notebooks/fttransformer-demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67febe95-dd8e-4564-8a5e-e641bb16906e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/antonsruberts/miniconda/envs/blog/lib/python3.9/site-packages/tensorflow_addons/utils/ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.4.0 and strictly below 2.7.0 (nightly versions are not supported). \n",
      " The versions of TensorFlow you are currently using is 2.7.0 and is not supported. \n",
      "Some things might work, some things might not.\n",
      "If you were to encounter a bug, do not file an issue.\n",
      "If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. \n",
      "You can find the compatibility matrix in TensorFlow Addon's readme:\n",
      "https://github.com/tensorflow/addons\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import tensorflow_addons as tfa\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score, average_precision_score, accuracy_score\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "from tabtransformertf.models.fttransformer import FTTransformerEncoder, FTTransformer\n",
    "from tabtransformertf.utils.preprocessing import df_to_dataset, build_categorical_prep\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a44d4949-4d7f-4f55-abcf-5184756d4814",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.rcParams.update({'font.size': 15})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f89b597-3f3a-4079-bc7c-ad3fbf16dba8",
   "metadata": {},
   "source": [
    "## Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0c2547d-a0b7-478c-ae45-92e99dc6f113",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset shape: (32561, 15)\n",
      "Test dataset shape: (16282, 15)\n"
     ]
    }
   ],
   "source": [
    "CSV_HEADER = [\n",
    "    \"age\",\n",
    "    \"workclass\",\n",
    "    \"fnlwgt\",\n",
    "    \"education\",\n",
    "    \"education_num\",\n",
    "    \"marital_status\",\n",
    "    \"occupation\",\n",
    "    \"relationship\",\n",
    "    \"race\",\n",
    "    \"gender\",\n",
    "    \"capital_gain\",\n",
    "    \"capital_loss\",\n",
    "    \"hours_per_week\",\n",
    "    \"native_country\",\n",
    "    \"income_bracket\",\n",
    "]\n",
    "\n",
    "train_data_url = (\n",
    "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\"\n",
    ")\n",
    "train_data = pd.read_csv(train_data_url, header=None, names=CSV_HEADER)\n",
    "\n",
    "test_data_url = (\n",
    "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\"\n",
    ")\n",
    "test_data = pd.read_csv(test_data_url, header=None, names=CSV_HEADER)\n",
    "\n",
    "print(f\"Train dataset shape: {train_data.shape}\")\n",
    "print(f\"Test dataset shape: {test_data.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75521624-3f40-4aad-bbd4-cea5c5cb5782",
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_bracket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education_num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital_status          occupation    relationship    race   gender  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week  native_country income_bracket  \n",
       "0          2174             0              40   United-States          <=50K  \n",
       "1             0             0              13   United-States          <=50K  \n",
       "2             0             0              40   United-States          <=50K  \n",
       "3             0             0              40   United-States          <=50K  \n",
       "4             0             0              40            Cuba          <=50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89bff333-3750-46c9-b687-3f06a8f43845",
   "metadata": {},
   "source": [
    "## Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "420201d9-eb7d-4873-bae8-2296fef046c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Column information\n",
    "NUMERIC_FEATURES = ['age', 'education_num', 'capital_gain', 'capital_loss', 'hours_per_week']\n",
    "CATEGORICAL_FEATURES = ['workclass', 'education', 'marital_status', 'occupation', 'relationship', 'race', 'gender', 'native_country']\n",
    "\n",
    "FEATURES = list(NUMERIC_FEATURES) + list(CATEGORICAL_FEATURES)\n",
    "LABEL = 'income_bracket'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aaa2d685-44be-410a-89cf-acdd4a069adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2408095574460244, 0.23621176759611842)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encoding as binary target\n",
    "train_data[LABEL] = train_data[LABEL].apply(lambda x: int(x == ' >50K')) \n",
    "test_data[LABEL] = test_data[LABEL].apply(lambda x: int(x == ' >50K.'))\n",
    "train_data[LABEL].mean(), test_data[LABEL].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "677298dc-05fe-47ef-b40a-f5b1d62e8187",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = test_data.iloc[1:, :] # drop invalid row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25986888-5dc1-497c-96c7-bc5e5411c105",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set data types\n",
    "train_data[CATEGORICAL_FEATURES] = train_data[CATEGORICAL_FEATURES].astype(str)\n",
    "test_data[CATEGORICAL_FEATURES] = test_data[CATEGORICAL_FEATURES].astype(str)\n",
    "\n",
    "train_data[NUMERIC_FEATURES] = train_data[NUMERIC_FEATURES].astype(float)\n",
    "test_data[NUMERIC_FEATURES] = test_data[NUMERIC_FEATURES].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ad185875-9f9e-43b2-9422-40fe9ff66d7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train/test split\n",
    "X_train, X_val = train_test_split(train_data, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56a4a4f7-47d3-46a5-a88c-fbff2264159b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train.loc[:, NUMERIC_FEATURES] = sc.fit_transform(X_train[NUMERIC_FEATURES])\n",
    "X_val.loc[:, NUMERIC_FEATURES] = sc.transform(X_val[NUMERIC_FEATURES])\n",
    "test_data.loc[:, NUMERIC_FEATURES] = sc.transform(test_data[NUMERIC_FEATURES])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a07ad92f-f1b9-46ad-9b61-831cbc903e22",
   "metadata": {},
   "source": [
    "## Modelling Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9bfdb641-4fec-4f2e-a84f-149a41e2a02b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/antonsruberts/personal/TabTransformerTF/tabtransformertf/utils/preprocessing.py:21: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  dataset[key] = value[:, tf.newaxis]\n",
      "2022-11-18 18:30:27.535732: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "/Users/antonsruberts/personal/TabTransformerTF/tabtransformertf/utils/preprocessing.py:21: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  dataset[key] = value[:, tf.newaxis]\n",
      "/Users/antonsruberts/personal/TabTransformerTF/tabtransformertf/utils/preprocessing.py:27: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  dataset[key] = value[:, tf.newaxis]\n"
     ]
    }
   ],
   "source": [
    "train_dataset = df_to_dataset(X_train[FEATURES + [LABEL]], LABEL)\n",
    "val_dataset = df_to_dataset(X_val[FEATURES + [LABEL]], LABEL, shuffle=False)  # No shuffle\n",
    "test_dataset = df_to_dataset(test_data[FEATURES + [LABEL]], shuffle=False) # No target, no shuffle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe05de6-639b-48b6-8387-e44be0d8db0c",
   "metadata": {},
   "source": [
    "# FTTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9342c4cd-d309-420f-b966-c6377030d6d4",
   "metadata": {},
   "source": [
    "## FT Transformer - Linear Numerical Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e29156d1-7d28-439b-9e00-2a98e38f1fb1",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "51/51 [==============================] - 16s 195ms/step - loss: 0.3909 - output_loss: 0.3909 - output_PR AUC: 0.6437 - val_loss: 0.3460 - val_output_loss: 0.3460 - val_output_PR AUC: 0.7198\n",
      "Epoch 2/1000\n",
      "51/51 [==============================] - 9s 179ms/step - loss: 0.3376 - output_loss: 0.3376 - output_PR AUC: 0.7224 - val_loss: 0.3369 - val_output_loss: 0.3369 - val_output_PR AUC: 0.7364\n",
      "Epoch 3/1000\n",
      "51/51 [==============================] - 9s 174ms/step - loss: 0.3272 - output_loss: 0.3272 - output_PR AUC: 0.7414 - val_loss: 0.3302 - val_output_loss: 0.3302 - val_output_PR AUC: 0.7458\n",
      "Epoch 4/1000\n",
      "51/51 [==============================] - 9s 175ms/step - loss: 0.3262 - output_loss: 0.3262 - output_PR AUC: 0.7452 - val_loss: 0.3278 - val_output_loss: 0.3278 - val_output_PR AUC: 0.7507\n",
      "Epoch 5/1000\n",
      "51/51 [==============================] - 11s 204ms/step - loss: 0.3230 - output_loss: 0.3230 - output_PR AUC: 0.7517 - val_loss: 0.3236 - val_output_loss: 0.3236 - val_output_PR AUC: 0.7555\n",
      "Epoch 6/1000\n",
      "51/51 [==============================] - 11s 207ms/step - loss: 0.3226 - output_loss: 0.3226 - output_PR AUC: 0.7553 - val_loss: 0.3239 - val_output_loss: 0.3239 - val_output_PR AUC: 0.7546\n",
      "Epoch 7/1000\n",
      "51/51 [==============================] - 11s 213ms/step - loss: 0.3201 - output_loss: 0.3201 - output_PR AUC: 0.7602 - val_loss: 0.3218 - val_output_loss: 0.3218 - val_output_PR AUC: 0.7623\n",
      "Epoch 8/1000\n",
      "51/51 [==============================] - 12s 233ms/step - loss: 0.3145 - output_loss: 0.3145 - output_PR AUC: 0.7716 - val_loss: 0.3172 - val_output_loss: 0.3172 - val_output_PR AUC: 0.7716\n",
      "Epoch 9/1000\n",
      "51/51 [==============================] - 11s 214ms/step - loss: 0.3087 - output_loss: 0.3087 - output_PR AUC: 0.7813 - val_loss: 0.3112 - val_output_loss: 0.3112 - val_output_PR AUC: 0.7826\n",
      "Epoch 10/1000\n",
      "51/51 [==============================] - 11s 205ms/step - loss: 0.3083 - output_loss: 0.3083 - output_PR AUC: 0.7841 - val_loss: 0.3102 - val_output_loss: 0.3102 - val_output_PR AUC: 0.7873\n",
      "Epoch 11/1000\n",
      "51/51 [==============================] - 12s 226ms/step - loss: 0.3055 - output_loss: 0.3055 - output_PR AUC: 0.7872 - val_loss: 0.3143 - val_output_loss: 0.3143 - val_output_PR AUC: 0.7852\n",
      "Epoch 12/1000\n",
      "51/51 [==============================] - 11s 213ms/step - loss: 0.3051 - output_loss: 0.3051 - output_PR AUC: 0.7887 - val_loss: 0.3067 - val_output_loss: 0.3067 - val_output_PR AUC: 0.7884\n",
      "Epoch 13/1000\n",
      "51/51 [==============================] - 11s 206ms/step - loss: 0.3032 - output_loss: 0.3032 - output_PR AUC: 0.7916 - val_loss: 0.3058 - val_output_loss: 0.3058 - val_output_PR AUC: 0.7882\n",
      "Epoch 14/1000\n",
      "51/51 [==============================] - 10s 199ms/step - loss: 0.3025 - output_loss: 0.3025 - output_PR AUC: 0.7916 - val_loss: 0.3092 - val_output_loss: 0.3092 - val_output_PR AUC: 0.7857\n",
      "Epoch 15/1000\n",
      "51/51 [==============================] - 10s 199ms/step - loss: 0.3025 - output_loss: 0.3025 - output_PR AUC: 0.7903 - val_loss: 0.3058 - val_output_loss: 0.3058 - val_output_PR AUC: 0.7902\n",
      "Epoch 16/1000\n",
      "51/51 [==============================] - 10s 195ms/step - loss: 0.3010 - output_loss: 0.3010 - output_PR AUC: 0.7942 - val_loss: 0.3085 - val_output_loss: 0.3085 - val_output_PR AUC: 0.7868\n",
      "Epoch 17/1000\n",
      "51/51 [==============================] - 10s 193ms/step - loss: 0.3031 - output_loss: 0.3031 - output_PR AUC: 0.7911 - val_loss: 0.3078 - val_output_loss: 0.3078 - val_output_PR AUC: 0.7884\n",
      "Epoch 18/1000\n",
      "51/51 [==============================] - 10s 194ms/step - loss: 0.3017 - output_loss: 0.3017 - output_PR AUC: 0.7911 - val_loss: 0.3076 - val_output_loss: 0.3076 - val_output_PR AUC: 0.7853\n",
      "Epoch 19/1000\n",
      "51/51 [==============================] - 10s 199ms/step - loss: 0.3022 - output_loss: 0.3022 - output_PR AUC: 0.7900 - val_loss: 0.3079 - val_output_loss: 0.3079 - val_output_PR AUC: 0.7862\n",
      "Epoch 20/1000\n",
      "51/51 [==============================] - 10s 198ms/step - loss: 0.3005 - output_loss: 0.3005 - output_PR AUC: 0.7935 - val_loss: 0.3077 - val_output_loss: 0.3077 - val_output_PR AUC: 0.7828\n",
      "Epoch 21/1000\n",
      "51/51 [==============================] - 11s 203ms/step - loss: 0.3000 - output_loss: 0.3000 - output_PR AUC: 0.7929 - val_loss: 0.3048 - val_output_loss: 0.3048 - val_output_PR AUC: 0.7919\n",
      "Epoch 22/1000\n",
      "51/51 [==============================] - 12s 231ms/step - loss: 0.2985 - output_loss: 0.2985 - output_PR AUC: 0.7970 - val_loss: 0.3049 - val_output_loss: 0.3049 - val_output_PR AUC: 0.7916\n",
      "Epoch 23/1000\n",
      "51/51 [==============================] - 11s 206ms/step - loss: 0.2967 - output_loss: 0.2967 - output_PR AUC: 0.7986 - val_loss: 0.3034 - val_output_loss: 0.3034 - val_output_PR AUC: 0.7907\n",
      "Epoch 24/1000\n",
      "51/51 [==============================] - 13s 256ms/step - loss: 0.2998 - output_loss: 0.2998 - output_PR AUC: 0.7945 - val_loss: 0.3041 - val_output_loss: 0.3041 - val_output_PR AUC: 0.7931\n",
      "Epoch 25/1000\n",
      "51/51 [==============================] - 13s 245ms/step - loss: 0.2970 - output_loss: 0.2970 - output_PR AUC: 0.7977 - val_loss: 0.3071 - val_output_loss: 0.3071 - val_output_PR AUC: 0.7910\n",
      "Epoch 26/1000\n",
      "51/51 [==============================] - 10s 198ms/step - loss: 0.2975 - output_loss: 0.2975 - output_PR AUC: 0.7959 - val_loss: 0.3063 - val_output_loss: 0.3063 - val_output_PR AUC: 0.7926\n",
      "Epoch 27/1000\n",
      "51/51 [==============================] - 10s 201ms/step - loss: 0.2987 - output_loss: 0.2987 - output_PR AUC: 0.7974 - val_loss: 0.3042 - val_output_loss: 0.3042 - val_output_PR AUC: 0.7928\n",
      "Epoch 28/1000\n",
      "51/51 [==============================] - 10s 199ms/step - loss: 0.2978 - output_loss: 0.2978 - output_PR AUC: 0.7975 - val_loss: 0.3054 - val_output_loss: 0.3054 - val_output_PR AUC: 0.7903\n",
      "Epoch 29/1000\n",
      "51/51 [==============================] - 11s 204ms/step - loss: 0.2963 - output_loss: 0.2963 - output_PR AUC: 0.7984 - val_loss: 0.3065 - val_output_loss: 0.3065 - val_output_PR AUC: 0.7849\n",
      "Epoch 30/1000\n",
      "51/51 [==============================] - 11s 207ms/step - loss: 0.2966 - output_loss: 0.2966 - output_PR AUC: 0.7985 - val_loss: 0.3027 - val_output_loss: 0.3027 - val_output_PR AUC: 0.7927\n",
      "Epoch 31/1000\n",
      "51/51 [==============================] - 11s 205ms/step - loss: 0.2953 - output_loss: 0.2953 - output_PR AUC: 0.8000 - val_loss: 0.3073 - val_output_loss: 0.3073 - val_output_PR AUC: 0.7903\n",
      "Epoch 32/1000\n",
      "51/51 [==============================] - 11s 212ms/step - loss: 0.2946 - output_loss: 0.2946 - output_PR AUC: 0.8015 - val_loss: 0.3071 - val_output_loss: 0.3071 - val_output_PR AUC: 0.7847\n",
      "Epoch 33/1000\n",
      "51/51 [==============================] - 11s 214ms/step - loss: 0.2941 - output_loss: 0.2941 - output_PR AUC: 0.8028 - val_loss: 0.3108 - val_output_loss: 0.3108 - val_output_PR AUC: 0.7880\n",
      "Epoch 34/1000\n",
      "51/51 [==============================] - 11s 215ms/step - loss: 0.2955 - output_loss: 0.2955 - output_PR AUC: 0.7995 - val_loss: 0.3088 - val_output_loss: 0.3088 - val_output_PR AUC: 0.7886\n",
      "Epoch 35/1000\n",
      "51/51 [==============================] - 10s 202ms/step - loss: 0.2938 - output_loss: 0.2938 - output_PR AUC: 0.8020 - val_loss: 0.3093 - val_output_loss: 0.3093 - val_output_PR AUC: 0.7892\n",
      "Epoch 36/1000\n",
      "51/51 [==============================] - 12s 228ms/step - loss: 0.2938 - output_loss: 0.2938 - output_PR AUC: 0.8025 - val_loss: 0.3042 - val_output_loss: 0.3042 - val_output_PR AUC: 0.7893\n",
      "Epoch 37/1000\n",
      "51/51 [==============================] - 10s 200ms/step - loss: 0.2934 - output_loss: 0.2934 - output_PR AUC: 0.8018 - val_loss: 0.3083 - val_output_loss: 0.3083 - val_output_PR AUC: 0.7883\n",
      "Epoch 38/1000\n",
      "51/51 [==============================] - 11s 203ms/step - loss: 0.2943 - output_loss: 0.2943 - output_PR AUC: 0.8011 - val_loss: 0.3056 - val_output_loss: 0.3056 - val_output_PR AUC: 0.7916\n",
      "Epoch 39/1000\n",
      "51/51 [==============================] - 10s 194ms/step - loss: 0.2935 - output_loss: 0.2935 - output_PR AUC: 0.8040 - val_loss: 0.3028 - val_output_loss: 0.3028 - val_output_PR AUC: 0.7919\n",
      "Epoch 40/1000\n",
      "51/51 [==============================] - 10s 188ms/step - loss: 0.2923 - output_loss: 0.2923 - output_PR AUC: 0.8053 - val_loss: 0.3060 - val_output_loss: 0.3060 - val_output_PR AUC: 0.7910\n",
      "Epoch 41/1000\n",
      "51/51 [==============================] - 10s 190ms/step - loss: 0.2932 - output_loss: 0.2932 - output_PR AUC: 0.8032 - val_loss: 0.3036 - val_output_loss: 0.3036 - val_output_PR AUC: 0.7903\n",
      "Epoch 42/1000\n",
      "51/51 [==============================] - 10s 191ms/step - loss: 0.2941 - output_loss: 0.2941 - output_PR AUC: 0.8024 - val_loss: 0.3077 - val_output_loss: 0.3077 - val_output_PR AUC: 0.7896\n",
      "Epoch 43/1000\n",
      "51/51 [==============================] - 10s 190ms/step - loss: 0.2916 - output_loss: 0.2916 - output_PR AUC: 0.8048 - val_loss: 0.3035 - val_output_loss: 0.3035 - val_output_PR AUC: 0.7908\n",
      "Epoch 44/1000\n",
      "51/51 [==============================] - 10s 197ms/step - loss: 0.2951 - output_loss: 0.2951 - output_PR AUC: 0.8005 - val_loss: 0.3066 - val_output_loss: 0.3066 - val_output_PR AUC: 0.7931\n",
      "Epoch 45/1000\n",
      "51/51 [==============================] - 12s 231ms/step - loss: 0.2921 - output_loss: 0.2921 - output_PR AUC: 0.8046 - val_loss: 0.3060 - val_output_loss: 0.3060 - val_output_PR AUC: 0.7933\n",
      "Epoch 46/1000\n",
      "51/51 [==============================] - 10s 196ms/step - loss: 0.2923 - output_loss: 0.2923 - output_PR AUC: 0.8048 - val_loss: 0.3112 - val_output_loss: 0.3112 - val_output_PR AUC: 0.7880\n",
      "Epoch 47/1000\n",
      "51/51 [==============================] - 11s 204ms/step - loss: 0.2916 - output_loss: 0.2916 - output_PR AUC: 0.8040 - val_loss: 0.3094 - val_output_loss: 0.3094 - val_output_PR AUC: 0.7857\n",
      "Epoch 48/1000\n",
      "51/51 [==============================] - 11s 212ms/step - loss: 0.2925 - output_loss: 0.2925 - output_PR AUC: 0.8050 - val_loss: 0.3054 - val_output_loss: 0.3054 - val_output_PR AUC: 0.7908\n",
      "Epoch 49/1000\n",
      "51/51 [==============================] - 10s 198ms/step - loss: 0.2901 - output_loss: 0.2901 - output_PR AUC: 0.8074 - val_loss: 0.3059 - val_output_loss: 0.3059 - val_output_PR AUC: 0.7926\n",
      "Epoch 50/1000\n",
      "51/51 [==============================] - 10s 199ms/step - loss: 0.2932 - output_loss: 0.2932 - output_PR AUC: 0.8028 - val_loss: 0.3100 - val_output_loss: 0.3100 - val_output_PR AUC: 0.7842\n"
     ]
    }
   ],
   "source": [
    "ft_linear_encoder = FTTransformerEncoder(\n",
    "    numerical_features = NUMERIC_FEATURES,\n",
    "    categorical_features = CATEGORICAL_FEATURES,\n",
    "    numerical_data = X_train[NUMERIC_FEATURES].values,\n",
    "    categorical_data = X_train[CATEGORICAL_FEATURES].values,\n",
    "    y = None,\n",
    "    numerical_embedding_type='linear',\n",
    "    embedding_dim=16,\n",
    "    depth=4,\n",
    "    heads=8,\n",
    "    attn_dropout=0.2,\n",
    "    ff_dropout=0.2,\n",
    "    explainable=True\n",
    ")\n",
    "\n",
    "# Pass the encoder to the model\n",
    "ft_linear_transformer = FTTransformer(\n",
    "    encoder=ft_linear_encoder,\n",
    "    out_dim=1,\n",
    "    out_activation='sigmoid',\n",
    ")\n",
    "\n",
    "LEARNING_RATE = 0.001\n",
    "WEIGHT_DECAY = 0.0001\n",
    "NUM_EPOCHS = 1000\n",
    "\n",
    "optimizer = tfa.optimizers.AdamW(\n",
    "        learning_rate=LEARNING_RATE, weight_decay=WEIGHT_DECAY\n",
    "    )\n",
    "\n",
    "ft_linear_transformer.compile(\n",
    "    optimizer = optimizer,\n",
    "    loss = {\"output\": tf.keras.losses.BinaryCrossentropy(), \"importances\": None},\n",
    "    metrics= {\"output\": [tf.keras.metrics.AUC(name=\"PR AUC\", curve='PR')], \"importances\": None},\n",
    ")\n",
    "\n",
    "early = EarlyStopping(monitor=\"val_output_loss\", mode=\"min\", patience=20, restore_best_weights=True)\n",
    "callback_list = [early]\n",
    "\n",
    "ft_linear_history = ft_linear_transformer.fit(\n",
    "    train_dataset, \n",
    "    epochs=NUM_EPOCHS, \n",
    "    validation_data=val_dataset,\n",
    "    callbacks=callback_list\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "902ec742-d2c1-47ba-88d9-3ab4153108fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "ax[0].plot(ft_linear_history.history['loss'], label='Training Loss')\n",
    "ax[0].plot(ft_linear_history.history['val_loss'], label='Validation Loss')\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1].plot(ft_linear_history.history['output_PR AUC'], label='Training PR AUC')\n",
    "ax[1].plot(ft_linear_history.history['val_output_PR AUC'], label='Validation PR AUC')\n",
    "ax[1].legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb8d3a81-2009-4242-a196-a5ea25171e46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FT-Transformer with Linear Numerical Embedding\n",
      "Test ROC AUC: 0.9118\n",
      "Test PR AUC: 0.7842\n",
      "Test Accuracy: 0.8577\n"
     ]
    }
   ],
   "source": [
    "linear_test_preds = ft_linear_transformer.predict(test_dataset)\n",
    "print(\"FT-Transformer with Linear Numerical Embedding\")\n",
    "print(\"Test ROC AUC:\", np.round(roc_auc_score(test_data[LABEL], linear_test_preds['output'].ravel()), 4))\n",
    "print(\"Test PR AUC:\", np.round(average_precision_score(test_data[LABEL], linear_test_preds['output'].ravel()), 4))\n",
    "print(\"Test Accuracy:\", np.round(accuracy_score(test_data[LABEL], linear_test_preds['output'].ravel()>0.5), 4))\n",
    "\n",
    "# Reported accuracy - 0.858"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "343588c8-1298-47c9-bc29-99204e6c31a7",
   "metadata": {},
   "source": [
    "## Explainability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3d0936a3-d41c-4071-b46c-d7c73a403fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UWFiY/P39NXz4cP31118aMGCAvv/+e02ZMsWKaW3XhQsX5OXlpcjISLVp00aenp4aNGhQgjGdOnXSgAEDtGTJEjVt2tRKSW3XjBkz9M4770iSypcvz+++dHD16lVVqFAhwbENGzYoZ86c+vDDDyVJlStXVp06dXTgwAErJLQP//77rxo0aKBTp07JmMIiSYPBYKFU6cjKhT2k0kcffWQ0GAzGBg0aGI8dO/bU+WPHjhkbNmxodHBwMH700UdWSGgf3nrrrUSvYXBwsNHZ2dno6OhoXLJkiZXS2bbSpUsbs2bNaty8ebO1o9i1K1euGN3c3Ix+fn7Gy5cvWzuO3SpSpIixcePGCY516tTJ6ODgYHqNjouLMxYvXtxYuXJla0S0eYULFzYWLlzYGBkZae0oL4zIyEjj+PHjja+88oppFluBAgWMH3/8sfHo0aPWjmfT4u/jRo0aZTpmMBgSzPQxGo3G8uXLGytVqmTpeDavYMGCRl9f3wTHmjRpYnR0dDRGRESYjlWuXNlYrlw5S8ezGz169DA6ODgYZ8+ebTqW2M9x5cqVja+++qql49mF1157zejo6GhcuXKltaPYrZw5cxr9/PxMjx88eGDMkiWLsWnTpgnGdenSxZgtWzZLx7MbnTt3NhoMBqOnp6dx6dKlxpCQEOP58+eT/GPr6LFmI1xdXRUXF6fTp08re/bsiY65d++eSpcuLQcHB126dMnCCe1DbGys2rRpo7/++kvDhg3T8OHDtX37drVo0UL379/XggUL1KZNG2vHtEnZsmVTvXr19Ndff1k7il17++23FR4err/++ktZsmRRlSpVVKxYMWXNmvWpsfSlMl/WrFnl5+enuXPnmo65uroqV65cOnbsmOmYn5+fAgICFBkZaY2YNi179uxq1qyZlixZYu0oL6QdO3bot99+06JFi3T//n1Jkqenp3r16qWOHTum2I8UCZUsWVLZs2fXkSNHTMccHBzUvXt3TZ8+3XTM19dXwcHB9HFNo2zZsqldu3aaN2+eJMloNCpfvnwqV66cdu7caRrXuXNnrV69Ok0bVuH/K1q0qFxcXLRnzx7TscR+jtu2bastW7bo+vXr1ohp07Jmzao6depo06ZN1o5it1577TXFxMTo+PHjkqQ1a9bI19dXY8eO1f/+9z/TuFatWmn37t0KCwuzVlSblj9/fuXKlUvHjx9P9H2IvWEpqI2In3KdVFFNevwmxMvLSytXrrRgMvuSKVMmLVq0SE2aNNHIkSMVHh6uOXPm6MGDB1q4cKFat25t7Yg2q1ixYilOA8azmzlzpunvDx480I4dO7Rjx45Ex1JYM1/BggUTvPE9e/asLl++rHfffTfBOCcnJz18+NDS8exCmTJlKC5YUe3atVWwYEFlyZJFU6dOlSRt27ZN27dv1//+9z8NGzbMtGQGKQsLC0vVPUTWrFkp+pihaNGiOnfunOnxnj17TG1UnhQTE0ObhGcQGRmpOnXqpDjOYDDowYMHFkhkf/Lly8fSz3TWqlUrffvttxowYIDq1aunIUOGyMHB4anX6P3796t48eJWSmn7oqOjVb9+/ReiqCZRWLMZpUqV0o0bN1Icd+vWLZUsWdICiexX1qxZtWrVKvn4+Ojnn3+Wk5OTlixZopYtW1o7mk3r1q2bxo4dq7CwMLm4uFg7jt0KCAiwdoQXwiuvvKKgoCBdvHhRbm5u+u2332QwGNS8efME486fP8/Pu5n69u2rPn366PDhwypfvry147wwoqOjtXjxYv3+++/aunWrjEajChYsqB49eqhBgwZatGiR5s2bp48++kg3b97U559/bu3INsHZ2Vn//vtviuNOnTqlwoULWyCRfalVq5b+/PNPrVixQvXq1dPo0aNlMBjk6+ubYNyxY8dUtGhRK6W0fQUKFEhQwEwK19l8rVu31ooVK3T//n1ly5bN2nHs0v/+9z8tWrRIEyZM0MSJE2U0GjVw4ECVKVPGNGbXrl36999/1blzZysmtW3ly5d/oVbRsRTURkyYMEGffPKJgoODValSpUTHHDhwQK+//rq++eYbDRgwwLIBbVBiGxQ86erVq/r888/Vvn17NWvW7KnzXbt2Ta9odik2Nlbt27fXuXPnNGnSJLYHh01bv369mjVrJmdnZ5UsWVIHDhxQqVKldPToUdNsiFu3bqlQoULy9fXVokWLrJzYNn3yySeaN2+eRo8erUaNGvFGLR0dPnxYv//+u+bOnaubN2/KaDTKx8dHvXv3Vrt27RJsYHDhwgXVrFlTjo6OCg0NtWJq29G2bVutWbNG+/fv16uvvirp6SV027dvV926dfXmm2+meI+ChI4fP66qVauaZkkZjUbVq1dPmzdvNo05f/68SpUqpZ49e+q3336zVlSb1qVLF82fP19bt26Vp6enpKd/jlevXq1WrVrpvffeY5MIM9y4cUO1atVSxYoV9euvvypPnjzWjmSX7t69q8WLF+vatWuqWrWq6tevn+D8ihUrFBgYqB49ejy10QFSZ/ny5fLz89PmzZsTbPZlryis2ZC+fftq7ty56tu3r9544w3T1NQLFy5owYIF+umnn/Tmm29q4sSJVk5qGxwcHFLcgcRoND41Jv5YbGxsesazS1evXlWDBg107NgxOTk5qXDhwon+NzAYDDpz5owVEgKp98svv+ibb75ReHi4qlatqsmTJyeYWTVp0iT169dPU6ZM0XvvvWfFpLYpU6ZMkhJ/Hf4vg8GgmJgYS8SyO9OmTdNvv/2m3bt3m/pSde3aVe+99548PDyS/LquXbtq3rx5/C5MpW3btsnb21slSpTQ1KlTVa9ePWXOnNlUkNi2bZu6du2qS5cuadeuXapcubK1I9uc/fv3a8KECabX5EGDBilXrlym87/++qt++eUXjR49Wi1atLBiUtsVEhKiatWqKXv27Pruu+/UqlUrFSpUSN27d9fEiRO1fPlyDRgwQPfv39fBgwfl7u5u7cg25+2339bNmze1YsUK5c6dW9WqVZOrq2uS98u09EBGFRoaqsmTJ+vnn3/WwIED1ahRoyR/lqXHbYNsGYU1G5GaNxhJneMNR+JGjBjxTFv7Dh8+/DmmsX/Hjh1TvXr1FB4enqpea3FxcRZIZb8iIiI0d+5c7d69WxEREWrQoIEGDx4s6fGN8dmzZ9WwYcNk+zbi2dy/f18PHz5Uzpw5Ta/hSL0SJUqk6TU6NcuT8DQHBwdJj3uq9e7dO9UbE3z//fdas2YNy8/TYOLEiRo4cKCMRqNy5cqlqKgo5cqVS46Ojrp+/boMBoMmTpyoDz74wNpRgSQtWbJE3bp1M21qIj1+rxF/b5c1a1bNnTtXbdu2tVZEmxb/mpwafND//N2+fVsnT56Uq6sry/KfUfwklhflA1IKazYirW8w/os3HLC25s2ba926derevbsGDhyoUqVKUdRJJ/Pnz9e7776ru3fvmn6ZdevWzbRMY9myZfLz89PMmTP11ltvWTktAGvq06eP3n//fdPyRKSv4OBgffPNN/r77791584dSVKWLFnk4+Ojzz77THXr1rVyQiBl58+f148//qhNmzbp/Pnzio2Nlaurqxo2bKiPP/5YpUuXtnZEm7Vly5Y0jX8Rltg9bxs2bND8+fPVt2/fBLOD42dWPXz4UAaDQf3799f3339vxaS2zcfHJ031C1v/oI7CGgCLeLIXFdJPUFCQ6tWrp7x582ro0KHy9PRU9erVE/Q/efTokQoVKiRvb28tW7bMyokBWNOAAQOUL18+DR061NpRXihGo1GRkZGKjY1VgQIFmNX6jE6dOqWdO3eqbt26CTbx+ueff9SvXz8dPnxYxYsX19dff80O78ALrmPHjlqzZo2uXLliWi5+9OhRVahQQQ4ODqpevbqOHTumW7duafny5U9tggIkhl1BgUQcOHDAtITu1VdfVatWrSQ93i0tOjpauXPntnJC2+Pk5JRgtx2kjzFjxsjR0VGbNm1SxYoVEx3j6OiocuXK6ciRIxZOZz9KlSqV6rH0DERGNnnyZAoNVmAwGFSgQAFrx7Ab48aN0++//55ghUZ4eLgaN26s27dvy2Aw6NixY+rQoYN2796d5O9HAPZv3759qly5coIejDNmzJDRaNTMmTPl7++vCxcu6OWXX9aUKVMorCFVKKwBTzh27Jh69Oih3bt3m45169bNVFibPn26+vTpozVr1qhp06bWimmT6tWrp/3791s7ht0LDg5WzZo1U3zT4ObmpsOHD1solf05f/68tSPYna1bt0qSatSooaxZs5oepxY7DZvH1dWVnpYWcOTIES1ZskS+vr5Jbkywf/9+rVq1Sh06dNDLL79s4YS2bdu2bapQoYLc3NxMx6ZPn67bt2/r448/1ldffaW1a9eqXbt2GjduHLuuIsN79OiRli1bpqCgIF2+fFkGg0EuLi6qW7eu2rZtm2CnZqTN1atXVa1atQTHNm3apDx58qhTp06SpOLFi8vLy4sPoZ+jyMjIBD/L+fPnt3ak54rCWgbFGwzLu3Dhgry8vBQZGak2bdrI09NTgwYNSjCmU6dOGjBggJYsWUJhLY2++eYb1ahRQ4MHD9Y333yTpuasSL379++n6hdV/Cf4ME9ShQij0aiLFy9q/fr1GjZsmLp27apvv/3WwulsU3wvjmPHjsnDwyPNvTlo4Gyetm3batasWaZG+kgfEyZM0KxZs9SrV68kxxQuXFijR4/WlStXNGXKFAums31hYWFP3fuuXbtWWbJk0fDhw+Xk5KTWrVurZs2aCg4OtlJK25faJcuOjo7Knz+/KlWqpC5dusjf3z+dk9mX7du3y9/fX5cuXXpqw68pU6bIzc1Nf/zxh2rXrm2lhLYtc+bMevjwoenxnTt3FBISohYtWiR4f1KwYEGFh4dbI6Jd+eWXXzRx4kSdOHEiwfFy5cqpb9++eu+996yU7PmisJZB8QbD8r788ktdv35ds2bNMjV0/29hLW/evHrllVe0c+dOa0S0aXPnzlXLli01btw4LV++XD4+PipatGiSO9nS78c8xYsX16FDh5IdExMTo0OHDtFcOB0YDAYVK1ZMvXr1UtWqVVW7dm2VLl062TfTeKxr164yGAxydnZO8Bjpa8SIEQoICFDz5s01ceLEJGdT4dls2bJFlStXlouLS5JjXFxcVKVKFZtv4GwNDx48UNasWU2PY2NjtWfPHtWsWVM5c+Y0HS9RogS9Xp+Bm5ubDAaDLly4YDqWN29eGY1G3bx503SscOHCunr1qtauXat169Zp/vz5WrZsGb0EU+HkyZNq1qyZ7ty5o6pVq+rNN99UiRIlJD2eBDB37lzt2bNHzZo10549e2izYoYSJUpo7969psdr1qxRbGysGjVqlGBcZGSk3c2qsqS4uDh17NhRy5Ytk9FoVJ48eVS8eHFJUmhoqI4dO6YPP/xQmzZt0qJFi2z+no/CWgbFGwzLW79+vSpXrpziLonFixdP8449ePzmLX7L5dOnT+v06dNJjqWwZr6WLVvqhx9+0OTJk/Xhhx8mOmb8+PG6cuWK3XxClFFVqVJFNWrU0KRJkyispcLMmTOTfYz00bp1a2XJkkXbt29XtWrV5OLiomLFiiUoUsQzGAzavHmzFVLavkuXLqlKlSopjitRooTWrFljgUT2xc3NTcePHzc9DgoK0r1791SvXr0E4+7fv68cOXJYOp7dOH36tDp06CBJGj58uDp06GC6nnfv3tWiRYs0atQolS9fXgsXLtT+/fv1/vvva82aNZoyZYr69u1rzfg24auvvtKdO3f0ww8/qF+/fk+d/+ijjzRx4kT1799fX331Fb8rzdCpUycNGTJE7du3l7e3t8aOHSsnJye1adPGNMZoNGrv3r0qW7as9YLauKlTp2rp0qUqW7asvvvuO7Vs2TLB+TVr1mjQoEFatmyZpk6dqt69e1sp6fPBrqDA/8maNatat26tBQsWmI45ODgk2E1Rktq1a6d169bp3r171ohps2bNmpWm8d26dUunJPYtIiJClSpVUlhYmPz9/dW6dWt17NhRzZs313vvvafly5dr5syZcnNz04EDB0zFe6SP9u3ba+3atbxeIMNKy7J8g8HAjHgz5c2bV56enlq9enWy41q2bKmtW7fq9u3bFkpmHz788EP98ssvGj9+vOrVq6cPPvhAO3fu1L59+xL0HC1Tpoxy586dYLYKUu/LL7/U+PHjdfToURUtWjTRMZcuXdIrr7yigQMHasSIETp//rxeeeUVVahQgWW4qeDq6qpChQql+DNatWpVXb16VZcuXbJQMvtx7949NWrUyLQCycHBQd9//7369+9vGrN582Y1atRIQ4cO1ZdffmmlpLbt9ddf14kTJ3TixAkVKlQo0TFXrlxR2bJlVa5cOe3atcvCCZ8vCmvA/3F1dVWRIkX0zz//mI4lVlh7+eWXFRMTo1OnTlkjJpCi48ePq0OHDjpy5IhplmD8jFej0ahy5cpp2bJlfAqXzq5fv65XXnlFDg4Ounz5srXjAIl6cklXasQv40Da1KlTR4cPH9aFCxeUJ0+eRMfcuHFDJUqUsIs3GJZ26dIlVa5cWdevX5f0+HfdG2+8oT///NM05siRIypfvrz69OmjiRMnWiuqTXN3d1eFChW0bNmyZMe1bdtWhw4dMu2I7eXlpYMHD+rWrVuWiGnTsmTJIj8/P82bNy/ZcV26dNGSJUv04MEDCyWzL3FxcQoKCtK1a9dUqVKlp5bUBgYG6uDBg/L19U3TTvD4/3LlyqVGjRpp6dKlyY5r166dNm7cqKioKAslSx8sBQX+j7e3t+bPn6/t27fL09Mz0TGrV6/WiRMnWEKHDK1cuXI6ePCgVq5cqU2bNun8+fOKjY2Vq6urGjZsKD8/P/qcPKPkNpS5c+eOTp48qZ9//lnh4eG8Xjyje/fuKSAgQKdOnVJUVNRTjZwllo8/CwplluHv768+ffqoQ4cOmj179lO91sLCwtStWzfduXNHXbp0sVJK2+Xq6qoDBw7ot99+U3h4uKpWraru3bsnGLN//37TLG6Y5/Lly6pUqVKK4/77gZKrq6t2796djsnsR/78+XXy5MkUx508eVL58uWzQCL75ODgIG9v7yTP+/j4yMfHx3KBXmD20u6KGWs2JjAwUFu3blVYWJiio6MTHWMwGDRt2jQLJ7N9ISEhqlatmrJnz67vvvtOrVq1UqFChdS9e3dNnDhRy5cv14ABA3T//n0dPHhQ7u7u1o4MwEocHBxSvBEwGo3y9vbWypUr2W3RTDNnztSAAQMSLIt7cgbmk49ZomiekSNHqlKlSmrVqlWy41atWqX9+/dr2LBhFkpmX2JiYlS/fn1t27ZN2bNnV/PmzeXu7i6DwaDTp0/rr7/+0r1791S7dm0FBATI0dHR2pGBp5QpU0bh4eE6efKkXnrppUTHXL16VWXLllXBggVNqzsaNmyoY8eO6d9//7VkXJvUpUsXzZ8/X7/88kuS/Vl/++039e7dW/7+/po7d66FEwKpU61aNZ05c0anT59OchOIiIgIlSlTRu7u7tqzZ4+FEz5fFNZsxK1bt9S6dWsFBQUl+mn9k3iDYb4lS5aoW7duun//vulY/FI66XEftrlz56pt27bWimiz0jKN2mAwmJYPABlR9+7dkyysOTk5ycXFRd7e3k81zkbqbdq0SU2aNJGzs7M+/PBDBQQEaOfOnfrll1905swZLVu2TKdOnVKfPn1UtWpV+jKaKbGWB4np1auXpk+fzv3FM7h3754++ugjzZo166nrmClTJnXt2lUTJkxIsIslkJF89dVXGjp0qDw8PDRq1Ci1bdtWmTM/XgAVExOjZcuWadiwYTp58qRGjhypzz//XDExMXJxcVHVqlW1bt06K38HGd+xY8dUrVo1PXjwQF5eXvL391eJEiVkMBh07tw5zZs3T0FBQcqWLZt2796tl19+2dqRbRYz4tPXpEmT1K9fP1WsWFE//vjjUzMEAwMDNWDAAB06dEgTJkxQnz59rJT0+aCwZiPee+89TZ06VaVLl9b7778vDw+PZG+8kpvaiuSdP39eP/74Y6JL6D7++GOVLl3a2hFtUloaZEuPex8gZcktSUwNLy+v55QEeL6aNWumjRs3au/evapYsaJ69Oih2bNnmwoSsbGxGjRokKZOnarg4GC99tprVk5sm1JbWHv77bc1Z84cPXr0yELJ7NeVK1cUEBCgixcvSnq8o6WPj89Ty0ORdiEhIZoyZYqCgoJ0+fJlGQwGubi4yMvLS++//z6vE88oJiZGfn5+WrlypQwGgxwcHFSoUCEZDAZduXJFcXFxMhqN8vX11ZIlS5Q5c2aFhIRo0KBB6tGjB8twU2nz5s3q0qWLrl279tSHeEajUYUKFdK8efNUv359KyW0fcyIT3+xsbFq1aqV1q5dK4PBoMKFCycoEl+5ckVGo1HNmzfXypUr0/xeMaOhsGYj4m+2jhw5wnp62BWj0aiLFy9q/fr1GjZsmLp27apvv/3W2rFsRmqWJCaHmwXzhIaGKmfOnCm+Ht+4cUNRUVEqVqyYhZLZjwIFCqhcuXLatm2bJD1VWJMeF+BLly6typUra8mSJdaKatNSW1h7/fXXde7cOV27ds1CyYC0mTBhggYNGqTY2NhEZ55kzpxZ3333nfr162eFdPZl7ty5+uWXX7Rnzx49fPhQkuTo6Kjq1aurd+/eeuutt6yc0Pbdu3dPCxcuNBWJJalIkSKqW7euOnbsqOzZs1s5oe1iRrzlxMXF6YcfftDEiRNNHybFK1asmPr27asBAwbYfFFNorBmM7Jnz65mzZrxxgF2bd++fapdu7YmTZqUZF8JJJTYksTIyEitXr1aBoNBlStXNhV1QkNDdeDAARmNRrVo0UL58+fXjBkzrBHb5mXKlEndu3dPsZ9lr169NGPGDMXExFgomf3ImjWr2rVrpz/++EOS9P7772vq1Km6efNmgp51nTt31ubNmyn4pMHbb79t+vvMmTNVunRp1alTJ9GxMTExOnHihPbs2aM2bdpwH4IMaePGjWrSpImyZ8+u9957T2+99ZZpZsT58+c1Z84c/fLLL7p37542bNigBg0aWDuyXYiJiVFkZKSkx03345eFAhkZM+Kt4+LFiwmKxG5ublZO9Hzx6mcj4puFIv1FRERo7ty52r17tyIiItSgQQMNHjxY0uMlBmfPnlXDhg35pCgdVKlSRTVq1KCwlgYzZ85M8DgsLEw1a9ZUo0aNNGnSJHl4eCQ4f/LkSfXr108HDx5UcHCwBZPaF6PRmGK/yyfHIu0KFy6siIiIBI+lxz/DVatWNR2/fv16gr6YSNmTrxvxzfNPnz6d7NdUqFBB3333XTons3/09Ekf48ePV+bMmbVhwwbVrl07wbn4n9127drJy8tL48aNo7D2nGTOnFmFChWydgwgTXbv3q2aNWuqYsWKiZ7PlCmTvv/+ey1fvlzDhw/nA6XnxM3Nze6KaU+isGYj+vbtqz59+ujw4cMqX768tePYrfnz5+vdd9/V3bt3TevqixYtajp/6tQp+fn5aebMmUxzTycFCxa0+V1hrOnTTz9VbGysVqxYoaxZsz513sPDQ0uXLlWZMmX0ySefaPbs2VZI+eKIiIhQtmzZrB3DJpUrV07Hjx83Pa5du7aMRqO+/fZbLViwQAaDQTt27NDff/+d5M0xEhcQECDpcdG3fv36atq0qT755JNExzo5OalIkSIqXry4JSPapbT09KGwljb//POPvL29nyqqPalWrVry8fHRrl27LJjM/hiNRs2bN08rVqxIsUDMRlQpCw0NlSQVLVpUmTJlMj1OLVpNpN2dO3cSXLf4++WoqCjTjHgHBwe9/vrr2rx5s1UywvZQWLMR77zzjk6dOqVmzZpp9OjRatSoUYKCD55dUFCQ3nzzTeXNm1ejR4+Wp6enqlevnmBMy5Yt5ezsrKVLl1JYSwfXr1/X9u3blSdPHmtHsVnr169XvXr1Ei2qxcuWLZvq1q2r9evXWzCZ7fvvRhFXrlxJcvOI+OVz69atYwmBmVq0aKENGzYoODhYNWvWVIMGDVShQgUtWbJERYsWlYuLi0JCQhQXF6f+/ftbO65NeXKDo27duqlu3bpsepTONm3apJ49e8rZ2VmfffaZqafPr7/+mmhPH6TNvXv3VLBgwRTHFSxYUPfu3bNAIvv08OFDtWjRQn///XeSs7ENBgMztdOgRIkScnBw0NGjR+Xh4WFawpwaBoOBVhNmYEZ8+nj77bdlMBj09ddfq1ChQgnaTqTEYDCk2F4lo6OwlkFlypQp0eNGo1E9e/ZM9mt5kTXPmDFj5OjoqE2bNiU5+8HR0VHlypXTkSNHLJzO9iW3e+WdO3d08uRJ/fzzzwoPD9d7771nwWT25fbt2wluFpISERGhqKgoCySyHz4+PgludtevX59scTJ+5snHH39siXh2p2vXrvLw8DBt3uPg4KA1a9aoZ8+e2rRpk65cuSJnZ2cNHjxYb775ppXT2i76LFrGuHHjZDAYFBAQYOrps3PnTlPbg6+++srU0+fdd9+1clrb4+bmpp07dyo2NjbJe+iYmBjt3LnTrpcipbdx48Zp8+bN8vX11fjx4zVy5EjNnTtXDx480NmzZ7VgwQJ9//33ev/999mIKpW8vLxkMBhMLWbiHyP9MCM+fcycOVMGg0GffPKJChUq9FS7muRQWEO6cXNz40XVwuJnRaT0Aurm5qbDhw9bKJX9+G9RIjFGo1He3t765ptvLJTK/rz22mvasmWLgoKCVLdu3UTHbNu2TYGBgapcubKF09m2rl27mn6GZ82aJXd3d3l6eiY6Nn75nK+vr6pUqWLJmHbD2dlZTZo0SXCsaNGiWrdune7du6dbt27ppZdeSvJNNNIuvhF5dHR0kmNYdmQeevqkr9atW2vcuHF65513NGHCBOXOnTvB+du3b6tfv34KDQ3lw45nsGDBAuXLl09//PGHcuTIYdrJz9HRUWXLltWwYcNUr1491atXT2XLlk3TjJUXVWBgYLKP8fwxIz59xLeZiL9PiH/8oqCwlkGdP3/e2hFeOPfv31f+/PlTHHf79m2KnmZ4sijxX05OTnJxcZG3t7fq1atn4WT25ZNPPlGHDh3UpEkTdevWTR06dFCxYsVkMBh04cIFLVq0SLNnz1ZcXFySPZWQuCc/eZs1a5bq1Kmj6dOnWy/QCyx79uxsIPMcbdq0SaNHj1ZwcLAePXqU5DhmxJuPnj7pa8iQIVq6dKlmz56t5cuXq3nz5qYldefOndOaNWt0+/ZtlSpVSkOGDLF2XJt1+vRpeXl5KUeOHJJkKqw9OVOwbt268vT01JQpUyisIUNiRnz6+G9LiRetxQSFNeD/FC9eXIcOHUp2TExMjA4dOqTSpUtbKJX9SMt0YJivffv2mjhxogYNGqRff/1VU6dOTXDeaDTKyclJ48ePV/v27a2U0vbFxcVZOwLwXKxevVpt27ZVbGys8ubNq1KlSilnzpzWjmV36OmTvvLly6egoCD17t1ba9as0Z9//vnUmBYtWujXX39V3rx5rZDQPmTKlCnBbMD4Alt4eLjpZ1p6PLt41apVFs9nD+I3lBk8eHCy477//nv99ddf+vvvvy2UzH4wI94ytm7dqsKFC8vDwyPZcadOnVJYWJi8vLwslCx9UFizA7dv39bJkyfl6uqa4Jca0qZly5b64YcfNHnyZH344YeJjhk/fryuXLlCDzBkaH369JGvr6+mTZum7du36/LlyzIajSpSpIjq1KmjHj16qGTJktaOCSSrfv36qRrn5OSk/Pnzq1KlSurYsSO7V6bRl19+qbi4OP3444/68MMPeSORTujpk/6KFCmiVatW6dy5c9q2bZsuX75sOl6nTh1+7z0HRYsWTbBrZfwHzcHBwWrTpo3p+KFDhyjQmykwMFAlSpRIcdyJEye0ZcuW9A/0gmFG/PPj4+OjHj16pNg7bezYsZo+fbpiY2MtlCx9UFizERs2bND8+fPVt2/fBH2Rfv75Zw0cOFAPHz6UwWBQ//799f3331sxqe369NNPNX/+fH300UcKDg5W69atJUnXrl3T6tWrtXz5cs2cOVPFihXTRx99ZOW0QPKKFy+ukSNHWjuG3QsNDdWqVat06tQpRUVFJboTmj00ZLWG+D4zye0w9+S5P//8U1988YW++uor/e9//7NUTJt35MgR1apVi99r6YyePpZTsmRJimjppGbNmlqyZInu37+vbNmyqXnz5howYID69eunLFmyyNXVVVOnTtWxY8fk6+tr7bh27cGDB8qcmbfyyNhSs0OwvewibDDay3di5zp27Kg1a9boypUrpl4cR48eVYUKFeTg4KDq1avr2LFjunXrlpYvX84vMzMdP35cHTp00JEjR0xv2OL7ghmNRpUrV07Lli1T2bJlrZzUNl27dk1TpkzR1q1bFRYWlmSDbIPBoDNnzlg4HZA2I0eO1KhRoxIsC43/lfrk64bBYLD5T+Gs4cKFC/rhhx/0yy+/qFOnTqZ+gUajURcvXtSiRYs0f/589e7dW2+88YaCgoI0ZswYRUVFadWqVWrevLm1vwWb8NJLL6lu3bo0y09nt27dUnBwsMqVK2eaVfnvv/+aevrExcWZevrQAwwZ1Zo1a/TOO+/o119/VatWrSRJH3/8sX744YcEv/dy5MihPXv2cL9sBgcHB3Xv3j3ZHq63b99W5cqVFRsbS1/uVHiWXn98OGq+1PwsS1Lz5s21fft23bp1y0LJ0geFNRtRunRpFS5cWNu2bTMdGzRokMaPH685c+bI399fFy5c0Msvvyxvb2+tXbvWimltW1xcnFauXKlNmzbp/Pnzio2Nlaurqxo2bCg/Pz+WyZjp2LFj8vb2VmRkZKo+maCH1bMLDQ1NtoApyeb7GVjLggUL1LlzZ5UoUUKff/65Fi1apI0bN2rdunU6e/asFixYoMDAQA0cOFC+vr4vXAPX5+HPP/9U165dtWHDhiQ3NQkMDFSjRo00c+ZMdenSRVu3bpWPj4+aNm2qv/76y8KJbZO/v7927typM2fOmBqR4/kbOHCg8ubNq6FDhz51jp4+aTd79uxn+vquXbs+pySQpPnz52v58uW6ceOGPDw89NFHH6lMmTLWjmUzSpUqZfr7+fPnlTNnThUoUCDRsTExMbp69apiYmLUp08fTZgwwVIxbdaz/G7jw9G0efK1uXv37qpTp47eeeedRMfGxMToxIkT+uGHH1SzZk1t3brVUjHTBYU1G5ErVy61aNFC8+fPNx2rXLmyQkNDFR4ebnrBaNq0qY4ePZqg/wGQEbRs2VJ//fWX2rdvr88++0weHh6mprd4vqZPn65Ro0al6nWAmwXzeHt7a9euXTpx4oSKFy+uHj16aPbs2Qmu5w8//KDBgwcrICBAderUsWJa21S1alXlyZMnxR0SGzZsqOvXr2vfvn2mr4v/3YiUXbx4UdWrV1fnzp31/fffU9hJJ05OTmrdurUWLVpk7Sh2wcHBwawd2plFjIzoycJPcu0PJMnR0VFFihRRq1atNGbMGPqBpcKz9qLjw9HUS+trs9FoVNasWbVy5Uo1bNgwHZOlPxZm24jMmTPr4cOHpsd37txRSEiIWrRokeDFuGDBgryZMNOxY8f08ssvWzuG3QoKClLZsmW1cOFCs26GkTozZswwfTJUvnx5eXh40EA4HRw6dEi1a9c2Len679JPSRowYICmTZum0aNHa926dVbLaquOHz9u6nWZnJdeekk7d+40PS5durRCQkLSM5pdmTFjhpo1a6aJEydq9erV8vHxkaura6Kv0waDIdEZV0iZq6srM7Gfo2HDhnEvAbvx5GtDapfPIfUojFlO/Guz0WjUyJEjValSpSTv5ZycnFSkSBE1btxYLi4uFk76/FFYsxElSpTQ3r17TY/XrFmj2NhYNWrUKMG4yMhI5c+f39Lx7MKrr76ql156SV5eXvLx8ZG3t7deffVVa8eyG0ajUeXLl+dGOJ2NHz9emTNn1pIlS+i1mI6io6MT7MKcNWtWSdLNmzeVN29e0/GKFStSVDNTrly5tGPHDj169EiOjo6Jjnn06JF27Nhh6j0qSXfv3pWzs7OlYtq8ESNGmG6Cz5w5k2x/Swpr5mvbtq1mzZqlqKioBD+vMM+IESOsHQFIFzNmzDDttgrYmidfm2fOnKmGDRtq+PDh1gtkQRTWbESnTp00ZMgQtW/fXt7e3ho7dqycnJwSbG1tNBq1d+9eGoWaKb5x4uLFi01NnAsUKCAvLy95e3vLx8dHr732mpVT2q5q1arp9OnT1o5h906dOiUvLy+KaunMxcVFV65cMT0uWrSopMc7LD657PPSpUssOTKTr6+vpk2bJn9/f/3444+maxzv8uXLGjBggC5evJigMfGxY8cS9KtB8mbMmGHtCC+EESNGKCAgQM2bN9fEiRMT7PAO65o2bZq2b9/ODCFkCN26dbN2BLt35MgR0wfQSb0W79+/X6tWrVKHDh1Y0WSmF21jDXqs2Yh79+6pUaNGpuUuDg4O+v777xNsyb5582Y1atRIQ4cO1ZdffmmlpLYtLi5O+/fv15YtWxQQEKDt27fr5s2bkh5/Up8vXz55eXmpXr166tOnj3XD2pitW7eqQYMGmjdvnjp27GjtOHarSJEiqlGjhpYvX27tKHatTZs2+ueff3T58mVJ0vbt21W3bl35+Pho5cqVypkzpxYuXKhOnTqpVq1a2r59u5UT257w8HDVqlVLZ8+elaOjo2rWrCk3NzcZDAaFhoYqODhYjx49UsmSJRUcHKyCBQtq//79ql27tj777DNmViFDqV+/vu7fv69du3bJYDDIxcVFxYoVM812fZLBYEixtyCen8R6ZAIZQVRUlM6cOaOoqKgk+66xCVXavfvuu5o1a5bOnz+f5BLEsLAwFS9eXO+8846mTJli4YSwRRTWbEhcXJyCgoJ07do1VapU6anddgIDA3Xw4EH5+vryaf1zYjQadeDAAQUGBiowMFAbN25UdHS0DAaDYmJirB3PpmzdulUrV67UhAkT1KVLFzVq1CjJPj4SNwrmev/997VixQqdOXNG2bJls3YcuzVt2jT16tVLGzZsMDVbbdCggQICApQ5c2blypXLVJRfs2aNmjZtasW0tisyMlKffPKJ/vjjDz148CDBuSxZssjf31/ffPONChYsaKWEQOqkZVc6mutbFoU1ZDQhISHq37+/AgMDk93IQGITKnOULVtWefPmVXBwcLLjatasqVu3bunYsWMWSmaftm3bphUrVujUqVNJFont4QMlCmtAMkJDQ01FtcDAQNOUVicnp6fe5CF58bvExL/kpNRrjRsF89y4cUO1atVSxYoV9euvvypPnjzWjmSXoqOjdf78eRUsWFD58uWTJN2+fVuDBw/W8uXLdePGDXl4eGjIkCHy9/e3clrbd/fuXe3bt880Q9DFxUVVqlRhYw7YjAsXLqRpfPzGKEh/FNaQkZw6dUrVq1fX7du35enpqbCwMJ07d06dOnXS2bNntW/fPsXExKhVq1bKkycPy/nNkCNHDrVq1Up//vlnsuM6deqkNWvWKCoqykLJ7IvRaFTPnj01a9asBO//niw/xT+2hw+U6LEGPOG/hbQLFy7IaDTKyclJ1atXV5cuXeTj46PatWtbO6rN6dq1KxsXWMDHH3+sV155RYsXL9aGDRtUrVq1ZHf4mzZtmhVS2r4sWbI81c8yd+7c+uWXX/TLL79YKZX9ypEjh+rWrZvq8fRMSr20zHA3GAzJbm6ApFEoA5Aao0ePVlRUlGbMmKFu3bqpR48eOnfunObNmydJOn36tHr27KmjR4+mOOMKiXNyckpVsezOnTu8d3kGv/zyi2bOnKlq1appzJgx+vnnn7Vs2TKdOHFCZ8+e1YIFCzRnzhwNHDhQH3zwgbXjPjNmrNmYwMBAbd26VWFhYYqOjk50DG+WzePu7m6akebo6Kjq1avLx8fHVEhjWR1sAcuNLGPr1q0qXLiwPDw8kh136tQphYWFsbTZwpiBknppec2QHrelAOwJrxfISNzc3OTs7KyQkBBJif983rx5U6VKlZK/v79++ukna0W1WXXq1NHhw4d14cKFJFd23LhxQyVKlFC5cuW0a9cuywa0E6+//rqOHj2q8+fPK3/+/In+LC9evFhvvPGGli5dqtatW1sx7bNjxpqNuHXrllq3bq2goKAU19pTWDPPuXPnZDAY9Oqrr2rIkCFq2rSp8ubNa+1YELNP0iIgIMDaEV4IPj4+6tGjR4qvtWPHjtX06dN5w4YMK6lCmdFo1MWLF7V+/XoNGzZMXbt21bfffmvhdADwYrl27Zpq1apleuzo6ChJevDggWmzkzx58sjHx0erV6+msGYGf39/9enTRx06dNDs2bOf2sAgLCxM3bp10507d9SlSxcrpbR9x44dU61atZQ/f35J/78NUGxsrDJlyiRJ8vPzU9WqVfX9999TWINlfPLJJ9q6datKly6t999/Xx4eHvSWec4++OADbdmyRUeOHNGbb74pg8Gg8uXLq169evL29pa3tzf9qqxk27Ztmj17NoW1VPD29rZ2hBdGaiZ8MykctspgMKhYsWLq1auXqlatqtq1a6t06dLq1auXtaMBgN3Kly9fgj7O8X1cL1y48FQLimvXrlk0m7149913NX/+fG3evFllypRR8+bN5e7uLoPBoNOnT+uvv/7SvXv3VLt2bb3//vvWjmuz4uLiVKBAAdPj7NmzS3o8G/DJ42XKlNGaNWssnu95o7BmI1asWKFChQopODjY9AKL5yv+E5/IyEhTj7UtW7ZowoQJ+vHHH+Xg4KAKFSqYloe2atXKyokBZHSXL1/mQxDYvCpVqqhGjRqaNGkShTUASEclS5bUuXPnTI8rVaoko9Go+fPna/jw4ZKkiIgIBQYGqlixYtaKadMyZ86sdevW6aOPPtKsWbO0ePHiBOczZcqkHj16aMKECaYZg0i7okWL6tKlS6bH8b1G9+/fr0aNGpmOnzx5Upkz235Zyva/gxfErVu31KxZM4pqFpA/f361b99e7du3lyRdv35dW7Zs0bp16zRnzhwdPHhQEydOVExMjJWTAkmLiIjQ3LlztXv3bkVERKhBgwYaPHiwpMfbuJ89e1YNGzY0fXqElM2ePTvB49OnTz91LF5MTIxOnDihTZs2qWbNmpaIB6SrggULas+ePdaOAQB2rXHjxho1apTOnTunkiVLytfXVwUKFNDIkSN19OhRubq6aunSpbp165b69Olj7bg2K3v27Pr99981evRoBQQE6OLFi5Ie97jz8fF5anko0q5KlSrauHGjYmJilDlzZjVu3FiffPKJBg0apD///FOurq765ZdftHfvXjVo0MDacZ8ZhTUbUaZMGYWHh1s7xgvl4cOHCg4ONs1eCw4OTjA1G8io5s+fr3fffVd37941bWFdtGhR0/lTp07Jz89PM2fO1FtvvWXFpLale/fupv4QBoNB27dv1/bt25McbzQalTVrVg0bNsxSEYF0cf36dW3fvp12CLBLLNlHRvLWW28pOjpa4eHhKlmypHLkyKH58+erY8eOWrRokWlco0aN9Pnnn1sxqX0oXLiwOnfunOT527dvK3fu3BZMZD9atWqlBQsWaPXq1WrTpo0qVqyoTp06af78+XrttddM4zJnzqyvvvrKikmfDwprNqJv377q06ePDh8+rPLly1s7jl1KrJAWHR1tuuFydXWVt7e3aSkokBEFBQXpzTffVN68eTV69Gh5enqqevXqCca0bNlSzs7OWrp0KYW1NBg2bJgMBoOMRqNGjhypSpUqJdlo1cnJSUWKFFHjxo351BMZ2tatW5M8d+fOHZ08eVI///yzwsPD9d5771kwGWAZQ4YMUY8ePawdA5Akubu7a8yYMQmO1a9fXxcuXFBQUJBu3LghDw8PVa1a1UoJbd/AgQM1fvz4FMfdvHlTjRs31j///GOBVPanc+fOateuXYJlnrNmzVKFChW0fPly08/y4MGDVaNGDSsmfT4MRj6msRmffPKJ5s2bp9GjR6tRo0YJZqDg2eXIkUMPHjwwFdKKFStm2rTAx8dHpUqVsnLCF1di2zMjcc2bN1dAQICCg4NVsWJFSZKDg4O6d++eYPOH2rVrKyIiQidPnrRWVJtWokQJdezYUWPHjrV2FCSC14zUc3BwMM3ETIrRaJS3t7dWrlypXLlyWSgZAADPn4ODg0aNGpXsjL8bN26oQYMGOnjwIPcSSBVmrNmI+C1pjUajevbsmexYg8FA/y8zFCxY0DQbzdvbWyVLlrR2JCDNgoODVbNmTVNRLSlubm46fPiwhVLZn/Pnz1s7AvBcdO3aNcnCmpOTk1xcXOTt7a169epZOBmQtPj7YnNwn4yMLDo6WlevXlXevHmT/CAjKipKN27cUOHCheXk5GThhLavTp06GjZsmPLly5forp/Xr183FdU++ugjKySELaKwZiPc3NxS/EQZz+ZZ3iifPHlSV65ckZeX1/MLBJjh/v37yp8/f4rjbt++zWtKOoiJidG0adN0+PBhFStWTL1795azs7O1Y71wmIyfejNnzrR2BCDNuC+GvRo/fry++OILbdmyRXXq1El0zMGDB+Xt7a1vvvlGgwYNsnBC27d69Wp5e3urb9++yps3rzp16mQ6F7/h1+HDhzVgwACNGzfOikntw8OHD7Vs2TJt27ZNly9fliQVKVJEnp6eatu2rbJkyWLlhM8HS0GB54BlR+mL65t65cqVU1xcXIIlnv9dChoTE6PixYurUKFC2rdvn7Wi2rSRI0fqyy+/1N9//y1vb29Jj4s5Pj4+2rZtm2nTCHd3d+3Zs4fGtxZ24sQJXblyxfTfBgAAW/D6668rMjJSp0+fTnacu7u7ChUqpB07dlgomX25du2aPD09FRoaqhUrVqhp06a6evWqGjZsqCNHjujjjz/Wd999Z+2YNm/z5s3q3r27Ll++/NSHngaDQS4uLpoxY4YaNWpkpYTPj4O1AwBASqj/p17Lli115swZTZ48Ockx48eP15UrV9S2bVsLJrMvGzduVNGiRRMUbpYuXaqgoCCVL19ev/76q9q2bavTp08n+98C6aNs2bIU1cwQFhamJUuWaNKkSfrpp5+0ZMkShYWFWTsWALwwzpw5o1deeSXFca+++qrOnDljgUT26aWXXtLGjRtVoEAB+fn5afHixapXr56OHDmiQYMGUVR7Dnbt2qUWLVro33//VY0aNfTjjz9q+fLlWrZsmSZMmKDXX39dly9flq+vr3bt2mXtuM+MGWs2LCoqSgaDQTlz5rR2lBceM6rSF7NPUi8iIkKVKlVSWFiY/P391bp1a3Xs2FHNmzfXe++9p+XLl2vmzJlyc3PTgQMHWKZopqJFi+q1117T+vXrTcc6d+6shQsX6siRIypXrpyMRqNKliypfPnyMTMwFeiZZD3h4eHq27evlixZori4uATnHBwc1L59e02aNEkFCxa0UkIAeDFky5ZNbdq00Z9//pnsuE6dOmnFihW6f/++hZLZpyNHjsjLy0s3b96U0WjUJ5988tSurDBP48aNtXnzZk2ZMkW9e/dOdMzUqVP13nvvqVGjRgnuqW0RhTUbs27dOk2cOFHbt2/XnTt3JD3ezbJu3brq27evmjZtauWELyYKa2kTGhqqsLAwRUdHJzmGfnXmO378uDp06KAjR47IYDCYliVKj2f/lStXTsuWLVPZsmWtnNR2Zc2aVX5+fpo7d67pmKurq3LlyqVjx46Zjvn5+SkgIECRkZHWiGlTSpQo8Uw9k86dO/cc07w4bt26pZo1a+rEiRPKli2bGjdurBIlSkiSLly4oA0bNujevXsqW7asgoODKcYjw4uKitKZM2cUFRWV5Ix37jGQUXl4eMhoNOrUqVPJjitTpoxiY2N19uxZCyWzXaGhocme37lzp9566y35+/tr5MiRT50vVqxYekWza87OzipXrlyKs9Fef/11HT9+XLdu3bJQsvTB5gU2ZODAgZowYYLpJiH+5vbWrVtau3at1q1bp379+mn8+PHWjAkkafr06Ro1alSKv+AkUaR8BuXKldPBgwe1cuVKbdq0SefPn1dsbKxcXV3VsGFD+fn5PdPsIDzeRTg8PNz0+OzZs7p8+bLefffdBOOcnJz08OFDS8ezSey0ah3ffPONTpw4oQ4dOuinn356alZaRESE+vTpo4ULF+rbb7/V119/baWkQPJCQkLUv39/BQYGpthCgnsMZFSNGzfWzz//rEmTJqlv376Jjpk8ebLOnDmT5CwgJJSaD+6MRqPmzJmjOXPmJDjOjHjzOTg4qHTp0imOK126tE6cOGGBROmLGWs2YsGCBercubNeeuklffHFF3rrrbdMhbXbt29rzpw5Gj16tK5du6Y///xTHTt2tHLiFwsz1lI2Y8YM9ezZU5JUvnx5eXh4JLuMecaMGZaKBqRZkyZNFBQUpBMnTsjNzU1DhgzR2LFjtWzZMrVq1co0rnbt2oqIiEiwmQSQkZQrV07379/X6dOn5ejomOiYR48eqXTp0sqaNatd3PzC/pw6dUrVq1fX7du35enpqbCwMJ07d06dOnXS2bNntW/fPsXExKhVq1bKkycP9xjIsC5evKjy5csrKipKvr6+evfdd+Xu7i6DwaDTp09r6tSpWrVqlXLlyqUDBw6YZhgjaT4+Ps80Iz4gIOA5pnlxNGnSRJcvX9bhw4eTHVe+fHm5uLhow4YNFkqWPiis2Qhvb2/t3r1bBw4ckIeHR6JjTp48qUqVKqlGjRoKDAy0bMAXHIW1lJUvX14nTpzQkiVL5Ovra+04dmvr1q2pGufk5KT8+fOrdOnSz3Sz8aJav369mjVrJmdnZ5UsWVIHDhxQqVKldPToUTk5OUl6PJu4UKFC8vX11aJFi6ycGEhctmzZ1LZtW/3xxx/JjvP399eyZcvo54MMqVu3bpo7d66mT5+ubt26PXVfdvr0afXs2VNXrlxRcHCw8ubNa+XEQNK2bt0qPz8/RUREPHWPZjQaVaBAAS1cuFA+Pj7WCQikwj///CMvLy8NHjxYI0aMkINDwn0zjUajRowYoW+//VZbtmzR66+/bqWkzwdLQW3EwYMHVb9+/SSLatLjNfn169fXtm3bLJgMSJ1Tp07Jy8uLolo6S+uncjly5FCnTp301Vdf0Zg8DZo0aaIpU6aYltHVqVNHkydPNhXVJGn27Nl6+PChGjRoYMWk9oGeSeknW7ZsioiISHFcRESEsmXLZoFEQNr9/fffevnll9WtW7dEz5cuXVorVqxQqVKlNHToUP30008WTgiknpeXl06ePKmpU6dq8+bNunjxoiTJzc1NDRs21DvvvENx+BmsXLlSjo6OatasmbWj2LXjx4+rW7du+uqrrzR37ly1b99exYsXl/S4h+uSJUt04cIF9erVSydOnHhqRnzXrl2tEdtszFizEdmzZ5evr68WLFiQ7Lg33nhDq1at0r179yyUDBIz1lKjSJEiqlGjhpYvX27tKHate/fuunnzplauXCkHBwdVrlxZxYoVk9Fo1MWLF7V//37FxcXJ19dX9+/f14EDBxQREaFSpUpp165dyp8/v7W/Bbtx//59PXz4UDlz5qSnnZnomZT+GjVqpK1bt2rHjh2qWrVqomP27t2rWrVqycfHx+aXasA+ZcmSRa1bt9bChQslSe+++66mTZumu3fvKmvWrKZx7dq10759++jpCLzAMmXKpMaNG2vt2rXWjmLXHBwcTJuoxXtyM7X/HosXv+mard3XMWPNRri7u2vLli26d++esmfPnuiYe/fuacuWLXJ3d7dwOiBlrVu3Nm0LzqyH9DN27FjVrFlTLVq00IQJE1SqVKkE58+dO6d+/frp0KFDCg4OVq5cudS7d2/NmzdPY8eO1bfffmul5PYnW7Zsif6sDxo0SEuXLtWZM2eskMp2nDp1SnXq1El1zySYZ8CAAdq8ebMaNGigAQMGyN/fP8EnyvPmzdOECRMUGxurAQMGWDktkLh8+fLpwYMHCR5Lj3+G/7sD9rVr1yyaDUDGUrBgQWb8WcCwYcNeqHYzzFizEaNGjdLw4cPVuHFjTZky5ak3y2fOnNGHH36ojRs36ssvv9QXX3xhpaS2a+DAgcqbN6+GDh2a5q+dNm2atm3bRjPcZNy4cUO1atVSxYoV9euvv/JGOJ28/fbb2rRpk06fPp1gWeKToqOjVaZMGTVo0EAzZszQ3bt3VbJkSeXPn1/Hjh2zcOIXDzNcU4eeSZYzZswYDR061PQJ8n8/UTYYDBo1apSGDBlitYxAcmrXrq2oqChTk+z58+fL399fw4cP1/DhwyU9Xs7s4eGhl156ScePH7dmXCBJoaGhaRpfrFixdEpivzp16qQ9e/bo1KlTL1ThB+mLwpqNuH//vurWrat9+/YpU6ZMqlGjhmnr4HPnzumff/5RbGysqlWrpi1btjAjyAxOTk5q3bo1TcbTydtvv62bN29qxYoVyp07t6pVqyZXV9dEf6EZDAZNmzbNCiltX+HChVW/fv0UG5F37txZAQEBunLliqTHPcO2bdumu3fvWiLmC43CWuq4ubnJ2dlZISEhkhK/bjdv3lSpUqXk7+9Pz6RntGfPHk2aNEnbtm3T5cuXJT1ewl+3bl19+OGHql69upUTAkkbMWKERo0apdOnT6tkyZKmD4wiIyPl5+cnV1dXLV26VKGhofr88881cuRIa0cGEhW/fC41DAaDYmJi0jmR/YnfRbh79+765ptvEiwXB8zFUlAbkS1bNgUGBmrIkCGaPn26du7cqZ07dyY4//bbb2vMmDEU1czk6uqquLg4a8ewWzNnzjT9/datW9q8eXOSYymsme/27dupakQeGRmp27dvmx7nzZuXT+2QoVy7dk21atUyPXZ0dJQkPXjwwHQTnCdPHvn4+Gj16tUU1p5RtWrVNGvWLGvHAMzy1ltvKTo6WuHh4SpZsqRy5Mih+fPnq2PHjgk+MG3UqJE+//xzKyYFkufl5ZXo/VhcXJwuXryo0NBQxcXFqVatWkmuTEDy/vzzTzVv3lyTJk3S/Pnz1bBhQxUrVizRApvBYDBrNRNePBTWbEjOnDk1adIkffvtt9q7d2+CT5SrVq2aZO81pE7btm01a9YsRUVFKVeuXNaOY3cCAgKsHeGF8OqrryowMFA7duxQ7dq1Ex2zc+dOBQQEqFKlSqZjly5d0ksvvWShlEDK6JkEILXc3d01ZsyYBMfq16+vCxcuKCgoSDdu3JCHh0eSG3QAGUVgYGCy50+cOKGePXvKYDDQfN9MI0aMMDXVv3btWrKrPCisma9+/fqpHmswGJKddGELWAoK/J+oqCh5e3srR44cmjhxoipXrmztSECaLVy4UJ06dVK2bNnUvXt3+fn5yc3NTQaDQaGhoVq8eLFmzpypBw8e6M8//1THjh11+/ZtFSpUSG3btk1xCSmeHUtBU4eeSZaxY8cO/f777+rVq1eCGYKJjendu7def/11CycEADwpPDxcZcuWVa9evdh0ygxpnZ3drVu3dEpi3xwcHFIcE1/gtMVdQP+Lwhrwf+rXr6/79+9r165dMhgMcnFxSXZasK1X1WG/fvzxR3366ad6+PBholtYOzo66uuvv9bHH38s6fFOoQsXLlSDBg1UrVo1a0R+oVBYSx16JllGly5dtHz5cv37779Jbipz48YNFS1aVB06dGC5KDKkUqVKqUOHDikWGYYMGaKFCxeyKzNsXvPmzXXkyBFduHDB2lGARCX1sxm/rHn9+vWaMGGCPvzwQ33wwQemHcltFUtBM6iRI0fKYDDoww8/VL58+dL0hoEpq+Z5cuq10WjU5cuXTctt/4teVOZ79OiRli1bpqCgIF2+fNlUxKxbt67atm1r6qME8/Xv31+tW7fWtGnTtHPnTtPPsYuLi2rXrq0ePXrI3d3dNL5kyZL65JNPrBUXSBQ9kywjODhYlStXTnan5rx586pKlSravn275YIBaXD+/HmFh4enOC4iIkLnz59P/0CABVy9etXaEYAkJVcoK1mypLy8vFSvXj01a9ZMNWvWtPnCGjPWMqj4HWGOHTsmDw8P0+PU/Oeyh6mU1pDWT3xs/X9+a9i+fbv8/f116dKlp36WDQaD3Nzc9McffyTZGwywB8xYezZ3796lZ9JzlC1btlQtA/f399eKFSvYORgZkoODg7p3767p06cnO87f319Lly5N0L8RsDX79++Xp6en3NzcdOLECWvHsWn//PNPoh/216hRw9rRXhg1a9aU0WjUrl27rB3lmTBjLYOaMWOGpMczTJ58jPRDoSx9nTx5Us2aNdOdO3dUtWpVvfnmmypRooSkx0XNuXPnas+ePWrWrJn27NmjMmXKWDcwgAwpR44catq0qbVj2I0cOXKkaifhiIgIdqCDzYqLi9OJEycUEBCgYsWKWTsOkKTkVinduXNHJ0+e1Nq1axUTE6PevXtbMJl9OXnypLp27ardu3dLkukD//hVSTVq1NDs2bN5P2IBbm5udrERBzPWAFhEt27dNGfOHP3www/q169fomMmTpyo/v37q2vXrpo5c6ZlAwIW0r17d82ePVtxcXHWjpKh0TPJMho0aKCdO3fq5MmTcnV1TXTMpUuXVKZMGdWoUUNbtmyxcEIgcZkyZTL9Pb75dUqMRqOGDh2qL7/8Mj2jAWZLzSql7Nmz63//+59GjBhhuWB2JCwsTFWqVNHVq1dVpEgRdejQIcGH/YsWLdK///4rFxcX7dmzxzTRBc/f/fv39fLLL+vOnTup+pAvI6OwZiNCQ0OVM2dO5cuXL9lxN27cUFRUFJ/GPYOQkBD9/vvv2r17tyIiItS6dWuNHTtW0uOljHv37tWbb76Z4n8LJOTq6qpChQpp7969yY6rWrWqrl69qkuXLlkoGYCMKLVLu3r16qXp06eztNZM8+bN01tvvaWKFStq+vTpT+2IvX//fvXs2VMHDx7U9OnT2R0NGUaJEiVMxbTQ0FBlz55dBQoUSHSsk5OTihQpolatWumjjz5KUJQDMpLkNohxcnKSi4uLqlevrhw5clgwlX358MMP9fPPP2vAgAEaM2bMU7OxHz16pCFDhmj8+PH68MMPNWnSJCsltW2hoaFJnouffTlu3Djt2LFDnTt31ty5cy2Y7vljKaiNKFmypLp3765p06YlO27w4MGaMWOGYmJiLJTMvowdO1ZffPGF6foZDIYE1fN79+5pwIABypIlC9Ov0yg8PFze3t4pjitXrpyOHDligURA6m3duvWZvt7Ly+s5JcF/3b17l01PnkH8rqBLlixR9erVVaVKFbm7u8tgMOj06dPat2+f4uLi1LZtW4pqyFCe3ITAwcFBHTp0SLEQD2R0vM6mv7/++ktly5bVuHHjEj3v6Oio7777TmvWrNHq1asprJnpyQ8/kmI0GlW2bFl99913FkqVfiis2Qij0ZiqjQvixyLtVqxYoU8//VTu7u4aN26cPD09VbBgwQRjGjZsqAIFCmj58uUU1tIof/78OnnyZIrjTp48yWxAZDg+Pj7PtBsws6meP3omPT8LFizQ119/rfHjx2vPnj3as2eP6VyePHk0YMAAffbZZ1ZMCCQvICBAhQsXtnYMADYgLCxM7du3T3aMwWBQlSpVtGTJEgulsj9eXl5J3jvHz7709vZW586dlTVrVgune/4orNmZiIgIZcuWzdoxbNIPP/ygnDlzauPGjaZ19v9lMBhUtmzZVBWIkFC9evU0f/58/fbbb+rVq1eiY3777Tft3btX/v7+Fk4HJK9r167PVFhD6vx3edasWbOSXRYjPf4w6d13303PWHbPwcFBX3zxhT755BPt2bNHFy9elPS4oXC1atWYEYgMLzUz4gFAknLnzm36PZecixcvKnfu3BZIZJ8CAwOtHcGiKKxlYP9denTlypUklyPFxMToxIkTWrdunV577TVLxLM7+/fvV61atZIsqsUrWrRogk/zkTpffPGFli9frvfee09//PGH/P39TVOEz507p3nz5ikoKEjZs2fX559/bu24QAJspmEZbm5uZvdMwrNzdHRUrVq1VKtWrVSNnzZtmrZv387yO1hc/P1wjRo1lDVr1jQv12d5PjKKZ+n3ZzAYaP9jhlq1amnNmjVau3atmjVrluiYv/76S9u3b5evr6+F08FWsXlBBha/K4yUut2O4sfMmzdPnTp1skREu5IjRw41btxYy5YtMx1LrHl2w4YNtWfPHt28edMKKW3b5s2b1aVLF127du2pn2ej0ahChQpp3rx5ql+/vpUSAsgoUrt5AaynR48emj17NkudYXHx98jHjh2Th4dHgnvm1OBnFhlFYn2ojEZjgsbvefLkkaQE7z2KFStm+nAaabNz5055eXnJwcFB/v7+iX7Y/+eff8poNGrr1q2qWbOmtSPbpBs3bujw4cMqXbq0ihQpkuiYf//9V2fOnFGFChVMP+e2ihlrGdiTS49mzZold3d3eXp6Jjo2/pN7X19fValSxZIx7Ya7u7v27t2r2NjYJD89unv3rg4cOKBXXnnFwunsQ4MGDXT27FktXLhQQUFBunz5siSpSJEiqlu3rjp27Kjs2bNbOSWAjICeSQCSEn+P7OzsnOAxYGue3IRDerwjZbt27RQXF6ehQ4eqc+fOypkzp6THOyn++eefGj16tMqXL6+lS5daIbHtq1WrlmbMmKHevXtr1qxZmj17doLzRqNR2bJl09SpUymqPYNx48ZpzJgx2rdvX5KFtYiICNWrV09Dhw7ViBEjLBvwOWPGmo3gk/v0N3LkSI0YMUKffvqpvv76a0lPX/eBAwdqwoQJGj9+vPr162fNuAAygHv37ikgIECnTp1SVFRUopvHGAwGDR061ArpgPTFjDUAeL6GDRumH374QSEhISpevHiiYy5cuKDXXntN/fr10+jRoy2c0H5cunRJv/32m7Zt2/bUh/09e/aUm5ublRPatsqVK+vhw4c6cuRIsuNeffVVZc2aVXv37rVQsvRBYQ34P3fv3lWNGjV0/PhxeXp6qlWrVho8eLC8vLzk5+en5cuX6++//1bFihUVHBysLFmyWDsyACuaOXOmBgwYoNu3b5uO/XfZfvxjCg8po2eS7aGwBgDPl7u7uypUqJCgNU1i2rZtq4MHD+rs2bMWSmY/7ty5Y5oFiPSTN29e1atXL8WZle3atdPWrVsVERFhoWTpg6WgwP/JkSOHAgIC1L17d61bt07bt2+X9PjNXlBQkIxGoxo0aKB58+ZRVEuF+N4QRYsWVaZMmRL0ikiNYsWKpUcs4LnYtGmTevbsKWdnZ3322WcKCAjQzp079euvv+rMmTNatmyZTp06pT59+qhq1arWjmsTfHx8EvRMin+cWhR3AMSLjIw0zUBxcXFJchMUIKO5fPmyKlasmOI4g8GgsLAwCySyP/ny5VPlypXl7e2tevXqydPTk90/08GjR49StTlH5syZde/ePQskSl/MWLMxoaGhWrVqVYrLjqZNm2aFdPbj4MGD2rhxo86fP6/Y2Fi5urqqYcOGev31160dzWY4ODjIwcFBR48eTXNjYXY5QkbXrFkzbdy4UXv37lXFihWfmrkTGxurQYMGaerUqQoODma35lTo3r27DAaDvvnmGxUqVMj0OLVmzJiRjumQGGasISMxGo2aPHmyJk+erJMnTyY45+HhoQ8++EAffvihHBwcrJQQSJmHh4euXr2qEydOJNln9MqVKypbtqxeeuklnTp1ysIJbd9rr72mY8eOmVYVODg4qFKlSvLx8ZG3t7e8vLwotD0H5cuX140bN3ThwoUkC2yxsbEqXry4cubMqePHj1s44fNFYc2GjBw5UqNGjVJcXJzpWPx/vv/uHspNLqwtfrbJnDlz5OrqmubZJwEBAemYDng2BQoUULly5bRt2zZJiRcY4uLiVLp0aVWuXFlLliyxVlQg3VBYQ0YRHR0tX19fbd68WUajUXnz5lXx4sVNuyveuHFDBoNB9evX1+rVq1l5gAxrzJgx+vzzz1W6dGmNGjVK7du3V+bMjxeZxcTEaMmSJRo2bJhOnz6tr776Sp9++qmVE9umyMhIBQYGasuWLQoMDNSRI0cSFNoqVqwob29v+fj4yNfX19pxbdKQIUM0duxY/e9//9O3336b6JhPP/1U3333nT7++GONHTvWwgmfLwprNmLBggXq3LmzSpQooc8//1yLFi3Sxo0btW7dOp09e1YLFixQYGCgBg4cKF9fX3l7e1s7sl2IioqSJOXKlcvKSQBkJFmzZlW7du30xx9/SJLef/99TZ06VTdv3kzwetG5c2dt3rxZ165ds1ZUIN1QWENGMXz4cI0aNUqvvfaavvvuOzVp0iTB+fXr12vw4MEKCQnRF198oS+//NJKSYHkxcbGqlOnTlqyZImpyFOoUCEZDAZduXJFcXFxMhqNatu2rRYuXJiqpXZIWWRkpLZu3arAwEAFBgYqJCREEqtonkVkZKQqVqyosLAwVaxYUe+8847c3d1lMBh0+vRp/f777zp48KAKFy6sAwcOqGDBgtaO/EyYC20jpkyZIicnJwUEBKhnz55ycXGRJDVq1Ei9e/fW33//rXHjxmnChAm8wD6j1atXq1mzZnJ2dlaePHmUJ08e5c6dW82aNdOqVausHQ9ABlC4cOEETVbjl2v8d/nR9evXdf/+fYtms1eRkZE6fPiwDh8+bPMNbgE8X3PnzlWePHkUEBDwVFFNkpo0aaLNmzfL2dlZc+bMsUJCIHUyZcqkRYsWad68efL09FSmTJl0+fJl/fvvv8qUKZM8PT01Z84cLVmyhPd8z1GuXLmUP39+5c+fX/ny5ZOjo6OMRmOibZeQOvnz59eGDRvk4eGhAwcOqG/fvmrevLmaNWumvn376sCBAypTpow2bNhg80U1iRlrNiNv3ryqXLmy/v77b0nS22+/rVmzZikmJibB8rrXXntNrq6uWrdunbWi2iyj0ah33nlHM2fONL2I5smTR0ajUbdu3ZL0+FOLt956SzNmzEjTskZI9evXV9OmTTV48OBkx33//ff666+/TD/rQEbUtGlTHT9+XOfPn5ckbdy4UU2aNJGfn58WLFggg8GgHTt2yNvbWxUrVtSePXusG9hG0TMpY+vevbvmzJnDjDVYXbZs2dSyZUstWrQo2XF+fn5as2YNH3jAZsTExCgyMlJGo1EFChQwLQvFs3n48KGCg4NNM9SCg4MVHR0to9EoV1dX0zJQHx8fubu7WzuuTYuNjdXSpUu1adMmXbx4UZLk5uamhg0bql27dnZTIOb/TBsRHR2doIFl1qxZJUk3b95U3rx5TccrVqxIUc1MEyZM0IwZM1SkSBENHTpU/v7+piVdUVFR+uOPPzRq1CjNmTNHlSpVUv/+/a0b2MYEBgaqRIkSKY47ceKEtmzZkv6BgGfQokULbdiwQcHBwapZs6YaNGigChUqaMmSJSpatKhcXFwUEhKiuLg4XivMlFLPpBMnTqh///5auXIlPZOsZMiQIerRo4e1YwAqWrSoHj58mOK4R48eqUiRIhZIBDybmJgYrV69Wrt371ZERIRef/11vf3225Ie7xwaERGhV155hUKbGRo0aKCdO3cmKKS1b9+eQlo6yZQpkzp06KAOHTpYO0q64iNeG+Hi4qIrV66YHhctWlSSdOTIkQTjLl26xCfHZpo6daqyZ8+uoKAg9e7dO0GfpFy5cql3794KCgpStmzZNHXqVCsmtW8PHjzgJgEZXteuXbV27VrTsnwHBwetWbNGjRo10rVr17R//35lz55do0eP1ptvvmnltLbp66+/1qZNm/Tqq69q7dq1ioyM1L59+7R//35FRkZq7dq1eu211/T333/r66+/tnZcmxcSEqL+/fvL09NTZcuWTTC7ePv27Zo4caKuX7+e4GvKli1LT1dkCF26dNHmzZt14cKFJMdcuHBBmzdvlr+/vwWTAWm3ZcsWlSpVSu3bt9eYMWP0+++/mzZLkqTNmzercuXKWrFihRVT2q6AgABFR0fr1Vdf1cKFCxUaGqo5c+aoZ8+eFNVgNgprNqJ8+fIJtqD18fGR0WjUsGHDdOfOHUnSwoULFRQUpFdffdVaMW3auXPn1KBBA5UsWTLJMSVLllSDBg107tw5CyZ7cdy+fVs7duwwFSuAjMrZ2VlNmjRR8eLFTceKFi2qdevW6fbt2/r3338VERGhIUOGWDGlbaNnkuWMHTtWVapU0cSJE7Vz506dPn06QR+7e/fuacCAASkuswOs5YsvvlCDBg3k5eWl6dOn6+7du6Zzd+/e1fTp0+Xt7a0GDRpo2LBhVkwKJO/w4cNq3ry5rl27pn79+mnRokVP9flq3769smfPzo7jZmrevLly586tkJAQvfHGG8qfP7/atWuniRMn6uDBg9aOZzfmzZunUqVKaePGjUmO2bBhg0qVKqUFCxZYMFn6YFqIjfD19dXKlSu1adMmNWzYUJ6enqpXr54CAgKUL18+5cqVSzdv3pTBYNDQoUOtHdcmFSxYUE5OTimOc3JyUoECBSyQyPaVKlUqwePFixcrMDAw0bExMTG6evWqYmJi1KdPHwukA8wXGhqqnDlzKl++fE+dy549u7Jnzy5JunHjhqKiolSsWDFLR7R5ly9fVsuWLZU/f/4kxxQoUED169fXmjVrLJjMvqxYsUKffvqp3N3dNW7cOHl6ej7VRLhhw4YqUKCAli9frt69e1spKZC0smXLymg06tKlS+rVq5d69eplapVy48YN0ziDwaCyZcsm+FqDwaAzZ85YNC+QlJEjRyo6OlobNmxQ/fr1Ex2TPXt2vfzyy9q/f7+F09mH1atXKy4uTvv37zf1WAsMDNTy5ctlMBiUN29eeXl5mZaGVqhQwdqRbdKcOXN09+5d1atXL8kx9evX1507dzRr1iy98cYbFkz3/FFYsxFvvvmm6tSpk+Bmd9myZRo8eLCWL1+uGzdu6JVXXtGQIUPUtGlTKya1XW3bttXcuXN148aNBH3rnnT9+nX9/fffLCNIpfjG7tLjG9c7d+6YZlj+l6Ojo4oUKaJWrVppzJgxFkoImKdkyZLq3r27pk2bluy4wYMHa8aMGWzVbgZ6JlnGDz/8oJw5c2rjxo1J9sGML0b8dwMJIKN48n4j3n+XLktKdqkokBFs2bJFNWvWTLKoFq9YsWLJzgRC8hwcHFS1alVVrVpVH3/8sYxGow4cOGAqsq1bt04rVqyQwWDgHs5MISEhqlChQrItfjJnzqyKFSsqJCTEgsnSB0tBbUSWLFlUtmzZBLMjcufOrV9++UVXrlxRdHS0Dh8+TMHnGYwePVqlSpVS/fr1E92R8u+//1ajRo1UqlQp+vmkUlxcnOmP0WhU9+7dExx78k90dLTOnTunCRMmmGb7ABlVWrZgZ/Nt89AzyTL279+vWrVqpbi5TNGiRRUWFmaZUEAaJXVvkdo/QEZx+/ZtUy/t5ERHR9NX+zm6ePGiDh8+bPrz4MGDNN3r4WkRERF66aWXUhz30ksv6dq1axZIlL6YsQb8n9atW8vJyUl79+5Vo0aNlC9fPlP/pNDQUEVGRkqSatasqdatWyf4WoPBoM2bN1s8sy2ZMWOGSpcube0YgEVFREQoW7Zs1o5hk7744gsdOHBAXl5eGj58uN544w3lyJFD0uOeSQsWLNDIkSPpmfSMYmJiUvVhRnh4eKraJQAAzOfi4qJjx46lOC4kJCRBn1ekTWhoaIJloPEf4hmNRjk5OcnT09O0FBTmyZ8/f6qW2Z85c0Z58uRJ/0DpjMIa8H+e7P1lNBoVGRlpKqY9aefOnU8dMxgM6RnNLnTr1s3aEYBnsnXr1gSPr1y58tSxeDExMTpx4oTWrVun1157zRLx7A49kyzD3d1de/fuVWxsrDJlypTomLt37+rAgQN65ZVXLJwOAF4sjRs31u+//65ly5apbdu2iY6ZOXOmLly4oP/9738WTmcf3N3dTcvHjUajsmTJojp16sjb21s+Pj6qXbu2smbNat2QdsDT01NLlixRUFCQ6tatm+iYbdu26Z9//lGbNm0sGy4dUFjLoFJaV58cZk+Zh50+LScqKkpnzpxRVFRUklOsvby8LJwKSJ6Pj0+CIvr69eu1fv36JMcbjUYZDAZ9/PHHlohnd+iZZBl+fn4aMWKEhg4dmmSbg6FDh+rGjRs231gYLwbuMWDLPvvsM82fP1+dO3fWoEGDTKtk7t27p5CQEC1fvlxff/218ufPr4EDB1o5rW36999/VbduXfn4+Mjb21u1a9dWlixZrB3L7gwYMEBLlixRq1atNHz4cPXq1SvByoPffvtNo0aNksFg0IABA6yc9tkZjCwczpAcHMxvf2cwGFhzb2HXr1/XnTt32PkvBSEhIerfv78CAwNT7FnAzzAymu7du5sKa7NmzVLp0qXl6emZ6FgnJycVKVJEvr6+qlKliiVjAmly9+5d1ahRQ8ePH5enp6datWqlwYMHy8vLS35+flq+fLn+/vtvVaxYUcHBwbz5QIbFPQbsxbZt29ShQwddvXr1qVUxRqNRBQsW1LJly1S7dm0rJbRt0dHR/C6zkB9/nnWBjgAAHIRJREFU/DHBB8wvvfSSDAaDrl69ajr23Xff2UWRmMJaBvWsn8Cz5t6yevTooTlz5rBrTDJOnTql6tWr6/bt2/L09FRYWJjOnTunTp066ezZs9q3b59iYmLUqlUr5cmTRzNmzLB2ZCBJDg4O6t69u6ZPn27tKMAzu3btmrp3765169bJYDCYihLxf2/QoIHmzZuXqibEgDVwjwF7ExUVpWnTpmnTpk06f/68YmNj5erqqoYNG6p379520ZMKL4atW7fqm2++0ZYtW3T//n1JUrZs2eTj46NPPvnEbmYQU1gDnoMePXpo9uzZfAKajG7dumnu3LmaPn26unXr9tQ1O336tHr27KkrV64oODjY1EsJAGAZBw8e1MaNG596E/f6669bOxqQLO4xACBji4uLU0REhCSpQIECz7RCLyOisAY8BxTWUubm5iZnZ2eFhIRISvya3bx5U6VKlZK/v79++ukna0UFkMHQMwlAcrjHAICM659//lFQUJAuX74sg8GgIkWKqG7duqpevbq1oz03bF5gYyIiIjR37lzt3r1bERERatCggQYPHizpcW+Js2fPqmHDhsqePbuVkwIJXbt2TbVq1TI9dnR0lCQ9ePDAtPNOnjx55OPjo9WrV3PTiwxl5MiRMhgM+vDDD5UvXz6NHDky1V9rMBg0dOjQdExnv+iZlP6mTZsmPz8/OTs7WzsKYDbuMQAg4zl58qS6du2q3bt3S1KCVhOSVKNGDc2ePVtlypSxWsbnhcKaDZk/f77effdd3b1717TbXNGiRU3nT506JT8/P82cOVNvvfWWFZMCT8uXL58ePHiQ4LH0uJ9g2bJlE4y9du2aRbMBKRkxYoQMBoPeeOMN5cuXz/Q4NZO+KayZ59SpU6pTp06qeybBPL169VKfPn3UokULdenSRS1atJCTk5O1YwFpwj0GAGQsYWFh8vb21tWrV1WkSBF16NBBJUqUkPT4tXnRokXatWuXfHx8tGfPHrm4uFg38DOisGYjgoKC9Oabbypv3rwaPXq0PD09n5o62bJlSzk7O2vp0qUU1pDhlCxZUufOnTM9rlSpkoxGo+bPn6/hw4dLejwjMzAwkN1VkeHEN7qO/6U/ffr0p3bqwvM1evRoRUVFacaMGaaeSefOndO8efMk/f+eSUePHlVwcLCV09qu9957T4sXL9bSpUu1bNky5c6dW35+fvL391e9evWsHQ9IFe4xACBjGT16tK5evaoBAwZozJgxT31o9+2332rIkCEaP368vv76a02aNMlKSZ8PeqzZiObNmysgIEDBwcGqWLGipMR3patdu7YiIiJ08uRJa0V9IdFjLWUjRozQqFGjdPr0aZUsWVJ3795VyZIlFRkZKT8/P7m6umrp0qUKDQ3V559/nqaldgDsDz2TLCc2NlYbNmzQvHnztHLlSt25c0cGg0EuLi7y9/eXv7+/KlWqZO2YQJK4xwCAjKVkyZLKmjWrjh07luQYo9GoV155RQ8ePEjw4Ygtsq+tGOxYcHCwatasaSqqJcXNzU1hYWEWSgWk3ltvvaXBgwcrPDxckpQjRw7Nnz9fefPm1aJFi/TDDz/owoULatiwoT7//HMrpwWSN3DgQI0aNcraMezatWvX9Morr5geP9kzKd6TPZNgvkyZMqlZs2aaO3eurl69qj/++EMtWrRQRESEvv/+e1WtWlWvvvqqvv76a2tHBRLFPQYAZCxhYWGqUqVKsmMMBoOqVKliF/ULloLaiPv37yt//vwpjrt9+zbLk5Ahubu7a8yYMQmO1a9fXxcuXFBQUJBu3LghDw8PVa1a1UoJgdT76aef1Lp1a2vHsGv0TLKObNmyqVOnTurUqZNu3rypRYsW6Y8//tCWLVs0dOhQffbZZ9aOCDyFewwAyFhy586tixcvpjju4sWLyp07twUSpS9mrNmI4sWL69ChQ8mOiYmJ0aFDh1S6dGkLpQKeXY4cOdS0aVN17tyZG17YDFdXV8XFxVk7hl1LrmdSPHompa+bN28qPDycwiUyvOjoaIWGhioqKirB8SfvMTw8PBQaGqqHDx9aKSUAvDhq1aqlHTt2aO3atUmO+euvv7R9+3bVrl3bgsnSB4U1G9GyZUudOXNGkydPTnLM+PHjdeXKFbVt29aCySBJ5cqVk5eXl7VjZGhJ3fQ+KSoqipte2IS2bdtqy5Ytyf4849k0btxYR48eNRXXfH19VaBAAY0cOVJvvPGGPv74Y1WvXl23bt1Sx44drZzWfkRERGjy5Mny9PSUu7u7hg4dqmPHjql27dr0sUOGNX78eJUsWVIHDx5McszBgwdVsmRJTZgwwYLJAODF9Omnn8pgMKhNmzbq0aOHNm7cqFOnTun06dPauHGjunfvrrZt2ypTpkz69NNPrR33mbF5gY2IiIhQpUqVFBYWJn9/f7Vu3VodO3ZU8+bN9d5772n58uWaOXOm3NzcdODAATk7O1s7sl1Zv369Dh8+rGLFiqldu3bKnJlV1Gk1ZswYffHFF9qyZYvq1KmT6Jht27bJ29tb33zzjQYNGmThhEDqRUVFydv7/7V350FR3/cfx19fEKTghZEICohS8YytWiXaogmxJqH1QI1jIDExxpK0jTgORmfiXW2TtKOtTuuMV6LxCokhwVRnqnhiMhgcY6JtALmRIxovcBVl2d8f4v40ghwJ+93F52PGGXf37czrD3S++/bzeb9HysfHR6tWrdLAgQPNjtTiZGdna/369YqOjtbQoUMlSfv379fkyZN14cIFe92vf/1rJScnq3Xr1mZFdXkWi0VJSUnaunWr9u3bJ6vVKpvNpj59+ig2NlYxMTEKCQkxOyZQp/DwcH333Xc6c+bMfetCQ0PVuXNnffbZZw5KBgAPri1btiguLk7Xrl27Z1yVzWbTT37yE61du1axsbEmJfzx0FhzIRkZGZo0aZJOnz4twzBks9nsP6A2m029e/dWUlLSPbNn0DD/+te/9Le//U2bN2++q/Hz7LPPKjEx0f46PDxcBw4c4EtcI/HQi5YkMjJS165dU1pamn17YnBwsLy8vO6pNQxDKSkpJqRsma5evcrMpB9RTEyMkpOTde3aNdlsNnXt2lVTpkxRbGwsm0DhMjp16qThw4crOTn5vnVjx45VWlqaysrKHJQMAB5sRUVFWrdunVJTU1VcXCxJ6tKliyIiIjR9+nQFBQWZnPDHwbEbFzF79mz5+vrq5MmTSk5O1r59+5SXlyer1arAwECNGjVKkyZNkru7u9lRXVZSUpKuXr161x3vvXv36v3331dgYKCef/557d+/X2lpadqwYYN+//vfm5jW9WRnZzfo/ny/fv2UlpbmgERA0x08eND+e5vNpuLiYvvDwvexUKZpKisrVVZWJl9fX7Vt29b+/u2ZSdL/Xx/39/eXp6enWVFd2o4dO9S+fXt7M+2xxx7jZxYu5+rVq/Lx8am3ztvbW1euXHFAIgCAdGsu8ZIlS8yO0exorLmI2xvo3NzcNH78eI0fP97sSC1ORkaG+vfvLze3/x89uG3bNhmGoQ8//FBDhw5VZWWlgoODtXnzZhprjcRDL1qSO4fqo3msWLGi3uvjJ0+e5Pr4D/Thhx/qt7/9LY1JuLSgoCClp6fXW3f8+HEFBAQ4IBEA4EHC8gIXwQa65nfu3Ll7HrYOHz6s4OBg+3yf1q1ba/jw4XypbgIeetGSdOvWrVG/0Hgff/yxunfvXmdTTZJ+9atfKSQkRElJSQ5M1rJMmDCBphpc3ujRo5WTk6PVq1fXWfPPf/5T2dnZevLJJx2YDADwIKCx5iLYQNf8OnTooEuXLtlfl5SUKDc3VyNHjryrzsfHRxUVFQ5O5/p46AXQGNnZ2erbt2+9df369VN2drYDEgFwVnPnzlXbtm01a9YsjR8/Xrt371ZGRoYyMzO1e/dujR8/XjNnzlS7du00d+5cs+MCAFoYroK6iMWLF+vAgQOKiopiA10z6dmzp1JTU3X58mW1b99eW7dulWEY9lk+txUVFcnf39+klK5r7ty52rJli2bNmqWUlBT97ne/U2hoqAzD0JkzZ7R27Vrt2rWLh164pEuXLqm8vFx17QMKDg52cCLXx/Xx5tGjRw8ZhqF9+/ape/fu6tGjR4P/rGEYNDHhlIKCgpScnKxJkyYpOTlZu3btuutzm82mTp06KTExkQ23AIAfHVtBXQQb6Jrfjh07FBMTox49emjAgAH697//LV9fX505c0Zt2rSRJF27dk1+fn56/PHH73loQ/0OHz6sSZMm6fz587WuXL790PvYY4+ZExBohNLSUs2fP1+ffPKJLly4UGedYRiqqqpyYLKWISwsTDabTVlZWfet69mzp6xWq3JychyUzLXdniP6zTffKCws7K65og3BWAo4s0uXLmnt2rVKSUlRYWGhpFtNt1GjRunll1+Wr6+vyQkBAC0RJ9ZcBBvomt+UKVP05Zdf6h//+IdycnIUGBioTZs22ZtqkpSYmCiLxaLIyEgTk7quESNGKDMzk4deuLySkhINGTJExcXF6tq1q/z8/PTtt99q2LBhysnJUVlZmQzD0LBhw+Th4WF2XJc0evRorVmzRqtXr9Zrr71Wa83t6+NxcXEOTue6vt8Yo1GGlqRDhw56/fXX9frrr5sdBQDwAOHEmovIz89vVD3DspuusrJSV65ckZ+f3z2fFRYW6sKFCwoNDb2r4QbgwfKHP/xBa9as0dKlSzV//nxNmzZNmzdvltVqlXTrdOarr76qjh07au/evbWeLsb9FRYW6pFHHlF5ebnGjBlT5/Xxtm3b6ssvv+R6FwAAAExBYw2oMXv2bPn6+mrBggVmRwHg5EJDQyXJPm/q+401STp79qx69eql+Ph4LV++3JScro7r481v6dKl+vnPf66xY8fet27Xrl06ceKEFi5c6KBkAAAAroHGGlDD09NT48aN0wcffGB2lBapoKCgUfUMe4cz8/Ly0m9+8xvt3LlTkvTyyy/rnXfekcViUevWre11Y8aM0TfffFPvnDDUjZlJzcvNzU0vvviiNm7ceN+6GTNmaOPGjXc1jwEAAMCMNcAuMDCQWTPNKCQkpMHz/xj2DmfXrl27uzaAdujQQdKtU2p3bln08vLS2bNnHR2vRWFmknOwWq2NXnQAAADwIKCxBtSIjo7Wpk2bVF5errZt25odp8UZMWJErY216upqFRYWqqCgQNXV1Ro2bJg8PT1NSAg0XHBwsPLy8uyv+/fvL0navXu3/vjHP0qSLBaLjh49qoCAADMiAj+q06dPczoQAACgFjTWgBqLFy/WgQMHFBUVpVWrVmngwIFmR2pR7txsW5uMjAxNnz5dhmFoz549jgkFNFFkZKT+/ve/q6ysTJ07d9bYsWPl4+OjhIQEFRYWKjAwUFu2bFFZWZleffVVs+MCd3nppZfuep2amnrPe7dVVVUpIyND6enpGj9+vAPSAQAAuBZmrAE1IiMjde3aNaWlpckwDAUEBCg4OLjWbX6GYSglJcWElC3buXPn1KtXL82YMUNvvfWW2XGAOp08eVJvvvmm4uLi7IPzt2/frmnTpunGjRsyDEM2m039+vVTamqq2rdvb25g4A53Xum8/bNanwEDBuijjz6666ozAAAAaKwBdo2ZHWMYBgOcm0lUVJROnz6t/Px8s6MAjVZQUKDdu3fr4sWLCgsL09ixY+Xh4WF2LOAuhw4dknRrs2pkZKSeeuopzZ07t9ZaT09PdenSRd26dXNkRAAAAJdBYw2o0dhGDl8ymkdUVJT279+v69evmx0FAFq8adOmKSIios6roAAAALg/GmsAnMaJEyf0y1/+UkFBQcrIyDA7DlCvvLw8HT58WCUlJaqsrKy1xjAMLViwwMHJAAAAADgCywsAOMTSpUvr/KyiokKZmZnas2ePqqqqFBcX58BkQONdv35dM2bM0LZt2yTpvjOqaKwBAAAALRcn1oAaBQUFjaoPDg5upiQtk5ubW71Dsr29vZWQkKDFixc7LhjQBPHx8Vq9erUefvhhxcbGKjQ0VD4+PnXWv/DCCw5MBzSOxWLRihUr9MknnygrK0vl5eW11hmGoaqqKgenAwAAcG401oAatxs/DcGXi8bbtGlTnZ95enoqICBAQ4YMuW9zAnAW/v7+qq6u1ldffSV/f3+z4wBNdvnyZUVEROj06dNyd3eXp6enLBaLAgICVFpaav/PkNtzRXNzc82MCwAA4HS4CgrUGDFiRK2NterqahUWFqqgoEDV1dUaNmyYPD09TUjo2jixg5akoqJCTz75JE01uLw333xTp06dUlxcnFauXKlXXnlF7733ns6ePavr168rMTFR8+bNU3h4uLZv3252XAAAAKfDiTWggTIyMjR9+nQZhqG9e/fKy8vL7EgATBIeHi4PDw+lpqaaHQX4Qfr06aPy8nLl5ubKw8ND06ZN0+bNm2W1Wu01//3vfzVw4EAtW7ZMc+bMMTEtAACA83EzOwDgKnr16qWkpCSdPn1aixYtMjsOABMlJCTo2LFj+uyzz8yOAvwg+fn5GjRokDw8PCTdGosgSTdv3rTX9O3bVyNHjrzvlX4AAIAHFVdBgUbw8/PTo48+qh07duitt94yO45Tc3d3b/KfZYYdnN0zzzyjoqIijRs3Tq+99ppGjRqlrl271jmnkWUncFZeXl5q3bq1/XW7du0kSaWlpQoKCrK/37FjRx09etTh+QAAAJwdjTWgCcrKysyO4PSCgoLuaTLYbLa7tq926NBBknTp0iX7e8HBwQ1eIgGYacCAAfL19dWSJUu0ZMmSOutoFMOZBQUFKT8/3/66d+/ekqRDhw7pueeekyRVVVXpiy++0EMPPWRKRgAAAGdGYw1ohBMnTujgwYP27WioW15e3l2vb968qQkTJqi6uloLFizQs88+qzZt2ki6NQh++/btWrZsmR555BF99NFHJiQGGu7TTz/VhAkTVFVVJT8/P3Xr1o2NtnBJERER2rhxoy5fvqz27dtrzJgxio+P18yZM1VeXq7AwEBt2LBBeXl5iomJMTsuAACA02F5AVBj6dKldX5WUVGhzMxM7dmzR1VVVfrrX/+q2bNnOzCd61u4cKFWrlypU6dO1dmYzM/PV//+/RUfH69ly5Y5OCHQcIMHD9ZXX32l9evXa+rUqZyyhMtKTU3VnDlztGjRIj311FOSpBUrVighIcH+c22z2eTv76/09HR16dLFzLgAAABOh8YaUMPNzU2GYeh+fyW8vb2VkJCgxYsXOy5YCxEaGqoBAwYoKSnpvnXR0dE6efKkcnJyHJQMaDxvb28NGzZMKSkpZkcBmkVaWpqSkpJ08eJFhYWFadq0aerYsaPZsQAAAJwOV0GBGu+8806dn3l6eiogIEBDhgzhulcTFRcX62c/+1m9dYZhqKSkxAGJgKbr1KmT/Soz0BKFh4crPDzc7BgAAABOjxNrABwiLCxMZWVlysjIkL+/f601paWl6tWrlx5++GFlZWU5OCHQcLNnz9bWrVuVlZVl36IIuDKLxaL09HSVlJSosrKyzrqpU6c6MBUAAIDzo7EGwCH+8pe/6I033tBPf/pT/elPf9LEiRPVqtWtQ7NVVVXauXOnFi5cqDNnzmj58uWaN2+eyYmBulksFj3xxBPy8fHRunXr1L17d7MjAU12ewamxWKps8Zms8kwDFmtVgcmAwAAcH401oDvuXnzppKSknTkyBEVFxfLMAwFBAQoIiJC0dHR8vDwMDuiS7JarZoyZYp27twpwzDk5uamzp07yzAMlZaWqrq6WjabTdHR0UpMTJS7u7vZkYE6RUZG6saNG/r888/l7u6ukJAQde3atdYlBoZhMIsNTuvtt9/WvHnz1KpVKz399NMKCwu77zXnRYsWOTAdAACA86OxBtzh6NGjiomJUVFR0T1LDAzDUFBQkLZt26bhw4eblND1bd++XWvWrNGxY8d048YNSbdm2A0dOlRxcXGKjY01OSFQPzc3twbXcsoHzqxnz54qLi7WkSNHNGjQILPjAAAAuBwaa0CNzMxM/eIXv1BFRYUGDx6s5557TiEhIZKk/Px8bdmyRenp6Wrbtq3S09PVs2dPcwO7uKqqKn333Xey2Wzq1KmT/Voo4Ary8/MbVd+tW7dmSgL8MF5eXoqMjNTu3bvNjgIAAOCS+CYL1Fi+fLkqKiq0cuVKxcfH3/P5zJkztWrVKs2aNUvLly/Xu+++6/iQLUBVVZU+/fRTffHFFzp//rzCw8P10ksvSbq1OfT8+fPq27cvjTY4NRplaCnqWiYDAACAhuHEGlAjMDBQnTt31vHjx+9bN3jwYJWVlamoqMhByVqOQ4cO6fnnn9fZs2ftg7BfeOEFbdy4UZL03nvv6cUXX1RiYqImTpxocloAaPnmzZun9evXKysrS76+vmbHAQAAcDkNHxIDtHDnzp1T7969663r3bu3zp8/74BELcvXX3+tqKgoffvtt4qPj9cHH3xwzxy7iRMnytvbWzt37jQpJQA8WBYvXqy+fftqwoQJys7ONjsOAACAy+GuFVDjoYceUmZmZr11mZmZ6tixowMStSxLly5VZWWl/vOf/ygyMrLWGm9vb/Xp00cnTpxwcDoAeDBFRUWpurpaR48eVe/evRUSEqLAwEA23AIAADQQjTWgxuOPP64dO3Zo3bp1mjFjRq0169at0/HjxxUTE+PgdK7v0KFDevTRR+tsqt0WHBysvXv3OigVADzYDh48aP+91WpVdnZ2nSfXamu2AQAAPOhorAE15s+fr48//livvPKKtm3bppiYGIWEhMgwDOXm5mrr1q06cuSIvL299cYbb5gd1+VcuXJFXbt2rbeusrJSVqvVAYkAALm5uWZHAAAAcGk01oAaffr0UXJysmJjY3Xo0CEdPnz4rs9tNps6d+6srVu3qk+fPialdF0BAQH63//+V2/dqVOn2LgIAA7Cv7cAAAA/DI014A5PPPGEcnJy9P777ys1NVXFxcWSpC5duigiIkKTJ0+Wt7e3ySld0+jRo7V+/XolJSUpOjq61pp3331X+fn5SkhIcHA6AAAAAAAaz7B9fy0fADSD/Px8DRgwQJWVlZozZ47GjRunoUOHavLkyfZruH/+85/l4+Ojr7/+Wv7+/mZHBgAAAADgvmisAd9jsViUnp6ukpISVVZW1lk3depUB6ZqGVJTU/XMM8+orKzsniHYNptNfn5+SkpK0vDhw01KCAAAAABAw9FYA+6wcOFCrVy5UhaLpc4am80mwzAYsN9E5eXl2rBhg/bt26e8vDxZrVYFBgZq1KhRiouLU4cOHcyOCAAAAABAg9BYA2q8/fbbmjdvnlq1aqWnn35aYWFhatOmTZ31ixYtcmA6AAAAAADgbGisATV69uyp4uJiHTlyRIMGDTI7DgAAAAAAcHJuZgcAnEVhYaFGjhxJUw0AAAAAADQIjTWgBlsoAQAAAABAY9BYA2pMmTJFx44d08WLF82OAgAAAAAAXAAz1oAa169f1+jRo+Xu7q7169crNDTU7EgAAAAAAMCJ0VjDAysyMvKe927cuKHPP/9cbm5uCgkJUWBgoAzDuKfOMAylpKQ4IiYAAAAAAHBSNNbwwHJza/pNaMMwZLVaf8Q0AAAAAADA1bQyOwBgltzcXLMjAAAAAAAAF8aJNQAAAAAAAKAJ2AoKAAAAAAAANAGNNQAAAAAAAKAJaKwBAAAAAAAATUBjDQAAAAAAAGgCGmsAAAAAAABAE9BYAwAAAAAAAJqAxhoAAAAAAADQBP8HFi240mW/aikAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_importances = linear_test_preds['importances']\n",
    "linear_importances_df = pd.DataFrame(linear_importances[:, :-1], columns = FEATURES)\n",
    "linear_total_importances = get_model_importances(\n",
    "    linear_importances_df, title=\"Importances for FT-Transformer with Linear Numerical Embedddings\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "27c656f9-da32-4e2b-bf83-1e74e35f819c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 contributions to row 3608 which was scored 0.9919\n"
     ]
    },
    {
     "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>Importance</th>\n",
       "      <th>Example Vlaue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.164829</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.076485</td>\n",
       "      <td>1.355023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital_loss</th>\n",
       "      <td>0.072796</td>\n",
       "      <td>-0.216317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <td>0.072420</td>\n",
       "      <td>Prof-specialty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship</th>\n",
       "      <td>0.071956</td>\n",
       "      <td>Wife</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Importance    Example Vlaue\n",
       "gender          0.164829           Female\n",
       "age             0.076485         1.355023\n",
       "capital_loss    0.072796        -0.216317\n",
       "occupation      0.072420   Prof-specialty\n",
       "relationship    0.071956             Wife"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 contributions to row 12682 which was scored 0.0016\n"
     ]
    },
    {
     "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>Importance</th>\n",
       "      <th>Example Vlaue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>relationship</th>\n",
       "      <td>0.099172</td>\n",
       "      <td>Other-relative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <td>0.086154</td>\n",
       "      <td>Transport-moving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hours_per_week</th>\n",
       "      <td>0.081941</td>\n",
       "      <td>-0.037162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital_gain</th>\n",
       "      <td>0.070829</td>\n",
       "      <td>-0.143609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education_num</th>\n",
       "      <td>0.069647</td>\n",
       "      <td>-3.146201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Importance      Example Vlaue\n",
       "relationship      0.099172     Other-relative\n",
       "occupation        0.086154   Transport-moving\n",
       "hours_per_week    0.081941          -0.037162\n",
       "capital_gain      0.070829          -0.143609\n",
       "education_num     0.069647          -3.146201"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Largest prediction\n",
    "max_idx = np.argsort(linear_test_preds['output'].ravel())[-1]\n",
    "example_importance_linear = linear_importances_df.iloc[max_idx, :].sort_values(ascending=False).rename(\"Importance\").to_frame().join(\n",
    "    test_data.iloc[max_idx, :].rename(\"Example Vlaue\")\n",
    ").head(5)\n",
    "print(f\"Top 5 contributions to row {max_idx} which was scored {str(np.round(linear_test_preds['output'].ravel()[max_idx], 4))}\")\n",
    "display(example_importance_linear)\n",
    "\n",
    "# Smallest one\n",
    "min_idx = np.argsort(linear_test_preds['output'].ravel())[0]\n",
    "example_importance_linear = linear_importances_df.iloc[min_idx, :].sort_values(ascending=False).rename(\"Importance\").to_frame().join(\n",
    "    test_data.iloc[min_idx, :].rename(\"Example Vlaue\")\n",
    ").head(5)\n",
    "print(f\"Top 5 contributions to row {min_idx} which was scored {str(np.round(linear_test_preds['output'].ravel()[min_idx], 4))}\")\n",
    "display(example_importance_linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "167d8831-5b0b-4292-bab9-cf1c179d0b66",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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