykuLJDLB8ZxZWJHdcwV8QIKNQLAG2+8wa233lrva126dGHjxo2tunyRX9teWMZ/1+3mg3V7nIZLhwf5cWn/WK5MjGNoQoTmexHxIgo1AsAVV1xBcnJyva81x/pKni5fBGBvSQUfrs/jv+t2s35XsWN7oJ+VC/vGcGViHOf3aq/FHEW8lEKNAOZw65CQkNO2fDl9lVRUs2RDPh+s28PX2wqpHWHtY7UwvEcUY86K48K+MbQN0H+XIt5Ov6XHOE36TMtJ6N9B61dRbeOLbHPkUsZm55FLgzqHc+XAjlw2IJaotgEerKWIuEqhhqO3N8rLyzUDrVBeXg7otldrcrC8ijU5B/h2xwG+21HE+l3FVNmOBpkeHdoyZmAcVyR2pHOkRi6JtFQKNYCPjw/h4eHs3bsXgKCgII1iOA0ZhkF5eTl79+4lPDxcK3m3UIZhsPvgYb7bcYBvdxTx3Y4DZBccqrNfbFggVyTGccXAOPrGhup3XqQVUKg5IiYmBsARbOT0FR4e7vj3IN7PZjfIzj/EdzlFjpaYvOKKOvt1iwpmaEIEQxLaMTQhgi6R+uNFpLVRqDnCYrEQGxtLhw4dnBZhlNOLn5+fWmi8XEW1jR92HuTbHWaIWZtzwGkSPDAXd+zXMYyhXdox5EiQUf8YkdavUaFm/vz5PPXUU+Tn55OYmMhzzz1HUlJSvfuOGDGCFStW1Nl+6aWX8vHHHwPmekCLFi1i586d+Pv7M3jwYB5//HGnIb5FRUXcddddfPjhh1itVsaOHcszzzxD27ZtG3MJx+Xj46MPNREvcqCsiu9yzBaYb3cU8ePuYqptzp25g/19GNSlnaMlZmB8OEH++ptN5HTj8m/922+/TWpqKgsWLCA5OZl58+YxevRosrOz6dChQ539Fy9eTFVVleP5/v37SUxMZNy4cY5tvXr14vnnn6dbt24cPnyYp59+mosuuoitW7fSvn17wFy0MS8vj2XLllFdXc2UKVO45ZZbePPNNxtz3SLihUoqqvk5/xCb8w+xcU8x3+44wNa9pXX26xAS4HQr6YyYEHx9NHeMyOnO5bWfkpOTGTp0KM8//zwAdrud+Ph47rrrLh588MGTHj9v3jxmzZpFXl4ewcHB9e5Tu87DZ599xqhRo9i0aRN9+/bl22+/ZciQIQAsWbKESy+9lF27dhEXF3fScl1ZO0JE3Kuyxsa2vWX8XGAGmOz8ErLzD7Gnnr4wYI5OGprQjiFdIhiaEEF8RBv1hxE5Tbht7aeqqirWrFnDzJkzHdusVispKSlkZmY26Bzp6elMmDDhuIGmqqqKl156ibCwMBITEwHIzMwkPDzcEWgAUlJSsFqtrF69mquuuqrOeSorK6msPLpGS2NXpxaRxrPbDXYdOMzmI6Elu+AQ2fmH2F5YRo29/r+nYsMC6RUdwhmxIQzpEsHgLu2ICPZv5pqLSEvkUqgpLCzEZrMRHR3ttD06OprNmzef9PisrCw2bNhAenp6ndc++ugjJkyYQHl5ObGxsSxbtoyoqCgA8vPz69za8vX1JSIigvz8/HrLSktL45FHHmnopYnIKSosrXTcOsrOP8TmgkNsKThEeZWt3v1DAn05IyaE3jEh9I4OoXdMKL2jQwgL0vxAItI4zdqTLj09nf79+9fbqXjkyJGsW7eOwsJCXn75Za699lpWr15dbz+dhpg5cyapqamO5yUlJcTHxze67iJy1N6SCr7cUshPe0rILjBbYQpLq+rd19/HSvcObX8VYEKIDQvULSQRaVIuhZqoqCh8fHwoKChw2l5QUHDSeT3KyspYtGgRjz76aL2vBwcH06NHD3r06MHZZ59Nz549SU9PZ+bMmcTExNSZP6ampoaioqLjlhsQEEBAgIZwijQFwzDYtq+UTzcWsOynAtbtPFhnH4sFOkcEmbeOjgSYM2JC6BIZjJ868YpIM3Ap1NQOt87IyGDMmDGA2VE4IyOD6dOnn/DYd999l8rKSiZOnNigsux2u6NPzLBhwzh48CBr1qxh8ODBAHz++efY7fbjruwsIqfGZjdYm3uAZT+ZQWZ7YZnT64mdwhjUpd2RABNKr+i2GkYtIh7l8v9AqampTJ48mSFDhpCUlMS8efMoKytjypQpAEyaNImOHTuSlpbmdFx6ejpjxowhMjLSaXtZWRmPP/44V1xxBbGxsRQWFjJ//nx2797tGPbdp08fLr74YqZNm8aCBQuorq5m+vTpTJgwoUEjn0SkYSqqbXy1pZBlP+WTsWkv+8uO3lLy97EyrHskF/WLJqVPNNGhgR6sqYhIXS6HmvHjx7Nv3z5mzZpFfn4+AwcOZMmSJY7Ow7m5uVitzk3N2dnZrFy5kqVLl9Y5n4+PD5s3b+a1116jsLCQyMhIhg4dyldffUW/fv0c+73xxhtMnz6dUaNGOSbfe/bZZ12tvoj8SlFZFRmbzNaYL7fso6L66EKPIYG+XHBGBy7qG8P5vaIICVQnXhHxXi7PU9NSaZ4akaNy9pex7KcClv5UwHc7ijh2dHVcWCAX9Yvhwr7RJHWNUH8YEfEot81TIyItk91u8OPu4iNBJp+fC5xn6e0bG8qFfaO5sG80/eK0YrWItEwKNSKtVFWNncxf9rN0Yz6fbSqgoOToZJQ+VgvJXSO4sK/ZPyY+IsiDNRURaRoKNSKthGEYbN1byldbClm5tZBvftnvNPFdkL8PI3q358K+0Yzs3YHwIM3SKyKti0KNSAu291AFq7YW8tWWQlZtLXRqjQFoHxJASp9oLuobzbDukQT6aQV6EWm9FGpEWpDyqhqythex8khrzOb8Q06vB/haSeoawfAeUZzbI4q+saFYreofIyKnB4UaES9msxts2F3Myq2FfLVlH2tzDlJlszvt0y8ulOE9ozivR3uGJLRTa4yInLYUakS8zM6i8iP9Yvbx9bb9HCyvdnq9Y3gbhveIYnjPKM7pHklkWy0HIiICCjUiHldcXs3X2wr5amshK7cUkltU7vR6SIAvZ3eP5LyeUQzvEUXXqGANuRYRqYdCjUgzq6yxsTbnoNnBd2shP+466DT5na/Vwlmdwxneoz3De0aR2CkMX02AJyJyUgo1Im5mGAbZBYdYucUcpZS1vYjD1TanfXp0aGveUuoRxdndI2kboF9NkdNOVTlUlkBIjKdr0mLpf04RN8grPszKI8OsV27dT2Gp81DrqLYBDO8RyblH+sbEhrXxUE1FxOMO5EDWS7D231BZDLEDIXECnHkNtG3v6dq1KAo1Ik3gUEU13/xSdGTOmH1s21fm9HobPx+Su0U4Ovj2jg5RvxiR05lhQG4mfPMCbP4YjGNGNeatMx+fPgQ9UiBxPPS+FPz0x8/JKNSINEK1zc66nQcd88Ws23kQ2zEdY6wW6N8pnPOOzBczqEs4Ab4aai1y2quphI3vm2Em74ej27uNhLNvh7izzNd/WAR71sKWT81HQCj0vdJswel8Dli9rJ9dTZUZxHz8zGvwEK3SLdIAtUsQrDwyQumbX/ZTVuXcLyYhMojhR0YoDesWRViQn4dqKyJep3QffLcQvkuH0gJzm28gDBgPybdBdN+6x+z7GdYvgvXvQPHOo9vDOsOAa82AE9Wzeer/a1VlsDPLbG3K+Rp2fQc1h6HPFTD+301alCuf3wo1IsdxuMrGpxvz+XLLvnqXIGgX5Gf2iTnSGqNFIaVZVJXDL8uhpgIsPmCx1v+wHme74xhLPcf4OH9v9QOr75GHzzHfH3muW6gnl7ceVi+AH98FW5W5LSQWkqbBoBshOPLk57DbIWeVGXA2/heqjplJvONgGDABzhzbsHM1VnkR5H5j1iM302xlstc479MmAs64DK58vkmLVqiph0KNuGLFz/t46P0f2XXgsGObv6+VpIQIR2uMliCQZlV5CL79J3z9PJQXero2pl+HnDrPjxeKfI6Eq3qCVYOCmfVXwexXYSy8s9nZNnYABIQ0/8/FboOfl8A3L8KOr45u7zgYzr7DvI3k08iW3OrDkP2JeXtqawYYR1qMrb7Q40Kz9abXxeAXeGrXULz7aCtMztewb1PdfUI7QZdh0OUc85ZYVC+33BZTqKmHQo00RGFpJX/+6Cf+u24PALFhgVw5sCPDe0RpCQLxjMMHzZEx37wAhw+Y28LioV2C2bnUsJsforXfn+xht5mdVOu8dsw57Hbzr3B7DdirT1Q7L2eByB4QN9AMOXEDIWYABLrpM6CiBL5/HbL+AQd2HKmCjxlizr4D4oc2bXmle2HDe/DDW879cwLCoN8YSLwOOp998hY1w4D92462wuR8DQdz6u4X1Qs6HwkxXc4xw2MzUKiph0KNnIhhGPzfml08/skmDpZXY7XAjed05fcX9SJYc8aIJ5TtN4NM1kvm3CVgfkCfdz/0v6bxf+k3hlPIqX3YnIOP0/Nfv17PNkeYqidg1QlpJwlhx75uqzL7ouStg5Ld9V9PZI+jISd2IMQmnlrQ2b/NfJ++f+PoraHAcBgyBYbeDGGdGn/uhtq7+Wj/m2OvO7yL2W8ncQJEdje32W1QsAFyMiH3a/Nr2V7n81msENMfupxrBpnOwzw2vFyhph4KNXI82wvL+OPiH8n8ZT8AfWJDeeLq/iTGh3u2Yt7MbjP/EvULguD24NPCgp+tGrB4Z70PFUDmc/DtQqg+MjVA+z5w/v3Q7yrz9oo0TOk+M9zsWXf0a8mu+veN6O7cohObCIFhxz+3YZi3lr55EbL/Bxz5KI3qDWffZvZz8fdAPzu73azX+rfhp/9CVenR1zoNNcPWztVHg3ItH3/oOOTo7aROSe5r0XKRQk09FGrk16pq7Lz81S88k7GFqho7gX5W7kvpxU3Du+KnZQmcVR+G3WvMpuncb8xRD47/FC0QHAVtY6BtB3M21LYdjj5vG310mzv7N9RUQdk+8y/O0tqvRx6135ftM78eLjIDWfcLzI6NPUe7t5NlQxTvhq+fhTWvmp2AwbxV8psHoPdl3jeEt6UqKzwScr4/8vUH55FFx4roVrdFxzfQ7PS7eoHZ2lGrx4XmkOzuF3hPB+qqcnMOnPWLYNvnznPh+IdAfNLRW0lxg069H46bKNTUQ6FGjrU29wAz3/uR7AKzqfi8nlE8PqY/nSM1ggkw/+PP/QZ2fmN+3bOubt8K30Czqf/Y/yhPxi+4/uATEmOGn9ptwVFmi0RNVd1A4hRWjtlW29+kMSxWiD8bel9ihpzaZvrmcCAHVj4N6944Ojqm01A4/wHoeaH3fEC2ZmX7jwk562DPD1CcW/++fkFQXX70+4HXQ9Kt0L5Xc9W2cQ7lw08fmLftOg+D6DO9s6WyHgo19VCoETBn/n3q02z+/U0OhgERwf7M+m1frhwYd/rO8GsYUPSLGV5qW2L2b6m7X9sYs9Nh52Hm1+gzzQ/c8v3mvBuHCsyvpflm0Dh05Gvpke3HNoOfjMUK/m3rNpGf9DgfMxgFtz/ytYPZDyD4SIuR4/sO5l/n2f+DzZ9AwY/O54nqZc7g2vtS6DTEPbd89m+Dr+aaf0XXDo3tMhx+8wfo+huFGU8rL4I93zvfvjp4JOiExR8Zkj0J2rTzYCVPDwo19VCokaUb85n1343kl5hN+2MHdeKhy/oQEezf/JU5fABWPWP+pxkUdfwP4uD24NvE9bNVQ/76Y0LM6rqdBAHan+EcYsK7nNoHbWXpkYCz9zjBJ/9o68uxrT9W31+Fk9qfVXTdn1ubdo27TXMw1ww42Z/AjpXO828Et4deo81bQN1GnHo/ib2b4Ku/m6NWaq+z20jzNlOXc07t3OJe5UVQvAs69G0xrRytgUJNPRRqTl/5xRXM+WAjSzbmA9AlMoi/XNWfc3tENX9laqrMGUVX/LXht0sCw+tpdTj2A/6Y5/XdE688BLu+PRpidn13tPm8lo+/eU+9NsTEJ0FQxClfbqPYaszWn8oSCIo0r785+5NUFMOWZWbI2bLMXGCwlm8b6D7SvE3V62Lz599Qeevhy6dg0wdHt/W6GM7/g9kaJCL1Uqiph0LN6cduN3gjK5cn/7eZQ5U1+Fot3HJ+N+4e1bP555sxDNj0IXw227zVA2ZLSNItZsCo02dk35EWC9uJz/trAaHHtFxEmf01CjbU7fcSGG4GmPhkM8TEneW1nQQ9qqbKHPJae5vKqZ+Fxez7csaR21RRvepvydq1xgwzP//v6LY+V5ijmWIT3X4JIi2dQk09FGpOLz8XHGLm4h9Zk2O2hgyMDyft6v70ifXAe79rDSx9yGwlAbNFZeQf4azfnbgJ226HioP1j95xGuFz5GttJ9P6hHc+ehup8zBz2KlG07jGMKBgo3mLKvsT89bhsSK6H+1o3CkJdmWZYWbb5+brFiv0u9oMMx36NH/9RVoohZp6KNScHiqqbcxfvpUFK7ZRbTMI9vfhgYvPYOLZXfBp7iUNDuRAxiNm3wkwb12cMx3OvafphzYbhnnb5NjQU1Zo3kLqPAxC45q2PDGHYP+8xAw42790DpX+bY92jLb4mBOfDU+FqB6eqatIC6ZQUw+FmtYvc9t+/vj+j2wvNCcsS+kTzaNX9iMuvE3zVuTwQbMj6OoFRz7oLOawz5EPQVjH5q2LNI/KQ2aLzOZPYMunZn8pqx+cNRGG32suaSAijeLK57e6b0uLd7C8irRPNvP2d+YEWh1CAnj0yn6M7hfTvMO0bdXw3UL44glzcjeArufDRY+p70RrFxBiru/T90qzo3P+egjtCCHRnq6ZyGlFoUZaLMMw+HB9Ho9+uJHCUrPpf+LZnXng4jMIDWzGdXEMw5y1c9ksKNpmbovqDRf9GXpepPlGTjc+vtBxkKdrIXJaUqiRFqmqxs7sDzbyVpY5GqVnh7akXd2fIQnNPAx591pY+idzdVsw55wZ+UcYNFnzWIiINDP9rystTlFZFbe/vobV24uwWOCeUT25Y0QP/H2bcTTPwZ2Q8Sj8+I753DcQht0J597rNYvAiYicbhRqpEX5ueAQU1/7lp1Fh2kb4Mtz153FyDNcmADtVFWUwMq5kPkC2CrNbQMmwAV/gvD45quHiIjUoVAjLUbGpgLuWbSO0soaOkcEkT55CD2j3bjq87Fs1ebqyV88AeWF5rYuw2H0Y+bEdSIi4nEKNeL1DMPgpS9/4YklmzEMOLtbBC/eMJh2zbFmk2GYc5EsmwWFP5vbInvAhX82J1pTJ2AREa/RqE4I8+fPJyEhgcDAQJKTk8nKyjruviNGjMBisdR5XHbZZQBUV1czY8YM+vfvT3BwMHFxcUyaNIk9e/Y4nSchIaHOOZ544onGVF9akMoaG/e/u560/5mB5vrkzvx7anLzBJrCrfDa5fDWBDPQBEXCpX+DO74xp8ZXoBER8Sout9S8/fbbpKamsmDBApKTk5k3bx6jR48mOzubDh3q9m1YvHgxVVVHZ9rcv38/iYmJjBs3DoDy8nLWrl3Lww8/TGJiIgcOHOCee+7hiiuu4LvvvnM616OPPsq0adMcz0NCmunWg7jGboe9P5mrHeesgrB4SJkNvgEunWbfoUpu/fd3rM09iI/VwuzL+/K7s7s0z9wzOZlmmKk4CD4BcPbtcF4qBIa5v2wREWkUl0PN3LlzmTZtGlOmTAFgwYIFfPzxxyxcuJAHH3ywzv4REc5DbBctWkRQUJAj1ISFhbFs2TKnfZ5//nmSkpLIzc2lc+fOju0hISHExMS4WmVxN7sd9m2C7V/Bjq/MIPPrFaj3rIXxb0BwZINOuXFPMdNe+449xRWEBvrywg2DGd6zmVbV3vg+LL7V7AjccQhcsxDadWmeskVEpNFcCjVVVVWsWbOGmTNnOrZZrVZSUlLIzMxs0DnS09OZMGECwcHBx92nuLgYi8VCeHi40/YnnniCP//5z3Tu3Jnrr7+e++67D1/f+i+hsrKSyspKx/OSkpIG1U8aoDbE7Fhphpgdq47OoFvLLxi6DDNn0s36p7mY4z8vgOvfgfa9T3j6JRvyuO/tHzhcbaNb+2D+OWkI3dq3deMFHWEYkPm8Oe8MwBm/hatfBv8g95ctIiKnzKVQU1hYiM1mIzraeerv6OhoNm/efNLjs7Ky2LBhA+np6cfdp6KighkzZnDdddc5rfFw9913M2jQICIiIvj666+ZOXMmeXl5zJ07t97zpKWl8cgjjzTwyuSE7HbYt/lIiPny+CGm89mQMBwSzoO4geBzZFbfAePhzWvhwA7454Uw7hXoMapOMYZh8PznW/n7MrND7nk9o3j++kGEtWmG2YHtNlgyE7L+YT5PuhUuTgOrj/vLFhGRJuHSgpZ79uyhY8eOfP311wwbNsyx/YEHHmDFihWsXr36hMffeuutZGZmsn79+npfr66uZuzYsezatYsvvvjihAtXLVy4kFtvvZXS0lICAur21aivpSY+Pl4LWjaEU4g5cjupfL/zPicKMfUp2w9v32C22Fh84NInYejNjpcrqm384f/W8+EPZgfxKecm8NClffD1aYYJ9arKYfE02PyR+fyix2DYdHUEFhHxAm5b0DIqKgofHx8KCgqcthcUFJy0r0tZWRmLFi3i0Ucfrff16upqrr32WnJycvj8889PWvHk5GRqamrYsWMHvXvXvZ0REBBQb9iRehiGc4jZsbKeEBP0qxBz1olDzK8FR8Kk/8KH98APb8HHv4fCLXDR4+SX1nDLv79j/a5ifK0W/jzmTK5L6nzyczaFskJ4czzs/s7sEHz1P6DfVc1TtoiINCmXQo2/vz+DBw8mIyODMWPGAGC328nIyGD69OknPPbdd9+lsrKSiRMn1nmtNtBs2bKF5cuXExl58s6k69atw2q11jviSlyQ+QJ89fejE8rVOtUQUx/fABjzIkT1goxHYPUCSnZv5rr8qWwv9aFdkB8vThzM2d0a1pn4lO3fBq+PhQPboU07mPCW2Q9IRERaJJdHP6WmpjJ58mSGDBlCUlIS8+bNo6yszDEaatKkSXTs2JG0tDSn49LT0xkzZkydwFJdXc0111zD2rVr+eijj7DZbOTn5wPmyCl/f38yMzNZvXo1I0eOJCQkhMzMTO677z4mTpxIu3btGnvt8v0b8OmRTt9+QRCf7BxifN0wF4zFYg6NjuxOzXu3ELrrC160b+Px9rN4/Mbf0jmymTrl7swyh2yX74fwLjDxPYjq2Txli4iIW7gcasaPH8++ffuYNWsW+fn5DBw4kCVLljg6D+fm5mK1OveDyM7OZuXKlSxdurTO+Xbv3s0HH3wAwMCBA51eW758OSNGjCAgIIBFixYxZ84cKisr6dq1K/fddx+pqamuVl9q7Vhp3goCGH4fjPije0JMPex2g6d3ncEX5X/in/5/4wzrTl6z/RFreQJEJrm/Aps+hPduhpoKM7xd/w60VYufiEhL51JH4ZbMlY5Grd7+bfDPUeZcMv2ugrELwdo8K1yXVdaQ+s46Pt1o9su6/+y23Jn/EJb8H80+LVfOhwHj3FeBbxbAkgcBA3pdbM5B43/86QVERMSz3NZRWFqB8iJ4Y5wZaDoOMfu4NFOg2XWgnGn/WsOmvBL8faykXd2fsYM7QeUSWHwLZH8Mi2+G/VthxINNO/rIbjfnn/lmvvl8yE1wyVPgo18BEZHWonk+zcQ71FTB27+Dom0Q1hmuewv82jRL0WtyihgzfxWb8kqIauvPW7ecbQYagIC2MP51OPfI7bAVT8B7U6H6cNMUXl0B/3fj0UCTMgcum6tAIyLSyuh/9dOFYcBH90HOSvAPgevfbrZ+JO9+t5OH3t9Alc1O39hQXp48hI7hvwpTVitc+Ki5AvZH98GG9+BADkx4E0Ki6z9xQ5QXwVvXwc5vwMffbJnqf82pXZCIiHgltdScLlY+DeteB4sVxr0K0X3dXqTdbpD2ySb+8H/rqbLZubhfDP93+7C6geZYgybB7/5jDrHe/Z3Z9yd/Q+MqULQd0i80A01gGPzufQUaEZFWTKHmdLDxP+a8MACXPAk9U9xeZGWNjbsXfc8/vvwFgLsv6MELNwwiyL8BjYNdz4ObM8xWm+KdsHA0/PypaxXYtQb+mWL2zwmLh5s+NYeri4hIq6VQ09rtXgPv32p+n3wbJE1ze5ElFdVMXpjFR+vz8POxMG/8QFIv6o3V6kLH38juMHWZOWdOVak5p8w3L5q30U4m+3/w6mXmhIIxA8zzdOjT+AsSEZEWQaGmNTu40+xPUlMBPS+C0X9xe5H5xRVcuyCTb34pom2AL69OSWLMWR0bd7KgCPOW0aBJYNjNodgfp4Kt+vjHfPtPWHQ91ByGHikw5RMIjW1c+SIi0qIo1LRWlYfM1o3SAujQz5yPxc0rTm/de4irX1jF5vxDtA8J4O1bz+bcHlGndlIfP7j8WXORSSzw3UJ44xo4fNB5P7sdls0y15Qy7GYQum4RBIScWvkiItJiKNS0RnYb/N9UKNgAwR3MkU5u/nD/dkcRY1/MZE9xBd3aB7P49nPoFxfWNCe3WOCcu8yRUH7B8MsXZgfgIrO/DjWV5vw2q54xn1/wJzMInepaVSIi0qIo1LRGnz4EWz4F30CztSI83q3FLdmQz8R/rqb4cDVndQ7nvdvOIT7CDWs4nXEpTP0UQjtC4c/w8iiz/8y/rzKHgFt94ap/wPl/aNqJ+0REpEVQqGltsl6G1S+a31/1D+g02K3F/TtzB7e/sYbKGjspfaJ58+azaRfsxjWkYvrDtM8hbhAcLjJvseWsgoBQc1HKxAnuK1tERLyaQk1rsvUz+N8M8/tRs6DfGLcVZRgGT326mYf/uxHDgOuSOrNg4iDa+Lu33w4AITFw48fQ98ojz+PgpiXQbYT7yxYREa+lGYVbi4Kf4N0pYNgg8XoY7r4VzKttdh5870feW7sLgNQLe3HXBT2wNOctH/8guOZVs5Umup85UkpERE5rCjWtQeleeHM8VJZAl+Fw+TNu61NSVlnD7W+s5cuf9+FjtfCXq85k/NDObinrpKxWc6I+ERERFGpavurD5rwsxbkQ0R3G/xt83dOnZd+hSm569Vt+3F1MGz8f5t9wFheccQrrMomIiDQhhZqWzG6H/9wBu76FwHC4/h233YbZUVjG5FeyyNlfTkSwPwtvHMrA+HC3lCUiItIYCjUt2RdpsHGxOZR5/OsQ1cMtxazbeZCpr37L/rIq4iPa8K+bkukaFeyWskRERBpLoaal+mERfPmk+f3lz7itb8ny7L3c8fpaDlfbOLNjKK/cmET7kAC3lCUiInIqFGpaopxM+OAu8/vh98FZE91SzDvf7WTm4h+x2Q3O79WeF24YRNsA/ZMRERHvpE+olqboF7NjsK0K+lwBF8xq8iIMw+D5z7fy92U/A3D1oI78dewA/Hw0rZGIiHgvhZqW5PABeONacybduLPMGYOtTRs0bHaDWf/dwBurcwG4Y0R3/jC6d/POQSMiItIICjUtha0a3pkE+7eYax9dt8icgK4JVVTbuPut71n6UwEWC8y5vB+Tz0lo0jJERETcRaGmJTAM+DgVtn8J/m3NVbdDYpq0iANlVdz8r+9Yk3MAf18rz04YyMVnxjZpGSIiIu6kUNMSfP0crP0XWKxwzUJzUccmtOtAOZMXZrFtXxmhgb6k3ziUoQladkBERFoWhRpv9/NSWHakM/Dov0Cv0U16+k15JUxemMXeQ5XEhQXy2k1J9IwOadIyREREmoNCjTerKIYP7wYMGHITJN/WpKevrLFxy7+/Y++hSnpHh/DqTUOJDWvTpGWIiIg0F4Uab7ZsFhzKM9d0Gv2XJl+k8t+ZOewsOkyHkADeuW0YYW38mvT8IiIizUkTj3irHSthzavm91c8C35N24JSXF7Nc59vBeD3F/VSoBERkRZPocYbVR+GD+42vx98IyQMb/Iinl++heLD1fSODuGawfFNfn4REZHmplDjjVY8CUXboG0MXPhok59+Z1E5r32dA8CDl56Bj1UT64mISMunUONt8tbDqmfM7y/7OwSGNXkRT32aTZXNzrk9IhnRq32Tn19ERMQTFGq8ia3GXKjSsEHfK6HPb5u8iPW7DvLBD3uwWGDmJX20/IGIiLQaCjXe5JsXIG+d2TpzyVNNfnrDMHj8400AXDWwI2d2bPpWIBEREU9RqPEWRb/A8r+Y31/0OIREN3kRn2/ey+rtRfj7Wvn96N5Nfn4RERFPUqjxBoYBH94DNYeh6/lw1sQmL6LGZiftf5sBuOncrnQM1yR7IiLSuijUeIPvXzcXq/RtA5c/0+ST7AG8890utu4tpV2QH3eM7N7k5xcREfG0RoWa+fPnk5CQQGBgIMnJyWRlZR133xEjRmCxWOo8LrvsMgCqq6uZMWMG/fv3Jzg4mLi4OCZNmsSePXuczlNUVMQNN9xAaGgo4eHhTJ06ldLS0sZU37scKoClD5nfj/wjRHRr8iLKKmuYu+xnAO4e1ZPQQE20JyIirY/Loebtt98mNTWV2bNns3btWhITExk9ejR79+6td//FixeTl5fneGzYsAEfHx/GjRsHQHl5OWvXruXhhx9m7dq1LF68mOzsbK644gqn89xwww1s3LiRZcuW8dFHH/Hll19yyy23NOKSvcz//mCu8RQ7EM6+wy1FvPTlLxSWVpIQGcQNyV3cUoaIiIinWQzDMFw5IDk5maFDh/L8888DYLfbiY+P56677uLBBx886fHz5s1j1qxZ5OXlERwcXO8+3377LUlJSeTk5NC5c2c2bdpE3759+fbbbxkyZAgAS5Ys4dJLL2XXrl3ExcWdtNySkhLCwsIoLi4mNDTUhSt2o00fwds3gMUHbvkCYgc0eRF7Syr4zVNfcLjaxgs3DOLS/rFNXoaIiIi7uPL57VJLTVVVFWvWrCElJeXoCaxWUlJSyMzMbNA50tPTmTBhwnEDDUBxcTEWi4Xw8HAAMjMzCQ8PdwQagJSUFKxWK6tXr673HJWVlZSUlDg9vMrhg/Dx783vz73HLYEG4OnPfuZwtY1BncO55MwYt5QhIiLiDVwKNYWFhdhsNqKjnYcbR0dHk5+ff9Ljs7Ky2LBhAzfffPNx96moqGDGjBlcd911jkSWn59Phw4dnPbz9fUlIiLiuOWmpaURFhbmeMTHe9n6Rp/NhtJ8cwXu3zzgliJ+LjjE29/uBOCPl2qiPRERad2adfRTeno6/fv3Jykpqd7Xq6urufbaazEMgxdffPGUypo5cybFxcWOx86dO0/pfE3KzStw13rif5uxGzC6XzRDEiLcUoaIiIi38HVl56ioKHx8fCgoKHDaXlBQQEzMiW9tlJWVsWjRIh59tP4FGmsDTU5ODp9//rnTfbOYmJg6HZFramooKio6brkBAQEEBAQ05LKaVzOswA3w9bZCPt+8F1+rhRkXn+GWMkRERLyJSy01/v7+DB48mIyMDMc2u91ORkYGw4YNO+Gx7777LpWVlUycWHdiudpAs2XLFj777DMiIyOdXh82bBgHDx5kzZo1jm2ff/45drud5ORkVy7B89y8AjeA3W7wl0/M5RCuT+5Mt/Zt3VKOiIiIN3GppQYgNTWVyZMnM2TIEJKSkpg3bx5lZWVMmTIFgEmTJtGxY0fS0tKcjktPT2fMmDF1Akt1dTXXXHMNa9eu5aOPPsJmszn6yURERODv70+fPn24+OKLmTZtGgsWLKC6uprp06czYcKEBo188hrNsAI3wAc/7GHD7hLaBvhyz6iebilDRETE27gcasaPH8++ffuYNWsW+fn5DBw4kCVLljg6D+fm5mK1OjcAZWdns3LlSpYuXVrnfLt37+aDDz4AYODAgU6vLV++nBEjRgDwxhtvMH36dEaNGoXVamXs2LE8++yzrlbfc2w18MF0t67ADVBRbeOpT7MBuH1EdyLbeuEtOBERETdweZ6alsrj89SsehaWPWy2ztz5rVsWrAT4x4ptpP1vMzGhgSy/fwRt/H3cUo6IiEhzcNs8NdJIzbACN8CBsiqeX74VgN9f1EuBRkRETisKNe7WDCtw13ru860cqqjhjJgQrh7UyW3liIiIeCOFGndrhhW4AXL2l/Hvb3YA5kR7PlZNtCciIqcXhRp3OpTv9hW4az35aTbVNoPzekZxfq/2bitHRETEWynUuNP/HnD7CtwA3+ce4OP1eVgsZiuNiIjI6Uihxl02fQQ//ddcgfuK58DH5dHzDWIYBmmfbAZg7KBO9In1khXIRUREmplCjTs00wrcAMt+KiBrRxGBflZ+f1Evt5UjIiLi7RRq3KEZVuAGqLbZeWKJ2UozdXhXYsPcszCmiIhIS6BQ09SaaQVugEXf7uSXfWVEBvtz22+6u60cERGRlkChpik10wrcAKWVNTzz2c8A3JPSk5BAP7eVJSIi0hIo1DSlFX91+wrctf6xYhuFpVV0jQrmuqTObi1LRESkJVCoaSp56831ncCtK3AD5BdX8PJXvwAw4+Le+PnobRQREdGnYVNophW4a81dlk1FtZ0hXdoxul+MW8sSERFpKRRqmsI3L0DeD2brzCVPubWozfklvLtmFwAzL+2DxU3LLoiIiLQ0CjWnqplW4K6V9slmDAMu7R/D4C7t3FqWiIhIS6JQc6p2ZoG92u0rcAOs3FLIip/34edj4YHRZ7i1LBERkZbGPXP3n04SJ0BMf/APdtsK3AB2u8FfPtkEwA3JXUiICnZbWSIiIi2RQk1TiO7n9iLe/343P+WVEBLgy92jerq9PBERkZZGt59agIpqG39fmg3AHSN7EBHs7+EaiYiIeB+FmhZg4art7CmuIC4skCnnJni6OiIiIl5JocbL7S+t5MXl2wC4f3RvAv18PFwjERER76RQ4+XeXJ3Locoa+sWFMmZgR09XR0RExGsp1Hi5zQWHALjqrI5YrZpoT0RE5HgUarxczv4yALpEagi3iIjIiSjUeDHDMMjZXw5Al8ggD9dGRETEuynUeLGD5dUcqqgBoHOEQo2IiMiJKNR4sR1Hbj3FhAZq1JOIiMhJKNR4sdwi3XoSERFpKIUaL6b+NCIiIg2nUOPFdmjkk4iISIMp1HixXLXUiIiINJhCjRfbURtqItRSIyIicjIKNV6qrLKGwtJKADqrpUZEROSkFGq8VO3Ip3ZBfoS18fNwbURERLyfQo2Xql0eobM6CYuIiDRIo0LN/PnzSUhIIDAwkOTkZLKyso6774gRI7BYLHUel112mWOfxYsXc9FFFxEZGYnFYmHdunUNOs9tt93WmOq3CLXDuRN060lERKRBXA41b7/9NqmpqcyePZu1a9eSmJjI6NGj2bt3b737L168mLy8PMdjw4YN+Pj4MG7cOMc+ZWVlDB8+nL/+9a8nLHvatGlO53ryySddrX6LkVM78Z6WRxAREWkQX1cPmDt3LtOmTWPKlCkALFiwgI8//piFCxfy4IMP1tk/IiLC6fmiRYsICgpyCjW/+93vANixY8cJyw4KCiImJsbVKrdIuv0kIiLiGpdaaqqqqlizZg0pKSlHT2C1kpKSQmZmZoPOkZ6ezoQJEwgOdv3D+o033iAqKoozzzyTmTNnUl5eftx9KysrKSkpcXq0JLr9JCIi4hqXWmoKCwux2WxER0c7bY+Ojmbz5s0nPT4rK4sNGzaQnp7uWi2B66+/ni5duhAXF8f69euZMWMG2dnZLF68uN7909LSeOSRR1wuxxtU1djZc/AwoOHcIiIiDeXy7adTkZ6eTv/+/UlKSnL52FtuucXxff/+/YmNjWXUqFFs27aN7t2719l/5syZpKamOp6XlJQQHx/fuIo3s10HyrEbEOTvQ/u2AZ6ujoiISIvg0u2nqKgofHx8KCgocNpeUFBw0r4uZWVlLFq0iKlTp7pey3okJycDsHXr1npfDwgIIDQ01OnRUtR2Eu4cEYTFYvFwbURERFoGl0KNv78/gwcPJiMjw7HNbreTkZHBsGHDTnjsu+++S2VlJRMnTmxcTX+ldth3bGxsk5zPm+QU1i5kqVtPIiIiDeXy7afU1FQmT57MkCFDSEpKYt68eZSVlTlGQ02aNImOHTuSlpbmdFx6ejpjxowhMjKyzjmLiorIzc1lz549AGRnZwMQExNDTEwM27Zt48033+TSSy8lMjKS9evXc99993H++eczYMAAly/a2zmGc2vkk4iISIO5HGrGjx/Pvn37mDVrFvn5+QwcOJAlS5Y4Og/n5uZitTo3AGVnZ7Ny5UqWLl1a7zk/+OADRygCmDBhAgCzZ89mzpw5+Pv789lnnzkCVHx8PGPHjuVPf/qTq9VvEbQ6t4iIiOsshmEYnq5EcygpKSEsLIzi4mKv718z6u9fsG1fGa9PTWZ4zyhPV0dERMRjXPn81tpPXsZmN9hZZA7nVkuNiIhIwynUeJn8kgqqbHb8fCzEhbfxdHVERERaDIUaL1O7PEKndkH4WDWcW0REpKEUaryMOgmLiIg0jkKNl9mxX6tzi4iINIZCjZfJLdLq3CIiIo2hUONltDq3iIhI4yjUeBHDMByhRn1qREREXKNQ40WKyqoorazBYjFHP4mIiEjDKdR4kdo1n2JDAwn08/FwbURERFoWhRovUjtHTWfdehIREXGZQo0XOdpJWCOfREREXKVQ40VqQ41aakRERFynUONFam8/dYlQS42IiIirFGq8SG6RhnOLiIg0lkKNlyitrKGwtApQqBEREWkMhRovUXvrKTLYn5BAPw/XRkREpOVRqPESueokLCIickoUaryEVucWERE5NQo1XqJ2de4umqNGRESkURRqvMSOQo18EhERORUKNV5Cw7lFREROjUKNF6issbGn+DCg208iIiKNpVDjBXYWHcYwINjfh8hgf09XR0REpEVSqPECx3YStlgsHq6NiIhIy6RQ4wVqF7JUfxoREZHGU6jxAlqdW0RE5NQp1HiB2iUSEtRJWEREpNEUarxAjmYTFhEROWUKNR5msxvsPKDbTyIiIqdKocbD8ooPU20z8PexEhvWxtPVERERabEUajys9tZTp4g2+Fg1nFtERKSxFGo8rDbUqJOwiIjIqVGo8bCcIxPvdVYnYRERkVOiUONhOVqdW0REpEk0KtTMnz+fhIQEAgMDSU5OJisr67j7jhgxAovFUudx2WWXOfZZvHgxF110EZGRkVgsFtatW1fnPBUVFdx5551ERkbStm1bxo4dS0FBQWOq71VyinT7SUREpCm4HGrefvttUlNTmT17NmvXriUxMZHRo0ezd+/eevdfvHgxeXl5jseGDRvw8fFh3Lhxjn3KysoYPnw4f/3rX49b7n333ceHH37Iu+++y4oVK9izZw9XX321q9X3KoZhOCbe03BuERGRU+Pr6gFz585l2rRpTJkyBYAFCxbw8ccfs3DhQh588ME6+0dERDg9X7RoEUFBQU6h5ne/+x0AO3bsqLfM4uJi0tPTefPNN7ngggsAeOWVV+jTpw/ffPMNZ599tquX4RUKS6sor7JhsUCndhrOLSIicipcaqmpqqpizZo1pKSkHD2B1UpKSgqZmZkNOkd6ejoTJkwgOLjht1vWrFlDdXW1U7lnnHEGnTt3bnC53qh2de64sDYE+Pp4uDYiIiItm0stNYWFhdhsNqKjo522R0dHs3nz5pMen5WVxYYNG0hPT3epkvn5+fj7+xMeHl6n3Pz8/HqPqayspLKy0vG8pKTEpTKbww51EhYREWkyzTr6KT09nf79+5OUlOT2stLS0ggLC3M84uPj3V6mq2o7CXdRJ2EREZFT5lKoiYqKwsfHp86oo4KCAmJiYk54bFlZGYsWLWLq1KkuVzImJoaqqioOHjzY4HJnzpxJcXGx47Fz506Xy3W33COdhNVSIyIicupcCjX+/v4MHjyYjIwMxza73U5GRgbDhg074bHvvvsulZWVTJw40eVKDh48GD8/P6dys7Ozyc3NPW65AQEBhIaGOj28zQ6tzi0iItJkXB79lJqayuTJkxkyZAhJSUnMmzePsrIyx2ioSZMm0bFjR9LS0pyOS09PZ8yYMURGRtY5Z1FREbm5uezZswcwAwuYLTQxMTGEhYUxdepUUlNTiYiIIDQ0lLvuuothw4a12JFPALm6/SQiItJkXA4148ePZ9++fcyaNYv8/HwGDhzIkiVLHJ2Hc3NzsVqdG4Cys7NZuXIlS5curfecH3zwgSMUAUyYMAGA2bNnM2fOHACefvpprFYrY8eOpbKyktGjR/PCCy+4Wn2vUVJRTVFZFaA5akRERJqCxTAMw9OVaA4lJSWEhYVRXFzsFbeiNuwu5rfPrSSqrT/f/elCT1dHRETEK7ny+a21nzykdnVu3XoSERFpGgo1HrKjduSTOgmLiIg0CYUaD8lVS42IiEiTUqjxkJwizVEjIiLSlBRqPKS2T41GPomIiDQNhRoPqKi2kVdcAUCCbj+JiIg0CYUaD9h5ZNK9kABf2gX5ebg2IiIirYNCjQcce+vJYrF4uDYiIiKtg0KNB9Suzq1bTyIiIk1HocYDco7MUaNOwiIiIk1HocYDcrQ6t4iISJNTqPEArc4tIiLS9BRqmlmNze4Y/aSJ90RERJqOQk0zyyuuoMZu4O9rJSY00NPVERERaTUUappZ7UKWnSOCsFo1nFtERKSpKNQ0M3USFhERcQ+FmmamTsIiIiLuoVDTzHYUanVuERERd1CoaWa1LTWaeE9ERKRpKdQ0I8MwHH1qtESCiIhI01KoaUb7DlVyuNqG1QIdw9t4ujoiIiKtikJNM6pdyLJjuzb4++pHLyIi0pT0ydqMHJ2EI3TrSUREpKkp1DQjdRIWERFxH4WaZnS0k7BCjYiISFNTqGlGOY4lEnT7SUREpKkp1DSjHK3OLSIi4jYKNc2kuLyag+XVgEKNiIiIOyjUNJOcIvPWU/uQAIL8fT1cGxERkdZHoaaZqJOwiIiIeynUNBN1EhYREXEvhZpmUttSo/40IiIi7qFQ00w08klERMS9FGqaSe3tpy5anVtERMQtFGqaQUW1jYKSSgC6RKilRkRExB0UappB7ZpPoYG+hAf5ebg2IiIirVOjQs38+fNJSEggMDCQ5ORksrKyjrvviBEjsFgsdR6XXXaZYx/DMJg1axaxsbG0adOGlJQUtmzZ4nSehISEOud44oknGlP9ZudYnTsyGIvF4uHaiIiItE4uh5q3336b1NRUZs+ezdq1a0lMTGT06NHs3bu33v0XL15MXl6e47FhwwZ8fHwYN26cY58nn3ySZ599lgULFrB69WqCg4MZPXo0FRUVTud69NFHnc511113uVp9j8hVJ2ERERG3cznUzJ07l2nTpjFlyhT69u3LggULCAoKYuHChfXuHxERQUxMjOOxbNkygoKCHKHGMAzmzZvHn/70J6688koGDBjAv/71L/bs2cN//vMfp3OFhIQ4nSs4uGV0ut3h6CSsUCMiIuIuLoWaqqoq1qxZQ0pKytETWK2kpKSQmZnZoHOkp6czYcIERyDZvn07+fn5TucMCwsjOTm5zjmfeOIJIiMjOeuss3jqqaeoqak5bjmVlZWUlJQ4PTzFMUeNJt4TERFxG5cWISosLMRmsxEdHe20PTo6ms2bN5/0+KysLDZs2EB6erpjW35+vuMcvz5n7WsAd999N4MGDSIiIoKvv/6amTNnkpeXx9y5c+stKy0tjUceeaTB1+ZOuv0kIiLifs26smJ6ejr9+/cnKSnJ5WNTU1Md3w8YMAB/f39uvfVW0tLSCAgIqLP/zJkznY4pKSkhPj6+cRU/BdU2O7sOHAY0R42IiIg7uXT7KSoqCh8fHwoKCpy2FxQUEBMTc8Jjy8rKWLRoEVOnTnXaXnucq+dMTk6mpqaGHTt21Pt6QEAAoaGhTg9P2HPwMDa7QYCvlQ4hdcOXiIiINA2XQo2/vz+DBw8mIyPDsc1ut5ORkcGwYcNOeOy7775LZWUlEydOdNretWtXYmJinM5ZUlLC6tWrT3jOdevWYbVa6dChgyuX0OyOXfPJatVwbhEREXdx+fZTamoqkydPZsiQISQlJTFv3jzKysqYMmUKAJMmTaJjx46kpaU5HZeens6YMWOIjIx02m6xWLj33nt57LHH6NmzJ127duXhhx8mLi6OMWPGAJCZmcnq1asZOXIkISEhZGZmct999zFx4kTatWvXyEtvHlqdW0REpHm4HGrGjx/Pvn37mDVrFvn5+QwcOJAlS5Y4Ovrm5uZitTo3AGVnZ7Ny5UqWLl1a7zkfeOABysrKuOWWWzh48CDDhw9nyZIlBAYGAuatpEWLFjFnzhwqKyvp2rUr9913n1OfGW9V21KToE7CIiIibmUxDMPwdCWaQ0lJCWFhYRQXFzdr/5qbX/uOzzYV8Ocr+/G7YQnNVq6IiEhr4Mrnt9Z+crPcoiO3nzTySURExK0UatzIbjccc9To9pOIiIh7KdS40d5DlVRU2/GxWogLb+Pp6oiIiLRqCjVuVDvyqWN4G/x89KMWERFxJ33SulGOlkcQERFpNgo1bpSj1blFRESajUKNGx2do0Yjn0RERNxNocaNakNN5wi11IiIiLibQo0bHb39pJYaERERd1OocZOD5VWUVNQAaqkRERFpDgo1brLjyK2n6NAA2vj7eLg2IiIirZ9CjZs4bj1pdW4REZFmoVDjJrn7NUeNiIhIc1KocZMdCjUiIiLNSqHGTWpX59bIJxERkeahUOMmaqkRERFpXgo1blBeVcO+Q5WAOgqLiIg0F4UaN8g9spBleJAfYUF+Hq6NiIjI6UGhxg12FB659aRJ90RERJqNQo0b1HYS7qxOwiIiIs1GocYNjq7OrZYaERGR5qJQ4wZanVtERKT5KdS4QY7mqBEREWl2CjVNrKrGzu4DhwHdfhIREWlOCjVNbPfBw9gNaOPnQ/uQAE9XR0RE5LShUNPEHKtzRwZhsVg8XBsREZHTh0JNE1MnYREREc9QqGliOVrzSURExCMUapqYVucWERHxDIWaJqbVuUVERDxDoaYJ2e2GYzFLrc4tIiLSvBRqmlB+SQVVNXZ8rRbiwgM9XR0REZHTikJNE6rtJNypXRt8ffSjFRERaU765G1C6iQsIiLiOQo1TUidhEVERDynUaFm/vz5JCQkEBgYSHJyMllZWcfdd8SIEVgsljqPyy67zLGPYRjMmjWL2NhY2rRpQ0pKClu2bHE6T1FRETfccAOhoaGEh4czdepUSktLG1N9t8nVxHsiIiIe43Koefvtt0lNTWX27NmsXbuWxMRERo8ezd69e+vdf/HixeTl5TkeGzZswMfHh3Hjxjn2efLJJ3n22WdZsGABq1evJjg4mNGjR1NRUeHY54YbbmDjxo0sW7aMjz76iC+//JJbbrmlEZfsPrWrcyfo9pOIiEjzM1yUlJRk3HnnnY7nNpvNiIuLM9LS0hp0/NNPP22EhIQYpaWlhmEYht1uN2JiYoynnnrKsc/BgweNgIAA46233jIMwzB++uknAzC+/fZbxz7/+9//DIvFYuzevbtB5RYXFxuAUVxc3KD9XWW3240zZy0xusz4yPg5v8QtZYiIiJxuXPn8dqmlpqqqijVr1pCSkuLYZrVaSUlJITMzs0HnSE9PZ8KECQQHm60Z27dvJz8/3+mcYWFhJCcnO86ZmZlJeHg4Q4YMceyTkpKC1Wpl9erVrlyC2xwor+ZQZQ0A8br9JCIi0ux8Xdm5sLAQm81GdHS00/bo6Gg2b9580uOzsrLYsGED6enpjm35+fmOc/z6nLWv5efn06FDB+eK+/oSERHh2OfXKisrqaysdDwvKSk5af1OxY4jq3PHhgUS6Ofj1rJERESkrmYd/ZSenk7//v1JSkpye1lpaWmEhYU5HvHx8W4tT52ERUREPMulUBMVFYWPjw8FBQVO2wsKCoiJiTnhsWVlZSxatIipU6c6ba897kTnjImJqdMRuaamhqKiouOWO3PmTIqLix2PnTt3nvwCT0HtxHvqJCwiIuIZLoUaf39/Bg8eTEZGhmOb3W4nIyODYcOGnfDYd999l8rKSiZOnOi0vWvXrsTExDids6SkhNWrVzvOOWzYMA4ePMiaNWsc+3z++efY7XaSk5PrLS8gIIDQ0FCnhzvlHLn91Flz1IiIiHiES31qAFJTU5k8eTJDhgwhKSmJefPmUVZWxpQpUwCYNGkSHTt2JC0tzem49PR0xowZQ2RkpNN2i8XCvffey2OPPUbPnj3p2rUrDz/8MHFxcYwZMwaAPn36cPHFFzNt2jQWLFhAdXU106dPZ8KECcTFxTXy0ptWTpEm3hMREfEkl0PN+PHj2bdvH7NmzSI/P5+BAweyZMkSR0ff3NxcrFbnBqDs7GxWrlzJ0qVL6z3nAw88QFlZGbfccgsHDx5k+PDhLFmyhMDAo4tCvvHGG0yfPp1Ro0ZhtVoZO3Yszz77rKvVdxvdfhIREfEsi2EYhqcr0RxKSkoICwujuLi4yW9FlVbWcObsTwFYP+ciQgP9mvT8IiIipytXPr+19lMTqB351C7IT4FGRETEQxRqmkBtJ2Gtzi0iIuI5CjVNQJ2ERUREPE+hpgnUdhJWS42IiIjnKNQ0AcftJ80mLCIi4jEKNU3gaEuNQo2IiIinKNScosoaG3nFhwHdfhIREfEkhZpTtOvAYewGBPn7ENXW39PVEREROW0p1JyiY1fntlgsHq6NiIjI6cvlZRLEWefIIO5L6UVoG/0oRUREPEmfxKeoe/u23JPS09PVEBEROe3p9pOIiIi0Cgo1IiIi0ioo1IiIiEiroFAjIiIirYJCjYiIiLQKCjUiIiLSKijUiIiISKugUCMiIiKtgkKNiIiItAoKNSIiItIqKNSIiIhIq6BQIyIiIq2CQo2IiIi0CqfNKt2GYQBQUlLi4ZqIiIhIQ9V+btd+jp/IaRNqDh06BEB8fLyHayIiIiKuOnToEGFhYSfcx2I0JPq0Ana7nT179hASEoLFYmnSc5eUlBAfH8/OnTsJDQ1t0nN7G11r63U6Xa+utfU6na73dLlWwzA4dOgQcXFxWK0n7jVz2rTUWK1WOnXq5NYyQkNDW/U/rGPpWluv0+l6da2t1+l0vafDtZ6shaaWOgqLiIhIq6BQIyIiIq2CQk0TCAgIYPbs2QQEBHi6Km6na229Tqfr1bW2XqfT9Z5O19pQp01HYREREWnd1FIjIiIirYJCjYiIiLQKCjUiIiLSKijUiIiISKugUNNA8+fPJyEhgcDAQJKTk8nKyjrh/u+++y5nnHEGgYGB9O/fn08++aSZatp4aWlpDB06lJCQEDp06MCYMWPIzs4+4TGvvvoqFovF6REYGNhMNT41c+bMqVP3M84444THtMT3FSAhIaHOtVosFu688856929J7+uXX37J5ZdfTlxcHBaLhf/85z9OrxuGwaxZs4iNjaVNmzakpKSwZcuWk57X1d/55nKi662urmbGjBn079+f4OBg4uLimDRpEnv27DnhORvzu9AcTvbe3njjjXXqffHFF5/0vN743p7sWuv7/bVYLDz11FPHPae3vq/upFDTAG+//TapqanMnj2btWvXkpiYyOjRo9m7d2+9+3/99ddcd911TJ06le+//54xY8YwZswYNmzY0Mw1d82KFSu48847+eabb1i2bBnV1dVcdNFFlJWVnfC40NBQ8vLyHI+cnJxmqvGp69evn1PdV65cedx9W+r7CvDtt986XeeyZcsAGDdu3HGPaSnva1lZGYmJicyfP7/e15988kmeffZZFixYwOrVqwkODmb06NFUVFQc95yu/s43pxNdb3l5OWvXruXhhx9m7dq1LF68mOzsbK644oqTnteV34XmcrL3FuDiiy92qvdbb711wnN663t7sms99hrz8vJYuHAhFouFsWPHnvC83vi+upUhJ5WUlGTceeedjuc2m82Ii4sz0tLS6t3/2muvNS677DKnbcnJycatt97q1no2tb179xqAsWLFiuPu88orrxhhYWHNV6kmNHv2bCMxMbHB+7eW99UwDOOee+4xunfvbtjt9npfb6nvK2C8//77jud2u92IiYkxnnrqKce2gwcPGgEBAcZbb7113PO4+jvvKb++3vpkZWUZgJGTk3PcfVz9XfCE+q518uTJxpVXXunSeVrCe9uQ9/XKK680LrjgghPu0xLe16amlpqTqKqqYs2aNaSkpDi2Wa1WUlJSyMzMrPeYzMxMp/0BRo8efdz9vVVxcTEAERERJ9yvtLSULl26EB8fz5VXXsnGjRubo3pNYsuWLcTFxdGtWzduuOEGcnNzj7tva3lfq6qqeP3117nppptOuLhrS35fa23fvp38/Hyn9y0sLIzk5OTjvm+N+Z33ZsXFxVgsFsLDw0+4nyu/C97kiy++oEOHDvTu3Zvbb7+d/fv3H3ff1vLeFhQU8PHHHzN16tST7ttS39fGUqg5icLCQmw2G9HR0U7bo6Ojyc/Pr/eY/Px8l/b3Rna7nXvvvZdzzz2XM88887j79e7dm4ULF/Lf//6X119/HbvdzjnnnMOuXbuasbaNk5yczKuvvsqSJUt48cUX2b59O+eddx6HDh2qd//W8L4C/Oc//+HgwYPceOONx92nJb+vx6p9b1x53xrzO++tKioqmDFjBtddd90JFzx09XfBW1x88cX861//IiMjg7/+9a+sWLGCSy65BJvNVu/+reW9fe211wgJCeHqq68+4X4t9X09FafNKt3imjvvvJMNGzac9P7rsGHDGDZsmOP5OeecQ58+ffjHP/7Bn//8Z3dX85Rccsklju8HDBhAcnIyXbp04Z133mnQX0AtVXp6OpdccglxcXHH3aclv69iqq6u5tprr8UwDF588cUT7ttSfxcmTJjg+L5///4MGDCA7t2788UXXzBq1CgP1sy9Fi5cyA033HDSzvst9X09FWqpOYmoqCh8fHwoKChw2l5QUEBMTEy9x8TExLi0v7eZPn06H330EcuXL6dTp04uHevn58dZZ53F1q1b3VQ79wkPD6dXr17HrXtLf18BcnJy+Oyzz7j55ptdOq6lvq+1740r71tjfue9TW2gycnJYdmyZSdspanPyX4XvFW3bt2Iioo6br1bw3v71VdfkZ2d7fLvMLTc99UVCjUn4e/vz+DBg8nIyHBss9vtZGRkOP0le6xhw4Y57Q+wbNmy4+7vLQzDYPr06bz//vt8/vnndO3a1eVz2Gw2fvzxR2JjY91QQ/cqLS1l27Ztx617S31fj/XKK6/QoUMHLrvsMpeOa6nva9euXYmJiXF630pKSli9evVx37fG/M57k9pAs2XLFj777DMiIyNdPsfJfhe81a5du9i/f/9x693S31swW1oHDx5MYmKiy8e21PfVJZ7uqdwSLFq0yAgICDBeffVV46effjJuueUWIzw83MjPzzcMwzB+97vfGQ8++KBj/1WrVhm+vr7G3/72N2PTpk3G7NmzDT8/P+PHH3/01CU0yO23326EhYUZX3zxhZGXl+d4lJeXO/b59bU+8sgjxqeffmps27bNWLNmjTFhwgQjMDDQ2LhxoycuwSW///3vjS+++MLYvn27sWrVKiMlJcWIiooy9u7daxhG63lfa9lsNqNz587GjBkz6rzWkt/XQ4cOGd9//73x/fffG4Axd+5c4/vvv3eM9nniiSeM8PBw47///a+xfv1648orrzS6du1qHD582HGOCy64wHjuueccz0/2O+9JJ7reqqoq44orrjA6depkrFu3zun3uLKy0nGOX1/vyX4XPOVE13ro0CHj/vvvNzIzM43t27cbn332mTFo0CCjZ8+eRkVFheMcLeW9Pdm/Y8MwjOLiYiMoKMh48cUX6z1HS3lf3UmhpoGee+45o3Pnzoa/v7+RlJRkfPPNN47XfvOb3xiTJ0922v+dd94xevXqZfj7+xv9+vUzPv7442auseuAeh+vvPKKY59fX+u9997r+LlER0cbl156qbF27drmr3wjjB8/3oiNjTX8/f2Njh07GuPHjze2bt3qeL21vK+1Pv30UwMwsrOz67zWkt/X5cuX1/vvtvZ67Ha78fDDDxvR0dFGQECAMWrUqDo/gy5duhizZ8922nai33lPOtH1bt++/bi/x8uXL3ec49fXe7LfBU850bWWl5cbF110kdG+fXvDz8/P6NKlizFt2rQ64aSlvLcn+3dsGIbxj3/8w2jTpo1x8ODBes/RUt5Xd7IYhmG4tSlIREREpBmoT42IiIi0Cgo1IiIi0ioo1IiIiEiroFAjIiIirYJCjYiIiLQKCjUiIiLSKijUiIiISKugUCMiIiKtgkKNiIiItAoKNSIiItIqKNSIiIhIq6BQIyIiIq3C/wOML2F6AnXVYQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "this_history_df[['auc_roc', 'val_auc_roc']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e184b0ca-1d31-4798-88a0-4e590b67062e",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_model.save(Path(setup.temp_path).joinpath('22_DATA_model.keras'),save_format='tf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d326c9ea-4759-4418-a64d-56c151049c25",
   "metadata": {},
   "source": [
    "## Predictions on all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cd6910ee-5ffe-4def-9aa8-7dba76446b08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   99/21503 [..............................] - ETA: 33s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-29 09:55:08.750673: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21503/21503 [==============================] - 32s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "all_predictions = this_model.predict([X.drop(columns='dset'), \n",
    "                                      X_naics.drop(columns='dset')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "98a2cce1-b7d3-4ebc-8869-80705297e145",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions_df = pd.DataFrame(all_predictions, index=X.index) \\\n",
    "    .set_axis(['predict_prob'], axis=1) \\\n",
    "    .reset_index() \\\n",
    "    .merge(business_data[['target', 'LoanNr_ChkDgt', 'dset', 'dset_naics_holdout', 'NAICS']], \n",
    "           on='LoanNr_ChkDgt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3fc7da9c-6bd2-4d1b-b77f-b62679825d5d",
   "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>predict_prob</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>predict_prob</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.319383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0.319383</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              predict_prob    target\n",
       "predict_prob      1.000000  0.319383\n",
       "target            0.319383  1.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions_df[['predict_prob', 'target']].corr(method='spearman')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e79afc4e-cb67-4064-9b19-dc52500baef2",
   "metadata": {},
   "source": [
    "##### Threshold Tune & Binary Predictions\n",
    "Using training probability predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ccd0bc23-fbc0-40d0-8d8b-f2302405a875",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_pred_train = all_predictions_df[all_predictions_df['dset'] == 'train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "df36d9e9-f35e-46b3-a5be-08081f998319",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_tune_data = sg_plot.get_f1_frame(all_pred_train['target'], \n",
    "                                        all_pred_train['predict_prob'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "de634156-caf6-4a52-b5d7-44b8e986aa1d",
   "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>thresh</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.460283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.459606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.458480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    thresh        f1\n",
       "12    0.24  0.460283\n",
       "11    0.22  0.459606\n",
       "13    0.26  0.458480"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresh_tune_data.sort_values('f1', ascending=False, inplace=True)\n",
    "thresh_tune_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c746b880-d781-4bc1-9df2-89211127c22a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_thresh = thresh_tune_data['thresh'].iloc[0]\n",
    "best_thresh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c0ac5e2-be0d-42f0-960d-1f81b4a5d4e2",
   "metadata": {},
   "source": [
    "##### Append binary predictions to probability predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "641da72e-1bca-4fe7-96c4-6c029b521bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions_df['predict_bin'] = sg_plot.get_binary_predictions(all_predictions_df['predict_prob'], best_thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9f6ecdf4-ac01-4c39-8315-e8583daa337e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predict_bin\n",
       "0    0.577865\n",
       "1    0.422135\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions_df['predict_bin'].value_counts(normalize=True, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cbfed830-7ff8-40f6-a691-6b894b26d6bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions_df.to_parquet(Path(setup.temp_path).joinpath('22_DATA_predictions.parquet'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb0ecbd2-abff-46d8-8c70-f7bccd363b69",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "be4ceefe-c7a8-45bd-8573-2daefe99a1c2",
   "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>dset</th>\n",
       "      <th>accuracy_score</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>precision_score</th>\n",
       "      <th>recall_score</th>\n",
       "      <th>average_precision_score</th>\n",
       "      <th>roc_auc_score</th>\n",
       "      <th>dset_naics_holdout</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.601532</td>\n",
       "      <td>0.423565</td>\n",
       "      <td>0.295507</td>\n",
       "      <td>0.747487</td>\n",
       "      <td>0.340208</td>\n",
       "      <td>0.705469</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>0.673688</td>\n",
       "      <td>0.460283</td>\n",
       "      <td>0.347907</td>\n",
       "      <td>0.679891</td>\n",
       "      <td>0.402345</td>\n",
       "      <td>0.741234</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>val</td>\n",
       "      <td>0.665375</td>\n",
       "      <td>0.450443</td>\n",
       "      <td>0.341632</td>\n",
       "      <td>0.660959</td>\n",
       "      <td>0.389446</td>\n",
       "      <td>0.727766</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.665786</td>\n",
       "      <td>0.449330</td>\n",
       "      <td>0.339841</td>\n",
       "      <td>0.662904</td>\n",
       "      <td>0.383644</td>\n",
       "      <td>0.728407</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.509223</td>\n",
       "      <td>0.395913</td>\n",
       "      <td>0.254990</td>\n",
       "      <td>0.885035</td>\n",
       "      <td>0.327247</td>\n",
       "      <td>0.716412</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dset  accuracy_score  f1_score  precision_score  recall_score  \\\n",
       "0   test        0.601532  0.423565         0.295507      0.747487   \n",
       "1  train        0.673688  0.460283         0.347907      0.679891   \n",
       "2    val        0.665375  0.450443         0.341632      0.660959   \n",
       "0   test        0.665786  0.449330         0.339841      0.662904   \n",
       "1   test        0.509223  0.395913         0.254990      0.885035   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  \n",
       "0                 0.340208       0.705469                 NaN  \n",
       "1                 0.402345       0.741234                 NaN  \n",
       "2                 0.389446       0.727766                 NaN  \n",
       "0                 0.383644       0.728407                 0.0  \n",
       "1                 0.327247       0.716412                 1.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_dset_df = all_predictions_df.groupby('dset') \\\n",
    "    .apply(lambda x: sg_plot.dset_metrics(x.target, x.predict_bin, x.predict_prob)) \\\n",
    "    .reset_index()\n",
    "metrics_test_df = all_predictions_df[all_predictions_df['dset'] == 'test'] \\\n",
    "    .groupby(['dset', 'dset_naics_holdout']) \\\n",
    "    .apply(lambda x: sg_plot.dset_metrics(x.target, x.predict_bin, x.predict_prob)) \\\n",
    "    .reset_index()\n",
    "metrics_df = pd.concat([metrics_dset_df, metrics_test_df])\n",
    "metrics_df.to_csv(Path(setup.temp_path).joinpath('22_REPORT_metrics.csv'), index=True)\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bf6b2f6-992d-4c4b-88cb-02eb418ae751",
   "metadata": {},
   "source": [
    "## Embeddings\n",
    "Get the NAICS embeddings for all codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "802d63c7-6106-4d32-8c2b-bfabf7db8b06",
   "metadata": {},
   "outputs": [],
   "source": [
    "#this_model = keras.models.load_model(Path(setup.temp_path).joinpath('22_DATA_model.keras'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b647025a-000d-4d60-bb42-e623160d0f4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_layer_name = 'naics_reshape'\n",
    "#embed_layer_name = 'reshape_4'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "25b09f02-4c5b-42ad-99a8-0e0c11f23be9",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_naics_x = business_data.drop_duplicates('NAICS_orig') \\\n",
    "    [['LoanNr_ChkDgt'] + numeric_features].set_index('LoanNr_ChkDgt').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a4bd9290-cf45-437d-9986-a29c1dcc8547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1311, 10)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_naics_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1cd7de97-c893-4697-9545-b31dc12c3c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_naics_x_naics = business_data.drop_duplicates('NAICS_orig')[['LoanNr_ChkDgt', 'NAICS']] \\\n",
    "    .set_index('LoanNr_ChkDgt').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f8bf0603-5235-4344-a73d-a5d632befd0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_model = Model(inputs=this_model.inputs, outputs=this_model.get_layer(embed_layer_name).output) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0ee68a3c-a630-4fde-b765-f441d2bb3895",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41/41 [==============================] - 0s 877us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-29 09:56:00.839921: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "embed_out = embed_model.predict([unique_naics_x, unique_naics_x_naics])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f622987d-4206-494f-8482-08ac99fff2f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1311, 8)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embed_out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2193800a-3e35-460c-8ee0-576d460f53bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_df = pd.DataFrame(embed_out, index=unique_naics_x.index) \n",
    "embed_df.columns = [f'emb_{i:03d}' for i in range(len(embed_df.columns))]\n",
    "embed_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5ba4cb2e-4321-4dd4-89cf-5b9b48d90c71",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_df = pd.DataFrame(embed_out, index=unique_naics_x.index)  \n",
    "embed_df.columns = [f'emb_{i:03d}' for i in range(len(embed_df.columns))]\n",
    "embed_df = embed_df.reset_index() \\\n",
    "    .merge(business_data[['LoanNr_ChkDgt', 'NAICS', 'NAICS_orig']], how='left', on='LoanNr_ChkDgt') \\\n",
    "    .drop(columns='LoanNr_ChkDgt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "17f1e84b-2aec-4b6b-bd27-04a79008b2de",
   "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>emb_000</th>\n",
       "      <th>emb_001</th>\n",
       "      <th>emb_002</th>\n",
       "      <th>emb_003</th>\n",
       "      <th>emb_004</th>\n",
       "      <th>emb_005</th>\n",
       "      <th>emb_006</th>\n",
       "      <th>emb_007</th>\n",
       "      <th>NAICS</th>\n",
       "      <th>NAICS_orig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.255826</td>\n",
       "      <td>-0.729110</td>\n",
       "      <td>-0.407601</td>\n",
       "      <td>-0.766506</td>\n",
       "      <td>-0.430174</td>\n",
       "      <td>-0.757454</td>\n",
       "      <td>-0.372485</td>\n",
       "      <td>-0.662722</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>722410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.973158</td>\n",
       "      <td>-1.763451</td>\n",
       "      <td>-1.429178</td>\n",
       "      <td>-0.376610</td>\n",
       "      <td>-1.121319</td>\n",
       "      <td>-0.690342</td>\n",
       "      <td>-0.219163</td>\n",
       "      <td>-1.379903</td>\n",
       "      <td>398.0</td>\n",
       "      <td>332721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.047049</td>\n",
       "      <td>0.031932</td>\n",
       "      <td>0.034413</td>\n",
       "      <td>-0.034623</td>\n",
       "      <td>0.049704</td>\n",
       "      <td>0.019276</td>\n",
       "      <td>0.030296</td>\n",
       "      <td>-0.017976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>445299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    emb_000   emb_001   emb_002   emb_003   emb_004   emb_005   emb_006  \\\n",
       "0 -0.255826 -0.729110 -0.407601 -0.766506 -0.430174 -0.757454 -0.372485   \n",
       "1 -0.973158 -1.763451 -1.429178 -0.376610 -1.121319 -0.690342 -0.219163   \n",
       "2  0.047049  0.031932  0.034413 -0.034623  0.049704  0.019276  0.030296   \n",
       "\n",
       "    emb_007   NAICS NAICS_orig  \n",
       "0 -0.662722  1095.0     722410  \n",
       "1 -1.379903   398.0     332721  \n",
       "2 -0.017976     0.0     445299  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embed_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6a8d3c99-24d1-404c-a867-b67f6dd6a4a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_df.to_parquet(Path(setup.temp_path).joinpath('22_DATA_embeddings.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52e382b5-e9dc-47a1-9bf4-6d0b6b4a0c61",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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