EX12]영화추천.sehyun.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "velvet-finish",
   "metadata": {},
   "source": [
    "# 프로젝트 - Movielens 영화 SBR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medium-circuit",
   "metadata": {},
   "source": [
    "## 프로젝트\n",
    "\n",
    "저번에 했던 추천시스템을 기반으로 하는 프로젝트입니다. 주어진 데이터셋을 전처리하고 예측 모델을 설계한 후,  모델 개선을 위해서 hyperparameter 조정합니다!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "related-biotechnology",
   "metadata": {},
   "source": [
    "## 루브릭 \n",
    "\n",
    "1. Movielens 데이터셋을 session based recommendation 관점으로 전처리하는 과정이 체계적으로 진행되었다.\n",
    "\n",
    "- 데이터셋의 면밀한 분석을 토대로 세션단위 정의 과정(길이분석, 시간분석)을 합리적으로 수행한 과정이 기술되었다.\n",
    "\n",
    "\n",
    "\n",
    "2. RNN 기반의 예측 모델이 정상적으로 구성되어 안정적으로 훈련이 진행되었다.\n",
    "\n",
    "- 적절한 epoch만큼의 학습이 진행되는 과정에서 train loss가 안정적으로 감소하고, validation 단계에서의 Recall, MRR이 개선되는 것이 확인된다.\n",
    "\n",
    "\n",
    "\n",
    "3. 세션정의, 모델구조, 하이퍼파라미터 등을 변경해서 실험하여 Recall, MRR 등의 변화추이를 관찰하였다.\n",
    "\n",
    "\n",
    "- 3가지 이상의 변화를 시도하고 그 실험결과를 체계적으로 분석하였다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "variable-glass",
   "metadata": {},
   "source": [
    "# Data Preprocess\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charged-outreach",
   "metadata": {},
   "source": [
    "#### 필요한 라이브러리 이용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "adapted-rebel",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "from pathlib import Path\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Input, Dense, Dropout, GRU\n",
    "from tensorflow.keras.losses import categorical_crossentropy\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tqdm import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unexpected-employee",
   "metadata": {},
   "source": [
    "#### 데이터 설명\n",
    "\n",
    "\n",
    "- 명확한 1회 세션의 SessionID포함하지 않고 있기에 UserID가 SessionID 역할 해야 함\n",
    "\n",
    "- Rating정보가 포함되어 있음\n",
    "\n",
    "  -  직전 봤던 영화가 맘에 들었는지 여부 파악\n",
    "  \n",
    "  - 비슷한 영화를 더 고르게 함\n",
    "  \n",
    "  - 낮은 데이터 처리 고민\n",
    "  \n",
    "\n",
    "- Time 항목에는 UTC time가 포함되어 있고, 1970-01-01부터 경과된 초단위 시간이 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "warming-public",
   "metadata": {},
   "source": [
    "## Step 1. 데이터의 전처리\n",
    "\n",
    "1. 항목별 기본 분석\n",
    "\n",
    "2. session length\n",
    "\n",
    "3. session time\n",
    "\n",
    "4. cleaning 작업 진행\n",
    "\n",
    "+ 주의사항) Session단위로 해석을 어떻게 할 건가!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accessible-glance",
   "metadata": {},
   "source": [
    "### 항목별 기본 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "regular-organ",
   "metadata": {},
   "source": [
    "#### 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "legendary-roman",
   "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",
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       "    }\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>UserId</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>3186</td>\n",
       "      <td>4</td>\n",
       "      <td>978300019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1270</td>\n",
       "      <td>5</td>\n",
       "      <td>978300055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1721</td>\n",
       "      <td>4</td>\n",
       "      <td>978300055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>1022</td>\n",
       "      <td>5</td>\n",
       "      <td>978300055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2340</td>\n",
       "      <td>3</td>\n",
       "      <td>978300103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000019</th>\n",
       "      <td>6040</td>\n",
       "      <td>2917</td>\n",
       "      <td>4</td>\n",
       "      <td>997454429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999988</th>\n",
       "      <td>6040</td>\n",
       "      <td>1921</td>\n",
       "      <td>4</td>\n",
       "      <td>997454464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000172</th>\n",
       "      <td>6040</td>\n",
       "      <td>1784</td>\n",
       "      <td>3</td>\n",
       "      <td>997454464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000167</th>\n",
       "      <td>6040</td>\n",
       "      <td>161</td>\n",
       "      <td>3</td>\n",
       "      <td>997454486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000042</th>\n",
       "      <td>6040</td>\n",
       "      <td>1221</td>\n",
       "      <td>4</td>\n",
       "      <td>998315055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000209 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         UserId  ItemId  Rating       Time\n",
       "31            1    3186       4  978300019\n",
       "22            1    1270       5  978300055\n",
       "27            1    1721       4  978300055\n",
       "37            1    1022       5  978300055\n",
       "24            1    2340       3  978300103\n",
       "...         ...     ...     ...        ...\n",
       "1000019    6040    2917       4  997454429\n",
       "999988     6040    1921       4  997454464\n",
       "1000172    6040    1784       3  997454464\n",
       "1000167    6040     161       3  997454486\n",
       "1000042    6040    1221       4  998315055\n",
       "\n",
       "[1000209 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = Path(os.getenv('HOME')+'/aiffel/yoochoose/data/') \n",
    "train_path = data_path / 'ratings.dat'\n",
    "\n",
    "def load_data(data_path: Path, nrows=None):\n",
    "    data = pd.read_csv(data_path, sep='::', header=None, usecols=[0, 1, 2, 3], dtype={0: np.int32, 1: np.int32, 2: np.int32}, nrows=nrows)\n",
    "    data.columns = ['UserId', 'ItemId', 'Rating', 'Time']\n",
    "    return data\n",
    "\n",
    "data = load_data(train_path, None)\n",
    "data.sort_values(['UserId', 'Time'], inplace=True)  # data를 id와 시간 순서로 정렬해줍니다.\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "neutral-captain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6040, 3706)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 유저 수(세션 수)와 아이템 수\n",
    "data['UserId'].nunique(), data['ItemId'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "configured-cheat",
   "metadata": {},
   "source": [
    "### Session Length (유저별 리뷰 갯수)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "nuclear-aircraft",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UserId\n",
       "1        53\n",
       "2       129\n",
       "3        51\n",
       "4        21\n",
       "5       198\n",
       "       ... \n",
       "6036    888\n",
       "6037    202\n",
       "6038     20\n",
       "6039    123\n",
       "6040    341\n",
       "Length: 6040, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session_length = data.groupby('UserId').size()\n",
    "session_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "unlike-husband",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96.0, 165.5975165562914)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# 평균 리뷰 수\n",
    "session_length.median(), session_length.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "sapphire-roads",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 2314)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# 리뷰 최소값, 리뷰 최댓값\n",
    "session_length.min(), session_length.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "tight-greece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1343.181000000005"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 리뷰 분위수( 0.999%)\n",
    "session_length.quantile(0.999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "twelve-texas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>UserId</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>696969</th>\n",
       "      <td>4169</td>\n",
       "      <td>1268</td>\n",
       "      <td>5</td>\n",
       "      <td>965333392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697168</th>\n",
       "      <td>4169</td>\n",
       "      <td>2617</td>\n",
       "      <td>4</td>\n",
       "      <td>965333392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697185</th>\n",
       "      <td>4169</td>\n",
       "      <td>2628</td>\n",
       "      <td>4</td>\n",
       "      <td>965333392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697219</th>\n",
       "      <td>4169</td>\n",
       "      <td>2653</td>\n",
       "      <td>4</td>\n",
       "      <td>965333392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697275</th>\n",
       "      <td>4169</td>\n",
       "      <td>423</td>\n",
       "      <td>3</td>\n",
       "      <td>965333392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697882</th>\n",
       "      <td>4169</td>\n",
       "      <td>3754</td>\n",
       "      <td>2</td>\n",
       "      <td>1024174347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695702</th>\n",
       "      <td>4169</td>\n",
       "      <td>1413</td>\n",
       "      <td>3</td>\n",
       "      <td>1024175031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697358</th>\n",
       "      <td>4169</td>\n",
       "      <td>494</td>\n",
       "      <td>4</td>\n",
       "      <td>1024175760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695945</th>\n",
       "      <td>4169</td>\n",
       "      <td>1804</td>\n",
       "      <td>2</td>\n",
       "      <td>1024175783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696446</th>\n",
       "      <td>4169</td>\n",
       "      <td>1548</td>\n",
       "      <td>3</td>\n",
       "      <td>1024176337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2314 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        UserId  ItemId  Rating        Time\n",
       "696969    4169    1268       5   965333392\n",
       "697168    4169    2617       4   965333392\n",
       "697185    4169    2628       4   965333392\n",
       "697219    4169    2653       4   965333392\n",
       "697275    4169     423       3   965333392\n",
       "...        ...     ...     ...         ...\n",
       "697882    4169    3754       2  1024174347\n",
       "695702    4169    1413       3  1024175031\n",
       "697358    4169     494       4  1024175760\n",
       "695945    4169    1804       2  1024175783\n",
       "696446    4169    1548       3  1024176337\n",
       "\n",
       "[2314 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최다 리뷰자 \n",
    "long_session = session_length[session_length==2314].index[0]\n",
    "data[data['UserId']==long_session]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "spare-exhibition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20      0.014238\n",
       "21      0.029305\n",
       "22      0.042053\n",
       "23      0.055464\n",
       "24      0.068874\n",
       "          ...   \n",
       "1271    0.998179\n",
       "1277    0.998344\n",
       "1286    0.998510\n",
       "1302    0.998675\n",
       "1323    0.998841\n",
       "Length: 736, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Session length의 분포 확인\n",
    "\n",
    "length_count = session_length.groupby(session_length).size()\n",
    "length_percent_cumsum = length_count.cumsum() / length_count.sum()\n",
    "length_percent_cumsum_999 = length_percent_cumsum[length_percent_cumsum < 0.999]\n",
    "\n",
    "length_percent_cumsum_999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "quick-welding",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Session length 시각화\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.bar(x=length_percent_cumsum_999.index,\n",
    "        height=length_percent_cumsum_999, color='red')\n",
    "plt.xticks(length_percent_cumsum_999.index)\n",
    "plt.yticks(np.arange(0, 1.01, 0.05))\n",
    "plt.title('Cumsum Percentage Until 0.999', size=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "patent-infrared",
   "metadata": {},
   "source": [
    "### Rating\n",
    "\n",
    "- 이번 실습에서 Rating이 큰 의미를 주지는 못한다고 했으나 실제로 저희가 예측을 하거나 맘에 들었는지 여부 판단을 하려면 이 수치가 중요할 것 같습니다. 그러므로 간단한 데이터 전처리를 하겠습니다.\n",
    "\n",
    "\n",
    "- Rating에 대한 정보 확인 및 변경\n",
    "\n",
    "- 최고 점수는 별 다섯 개처럼 평점 5점으로 하겠습니다.\n",
    "\n",
    "- 평점 5점 기준으로 영화가 맘에 들었는지 여부 판단\n",
    "\n",
    "- Rating을 통해서 평점이 비슷한 영화를 고를 수 있을 것 같습니다.\n",
    "\n",
    "- 낮은 데이터 삭제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "athletic-structure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['Rating'], density=True,bins=5)\n",
    "plt.title('Rating')\n",
    "plt.axis([1, 5, 0, 0.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "shared-sydney",
   "metadata": {},
   "source": [
    "그래프를 보아하니 Rating분포가 적은 평점 1,2를 삭제하면 될 것 같습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "occasional-romantic",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "836478\n"
     ]
    }
   ],
   "source": [
    "\n",
    "change_Rating = data[data['Rating']>=3]\n",
    "print(len(change_Rating))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "intelligent-insight",
   "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>UserId</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>3186</td>\n",
       "      <td>4</td>\n",
       "      <td>978300019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1270</td>\n",
       "      <td>5</td>\n",
       "      <td>978300055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1721</td>\n",
       "      <td>4</td>\n",
       "      <td>978300055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>1022</td>\n",
       "      <td>5</td>\n",
       "      <td>978300055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2340</td>\n",
       "      <td>3</td>\n",
       "      <td>978300103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000019</th>\n",
       "      <td>6040</td>\n",
       "      <td>2917</td>\n",
       "      <td>4</td>\n",
       "      <td>997454429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999988</th>\n",
       "      <td>6040</td>\n",
       "      <td>1921</td>\n",
       "      <td>4</td>\n",
       "      <td>997454464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000172</th>\n",
       "      <td>6040</td>\n",
       "      <td>1784</td>\n",
       "      <td>3</td>\n",
       "      <td>997454464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000167</th>\n",
       "      <td>6040</td>\n",
       "      <td>161</td>\n",
       "      <td>3</td>\n",
       "      <td>997454486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000042</th>\n",
       "      <td>6040</td>\n",
       "      <td>1221</td>\n",
       "      <td>4</td>\n",
       "      <td>998315055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>836478 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         UserId  ItemId  Rating       Time\n",
       "31            1    3186       4  978300019\n",
       "22            1    1270       5  978300055\n",
       "27            1    1721       4  978300055\n",
       "37            1    1022       5  978300055\n",
       "24            1    2340       3  978300103\n",
       "...         ...     ...     ...        ...\n",
       "1000019    6040    2917       4  997454429\n",
       "999988     6040    1921       4  997454464\n",
       "1000172    6040    1784       3  997454464\n",
       "1000167    6040     161       3  997454486\n",
       "1000042    6040    1221       4  998315055\n",
       "\n",
       "[836478 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data = data[data['Rating']>=3]\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "detailed-valve",
   "metadata": {},
   "source": [
    "Rating기준을 이용하여 데이터1000209를  836478로 줄였습니다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "israeli-aruba",
   "metadata": {},
   "source": [
    "### Session Time\n",
    "\n",
    "-  최근 소비 트렌드 파악 가능.\n",
    "\n",
    "- 데이터의 시간 정보 확인 및 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "spatial-skirt",
   "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>UserId</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>3186</td>\n",
       "      <td>4</td>\n",
       "      <td>978300019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1270</td>\n",
       "      <td>5</td>\n",
       "      <td>978300055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1721</td>\n",
       "      <td>4</td>\n",
       "      <td>978300055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>1022</td>\n",
       "      <td>5</td>\n",
       "      <td>978300055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2340</td>\n",
       "      <td>3</td>\n",
       "      <td>978300103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    UserId  ItemId  Rating       Time\n",
       "31       1    3186       4  978300019\n",
       "22       1    1270       5  978300055\n",
       "27       1    1721       4  978300055\n",
       "37       1    1022       5  978300055\n",
       "24       1    2340       3  978300103"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thorough-harrison",
   "metadata": {},
   "source": [
    "Time 안에 있는 데이터들을 보았을 때 전혀 무슨 말인지 감이 오지 않습니다. 그러면 우리는 저희가 익숙한 날짜와 시간 패턴인 yyyy-mm-dd hh:mm:ss를 해주면 편할 것 같다는 생각이 듭니다. 노드에 나와있는 방식이 있지만 차라리 datetime라이브러리를 이용하는 것이 더 간편할 것 같습니다\n",
    "\n",
    "\n",
    "고로, fromtimestamp()이용합니다\n",
    "\n",
    "\n",
    "https://docs.python.org/ko/3/library/datetime.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "presidential-pearl",
   "metadata": {
    "scrolled": true
   },
   "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>UserId</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>3186</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:00:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1270</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1721</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>1022</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2340</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:01:43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    UserId  ItemId  Rating                Time\n",
       "31       1    3186       4 2000-12-31 22:00:19\n",
       "22       1    1270       5 2000-12-31 22:00:55\n",
       "27       1    1721       4 2000-12-31 22:00:55\n",
       "37       1    1022       5 2000-12-31 22:00:55\n",
       "24       1    2340       3 2000-12-31 22:01:43"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "change_date = []\n",
    "for time in data['Time']:\n",
    "    temp = dt.datetime.fromtimestamp(time)\n",
    "    change_date.append(temp)\n",
    "\n",
    "data['Time'] = change_date\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greatest-hierarchy",
   "metadata": {},
   "source": [
    "한 눈에 보기 편해졌습니다!!\n",
    "\n",
    "이제 그러면 데이터가 몇 개월치 있는지에 대해서 확인해보겠습니다\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "portable-label",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000-04-25 23:05:32\n",
      "2003-02-28 17:49:50\n"
     ]
    }
   ],
   "source": [
    "oldest, latest = data['Time'].min(), data['Time'].max()\n",
    "print(oldest) \n",
    "print(latest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fixed-anxiety",
   "metadata": {},
   "source": [
    "데이터는 2000-04-25 ~ 2003-02-28까지의 데이터가 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spread-swift",
   "metadata": {},
   "source": [
    "### Data Cleansing\n",
    "\n",
    "- 너무 적게 된 것을 제거해줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "seventh-filter",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# short_session을 제거한 다음 unpopular item을 제거하면 다시 길이가 1인 session이 생길 수 있습니다.\n",
    "# 이를 위해 반복문을 통해 지속적으로 제거 합니다.\n",
    "def cleanse_recursive(data: pd.DataFrame, shortest, least_click) -> pd.DataFrame:\n",
    "    while True:\n",
    "        before_len = len(data)\n",
    "        data = cleanse_short_session(data, shortest)\n",
    "        data = cleanse_unpopular_item(data, least_click)\n",
    "        after_len = len(data)\n",
    "        if before_len == after_len:\n",
    "            break\n",
    "    return data\n",
    "\n",
    "\n",
    "def cleanse_short_session(data: pd.DataFrame, shortest):\n",
    "    session_len = data.groupby('UserId').size()\n",
    "    session_use = session_len[session_len >= shortest].index\n",
    "    data = data[data['UserId'].isin(session_use)]\n",
    "    return data\n",
    "\n",
    "\n",
    "def cleanse_unpopular_item(data: pd.DataFrame, least_click):\n",
    "    item_popular = data.groupby('ItemId').size()\n",
    "    item_use = item_popular[item_popular >= least_click].index\n",
    "    data = data[data['ItemId'].isin(item_use)]\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "favorite-bible",
   "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>UserId</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>3186</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:00:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1270</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1721</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>1022</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2340</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000019</th>\n",
       "      <td>6040</td>\n",
       "      <td>2917</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-08-10 14:40:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999988</th>\n",
       "      <td>6040</td>\n",
       "      <td>1921</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-08-10 14:41:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000172</th>\n",
       "      <td>6040</td>\n",
       "      <td>1784</td>\n",
       "      <td>3</td>\n",
       "      <td>2001-08-10 14:41:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000167</th>\n",
       "      <td>6040</td>\n",
       "      <td>161</td>\n",
       "      <td>3</td>\n",
       "      <td>2001-08-10 14:41:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000042</th>\n",
       "      <td>6040</td>\n",
       "      <td>1221</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-08-20 13:44:15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>835789 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         UserId  ItemId  Rating                Time\n",
       "31            1    3186       4 2000-12-31 22:00:19\n",
       "22            1    1270       5 2000-12-31 22:00:55\n",
       "27            1    1721       4 2000-12-31 22:00:55\n",
       "37            1    1022       5 2000-12-31 22:00:55\n",
       "24            1    2340       3 2000-12-31 22:01:43\n",
       "...         ...     ...     ...                 ...\n",
       "1000019    6040    2917       4 2001-08-10 14:40:29\n",
       "999988     6040    1921       4 2001-08-10 14:41:04\n",
       "1000172    6040    1784       3 2001-08-10 14:41:04\n",
       "1000167    6040     161       3 2001-08-10 14:41:26\n",
       "1000042    6040    1221       4 2001-08-20 13:44:15\n",
       "\n",
       "[835789 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = cleanse_recursive(data, shortest=2, least_click=5)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "difficult-stable",
   "metadata": {},
   "source": [
    "## Step2. 미니 배치의 구성\n",
    "\n",
    "- 데이터셋과 미니 배치 구성\n",
    "\n",
    "- 학습 속도의 최소화 구성 (by Mini-Batch)\n",
    "\n",
    "- Step 1에서 Session 단위를 어떻게 정의\n",
    "  \n",
    "  -> Session-Parallel Mini-Batch 필요 없다.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tough-honey",
   "metadata": {},
   "source": [
    "#### Trian/ Valid/ Test split\n",
    "\n",
    "- 기준: 기간\n",
    "\n",
    "- 모델 평가: Valid set 과 Test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "royal-democrat",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_by_date(data: pd.DataFrame, n_days: int):\n",
    "    final_time = data['Time'].max()\n",
    "    session_last_time = data.groupby('Time')['Time'].max()\n",
    "    session_in_train = session_last_time[session_last_time < final_time - dt.timedelta(n_days)].index\n",
    "    session_in_test = session_last_time[session_last_time >= final_time - dt.timedelta(n_days)].index\n",
    "\n",
    "    before_date = data[data['Time'].isin(session_in_train)]\n",
    "    after_date = data[data['Time'].isin(session_in_test)]\n",
    "    return before_date, after_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "clean-nature",
   "metadata": {},
   "outputs": [],
   "source": [
    "tr, test = split_by_date(data, n_days=180)\n",
    "tr, val = split_by_date(tr, n_days=180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "valued-identity",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stats_info(data: pd.DataFrame, status: str):\n",
    "    print(f'* {status} Set Stats Info\\n'\n",
    "          f'\\t Events: {len(data)}\\n'\n",
    "          f'\\t Sessions: {data[\"UserId\"].nunique()}\\n'\n",
    "          f'\\t Items: {data[\"ItemId\"].nunique()}\\n'\n",
    "          f'\\t First Time : {data[\"Time\"].min()}\\n'\n",
    "          f'\\t Last Time : {data[\"Time\"].max()}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "honey-trading",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* train Set Stats Info\n",
      "\t Events: 818736\n",
      "\t Sessions: 6036\n",
      "\t Items: 3307\n",
      "\t First Time : 2000-04-25 23:05:32\n",
      "\t Last Time : 2002-03-05 04:14:13\n",
      "\n",
      "* valid Set Stats Info\n",
      "\t Events: 9771\n",
      "\t Sessions: 390\n",
      "\t Items: 2348\n",
      "\t First Time : 2002-03-05 04:24:20\n",
      "\t Last Time : 2002-09-01 04:20:59\n",
      "\n",
      "* test Set Stats Info\n",
      "\t Events: 7282\n",
      "\t Sessions: 333\n",
      "\t Items: 2049\n",
      "\t First Time : 2002-09-01 19:41:39\n",
      "\t Last Time : 2003-02-28 17:49:50\n",
      "\n"
     ]
    }
   ],
   "source": [
    "stats_info(tr, 'train')\n",
    "stats_info(val, 'valid')\n",
    "stats_info(test, 'test')"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
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"
    },
    "image-4.png": {
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lLVeiVZ+jVQxTKWUHuOJz6PHMj/CEwLajTiuR6AyDIUvO5Wt9uesI2lK0jsZn1q/BJ7IDzIvWHEdPQHjHUTGoctZug69PuZTHyovcD28QENLFvu+KHpmhk6JVLYi6tf50j8+pcJCeLYMwDWuDw6n1a6UUrdWcGpOmmcoTe5NXt2cIT0mABEiABL6MAEXrl6H+7Q0t97QC4963MQGKWshqdWadRqntPH2uaAVQ5giVcLYqL2pLUH3TVGhyn3qHBNe9BApksV/mLRWjXuqusVOi9Z76S89zz3PebVlzVuTIkgM8Wwnvlpe9FK0Su84Pm1YdRYytEPqFXa2iPCQBEiABEngNAhStrzEOv8CKW0QrUArUzgKeHNFmZLFOGcM44kHseRXbovbpolWNqkref9hWC8MsG5vtAVF6HvW+PnUSKFEv9SmjisQvPbCduNARg7Si9Z76y0V1fc/5SONTl8ofJS0RWorWlogdPDsfAjJ4hBdIgARIgAS+lQBF67fi/02N3yZaKw+qvG5KUIrTkZCB8rozkipJz/hLRGu7+SLH6RjAcy1IZ4uw/Tq7Xe4Jx1Ue1ikRVy1m0sXgNibpROtd9autYcWUbar1HKd4auvVnv2laG2nwGp60HxOxxw3pfhJAiRAAiTwOgQoWl9nLN7ckhtFK6qdkOQuLr2T5Qr1sZ2bRuNcp5F+uWi9mFNlORjfnaoq9OiUV2Vfe8n3L+bUB6Ugbbui+wUmUl7dU3+54G6wm1Wv8VKETuWR7YnQ0vM6lYmg2lZYFwPda52nJEACJEACL0CAovUFBuF3mHCraAXK9FblIqpmW86xBPn1695a3PaZFqkPu7fw5rmiVeUJlWjH0HZsyg5wBim7riUevbmAqSd0TsTd62kd1m8oHssdrCZ41inSrpsPqJRpEzGteYSNaHnwr+h5RAIkQAIk8KIEKFpfdGDez6zbRWuZsupTYLvfw5lI5F+mvFJCcLBcvH513MsU8GzRqrYdHYq0amSLcttRvSfw0Z5WlCJtLqZ1fltTnWi9uf65BXSXP4TqB4vcVh73y+X6oBz73o+SdG9DjHnlUXu67T30Sc36LfCcBEiABEjguwlQtH73CPya9qsdinSbBcxhKF8hq0VVGtFSPd+IkR2Orc0FQs+GkOuBmL1HtKYqz+oqxEAftzpSbS7gYBedLhsXANfV+rb/tSmvLqv7WxsplLGiR5U9QJa5TudyHGhFa5ksYQ0pbHhL6i9TXU299r8CLb3lQsL142v2g1beVT/pWV+k2DsS1iZA0swHtTDOd8tNHwKDFF/X1nlEAiRAAiTw3QQoWr97BH5R+9lxC9eaWuFvYSA8Gj4qRlHa8GeFRncbV6FW7Psq5VRT0fXzso3r9dLlSIlSq7cZweWmOigS7N1qq9ipbVzPsdrG1cGHbPptwV55COKxEIdOC085qXaHcmA341BuK+sjTMzsUflNrYn408X1nwLYSzye5xiB5155WjZWXgA9zjPi/eba34n58BTgrJQESIAESOBhBChaH4aSFZEACZAACZAACZAACTyLAEXrs8iyXhIgARIgARIgARIggYcRoGh9GEpWRAIkQAIkQAIkQAIk8CwCFK3PIst6SYAESIAESIAESIAEHkaAovVhKFkRCZAACZAACZAACZDAswhQtD6LLOslARIgARIgARIgARJ4GAGK1oehZEUkQAIkQAIkQAIkQALPIkDR+iyyrJcESIAESIAESIAESOBhBChaH4aSFZEACZAACZAACZAACTyLAEXrs8iyXhIgARIgARIgARIggYcRoGh9GEpWRAIkQAIkQAIkQAIk8CwCFK3PIst6xwnkJ0R+ay94YcFebXFI8/HyL3q1SHawhMQ6Or+ohd9o1jlG4Ln4kAJCCFj2Bn50gm6Ez/EeG9sqywrLhRck2rLIUxy2K9hWVbeQH3C9AImucoVhoT3fSI5NkwAJkAAJTBCgaJ2Aw1sPJnCOsLEknG2INC+qyoscaejBVgIwzB7c4POqy49bitYxvFmItbThhWktPAvkyR6uFLD9FPWoX57MwjWk7SE81apTidKNBbkOMZgN5whrVY+n5k9dRZHhuHMg5Rqj02ehPRfDeEACJEACJPByBChaX25I3tWgAvFOQmyiUS9aFq4g5A5xX9W8K4637FeGgyuwGlGPebSBEB6OjdhU/c8OcIWLQ1+dFgn8TwmvU7ieP+5hKGZxRrgSkN6xN7cW2vOWY8JOkQAJkMD7EKBofZ+xfPGepNh/KkGjeZ2ehVgJAT958W68iXlFuocjHxveUMQ7yM890jFGyksqbASn6810/6n9EXNWP2KctkBt5k9f4Vb15UcPQvhoT5+l9lwt4xEJkAAJkMArEqBofcVReUubEvhiQrQiRxrFyOhp/ZLRT/d1DOkqhOZnxEI7CsRbgc/9qGQdqasSodqY4PJHjNPywp4Q2H3va6vaxO+J1qX2tOriIQmQAAmQwEsSoGh9yWF5R6NyHD0BsY0HcY3Tvc1xivzLQh35oRbqxFqhlScBvJUNS1wX6jibLQ6alTpLy19szY/wpIV5jZYjCTy4H7JeaGRj40doQjgv9an1QtEG1ibCuagWqzn1M/Jjjf2D7X+8pzWFLwV2bVdnu3P949LzKuFrNW71I6cbItCv5Hp+jtYQHS/vQnuuVfGIBEiABEjgRQlQtL7owLyjWUXqwxY2dnE7sHGqpwUS34Z0fcTn2gXbLNRxA6Q9r6wSLlK68OPsKoxbC736Amhp+Y6lZ5NwhgzhWkKuD62FZ+dq4ZDt4dhzcVavxNdYOw62aiFT3b/zcVdy6wvku+zvdOYBJyWPFVT0R36K4G+aHw4W7I2PqK/S0z0+RVV+vPUqTnUsPnZYPitjWjuhJ0vtGVbKKyRAAiRAAi9GgKL1xQbk3c3J0wCrSwaB6d4W8RZSbkcWZ6XY2wJ2R8VVIkdogmITX0Kso5aHdmn5nq0GojU7uBC2PxDXgHp1LQcLh0rRKmz4SU+NNwuNOm7JO+3vdefu0/L1vIfgsMbHan/9kXH50WDDO7biUcvy86JVN56NvUWeIvQcOH4vTdZSe5oK+UkCJEACJPCyBChaX3Zo3tkwleZqC1eJ102AYzbmea1EmTNYWl5xKVejd7IN1OEH3vHqZW0hLNIA26Cdcmlp+VZl6nBWtCbl6/JNNNa3auW801s5X4pWTYxp4gt0Bdyd9ve6c/dpKRIFxGokVZXqbpkdopU94E7RWmUjqENAVCiItcHh1BL7S+25GwArIAESIAESeDYBitZnE2b9EwTU4isfK0vAWu27CeJVzOhUNoFyoU4vJrLMydnLAzvROpaWb9c1J1qzAxz1+rvlXGw/DlQxm+2FSMtEa6kEsZYL+ts14LFnpUiU2OlylhUxtkJc01jdKVovxhc5suQAzxYQba/8UnsuFfKABEiABEjgVQlQtL7qyPwquzJEnl2+Sr+8Ga9FodpRaerfIBqgOCM+bKuFW5aNzfaAKD2Pel9LxEvLN+MyJ1oNRVk7DnOxaFW23Gp/049HfZb9HQvlaBqoPOeyCXF4aExrJeBVyrRL3PJSexoz+UkCJEACJPCyBChaX3Zo3s2wM06jYQB1P4sYOymu26KWorCd8ugGHkWO0zGA51qQzhZh+/XxWHVLyr+KaG33Y4n97ececVyKxG6e1G61vRjcxdkDcpxi/VawQLUY6xJCsdSerrE8IwESIAESeEECFK0vOCjvaFLpRZT+eOL5ssM9UVOHB2x1r5sXQaqyEHRTIk1VYFB+TrR+RXiAtgvz9j885VUZrtGKWR3YluHgCFxjlBfmaS1F6IKFW4vtGRjMCyRAAiRAAi9GgKL1xQbkbc1JfcjJFEeVp+wqaurXybvxvK5l+izZFkkqL2cvxrUNsxGRlzRTS8u3K3vAQqzy9Xh3C9Nl4QH32f/4zQWUCJ2Iac0jbITs7Hg2uyNWO+/qrGe2EsGX8AAst6c3wjwlARIgARJ4MQIUrS82IG9rTrmfvIDtJ6PxpUWicrh2RVyZ8mp0MVP9Kriz0r4SLVfR2yVZlNt8dkWu2lbWvHy3vvnsAcBNKa86fbq2OcwesLS/17rU0cM9rQDSvQ3htrdebdqsPb92b4vX7AC3N+blE+Vc6e9+leHgCkjN5hRVerT2+N5gT2MuP0mABEiABF6SAEXrSw7LexpVCVMJ14+RNZnzoXa82sGRFjZRf6l9I3Z2OLY2FwjVoi25HqzMr5LtO9hFp0tifpUTNYt9uFIJ5nbKK7UDldqMwLx8Z1TmwgPKwjdsLmAsWu+0v9OZB50UKfaOhLUJkDTjpRaK+W656UPQ3w2iXD+1hrQ9hM3mA83mEeth6qxqc4p6/jTZrVp5YAf5bW+w50EkWA0JkAAJkMATCFC0PgEqq9QTyLMjAm8F27pus+p6Kldro0L6z3a3cRUqI4AfItWkPz3HahtXBx+yyTpgwV55COJLXECngaXlLw+X27jaCE6XK5qDG7ZxHakp9SWszmYKVaGb7R9p4zGXzoj3m+v4zoyXavMc7y/b9ApLbdPb2yigbdg5RuC51/G1bKy8AJrhVbUvtqfdHI9JgARIgARehwBF6+uMBS0hARIgARIgARIgARLQEKBo1YDhZRIgARIgARIgARIggdchQNH6OmNBS0iABEiABEiABEiABDQEKFo1YHiZBEiABEiABEiABEjgdQhQtL7OWNASEiABEiABEiABEiABDQGKVg0YXiYBEiABEiABEiABEngdAhStrzMWtIQESIAESIAESIAESEBDgKJVA4aXSYAESIAESIAESIAEXocARevrjAUtIQESIAESIAESIAES0BCgaNWA4WUSIAESIAESIAESIIHXIUDR+jpjQUtIgARIgARIgARIgAQ0BChaNWB4+ckEihg7aSM4Pbmdd68+T3HYrmBbAkIICPkB1wuQ5JqOLy0P4BzvsbGtqn7LhRck6Fd/DlfVfWXDxD+bA64ZGF4mARIgARKYI0DROkeI959D4BxiJQT8xKz61Jew9qlZ4d9S6hxhLQVsL0TaqMgiw3HnQMo1wqwHYml5AFm4hrQ9hKe6ASV6NxbkOkS/+l5rndOyHmHDT4rOdZ6QAAmQAAmQgCkBilZTUiz3WAILRWviCwhThftYS1+0tgLxTkK4hxHxeEa4EpDeseURXVpeKdYDXOHi0FenRQL/U8I7Nkp5BlGmfqBIrAcqeuY53iYBEiABEiCBFgGK1hYMHn4hgV8mWot0D0dKrKPzgyCn2H8KrDRCMD96EMLH1ZG9tDyQ7j8hNlFL+F5NL8MBnDHBfC1THdUCeqFntl8Lz0mABEiABEiAopVz4HsI/DLRmu7rmNBViMfI1hMCe8Lbmfg90bq0fCVytSK79J46Qy9sbzYV8RZyzFvbK8dTEiABEiABEpgjQNE6R4j3n0PAULTm0WZkYc8GkfbNdI5T5F8WDskPtXAoHgrF/AjPWiE47OGqRUyWi+0xQ54GWH9ICCHhbI+DV+95EsBb2bCaxUbyA85mi4N25VOF7/Ge1ulhOUdriM89TKOAB+VV/KuQ8LUVJPCFmAkRSLG3BT4Zizw9WLxLAiRAAiRgRICi1QgTCz2cgKFobdo1i2ktkPg2pOsjPtcLfpqFQ26AtL0GqG6/WVCUxzt8SguW41cr70tPosA2vj6khJ2ULvw4w+VqkSMNPdhiwuvZdOLLPrMypnUVmvp0R8qne3yKFfRVVK/9deEJqqsqREFKD6ahr1+Ghw2RAAmQAAn8SAIUrT9y2N7A6CeI1vJVtNyipTNrUJXHz257/AbtV57Dq0htYkAb4VeJNN1isMSXEOto6NH94qEq8hSh58Dxh2mpxkzRli/DC+ZFq44HkOHg0ss6xpzXSIAESIAEbiNA0XobNz51L4GBaJyucN7TWolKZ7DUvaq3DDOQu6ugHbRfidZrgoLGk9iI1hxHT0B4x6uXtWVykQbYBunovVaxpx0OwiisDQ6niz940F5p1xwAACAASURBVO5s+XtFa+mpHck8MLCEF0iABEiABEjAjABFqxknlno0gYFonG5gVrSqGNWpvK912qVLjOag/TnRWiYtxVqqWFeVF1UvCKd78uS7RY4sOcCz1UYDY17nXvu68neK1tLzrMk80LOApyRAAiRAAiRgRICi1QgTCz2cwEA0TrcwK1rr+qZ2Y1L3Lp7UQfsGolWZWJwRH7bVQi/LxmZ7QJSev83DqqVWx+Quy6XaWlh1T0xrudtZNx5YaydvkAAJkAAJkIAhAYpWQ1As9mACA9E4Xb+ZaJ1PwXRpZdC+oWi9VKAEbI7TMYDnWpDOFuHE6/j2Y485znGKT6M5VKv6q8VV15jTheXvyR6Q+JDCwMv7GBCshQRIgARI4JcQoGj9JQP9ct0ciMZpC2dFax0ecF1INV0fBu3fIFovTVRZC6ZSTD085dXS1/dLy+P2PK3lpgSd3bguoHhAAiRAAiRAAjcToGi9GR0fvIvAQDRO1zYrWlEtnJK7ePRVfZH6sNvplwbtz4nWFL6cyFuaHeBMpIh6+OYCs57QSnRewgOWlofBjlijeWAzHBwB3YK46VHmXRIgARIgARLQE6Bo1bPhnWcSGIjG6cbmRStQ7b60wnBn0/pVeXs3qkH786JVbZuqE2NFuW2qPifpwz2tdUopudWIdLUTVVukLy6vFp4d4I7tZlUk8D81eWnnFsRNDzPvkgAJkAAJkICWAEWrFg1vPJXAQDROt5aqPKircLBDVfep+jW9vcOxtblA6NkQct0Vs4P250QrUG0u4GAXnXBNHlAgi324UsD2vzblVek9FhKuHyNrkhlcNjuw4SfNxYrS0vLqqSxcQ9oewlO9BVmzWcNaMxazC7i6I8YzEiABEiABEjAlQNFqSorlHkugXGFuIzgZVlsk2LtWvaWr+TauQq3w91WKql475wgbq+2VTbG3LFz3HzgjWg+9iedYbePq4EOK2hYL9spDEDf5XHvtPPv0HCPw3Ks9lo2VF0BrztLyAM7x/rItrtru1gsmNi44BbBt8+1jn42H9ZMACZAACbwPAYrW9xlL9oQESIAESIAESIAE3pYARevbDi07RgIkQAIkQAIkQALvQ4Ci9X3Gkj0hARIgARIgARIggbclQNH6tkPLjpEACZAACZAACZDA+xCgaH2fsWRPSIAESIAESIAESOBtCVC0vu3QsmMkQAIkQAIkQAIk8D4EKFrfZyzZExIgARIgARIgARJ4WwIUrW87tOwYCZAACZAACZAACbwPAYrW9xlL9oQESIAESIAESIAE3pYARevbDi07RgIkQAIkQAIkQALvQ4Ci9X3Gkj0hARIgARIgARIggbclQNH6tkP7eh0r0uC6h70QEIN/FvbpuN1FsoMlJNbRebzAD7+a7q0RHiOMPvYYQ/TufG4f3gKnyMfG+YAs55sFe+UhiPXz6Bzvr/PUcuEFCfIJA55dfqLp0VtL7elWkuPoWZDecbLP3WemzpbwPyNaj8z55v8Tlo+kmGrL5N4SewDkKQ7bFWyrtkt+wPUCJFMTwsSMVhnz8foCPov7u5Bnq988JIFbCFC03kKNz9xAIMX+U8A9ZDc8C+TH7UuJ1tSXsHQK+6Ye9h46h1gJAT/pXdecvhofjZlffLlA4tuQro/43KidAnkawrMl1uFwLmbhGtL2EJ5qVaK+xDcW5DrEsDTw7PJLgS21p19/EW8rcb8KoZf1/ad050v5V/+P+Hza39VCe84R1lLA9kKkjUgtMhx3DqRcY2T66EBory8bryfzWdzfhTy1FHiDBMwJULSas2LJuwgk8BeIsLua+oKHE19AmCrKW+xZKFpvaeLdnyniHaTcIm70arvD2QGucNH5DTV2TT1TJPA/Jbxjo1zqip5dvm2vyfFSe/p1Fgl2n7VH8QGidTH/IsZWiCHnvp03ni+zp0C8kxDuYeTHyhnhStzvjV46Xk/ls7y/y3jeOGh8jAR6BChae0B4+iwCFK2LyP4y0VqkezjyseEfyU5A7HSu6gwHR3TCTdL9J8QmGn0tfg5XEE5XwDy7/KL5AmCpPd36C6S+DSE9eBsB8QDRupQ/MvV24VMbItS1d/nZMnsqr+ZK407Njx6E8KGbXSbzefF43cFn3p7l/V3Gc/l48QkSGCNA0TpGhdeeQICidRHUXyZaLzG9DxBLFefKG7YKdS+5q/tXb3n1pa2NmS4Fg9PyzD67/KLZoiRrGX5jbn+v/nQPu44ZL98i3D0OS/kDSHwIofGM98xdfrrUnhMCe8S73jRc2qoXrfPz+YbxuoPPvD1L+7uUZwOOnyRwHwGK1vv48WljAneK1vwIT+oXal3MOEfYSBtBmiPZb6oFFNb44iX1TJ4E8FY2rGaxh/yAs9nioFlpkUebkQVTG0S9N8cXe249WCpa5/io+9YKwWEPVy0qsVxsjxnyNMD6Q0IICWd7HHkVmlcLmexqoZj8UAuT4gfEO3bBzHuCuuXnz9SilQnRgcrTeomfVPF8QsIfW+VWNlbN30uIwLPLz3ewW2KpPZ2nMxzcyruq4nYfJVoX8Qcw5s3umHnXycL5MNPWOVpDfOr/vzI7n28Yr3v4zNqzuL+P5TnTPG+TwIUAResFBQ+eS+BO0Woq4upytr2CF6bIx+IZ646qLx4pXfhxhkuxIkcaeqXX6SJQRsC8XEzrHJ/6frOgKI93+JQWLMevVkKXnkSBbScAdGShRbMwyQ2QXqCNAHrxSyrbwqeQ2DX9Tff4FCtoHbNoPEv1cqxnl1/Kb6k9rfozFfog7Mtr+ceI1lYDI4cD/spX7EuIXYzOavonrNYfMQdj9oyVq65lZUyr3ouvf/Jy54bx+j4+y/u7jOeFCg9IYJYARessIhZ4DIGvFa26WLRrXyoRcn09fL2jjhL1BbqOtB7Fnypar2vHqvG4itTqdWX7i7hcST66kCnF3hawn7bKuzsWjz5Tgt2RArI9vuWr13nRepkvzy6/tNNL7WnqVx4/tULeTy8/3J4tWkf5Q6XaUovAlMdfrdavfxEVGWLfLX9c7p/0K2ncngZQ97PIU4SeA8efToPWfWrkbPF4fQ+fW/q7hOcIGV4igUkCFK2TeHjzcQQqkTTMzdrkZZx59T/nSWwMNS3XfEl6x8uXdVOF+lQ5ZbfB9Yu8fU8dv4tovYrYxpPYxIBW505nef2VQhkmIXfjK/OvxV7sKEO0dcqUTrbXC4VYKiKeXP50WMFxnPl/uzqf6lJ7ypFRQkhCfO46+U/HROtie0ZHfoI/qtfNSgyOOfDTvQ1h61/HjzY3e3HKnu7Dg7Aga4PDaczS7nPas8Xj9bV8buuvOU8tF94ggRkCFK0zgHj7UQS+1tN6FWMT9mch1rLn2Zko3r719qJVxcBOpSgrwwmmYkDbtL7/OD8dsFGxvNLBNhrJuLpURDy7fH5CHMez/5KsDqZeak+ZyasKkeiHwYyJViy1pzfks/x75Qendbqn65uBQYlFF262p8iRJQd4tppLdywau2G8Jjv4YD6Xtgz7ezPPS0M8IAEzAhStZpxY6m4CLyhaVZ+KM+LDttoBybKx2R4QpedRb08bwduL1tpjrfeMVx5yox8HbXDfcJyFm3KhndpkQLO+TuWL+l0xrUUK3+6FSNRjMypa7xg3I/6z9Vee/8vCudny+gIPsaeOAe8Lfn2rvTtL51vv8eHp4/gM64baRaPc7GSsvw/hOdooL5LAkABF65AJrzyFwIuK1nZfixynYwDPtSCdLcKJ13+/Q7S2Uzy1Qf2c43LHIRUnuYtH869eerJ0Nfezy18MMzxYaE/5ul0tPBt1Oj8mT6uy3Jj/bDcrUXaJKZ4tP17A3J4cp/g0MWeqxUk327NwvMZ70756L5/b+mvOs20rj0ngdgIUrbez45OLCPwA0XrpT7VqfiqlzduL1jo84FGvYy9oNQf3puQZrbbccUgtMhqPk+w+szRv5rPLd62bP1tiTx3L2qR5m/yciTWfMmwR/6mK1L1KJOpirOeeLu8vsefO1/fz83nJeJn0bprPrD239HcJT5MusAwJGBCgaDWAxCKPIPBqojWFLydiMrMDnIkUSG8vWusUT3IXj4ZKFKkPW3ro72x660yZT36+tOapbSnH65rdoaiXl/PZ5cet1F9dao+upseEByzjnx+3UIvjtOmO8wgblUdXtwWVrjOX68vswawntBKdY6/LVZMm83nJeN3LZ9aexf1dyPMyDjwggfsIULTex49PGxN4PdG6/xTQeW6KcptGvSh7f9EKlCmvRl8h169G79416Tp5Zj1B16JmR7fs0156jtzWrld1U0UC/3Nko4Jnlzfr6bXUUnuuT3aOHiJaF/Iv97EXI+xLy+o3H/dkD1hoj/Lsqg0X5Fbzoy3eQk78aDOazwvG614+8/Ys7O9inp0pxhMSuJkARevN6PjgMgKvJlqBanMBB7vo1NqEoEAW+3B7uSv7fS0Tfa/CkR2k+iVvPDdO3VXXP1d+cL8/HlVMXDtPK9CIhR2O5zq9T5mnUu1Rvx6Nh7yxt49/bHahy3iTZYye7SE81T6/ZjOF9fhYP7v8uJX6q0vtGavpIaJ1Mf8M0caCsFbYdzb7aPK03jnfFtuj0t755SYjrh8ja7JbXTYfseEnzcUximbXzMfryXzKNH8L+nsDTzMiLEUC0wQoWqf58O7DCKi9re+Ijyu3KbURnGYMKsvpPDbDZ8+x2sbVwYe85ou1Vx6CuMlXOnymvFIk2LvV1qZCPGEb1yLGTm1HO9ffxrw5Pmp7W6u98CbF3mqPh25bxu42rkJlWPBV8vem4Rf9LGP0mjHVf1ojGyR0dmSy1La104nkn11+KeGl9vTrPwU25NSr+v4DY+c38Vdzbd/ZVll+ONj4EZrfEGNNGV27yR61t2yMwHOv/3+wbKy8AHP/ezCyqS5kPl5P5NMYbNrfW3k27fCTBG4kQNF6Izg+RgIkQAIkQAIkQAIk8HUEKFq/jjVbIgESIAESIAESIAESuJEAReuN4PgYCZAACZAACZAACZDA1xGgaP061myJBEiABEiABEiABEjgRgIUrTeC42MkQAIkQAIkQAIkQAJfR4Ci9etYsyUSIAESIAESIAESIIEbCVC03giOj5EACZAACZAACZAACXwdAYrWr2PNlkiABEiABEiABEiABG4kQNF6Izg+RgIkQAIkQAIkQAIk8HUEKFq/jjVbIgESIAESIAESIAESuJEAReuN4PgYCZAACZAACZAACZDA1xGgaP061r+8pRxHz8ImOv8KDuneghD6Pe8v9z72SEeIFMkOlpBY/xJeIwg0lwqcIh8b5wOy5GvBXnkIJjaDN9/bvWpyWfnl9mg6pr28yJ48xWG7gm3Vc09+wPUCJLm2+sU3zO05I1pP/A1YPpJicfODB8ztGTwKoPr/kvSOeCCia0OJD7mOYP5/vW+25wvmzxUOj0hgOQGK1uXM+MRNBM4IVwKr0Px/3zc1c+NDqS9h7cfk440V9h87h1gJAT/p3xg/z49bitYBmgKJb0O6PuJzo3YK5GkIz5ZYh9ngiSxcQ9oewlMtSdSX8saCXIcYlgaWlV9uz8DAmQuL7DlHWEsB2wuRNgqsyHDcOZByjRE8M60Pby+yByn2nwKfT/y7WmbPsD9FvK1+/KzCBcJyWI/uSnZwIBbU/a32fMH80XHidRIwJUDRakqK5e4k8NqiNfEFhKmivIXEQtF6SxPv/kwR7yDlFnGjV9sdzg5whYtDW4mOXVPPFAn8Twnv2Ci7uqKF5Rfb07bX5HiRPQXinYRwDyNivPrbu9ubuMgexTnGVoghZ5O+m5RZak+/ziLB7rP2BC8Qlv1qxs/Vj6lD+SPCWLR+qz1fMH/GQfEqCSwiQNG6CBcL306AonWJp/V2zq/xZJHu4cjHhjckOwGx07mqMxwc0QmnSPefEJto9LXvOVxBOF2Bt7T8UnuWjswyeyqv5krjTs2PHoTwoaNnYtsye6Dc1liJTzzL0brYnk4nC6S+DSE9eBsx6w1dMp8HoUFGgvi77Xn+/Ong5wkJ3EiAovVGcHxsKQGK1t8kWi9f3EZf2CZzaW7+VPev3vLqS1gbE1wKKqflmV1afqk9Jn1sl1lqzwmBPeI9bqpM/DtF61J7AJRtajzjjV03f95gT7utdA+7jhkv37LMzNNb57NJ3aVZ327Ps+dPGz6PSeB2AhStt7Pjk4sIzH3JayrLT4j8zXVhiWVjs4+18Wd5EsBb2bCaRVDyA85mi4NmJUoebUYWTG0Q9d4ca6wzv7w0PCA/wpOW3kul7lsrBIc9XLXoxnKxPWbI0wDrDwkhJJztceRVcV4tZLKrhWLyw4UX6Hmad7Bbcolnqvuk7kwt6pkQZag8rZf4SRWfJyR8bZhyAr/96nppeSy0R9ct3fXF9ugqqq6fozXE5/iiv+kn67s32DPmzTZqy6TQDfZcq81wcCvvqoomMRGWt85nk7qh5u5L2XMl1RzdPX+aivhJAncSoGi9EyAfNyVwg2gtUvi2gL07ImviGIszjjsbY/F56n+sUrrw4wxNcRQ50tArvSqDGMaW6eWXyyvFtM6J3Pp+s6Aoj3f4lBYsx69WipeeRIFtJwB0ZOFQszDJDZBeoLXA/JBDlW3hU0jsmv6me3yKFfTr/pr5WAfBLi0/w2Vgz0z5we2H2pPdvwjyBnvU4kaxi9FZ3f+obAY32NMwzlRoiLAvPwjNhGXz9LJPk7pfzZ5hDx8wf4aV8goJ3ESAovUmbHxoOYFGJJhnDyjj8ORIHF52gDMQJFX919fDXQsT9QU6kXrmp4rWq86uPIdXkVq9Pm1nayhXJo8uZEqxVz8OnhV82B2Kh58pwe5I0U0tVL6anhetl/mytPxEL0btmSg/eutB9hR5itBz4PjJaGzvaNtjFxfbo1I3qUVOyuOvshnUv4iKDLHvlj8u9/f8SlpsT90p5aFVGRb89PLD1kRYjiExuTZb96vZ0+vUw+ZPr16eksCtBChabyXH5xYSWC5aizTEPrx+uVwaHPVC1l+S3vHyZXQprxYypwG2wUhddaF3Ea1XEdvnXZ07neX1V0JlmITcja/MvxZ7saMM0dYpUxbZXi8UYqmoWVp+lITentNhBcdx5v/t6nyhd9ozCHuxNjicrq7059tThU8osXxt9Qot3dsQ9h3hCjfxUf+PkBCfu05+2FlheTV78dF03a9mz7V7c/PnWpJHJPC1BChav5b3L26tL6KWoiiQn09IogO2qyoe8yrQ6rqyEGvZ8+wYNvP2olXFwE7liS3DCaZiQA1BflGx/HTARsXySgfbqJ3nqjZgqahZWr7Xz1l78hPiOJ79l2R1MPWd9lzMK3JkyQGerVi1FkV9lz2NYXU6rOubgeaG4ecNfJqQjX6Y0LSwNLRHU2yq7lezZ7QLuvkzWpgXSeD5BChan8+YLZQEbhOtxSmCv1a7H0l8OCt4uwOOcaBP1F+cER+22KiFRmrR1vaAKD2PenvaA/P2orX2Tl924moWqvU+Bz8E2pBe5DgLN+VCO7XJgGZ9HbA05nFp+RYLI3ta5Y0O77BntP46xrkv2EbLjl18tD2o/n9wWTg31ubUtaX21PHxY7tTTQnLKRNM7mnrfjV75jpz7/yZq5/3ScCQAEWrISgWu5fADaI1DeBIG16YduPxRsMDRuwrcpyOATzXgnS2CFuvR/ulf4dobad46hP4GeflDkgqTnIXd+dE3/ylq8uXlq/bM7anb9/c+WJ7cpzi0wSTajHNJYZ3rv3+/cX29Cvon1f/P/gqe8pwBBUHP+qUn8/T2rfe9FwnWl/NHrWd7VPnjykwliOBGQIUrTOAePtRBJaK1ipGddQTYypaL6ZXq+anUv68vWitwwNufh17YWl2cGuKoMnayx2Q1CKa8TjJ7rNL83guLa+S56tduEzt6Vo3f7bQnhtel8/b0C6x0J72o6PHlYjWxViPPtK5uMSeOna091Zh/K3DeJq5W+fzuGh9NXuanLoLFi52xoInJPB1BChav471L29pqWitVsO3V79fAI6K1hS+nIjJHM04cKmxytX4zHfjozZf2x8czZUf3K94XbvQ512dy108GipRpD5s6aG/s+nALsMLtyZj11c/tc3k+FOzOyb18pYuK7/cnnEr9VcX2TPrCa1E3s3hAVARFzM7jLV45sct1OI4bbrjPMJG5dG9Y4uuJfboKZvlab11Po+L1ilrvsmeL5g/073mXRIwI0DRasaJpe4m0BdRcxVW5cc8reoLUQ4WFVVfyjrPTVFuY6kXZW/vaS23glfcxl6R1q+OZ3YFmhux9v1bPVPtOjrHt+xjv3Rv+iXlb7Gn0yGDkyX21Anq5VbzoyTeQt77o2SBPUW8gxRua8exdn/rNx/3ZA9Q1S2wp916/9hEWN46n03qfg17qg0Onjp/+h3lOQncQICi9QZofOQWAioFjoBc4FpReVrVF9++WW3TbBQgZSlavWM3mU61uYCDXXRCkxYSKJDFPtxebsZ+D8pE6KtwZAepfskbzwee0Zl65soP7s95WlV7jVjY4Xiu2ZV5PNUe7OvReL8ZK7/u9uzCm3FTyphT20N4qn1+zWYK6/GxNi5/oz3jVuqvGttTpnXzy000XD9ubcbRbK5hw0+6fy/6VvV3zO3JEG3UYsgV9p3NPpo8rY+Zb+b26Pt0i7DU19a9c0vdtzzTbVV/NlV3+bZFSDxz/ugt4x0SMCNA0WrGiaUeQKBI/DIJ/HgsmUpELrDp7Z+aHf16W1KVsucDzkotqDoh2qiMAgJiE3VeQZ5jtY2rgw9Z1SeEBXvlIYhnNjUoEuzdKpWWEE/YxrWIsZM2gpMhyHIb14ny5wgbq+01TbG32vF4um1Gu9u4lhkWfJX83dCu7ypWxmw2Y6r/tEY2SOjsyGSpbWunE+0blb/DnqUIjexpKj3HCDz3Ov8tGysvwNz0bx43+TS3R821fWdbZfnhYONHaH5DmLQ3V8bcnvGaTsH4DnvjpZddVbtdWVNhEiPVfas9XzB/RrrMSyRgTICi1RgVC5IACZAACZAACZAACXwXAYrW7yLPdkmABEiABEiABEiABIwJULQao2JBEiABEiABEiABEiCB7yJA0fpd5NkuCZAACZAACZAACZCAMQGKVmNULEgCJEACJEACJEACJPBdBChav4s82yUBEiABEiABEiABEjAmQNFqjIoFSYAESIAESIAESIAEvosARet3kWe7JEACJEACJEACJEACxgQoWo1RsSAJkAAJkAAJkAAJkMB3EaBo/S7ybJcESIAESIAESIAESMCYAEWrMSoWJAESIAESIAESIAES+C4CFK3fRZ7tkgAJkAAJkAAJkAAJGBOgaDVGxYIkQAIkQAIkQAIkQALfRYCi9bvIs10SIAESIAESIAESIAFjAhStxqhYkARIgARIgARIgARI4LsIULR+F3m2SwIkQAIkQAIkQAIkYEyAotUYFQuSAAmQAAmQAAmQAAl8FwGK1u8iz3ZJgARIgARIgARIgASMCVC0GqNiQRIgARIgARIgARIgge8iQNH6XeTZLgmQAAmQAAmQAAmQgDEBilZjVCxIAiRAAiRAAiRAAiTwXQQoWr+LPNslARIgARIgARIgARIwJkDRaoyKBUmABEiABEiABEiABL6LAEXrd5FnuyRAAiRAAiRAAiRAAsYEKFqNUbEgCZAACZAACZAACZDAdxGgaP0u8myXBEiABEiABEiABEjAmABFqzEqFiQBEiABEiABEiABEvguAhSt30We7ZIACZAACZAACZAACRgToGg1RsWCJEACJEACJEACJEAC30WAovW7yLNdEiABEiABEiABEiABYwIUrcaoWJAESIAESIAESIAESOC7CFC0fhd5tksCJEACJEACJEACJGBMgKLVGBULkgAJkAAJkAAJkAAJfBcBitbvIs92SYAESIAESIAESIAEjAlQtBqjYkESIAESIAESIAESIIHvIkDR+l3k2S4JkAAJkAAJkAAJkIAxAYpWY1QsSAIkQAIkQAIkQAIk8F0EKFq/izzbJQESIAESIAESIAESMCZA0WqMigVJgARIgARIgARIgAS+iwBF63eR77ebH+FJG8Gpf2P8vEh2sITEOjqPF5i7WsTYLWhPW11+QuRvYFsCQqh/FuzVFoc01z7yUjd+uv1fBfNR80Vrb47Yd2GVc+gD+1RbkDdIgARIgAR+KQGK1lcZ+HOIlRDwEzOD8uP2PtG6sL1Rq84RNpaEsw2R5kVVpMiRhh5sJajDbPSxWy6mvoT1aCVzh/1PsacF5tn1t5oyO3zEfJloqYh3kGKDW3+DTVTNWyRAAiRAAm9CgKL1VQbyyaJg0M272ysQ7yTEJsKYTzULVxByh7jWsoP2F15IfAFhquiN6r7P/sfb0zX62fV3WzM4u3u+TLdxVvNF+DD8zTZdGe+SAAmQAAm8JQGK1lcZ1ieLgkE3724vxf5TYBVqwhOyZZ7jgX29C48XcffZ/3h7uh1+dv3d1gzO7p4v021QtE7z4V0SIAESIAHgjUTrCQdvi6NGQ738YD9ZFAz6f3d7CXwxIVqRI41iZC/rab3P/meLymfXP5gPcxfuni/TDVC0TvPhXRIgARIggXcSrY/+Uj0FcISLgy4ss1yYIrE99lRZf2GPZWOzjzGrpZfaXy7csgwWrBQ4RT42tlUulJIfa+zjM7C0vcFfS46jJyC2MXoEBiW7F/KePS68QM8njzb1Aq9moZf63CAai0noNjRzdpv9i+1ZOB+W1p8nAbyVXS9gEhDyA85mi0NyN6AuP4P5co42sDYRzkW1OM/5kNc5p7PnFMAuF1+1x3dqIVaOJPDg1nUL9fflRzg9uLvdzvOMBEiABEjgFQi8j6fV4Et1GfDq9bGrUa3VwpEVOm/HixS+LWDvjlcPY3HGcWdDesfR2M+LTUvtNypfIPFtCHuH47mWlnmK0HOw9neLFn5d7GwdFKkPW9jYxaaKZn3QewAAIABJREFUobJHuj7ilj2HjQXpBkgn1O8zPI/L7b923sieO+aDSf3naA0pXfhxdv3h0FoI5x1Nx+XaL+2RwXwrvaXOGmvHwTZM0azNOx935TyZWkdn5mnNEK4l5PrQWvin/r4cSNv7uW9ZtNB5gwRIgARIoE3gR4vWU+CUniXX2yMMd3CEgHeIEe5rT4wdwDCDVJvJ5Tjdf0I4BwydrQXirRgsQsqPHoQcWUySHeCInsC9tFIfGIiCziMm5dM9bNXuoAMp9nbl2bp3bVOeBlhdMgh0LBycFPEWUm5HFmdV9tgTqsZExA0aNLiwxP52dSb23DMf5us/I1zpF6clvoRYR/Me/nanpo4N5lslPG34Sf/XR2Wr9PV5rExEa3ZwIWx/5MeN+nuU8z8Mp/rHeyRAAiRAAi9P4EeLVhQZ4nAPz/2AbL1ilB8uvH2IOLvT05Qd4IrP4St49WpeCGx7S+OLNMQ+TK9er2b4Db7wF7+un62zXh2v8fA2r6HvFa1VF1Waqy1cJV43AY6j3Cvh4mg816U9E9kG5kVcA/uWTxP7u/Wa2HPPfJivvw5v8I7D+QagSANsg5G52O2G+dnsfFMRJyuIVTgqlOf6My9aE/hSYKOLCyl/GE6E85j3lCVJgARIgARelMDPFq0XqOoLXEJICSnXI57FS8GFBxkOrsBnzwM4J7CqRgrk5xOS6IDtqoonnRSIBqKgY/xs+WZ1/MDNWlVTfsmb54XttK09UYuvfKwsAWu1RyeMsRb6WgZltgEJnTNuTvRoTVp0Y8L+Xj3L7Vk2H4zqz0KsZS9Pbs/Oh53Ozrcni9bmbYVmOgPVwrqbN9t4GChWRAIkQAIk8CwCbyFas3ANqRZNnaqYN+Xt0X63LSRZCtTPPa4vNnNEm6GQbaotThH8tfL8Snw4K3i7A45xMB8/aiAKmjbKz9ny1Zd43xt8qWP2+UvJGw4yRJ6KpfVxeVNct1ftmtVedNM91olaIxF3g6Xjj4zY3ytoas+t88G0fhRnxIdttdBOLUraHhCl51Hva68Ly04N5stTPa2JDzEZYlN58rUp2Jb1lqVJgARIgARekMDPF63nCGupUi/VMrXODzoVH7loHEoPobzuVFV+eY+EDKhK0wCOtOGpRSjtRgy+8B8fHvBs0XrGaTQMoO54mV1BXLeZLRk4+mwMbV4jx8YibuTZ8UsL7e9VYmTPHfPBqP6eTShynI4BPNeCdLYIT/3Y0v4DC84N5jBF6wKeLEoCJEACJLCYwFuIVs+LOp7VLPLgRY/ytVaxoXJXpXYqF4O4Y4uzqhjDfihBOSIGX/iPF63PDQ8oBYr0Wx7o/tzrLRTSxAH3n9Kd3yTidJWhfpW9xP5eXfP23Dcf5uvvGdQ5rbNGdN4QdAosPzGYw08VrQwPWD5mfIIESIAE3ozAzxetXzAgZXqrcpFQFeM6/gpyIlm9wRf+40XrkxdipT7k5OvarFzdfl14Va8gr8V/f9jK9FPSgy5L030irt+a8oovtb9bx7w9982H+fpT+FIfA4xG5M0mCO72S3tmMIefKlpVzOrUQqx0j8+pvMrajvEGCZAACZDATyFA0Wo0UpXXcrvfw9Emtq9E2ZinNT9uy+wGunjN0gQDUdAx1aT8M1NeFQn8TwHbT0bjJ4tE5XDtruYuU16NpuCqBK5u5bnq97yI69CZP7nB/nal8/bcNx/m66/m5PVHQds6oFDp14T+R0C3tMGZwXx7rmgFmPLKYJxYhARIgATemABFq+HgljlbVVqtiR2gVF5OtSBs3yybbxK9q6wGKodsf/esdtsGoqBd3Mwzq9tcwIa0bXyK+7IHVMJUwvVjZE0meagdr3ZwpIXNIERDb4+YyfqQqryjD1xgp1gut/86Aib23DMfTOqvNhdwsItOl0T+QIEs9uFK9YPinVJeKfbcXOA6A3lEAiRAAr+PAEWr6ZirBV7Shj+1bZP6Wj36WDdbTKotNVdqQcwJ0abOJbuJuou0mvZVzKe0EZjuhmBcvruNa7Xt5RFZHmO3pL3Gzt5nnh0ReCvYVp0FQH7A9VSuVt0ioO42rpU9IdLOyrVeI+q0SLB3q9Rhj9nGtWpjuf21bYb23DwfDOs/x2obVwcfssnCYMFeeQjUVr2P/K9cWDc9Py/buI60q0S41Usd1y5WCnyjzUC4jWubG49JgARI4DcRoGj9TaPNvpIACZAACZAACZDADyVA0fpDB45mkwAJkAAJkAAJkMBvIkDR+ptGm30lARIgARIgARIggR9KgKL1hw4czSYBEiABEiABEiCB30SAovU3jTb7SgIkQAIkQAIkQAI/lABF6w8dOJpNAiRAAiRAAiRAAr+JAEXrbxpt9pUESIAESIAESIAEfigBitYfOnA0mwRIgARIgARIgAR+EwGK1t802uwrCZAACZAACZAACfxQAhStP3TgaDYJkAAJkAAJkAAJ/CYCFK2/abTZVxIgARIgARIgARL4oQQoWn/owP1Es4s0wPpDQgih+WdhYnv6n9jlF7T5jDjwsLKtagzkB1wvQHzumnoOV5ox6o6dHZzKB5eW77bWOkt8yHWEnjmtAjwkARIgARL4rQQoWn/ryH95vzMcHAH3kH15y89oMPUlrJ+msIsEviNhbQ5I85pKcUbsu5ByjXDB0GThGlLY8JPCCK9p+ezgQKxCilYjqixEAiRAAr+LAEXr7xrvb+xtAl8I+Mk3mvDAphNfQPyozhRI/E8I94ChNlX3bIhNhEbLTqLKQqyExNpU5RqVL5CnB6yloGidhM+bJEACJPB7CVC0/t6x/+KeU7QuAV6kezhSYh096EV5EWMrBLaxxjOaR9gIF/OO8DPClYBchyPid6yH8+XTfR2q0ISN0NM6BpLXSIAESODXE6Bo/fVT4KsAULQuIX0Rco8ScOken2KFUKuBq/AN7zjtay3iLaSRuK16u7R86cF+VJ+XAGdZEiABEiCBlydA0fryQ/QuBi4UracAzpQ4KmLspMT22Pcc5jhFPjb1QiP54cIL4tEYyXO0gbWJcC5OiPwNnHqRmPxYY5+Mi7c82owsUNogGi+OPAngrWxYjRdRfsDZbHHQ1N+M9sM9rYkPMSlaK4+oM+lqTbG3BT6NY3mXlgcoWpsZwE8SIAESIIE+AYrWPhGeP4nAQtGKFPtP/cKtIt5BDkRYFZspXR/xuRazeYrDxoJ0A6Q9fVuueHfWWDsOtmGKvL5/Pu5gC3syk4FJTOs5WkNKF36c4dJ0kSMNPdhCYs6r+dCBmBWtWfnafypONz96kNLDjDP2YvbS8upBitYLPh6QAAmQAAn0CFC09oDw9FkElopWIN1/QjjjC4firRgsHCpfRcsthmGblcfP7nkIqzRNYyvg6zhMP9XCmBetVR06EZj4EuIrUzudq8VT2rVjZUzr1OKyDAd3iZd1afkKNUWrdsrxBgmQAAn8egIUrb9+CnwVgEq06nO0jqwazw5wxefQ45kf4Q0WFU2/3i5f68tdR9CWolUTPzknSufuAzmOnoDwjlcvawu1ylm7DdLRe61iDzzMEW0EpHccyRBQZw9QIQw6VVvGxJos1KpNXlq+foyi9YFDzqpIgARI4M0IULS+2YC+bneWe1qBcW/dmABFLWR1mgt12qW28/S5ohVQba6lhLMNkTaxB985QI09uyOyJl4hPyHaOXC8PXYrgZUmjVXpGTZNiVW+5pcDT7hJ1ylaTSixDAmQAAn8TgIUrb9z3L+h17eIVqAUqJ97XF/UVx7DwWKg8vV3d7emMa9uW9Q+XbQqyip5/2FbLQyzbGy2B0Tp+Qs9rL2hzk8Ity4+VD5UIfHhbLAvt8OqsgesxtILlIveJtJl9ZrA0vKt5ylaWzB4SAIkQAIk0CFA0drBwZPnEbhNtFYeVHndlKAUpyMhA+V1xyDP6LWHXyJar80BRY7TMYDnWpDOFuGpcXe2C33XcYKdbvMHtbWqGIsV1ti6tHyrGorWFgwekgAJkAAJdAhQtHZw8OR5BG4UrSgQ7yTkLi69k9nBHd/VaTTOdbo3Xy5aL+bUMaQdD/LlZnnw8JRX3eqHZ9kBjlhjbC+DckHcaCzssBp1ZWn5di0UrW0aPCYBEiABEmgToGht0+DxEwncKlqBMr1VuYiqinEdfYWNesV/LW77HSlSH3YvXdNzRWsKX0q0Y2g7NpUiUZ/s/+GbC6CyZzdMrVCapf0xoOKKHYHp/K3tni0t336WKa+6NHhGAiRAAiTQJkDR2qbB4ycSuF20os7Zut3v4Qh9Iv9q96UVhmuJ6hykvUwBzxatKs+sTuwVRw9C6HOePt7TWolJua081p2BLhLsPjV5Y+cWuHUqAmYXxPXL987pae0B4SkJkAAJkMCFAEXrBQUPnktAefr0mwXMtV2+clYpmcZE1+Xh+rW7vcOxtblA6NkQcj0Qs/eI1lTlWV2FyC5tDw+qzQUc7KLTZeMCoEAW+3ClgO1/ZcorpSc9SGFhc0gvaa+KLIbvynJnsNG+zG7/2uv30vK9xylae0B4SgIkQAIkcCFA0XpBwYNnE8iOW7jW1Ap/C36iWZykUlZJG35/W6uB0d1tXIVase+rlFODgrhs4zq8BSVKrd5mBJ1iRYK9a9Vbuuq9v+dYbePq1Kv1Vd8t2CsPQbliv1Pjl5yU28q6H5DltrJV9gBfiWpd66cAtt3O3qArWF9fWr5XXRauYC2In+09zlMSIAESIIE3JkDR+saDy66RAAmQAAmQAAmQwLsQoGh9l5FkP0iABEiABEiABEjgjQlQtL7x4LJrJEACJEACJEACJPAuBCha32Uk2Q8SIAESIAESIAESeGMCFK1vPLjsGgmQAAmQAAmQAAm8CwGK1ncZSfaDBEiABEiABEiABN6YAEXrGw8uu0YCJEACJEACJEAC70KAovVdRpL9IAESIAESIAESIIE3JkDR+saDy66RAAmQAAmQAAmQwLsQoGh9l5FkP0iABEiABEiABEjgjQlQtL7x4LJrJEACJEACJEACJPAuBCha32Ukf0o/8hMifwPbEhBC/bNgr7Y4pPlP6UFpZ5HsYAmJdXT+UXY/3dg8xWG7uo6v/IDrBUi0w3tGHHhY2VY1H+ry8QTWc7zHpilvufCCBNrqn95hNkACJEACJPBVBChav4o02wHOETaWhLMNkeZFRaTIkYYebCUAw+zHUMqPW4rW/midI6ylgO2p8a1vFhmOOwdSrjEY3iKB70hYm0Or/Bmx746XB5CFa0jbQ3iqG1AieWNBrkP8nNnTB8dzEiABEiABEwIUrSaUWOYBBArEOwmxiUa9Ylm4gpA7xLWWfUCDrOJLCdTj6x5GxOMZ4UpAesfW2BdI/E+I0fLqnj2cK9kBrnBx6KtTJX4/Jbxjo5S/tONsjARIgARI4IsIULR+EWg2k2L/KbAKNe99sxArIeAnJPUVBIp0D0c+MryhGd++oqx6kx89COHjMrxFjK0Q2Op+peQRNj2Bmu4/h0K2hnVWP3qcMcH8FTTZBgmQAAmQwFcQoGj9CspsA0ACX0yIVuRIoxgZPa1fMlvSfR1Dugqh+Rmx0I4TAnvC25n4XdGa7vEpVtD9hgEyHBzR8p5WolgbQ1z+6HGGXtiFvWBxEiABEiCB1yVA0fq6Y/NmluU4egJiG2OZLs1xivzLwhv5oRbexFqhlScBvJUNq1zkJSDkB5zNFgfNSqCl5S+Dkh/hSQv79HJFc5AjCTy4H7JaaGTZ2PgRmpDM9kPnaANrE+FcVIvVnPoZ+bHG/sH2P97T2u7J8PgcrSE+97jgKkXslGitQgqcJhZAxcsKCf9SQb+N6kcRQwT6XHhOAiRAAu9DgKL1fcby5XtSpD5sYWMXm8YeVrGN0vURn2up2yy8cQOkPfWrhJGULvw4uwrj1kKvvqBZWr4D+GwSzpAhXEvItVpoVBtbnKuFSbaHY8/FWb3iXmPtONiGKZpHzsddya0vkO+yv9OZZ59kZUxrJzRkVrRWz4gmXmTWM1uJ3NVgtdez+8b6SYAESIAEvooARetXkWY7JYE8DbC6ZBCYhlLEW0i5HVmclWJvC9gdFVeJlovI6VWd+BJiHbU8tEvL9yo0EK3ZwYWw/YG4BgrEW9lbmASUolXY8JOeGkdlq+y4Ge+0v9edZ50WeYrQc+D4vbRUJT+pj2EuY1oFLuM5K3KneTyrf6yXBEiABEjg6whQtH4da7Z0IaDSXG3hKvG6CXDMxjyvlQi5vB6+PFsd5NGml22gDj/wjlcva+uZIg2wDdLWvaXlW5Wpw1nRmsCXAptorG8qd9MBTm+hUSlaNTGmid8ScKUpd9rf686jT8vxaUI01Ke1weHUFuM5ok0/o0BjRZ09QD3XeFopWhs4/CQBEiCBX0uAovXXDv0rdFwtvvKxsgSs1b6bgF7FjE5lEygX3vRiHLMQa9nLAzvVzaXl23XNidZSlK6GuUkvdVQxmO2FRctEa5m0dFl/L21/4UGRI0sO8GwVX9zzmjf8d8frAjy1+cTOgePtsVuphXt1NgKK1i8cNDZFAiRAAq9JgKL1Ncfll1mVIfLs8lX65c14LQqrXbOa3bOGn40j7gKsOCM+bKuFW2rR0/aAKD23PKyXktXB0vLN43Oi1VBkteM8F4tWZcut9jf9+KrPOqVZP64Y+Qnh1sWHVGMr8eFssC+3w6qyB1z4MKb1q0aK7ZAACZDAyxKgaH3ZoXk3w844jYYB1P0sYuykuG6LWorCO1MYFTlOxwCea0E6W4Sd19MjfJeUfxXR2u7GEvvbzz3kOMcpPrU2D+hX2ltY1b89OE+wa3vamT1gQIgXSIAESOC3EaBo/W0j/k39Lb2I0r+mPBrY0VtIU4cHaJPPD56fulDHSLZTLk0Vh0H5OdH6FeEB2j7M2//wlFeGnuVLjKrW9vpGyW+N6JJhgXla55DxPgmQAAm8OwGK1ncf4VfpX+pDziSTV1t9XhdeVSJW7sbzupbps6SH686dKXzZi3Ft970RkS0RtKx8u7IHLMQqX3d3tyRdFh6wtL9d+x++ucCsJ7QSndfwgMr+nWZHrDLzQm+L19kdsYx/lHRZ8IwESIAESOBnEKBo/Rnj9POtLPeHF7D9ZDS+tEhUDteuiCtTXimhO9gZtH7V3FlpX4miq+jtIivKbUS7IldtK2tevlvffPYAlSDghpRXnT5d2xxmD1ja32td6ujhnla1g5UrIDWbR1Tpy9r8q5jV0fJFgt3nyO5a2QFub46UvSrn1kj5bpd5RgIkQAIk8MMJULT+8AH8SeZXwlTC9WNkTeZ8qB2vdnCkhU3UV6f1a257h2Nrc4FQLdqS64GYrZLtO9hFp0tifpUTNYt9uFIJ5nbKK2Bp+Q7rufCAsvANmwsYi9Y77e905jEn1eYR9fg22a0umzsM88/mRw9SWNgc0kssbJHF8F1Z7gzWnw3KyixcQ9oewmZLsWaziXWIsfKP6RlrIQESIAESeAUCFK2vMAq/yIY8OyLwVrCtOhOA/IDrqVytjcrpw+hu4yrKbVBDpJr0p+dYbePq1KvRVRsW7JWHoFyR3q8bWFr+UkO5jauN4HS5ojm4YRvXkZpSX8LqbKZQFbrZ/pE2HnLpHCPwmmwAKj+rjZUXQIMf5Ta67gdkmdO1yh7gqx8dE8ac4/1lW19hqW19exsXTDzLWyRAAiRAAj+XAEXrzx07Wk4CJEACJEACJEACv4YAReuvGWp2lARIgARIgARIgAR+LgGK1p87drScBEiABEiABEiABH4NAYrWXzPU7CgJkAAJkAAJkAAJ/FwCFK0/d+xoOQmQAAmQAAmQAAn8GgIUrb9mqNlREiABEiABEiABEvi5BChaf+7Y0XISIAESIAESIAES+DUEKFp/zVCzoyRAAiRAAiRAAiTwcwlQtP7csaPlJEACJEACJEACJPBrCFC0/pqhZkdJgARIgARIgARI4OcSoGj9uWNHy0mABEiABEiABEjg1xCgaP01Q/1iHS1i7KSN4PRidv04cwqcIh8b5wNSCAhhwV55COLzeE/yFIftCralygoI+QHXC5Dk48WHV3McPQvSO2L0kfyEyN906nc2exyzYlgVr5AACZAACZDAAgIUrQtgsegDCZxDrISAn5jVmfoS1j41K/xrShVIfBvS9RGfG1FYIE9DeLbEOsy6JM4R1lLA9kKkjeIsMhx3DqRco1+8+3B1VsTbShyvQgxkcZHAt/v1nxH7LqSw4SeNjWM18xoJkAAJkAAJTBOgaJ3mw7vPIrBQtCa+gDBVuM+y+cXqLeIdpNwiHtOC2QGucHG46NYC8U5CuAdcLl36c0a4EnrvaVOuSLD7rD20I6I13X9CfO4x/GlRIN7q2m4q5ycJkAAJkAAJTBOgaJ3mw7vPIvDLRGuR7uFIiXU08E/eTDjZCYidzlWd4eCIVnsp9p8CK407NT96EMKHrjagQOrbENKDtxEQA9FaCd9PjTdceWiF8HBsPLw395oPkgAJkAAJ/FYCFK2/deS/u9+/TLSme6uKIR2IvVsHohKJq1Angqv7V+/0CYEt4elUY+JPi9Z0D1tUorv0eg/6MWNPWf8KWnNvxcDnSIAESIAEfg0BitZfM9Qv1lFD0ZpHm0rslYuM6lfTYoNI67HLq4VJdiUS5YcLL4iH8Zf5EZ61QnDYw1WLkiwX22OGPA2w/pAQQsLZHgev0vMkgLeyYTX2yA84my0OMyuZHu9pPSNaT4hQVJ5WneezPxvO0Vrzal+VzHBwK++qCi2YEq269kpP62joQN8SnpMACZAACZDAOAGK1nEuvPpsAoaitTHDLKZ1ZGGSWi2/sSDdAGk79rNuX67DUpjm8Q6f0oLl+NVK+qxaKLZtBYwqYSelCz/OcKmqyJGGXumF1Hoxm0584WeR7PApJHYt+/XNZ2VMq85rm4UrCGGjefM/LlqB7OBqhK+KabVaoQp6S3iHBEiABEiABHQEKFp1ZHj9uQSeIFrLle2jC5NS7NWq9kZ1qZ4N2k/gC4GrSG1iQJvX7/3X7V08iS8h1tHQo9st9iVnSoA7UkAa2FPkKULPgeMn4ymsmowDfnoR6jrRCm32gDU2h9Pl+S+BwEZIgARIgATejgBF69sN6Q/p0EA0Tts972mtRKVzXS7fqbAMM5C760r7QfuVaL0mKOjHaKr8pALCO46KryINsA2uwq7T+JedZIi2TpmSyvaGoQ1tMwZhF9YGh9PFf1wXVX2WEJ87tLNVaUUrACWY7SZ0ov0pXew7ru62NTwmARIgARIggXkCFK3zjFjiGQQGonG6kVnRqmJUp/K+lq/7JfwmH9Og/TnRqkI7Q6ylinVVeU77Am/a/mffzU8HbFRsrnSwjYZJrbTtFzmy5ADPVs9202c1IQb9sAedaM3CdZmP1TskuO4lUCCLfbhyWL/WJt4gARIgARIggRECFK0jUHjpCwgMRON0m7Oita6v3OWp7eHrHV88qYP2DUSrMrE4Iz5ssVELvSwbm+0BUXoe9b5O9+hxd7NwUy4MU5sMzKwH0zdax/BeBGqRlhsFjIUYjIpW9aNB6lNqFYlfemB1cbN6w3iHBEiABEiABCoCFK2cCd9DYCAap80wE61OK5n+dH26mNaLqEU/PGCkviLH6RjAcy1IZ4tw8Hp95JkHX6q8mxLOLh6PSb20l+MUnybKVIuxmhRZ6d6GEKvRXbLGRGuVh7Xrqb00XR506+/e4xkJkAAJkAAJzBOgaJ1nxBLPIPBo0VqHB1wXUs0YPWjf0NM6Wm2VtWB8N6jqgcenvFLhCmrXKwHbT+Y9vbN5UtsLzepY1p6XetyLbZVZBc5lhoGpzQnqVFnXXwWjJHmRBEiABEiABHQEKFp1ZHj9uQQGonG6uVlPa+0Zlbt4VMAVqQ9btnZkGrQ/J1pT+LIVE9s3NzvAUZ7JJtlA7/7jNxeY2pa117g6VVkAxIT9qLIlXMIDRqpoLt3jadUtlGvq5icJkAAJkAAJ6AhQtOrI8PpzCQxE43Rz86IVKFNejb7Srl9Nt3dxGrQ/L1rVNqg60VWU26C2RHGvOw/3tBYxtkLod7jqtd9sECC3GlEfbyHbon7w/PXCmGhFHmEj5mJabQSnaz08IgESIAESIIElBChal9Bi2ccRGIjG6apTlQd1VW0EoC9Zv6a3dzie69X9ZR5SG0Kuu/GZg/bnRKtyVqrNBRzsohOuyQOuq+PtVi5TvY0PupPu8Tnh2R1rpfQ2CwnXj6+r+y+bI9jw23mtxiqor42K1jK5Qp09IExbfFTcr8oeIMtcsK+Vc2Gik7xFAiRAAiTwcgQoWl9uSH6JQUWMnVzgeSsS7N1qa1axYBvXcoW/r1JU9bieI2ys9kKjFHuris+sSo5vk3qO1TauDj5UCqcy5tOCvfIQxJq4gF6zDzstY1QbG/SfVntDBdX4OUbguVf7LRsrL8AS80+BDekdRxd15acI/saBrdJvKT7lNrc+wuSL+TwMNCsiARIgARJ4FQIUra8yErSDBEiABEiABEiABEhAS4CiVYuGN0iABEiABEiABEiABF6FAEXrq4wE7SABEiABEiABEiABEtASoGjVouENEiABEiABEiABEiCBVyFA0foqI0E7SIAESIAESIAESIAEtAQoWrVoeIMESIAESIAESIAESOBVCFC0vspI0A4SIAESIAESIAESIAEtAYpWLRreIAESIAESIAESIAESeBUCFK2vMhK0gwRIgARIgARIgARIQEuAolWLhjdIgARIgARIgARIgARehQBF66uMBO0gARIgARIgARIgARLQEqBo1aLhjUcTKNIAG9uq9qRX+9IP/lnYp+OtFskOlpBYR++5h326n+LSYvWxxxiid+czPitMrhY4RT42zgdkOd8s2CsPQayfR+d4f52nlgsvSJBPNLWs/HJ7JpoevbXInjzFYbuCbdVzTH7A9QIkUx0ebVV/0dyeM6J1a673//9g+UgKfTumd8ztGasxx9GzIL3j5JwYe3LjdqIhAAAgAElEQVT82r3z4fvtuY/nOBVeJQEdAYrW/9XeuXKpDkNROP8isnIksrISiUQiK5H8hEokEllZiaysrERWIisr913pA/pI24THwGW2uIs+TpOTL7nDJj05GSPD608mkGK/EFges7vKzU/bjxKtaSDhjCnsu1rYe+gSYiUEgqR3feT00/iMuPnLlwskgQu5DBBfGrVTIE9D+K7EOhyOxSxcQ7o+wnOt2pSo2ziQ6xBDa8DO3t4fW2BW/lwirKWA64dIG5FaZDjtPEi5hgaPrTuWfKq/EYsX/r+y4qNpbRFvqx8/qxDjP3s0D2ovPT4e3u3Pozy1WHiRBCYIULROwOGtZxJIEFiIsGfW/IqykkBAmCrKexywFK33VPHtzxTxDlJuETd6td3g7IilWKLzG0p3TT1TJAgWEv6pUXZ1QZb21v60/TU5tvKnQLyTEMujRoxfEK7E47OJVv4ozjG2Qgw5m7TdxMbWn36ZRYLdop4JfoJofXg8vNufR3n2+fKcBAwIULQaQKLJMwhQtFpR/GOitUj38ORzwz+SnYDYjU1VZzh6ohNuku4XEJtI+9r3Eq4gvK7As7W39cdqvACw86ea1VyNTKfmJx9CBBijZ+KbnT9Q09ZYicVoiJBJnVM21v50CiuQBi6E9OFvBMSMaDUZz4+Nh/f78xjPDlyekIAxAYpWY1Q0fIwARasVvz8mWq8xvTNiwJxhNVu4Csde4lb3b7PllYgbjZkuBZXXmpm1tbf1x7yllaWtP2ccXM3scVNtEjwoWm39AVDWOTIz3vh19+cd/rTrSvdw65j68i3LzDidH88Pjoe3+/MgzzZbHpOABQGKVgtYNH2EwIOiNT/Bl+MLta6eXSJspItDmiPZb6oFJo5+8ZJ6Jk8O8FcunGbRh/yBt9niOLISJY82mgVkG0S9N8dXf+49sBWtc3zUfWeFw3GPpVp04yyxPWXI0wPWPxJCSHjbk+ZVcV4tZKoX0MkftTApfkI8XxeMycxU94m5M7WoZ0KUoZppvcZPqvhOIRHoVrmVVVXj9xoiYGsPS3/mmte/b+1Pv4Du+SVaQyzG/990rTVnd/ijm83WlHzfpTv8uVWU4bisZldVXLOJaJ0fz4+Mhw/w5yGeN7I8IgFbAhSttsRofyeBB0WrqYir7Vx3BT9MkeviGesWqC9mKZcI4gxXsyJHGvrlrMpVoGha/HExrXN86vvNgqI83mEhHTheUK0UL2cSBbadAFDNQpFmYdLygPQKTQPowy+pbAsLIbFr2pvusRArjE7MopkZq5dj2drP8Bj4M2M/uP1Uf7IypnV8lnpQ+/DCHf6oxY1iF6OzGv1Z2Qzu8KdpVKZCQ4R7DVswEa3Ns/d+To2Hj/DnAZ73MuFzJKAIULRyHPwSgd8VrWOxerfG9l8P3+6oo0R9ga6j0RnF/1W03taOVf1xE6nV6762UClXJmsXMqXYuwLuC1d5d3vjuWdKsHtSQLb7t3w1PS9ar+EEtvYTTdD6M2GvvfUkf4o8Reh78ILpNF9aH9oXrf1RqZvUIic146+yGdS/iIoMcbAsf1zuH/mVZO1P3Rg1o6gyLATp9Yftq0Xr5Hj4FH/u5dkeIzwmgTsIULTeAY2P3EOgEknD3KxNXsaZV/9zM4mNS6Z2qL8k/dP1y6gpQn2qnLLbw+2Lqn1PHX+LaL2J2GYmsYkBrc69zvL6G4UyTELu9Cvzb2YfdpQh2nplyiLX74VC2H4J29prSYz7cz6u4Hne/L9dnS/0QX8GYS/OBsfzbSr99f5Ur8uVWL7VeoOW7l0I94Fwhbv4qL8REmKx6+SHfZ1oHR8PFYkP8ucunrf+5BEJ3EuAovVecnzOksDvzrTexNiEm1mItezN7EyYt299vWhVMbBTKcrKcIKpGNA2rfcf5+cjNiqWV3rYRpqMq7Zfwrb2PQSz/uRnxHE8+y/J6mDqB/25ulfkyJIjfFexai2Kepc/jWN1Oqzbm4HmhuHnHXyaV/T9MKFXiNbZ8VBmXqtCWj7Cnzt4GvYUzUhgkgBF6yQe3nwegQ8UrapxxQXxcVvtgOS42GyPiNKLdranzeLrRWs9Yz0+M17NkBv9OGiDe8NxFm7KhXZqk4GR9XUqX9SvxbQa+WPLydb/ufLrGOe+QJp77Hr/2f7UMcXXhXPXigwPbP0pUgRuL4SkrurZotVoPHyaP7Y8DbuJZiQwR4CidY4Q7z+JwIeK1nbrihzn0wH+0oH0tghbr0fbZur4b4jWdoqnPoH/47zcsUfFSe5ibf7VaytsV0Pb2tcVGftzdczwwNqfHOf4PMGkWox1jeE1dONqZu3P9cmRg+kY9JGHbpct/SnDEdTCPO2k/Hye1lvF00em4+HT/IElz2kKvEsC5gQoWs1Z0fIhAv+BaL22r1o1P5Xy5+tFax0ecPfr2CtLs4P5FEFm5XSsyh171CIafZxkxxa2eSdt7VXyfLULl6k/Xe/mzyz9efnrXUt/ZhtYieixGOvZx636t44dbdLgTX7qY/GNxrPxePg0fxTtZ/fvfA/SggQUAYpWjoNfIvBpojVFICdiMrMjvIkUSF8vWuvXsXIXa0MlijSAK330dza9dzDNJ2O3LXlqm1J9WbM7/PTyltrZ2/uj93L8qpU/szNllSi5OzzAZIeuFs/8tIVaHDea7jiPsFF5dB/YosuKzzhmozyt8+P5eePBJFzhFf48i+cEat4igQEBitYBEl54DYHPE637hcDYzE1RbmM5Lsq+X7SqreC3kNpXpPWr45ldgWzGkdHMlFWBd+xjb7uXuo19vZDoERE423wbf9TmCksBuR35UaL6/tEfJRb+FPEOUixbO461W1u/+Xgke4AqzsKfdu39YxORODuenzge3ubPk3j2+fKcBKYIULRO0eG9JxL4NNEKVJsLeNhF59YmBAWyOMCyl5uxD6JMhL4KNTtI9S3vPDdO3VWXP2c/uN/vj37KK1VuIxZ2OF3qRERlHk+1B/taG+93Z2uf/9jsQhF9lWWMoesjPNdzfs1mCmt9Xxvb3+mP3svxq8b+lGndgnITjWUQI2vyTF0313ARJM3F8frm7pj7kyHaOBDOCvvOZh9NntbnjDdzf8ZbZiISx5+u7zxxPLzTn2fwnGVFAxJoEaBobcHg4SsJqL3O9fFfRrWW25S6OJxnrEu7sRmb4bOXWG3j6uFH3vLFuisfh7jJVzp8prxSJNgvnXpL1xds41rE2KntaOfa27g3x0dtb+u0F5ak2Dvt/hjbVrK7jatQGRYClfy9qfhDP8uYzaZPxz8dzQYJnR2ZHLVt7XSifSP7B/yxJWzkT1PoJcbBX97Gv+Ni5R8wN/ybx00+zf1RY23f2VZZ/njYBBGa3xAm9c3ZmPujL+l8cCGnQhn0j3WvPnE8vNufR3l2wfCMBKYJULRO8+FdEiABEiABEiABEiCBDyBA0foBnUAXSIAESIAESIAESIAEpglQtE7z4V0SIAESIAESIAESIIEPIEDR+gGdQBdIgARIgARIgARIgASmCVC0TvPhXRIgARIgARIgARIggQ8gQNH6AZ1AF0iABEiABEiABEiABKYJULRO8+FdEiABEiABEiABEiCBDyBA0foBnUAXSIAESIAESIAESIAEpglQtE7z4V0SIAESIAESIAESIIEPIEDR+gGdQBdIgARIgARIgARIgASmCVC0TvPhXRIgARIgARIgARIggQ8gQNH6AZ3wN1zIcfIdbKLLn2huuncgxPie99d7P3ukGiJFsoMjJNZ/hJcGwcilAucowMb7gSz5OnBXPg7x+Liy3Rvdzt7en5GGjV628+eC+OBj5dbjT/5g6R8wgWe03rEbdv70SkkCyHWE8d7q2RucPuQPqr9L0j8hN6hr3sR+PDzm/5RHF0Trib9BToCk6D//+vHTr5HnJGBDgKLVhhZtHyBwQbgSWIXP/Lp6wJ3eo2kg4ex18rFneO/pJcRKCASJWQH5aUvROkBVIAlcyGWA+NJ82xbI0xC+K7EOs8ETWbiGdH2E51qS5CmOGwdyHWJoDdjZ2/szcHDmgpU/RYLAk3A2R6SNAisuiIMlpFxDg2em9uFtK3+GjyM7ehCr8Gmi9VF/inhb/fh5ik/24+FR/zWIW5dS7BcCC9O/a78wflrO8ZAE7iJA0XoXNj5kT+CzRWsSCAhTRWnfeMBStN5Txbc/U8Q7SLlF3OjVdoOzI5ZiiWNbiequqWfUl/NCwj81yq4uyNLe2p+2vybHVv4owbSAWB41YrwSU2ITPTabaOVPv4Hqx8URaymeJ1of8qcaB7tFPRP5BNFqPR4e9b+PuH9exNgKMRznfbvy/BfGj7ZeXiQBOwIUrXa8aH03AYpWm5nWuzF/yINFuocnnxvekOwExG5sqjrD0ROdcIp0v8CYULuEKwivK/Bs7W39se0aK39qgbLVKnoAeYRNX9RbOmTlT6vsQajMEwSiKv5efyrXCqSBCyF9+Jt5IW0ynm3HwyP+m/iDTL3dWcBoovUXxk9rSPCQBO4mQNF6Nzo+aEeAovUvidarUHmSQAHmxk91/zZbXr0aHY0JLr/QvdbMrK29rT92/1sAS3/SPRZihfHom0rUD2aXjd2y9Gek3PKNxlPGxIP+pHu4dcy4iU/z49l2PDzm/7w/AJIAQoy8mej3z8vHT79CnpPAfQQoWu/jxqesCcz9UR8pMD8jCjZwnfo1nuNis49HY+Ly5AB/5cJpFkHJH3ibLY5J71VwXV0ebTQLpjaI9OYjThpctg0PyE/wpTM+S6LuOyscjnssFRtnie0pQ54esP6REELC2540r4rzaiFTvVBH/izhH8Z5GrRMa2I0E6R9cuyiWlSieaV/Na9E2TV+7xJhLSSC0TDlBEH71amtPSz9ufppeGDrTylQpkRr9f/P68RPGPqizGz9GSnaRCCOPNq9/JA/GY7LanZVRZOY+DQ/ni3Hw0P+A/P+qIik4duELsTW2avHT6sqHpLAIwQoWh+hx2ctCNwhWosUgSvg7k7ImjjG4oLTzoVute8lWkPKJYI4Q2OOIkca+uWsytQsU/nF9UkxrXMit77fLCjK4x0W0oHjBSj1eTmTKNB9XaxZKNIsTFoekF6hWXTrh5iqbAsLIbFrXo/Pzhw147EOgrW1n2n3wJ8Z+8FtW39mRUdWLoS8zUQPapy+YOvPSGkmAnHk0e7lB/zJlJgT7vUH4dN86nrYORuMhwf87xQ8caIWl4pdjE52gjqbxOA3/KvHz4SfvEUCNgQoWm1o0fYBAo1IMM8ekJ98CBlgEMWYHeENXoVW5Y99KSfqD/hEqp3/VbTedHY1c3gTqdXrx3a2hnKltHYhU4q9+nFgFPz2wBB40aNKsHtSdFMpzX4J98aLrf1EW7T+TNhrb9n6U/6IkePZKcqY1gcWG9r6o22U2azmyKPdy/f6o2Y4pYAbpNcftq8WrdrxcK//XQoTZyqVl3o7pd64hEjz+hdpkdXZJJbYt3+lvnr8THjKWyRgQ4Ci1YYWbR8gYC9aizTEPrx9uVwr185C1n+k/dP1y+hqrxYKpwdsD5qyaqNvEa03EdvnXZ2PvR4uwyTkTr8yvw3yo44zRFuvTFnk+r1QCFtRYGuv5TDuz/m4gud58/92db5Qa39yRBuhfQMB1NkDVMhMPUBe748WkNGreP2TvavWfNTz6m+EhFjsOvlJXydax8dDFW86H87R9Fev9QanVbiCFyTav4fp3oVw2zmi7caPgQM0IYGXEKBofQlWFjok0BdRQ4vpKwXyyxlJdMR2VSVOvwm0+sksxFr2ZhamC73e/XrRqmJgp/LEluEEUzGgV1QfcZCfj9ioWF7pYRu181zV7tmKGlv7HoVZf/Iz4jie/ZdkdTD1Pf40478dTqNiwncePH+PXZknuWb1G/70GKnTpwnEO/g0r+j7YUJP86nV3tnxcIf/reIfP9RlC7AZP497wBJI4C4CFK13YeND9gTuE63FOUKwVrsfSfx4K/i7I07xYTxRv0qmftxioxYaqUVb2yOi9KKdbWi34etFaz07fd2Jq1mo1vsc/BBoQ/qQ4yzclAvt1CYDg9i8xkfbmEFb+6YeqA0JDPxp2Rsd3utPfka4XeJH5UMt/89ssC+3w6oWqrXDRYz8aIzu9ad5vv58mkC09aeOj9ftxvU0n+o2Go0HW/97HB8/rf4eXxcuNgW+avw05fOTBB4kQNH6IEA+bkrgDtGaHuBJF36YdpOia8MDNH4UOc6nA/ylA+ltEZ7HVxr9DdHaTvGk4fUfXCp3EFJxeru4Oyb6vtuuzra1r+sz9qfv39z5nf6MF5tgNzXTPv5gdedJ/jxNIFr6U74OV3Hw2kn5+Tytc3ia+8bjwdL/pvznfVZ/j83DDx4cP89znCX9cQIUrX98APxe821FaxWjOpgJUA6bitZr4+qYvkU7hut6szz4etFahwfcFmp12//sM5OUPNZ1ljsIqUU0+ji9bnm2eTBt7dUUq9qFy9SfrnfzZ3f4M1VouXhxjch8HWSvtOf48zTRapXHto5l7b1V0L910KeZMxrPVuPhMZ5G/vR6sHtaZZMYi3Hv2lZj3ROPjJ9BibxAAncRoGi9CxsfsidgK1qr1fDa15la0ZoikBMxmdqMA7dWfL1orZPzy12sDZUo0gCu9NHf2fRGyO7IKPm5VZEF4p0c2aZUX9DsjkO9HzF29vb+6L0cv2rnTzX+rym/esVmx6UVu97j5amdP7oSnhjTarIjVq9/9R6Z+TQ/nu3HwyM85/zJT1uoxYmj6abLbBLtbBOvHz9j/HmdBGwIULTa0KLtAwRsRWtlr5tpVX+Q5eBVZzVzMTZzUKj0WWJclH2/aAXKlFfaV6R1Ds+n7FRUDZHHZ4J6Q81qH/X6Wdu93W3s7/Gn16TZUxt/UMWsyq3mR0mRYLeY2phh1pPKwMoffZnPm2ltZrqXrV3N6jqLBIFFe018mh3P94yHB3jO+VPEO8jRbXvrN0+d7AG/MH70Q4JXScCKAEWrFS4a309ApWARkBYrfVSeVvWHd9+stmk2CpCyFK3+qRujWm0u4GEXndGkJVTpfrI4wLKXm7HfjjIR9yrU7CDVt7zzXDs7PFHWnP3gfjUzfcOr+5HQfFntcLrU7PIUoa/2YF9r4/0mPPzdW7MLV/TulDGGro/wXM85NZsprPV9bWx/pz96L8evGvujEjqV/18cbI63GPAiixEsJZxN9JSxbeOPrlUmAlH33Ni1R/1R5T7FpzvHwzP817PJEG3UYtQV9p3NVpo8rcP/778xfvS+8ioJmBOgaDVnRcsHCRRJUCaB18eSVdu0bnr7p2anoN6WVKU3+oG3Uguqzog2KqOAgNhEnVdgl1ht4+rVq6dVmQ7clY9DuYJ6ogFFgv2ySqUlxAu2cS1i7KSLw3nCh/atchvXCftLhI3TXliSYu+04/HGtpXsbuNaZlgIVPLxduUfeFymCKrGyNT4cTQbJHR2BHLUtrVJZ8z0W2tk/4A//frmzo38qQsptzFe1v836uwBgfoRN1eJxX0bf/rFqt2onKnX1v0HDM4f8UcVfz7od9gzqPpm8sB4eNT/mxP9I/V/fd/Z1lr+eNgEEZrfcIMn1DbYLx4//Tp5TgI2BChabWjRlgRIgARIgARIgARI4C0EKFrfgp2VkgAJkAAJkAAJkAAJ2BCgaLWhRVsSIAESIAESIAESIIG3EKBofQt2VkoCJEACJEACJEACJGBDgKLVhhZtSYAESIAESIAESIAE3kKAovUt2FkpCZAACZAACZAACZCADQGKVhtatCUBEiABEiABEiABEngLAYrWt2BnpSRAAiRAAiRAAiRAAjYEKFptaNGWBEiABEiABEiABEjgLQQoWt+CnZWSAAmQAAmQAAmQAAnYEKBotaFFWxIgARIgARIgARIggbcQoGh9C3ZWSgIkQAIkQAIkQAIkYEOAotWGFm1JgARIgARIgARIgATeQoCi9S3YWSkJkAAJkAAJkAAJkIANAYpWG1q0JQESIAESIAESIAESeAsBita3YGelJEACJEACJEACJEACNgQoWm1o0ZYESIAESIAESIAESOAtBCha34KdlZIACZAACZAACZAACdgQoGi1oUVbEiABEiABEiABEiCBtxCgaH0LdlZKAiRAAiRAAiRAAiRgQ4Ci1YYWbUmABEiABEiABEiABN5CgKL1LdhZKQmQAAmQAAmQAAmQgA0BilYbWrQlARIgARIgARIgARJ4CwGK1rdgZ6UkQAIkQAIkQAIkQAI2BChabWjRlgRIgARIgARIgARI4C0EKFrfgp2VkgAJkAAJkAAJkAAJ2BCgaLWhRVsSIAESIAESIAESIIG3EKBofQt2VkoCJEACJEACJEACJGBDgKLVhhZtSYAESIAESIAESIAE3kKAovUt2FkpCZAACZAACZAACZCADQGKVhtatCUBEiABEiABEiABEngLAYrWt2BnpSRAAiRAAiRAAiRAAjYEKFptaNGWBEiABEiABEiABEjgLQQoWt+CnZWSAAmQAAmQAAmQAAnYEKBotaFFWxIgARIgARIgARIggbcQoGh9C3ZWSgIkQAIkQAIkQAIkYEOAotWGFm1JgARIgARIgARIgATeQoCi9S3YWSkJkAAJkAAJkAAJkIANAYpWG1q0JQESIAESIAESIAESeAsBita3YP/gSvMzomAD1xEQQv1z4K62OKb5Bzvdcu1/97/VlJceFjF20sXh/KpacsTBEk45hn6wT19VD8slARIgARL4KwQoWg17Og0knA/65n2JP5cIG0fC24ZI86IiU+RIQx+ukFiHmSGtebNP8/8l/rQwvLr8VlVmh5cQKyEQJGbmtlZFvIMUG0QX2ydpTwIkQAIkQAJ6AhStei6Dq0kgIF71DT+obf7C8/0pEO8kxCaCbk41C1cQcoe41rLzHk5bfJr/z/en2/5Xl9+tzeDsxaL1osaLCPAiTWzQQJqQAAmQAAl8GwGKVsMe/TTR8Xx/UuwXAqtwZGose+7M3Kf5/3x/ugPr1eV3azM4o2g1gEQTEiABEiCBTyLwRaL1jKO/xWlEcz0K/dNEx/P9SRCICdGKHGkUI/vYmdbH/H8+z+6Ie3X53doMzihaDSDRhARIgARI4JMIfI9ofdGXcB5t6gVJzcIk9blBpHuHXvZsjnMUYOM65XPyZwn/EGNMS+fJAf7KrResCAj5A2+zxTHRV2Dvj+lwy3HyBcQ2hp0utWyvNc/X+m/Ns7/Qy3Gx2U/0r2V7bceDKZ2BncH/l0u0gbOJcCmqxXnej6zH9Br7kfGJ8wFuufiq/f9laiFWjuTgY1mXLRTPIMJZP/wHzeAFEiABEiCBv0OAotWwr81mygokgQu5DBBfaumXpzhuHMjlAWlPDV6iNaRcIoizm1BsLXzyT+Pf3Gb+GDauNivSAK5wsYvH6+2WaNfe9rOf4f/NIyN/ihSBK+DuTrcZ5+KC086F9E/aWOCmBpPyHxkPTT3GnyaiVcWlemusPQ/bMEWzNu9y2pXjZGpdollMa4ZwLSHXx9bCP8XTg3T9l701MWZEQxIgARIggY8i8F+L1vPBK2cml/4eYbiDJwT8Y4xwX8/cuAc8K6OPiego4i2k3GoWK6XYK7HT+Za/IFyNL+5KAgmxjkZnaE38uWek5ekBq2sGgekS7NrbLesT/G97ZOJPfvIhpGZxUXaEJ1YYCwdW9cyX/9h4aLfF6NhUtAoXQdL7tYXKVxmM57EyEa3ZcQnhBoMfc0CBeCtnfwgYtZNGJEACJEACX0PgvxatKDLE4R7+8gey9UqyfCW/DxFnpjOG8/1pKjq8oz4tVPkaurP6vn4d759us6wtN4r0gO0h1d5TZvP+tAqzPlRprrZYKvG6OeCk5VgJF/P2dp14v//2/hRpiH2o6RMDATjf3sfGQ7c1BmcGPpfCcxVqfzjNtWdetCYIpMBmLM6m/CGwxMh/J4MG0oQESIAESODbCPzfovXaG+oLX0JICSnXeGI60WsNc1/SyE/wp/JelqvvJTqTU1mItezlRb3WOH0w68/044Z31eKrACtHwFnt0QljvKe9rVrf7n/LF3Vo70+B/HJGEh2xXVXxy1MZ0YzKf2A89Jozf/pu0drMTut/46keKRcGrpnodb4vaUECJEACf4TAV4jWLFxDiiWO5ypGTqxCjH4X3tmxs6KjFgHVLlLtRSjd44GwKS6Ij9tq4ZZahLI9IkovozOsjfuz/jSGT/nMEPlu+Sr3+qb43vbW/rzd/x4XU3+Kc4RgrWb2JX68FfzdEaf4MJuo37R83Dkees2ZP323aE0CiMmQimomfzQF23wLaUECJEACJPBlBP5/0XqJsJYqVVMtU+t8ot340cd7bVZ0lCLAe+x1ZpHjfDrAXzqQ3hbhuR9LeGvHrD83U8OjC87aMID68XLbT4HrzNeD7X27/z0qRv6kB3jSha8WJbWfNxCARuW3y1THFuOh/+jsuYHPLw0PoGid7SIakAAJkAAJdAl8hWj1/agzs5pFPvzouXOts6Kjfl2+fcqWUdWqfLHYY2ypy6w/3X6ePSsFigxG60O9+Oa6K9iD7X27/z0i8/5UMaeLzmK6uhADAThffs+hzun8eOiYm5wY+PxS0crwAJNeog0JkAAJkECLwP8vWluNeeXhvOioV1Tv9HlOy3RS0scti1WKQPZiXNsNaL7URxK8zvvTLszgOA0gJ1/XZmW2g9vCK9v2dn14v/+2/kxsXmAgAOfb+9h46LbG4MzA55eKVhWzOrUQK91joUJ+nvvb0wAMTUiABEiABD6VAEWrYc/Miw6gTAGlhN/gi7YSfCrW9qZBq21TbyKw60ih0iuJtsjt3jfxp/vEzFmRIFgIuEGijactEpXDtSsi7Nrbrf8T/G97NO9PJdJ1M635aVtmrxjEK7cqmC//sfHQqsrs8O2iFWDKK7OuohUJkAAJkEBFgKLVcCSkKm/q7AKv+jWuu8OptblAqGJe8NgAACAASURBVBYxabIaVMnkPeyi8zVxu8pRmcUBllIJSE16pdpfM38MG1ebVcJUYhnEyJpM8lA7Xu3gSQebQciFXXvb3nyG/zePTPxReVrVgr/rblDNRhAqa4XKEXwaj0E2Kf+R8XBrieHRB4hWgJsLGPYWzUiABEiABABQtJoOgyLBflmlNrLZxrXaljJE2lm5c6v0EqttXD38yCbLgAN35eMQ3+Zkb9atI2N/Ws8YHObZCQd/Bdep/ZE/WPoqV+uYIOtu4zrX3qsLH+N/7ZGhP9kpwLrZclRtubtSC+bOiDZ1ruBN1F2k1TTYsPy7x0NTj+lnubDOxWFi943rNq6aMpUId3TxvbVtKfCNNvfgNq4avLxEAiRAAiSgIUDRqoHCSyRAAiRAAiRAAiRAAp9FgKL1s/qD3pAACZAACZAACZAACWgIULRqoPASCZAACZAACZAACZDAZxGgaP2s/qA3JEACJEACJEACJEACGgIUrRoovEQCJEACJEACJEACJPBZBChaP6s/6A0JkAAJkAAJkAAJkICGAEWrBgovkQAJkAAJkAAJkAAJfBYBitbP6g96QwIkQAIkQAIkQAIkoCFA0aqBwkskQAIkQAIkQAIkQAKfRYCi9bP6g96QAAmQAAmQAAmQAAloCFC0aqDwEgmQAAmQAAmQAAmQwGcRoGj9rP74am+K9ID1j4QQYuSfg4nt7L+azasbdwlXI8y7feEezj1XCpyjABvvB7LsNwfuyschvvTs6tP8jCjYwHXqcuUPvM0ep6zQ28Oy/JFSeJkESIAESOD7CVC0fn8ff0gLMxw9geUx+xB/HnMjDSScL1LYWbiGFC6CpC0uCySBC7kMEF+a6wXyNITvSqzDXl8WCQJXwPVDpHnNt7ggDpaastV9y/If6zI+TQIkQAIk8J8ToGj9zzvw/3E/QSAEguT/8XjK0yQQEN/SmCzESgxFaBHvIOUWcaNX20CyI5ZiifZvkHS/gFjskbbtyuMC8VZCLI9oy1zb8gfF8gIJkAAJkMCfIkDR+qe6+52NpWi1oV+ke3hSYh2NvIa3KWzS9oJwJSDXYUdQqkeSnYDYjf3KqGbOb/5V5SxGZp+LeAshfJyaGVjr8icbwZskQAIkQAJ/gABF6x/o5M9oIkWrTT+ke6eKQV2FeKVsVWJS9mZMKz8rEboKx2qv7t9mm2fskwBCrHArbsYe/fJt6NGWBEiABEjgGwlQtH5jr35kmyxF6/kATyum6sYVMXZSYnvqv7vOq4VDbiX65M8S/iHWCr9LtIGziXApqsVDXr1ITP6ssU9aU4Itnnm00Sxo2iDSmyNPDvBXLpxm8Vm5MGmL40j5TVW/M9OaYu8K6GdHL4jWEn57arRxrvysZlpvz1Yi83beMUY509oJHbAtv1sez0iABEiABP4eAYrWv9fnb2qxpWhFiv1ifOFWGQ/ZmblTzdIs7MlTHDcO5PKAtKdvyxX13hprz8M2TJHX9y+nHVzhTmYyMIlpvURrSLlEEGe4Vl3kSEMfrpgShL/TRfnJh5TdV/amNRfJDgshsWsFvGbH5URMq2MV6qAr39Q32pEACZAACXwnAYrW7+zXD2yVrWgFyoU9XnfxTtUwtbBHQGwitCc4y1fd2oVD1Yyi24u3rNJA9VfMqxqqWUMZDJcUNWDnRev06+0kkBDrSDsD3NTx2s8Mx+XYLOt0zXm8gydVHGzP/9HsAWtsjuebcJ8uHqPlzzzH2yRAAiRAAt9NgKL1u/v3g1pXidbxHK0Coh+/Wa5QXwxnPPMTfCGwbc3yNULTay9nb7W+fK0vd52V8KVo7ddZPzMnSufuAzlOvoDwT1qxpnLWbg+p9l7L7dcdpnsspsIvtDVniLZema/V9U+DhVvqESU43SYUov0pl9j3p7oHdcyXP3iEF0iABEiABP4MAYrWP9PV726o/UwroJ8N1AlQ1EJ2NAtVndapPXn6WtEKIAuxlhLeVuUtvQYIvLsjyvrLmd7eTPWUY/n5iI3aMEB62EbtxFW3p5pcr/4xwW0vgQJZHGAp1bMj6bOU2DUo/1YTj0iABEiABP4iAYrWv9jrb2nzPaIVKAVqZwFPjmijea19UblGu7s76WZ126L25aJVcVbJ9Y9bbNTCMMfFZntElF7eN8Na+qQWsfVnqscHRRZuyoVkapOB0fVj6keDFFj1Nxyoiy2SoJyB1WUjMCp/3D3eIQESIAES+CMEKFr/SEe/v5n3idZqBlXeNiUoxakmZKC87nWS3c+1+VdEa9uJIsf5dIC/dCC9LcLzm2ZfkwBSjM96tl2uZk8lvF3ciR9u26jjKg/rVJlZmQ/2liKrKsG0/H59PCcBEiABEvh7BCha/16fv6nFd4pWFIh3EnIXl7OT5Qr13s5KZYO0ca7TTf110Xp1p8pyoN89qjJ6ZcqrcoGbf5oUoaUXZUyxgBskszPDJUsRYGwrAlXeIA7YovwrOh6QAAmQAAn8WQIUrX+263+74feKVjWLp7YTVYuoqhhX3SvmZiFWI277rSvSAG4vvdNrRWuKQEq0Y2g7PmVHeIOUXTeL120uUOVXHVuwdvOg+rHQ33r1dr97ZDrTeqvXrvxubTwjARIgARL4iwQoWv9ir7+lzfeLVtQ5W7f7PTwxnsi/THmlhOBgnVD9arqXKeDVolXlmb2JtC704uQPtjVtW7xspnVuwVrjRBFjK8TE5gKNYf2ZR9iIuZhWF4dzbW9bfq86npIACZAACfw9AhStf6/P39RiNfM4vlnAnFPlK2210GpbhQno7evX7u4Op0sdL5qnCH0XQq4HYvYR0ZqqPKurUJv2qfGt2lzAwy46XzcuUBsgNKvp3eANKa/KVFft7VQbb3ufpnatx67ZA1obNajUX+eTyh4g4bXDDO4ov1UVD0mABEiABP4gAYrWP9jp72pydtpiqdImja7ydxAkI4uTVMoq6SKYzfXZ3ca1XLEfqJRTw1Zft3Ed3oISpU5vM4KOWZFgv6y2ihUTs7+XWG3j6uFHpXwq2+3AXfk4xJdOcb92cj7AdfcY3zah9iQJJvqpaYsYMMrPEYKNB7fp53Lb2gBh0mvvneX/GidWRAIkQAIk8HEEKFo/rkvoEAmQAAmQAAmQAAmQQJ8ARWufCM9JgARIgARIgARIgAQ+jgBF68d1CR0iARIgARIgARIgARLoE6Bo7RPhOQmQAAmQAAmQAAmQwMcRoGj9uC6hQyRAAiRAAiRAAiRAAn0CFK19IjwnARIgARIgARIgARL4OAIUrR/XJXSIBEiABEiABEiABEigT4CitU+E5yRAAiRAAiRAAiRAAh9HgKL147qEDpEACZAACZAACZAACfQJULT2ifCcBEiABEiABEiABEjg4whQtH5cl9AhEiABEiABEiABEiCBPgGK1j4Rnr+WQH5GFGxue9MLB+5qi2Oav7beJ5deJDs4QmIdXZ5c8n9eXJ7iuF3d+lf+YOkfkIx176vt/3OcdJ8ESIAESOBGgKL1xoJHryZwibBxJLxtiDQvqtqKHGnow1UCMMxe7cHTys9PW4rWPs1LhLUUcH3Vv/XNIsNp50HKNQbd+2r7vn88JwESIAES+K8JULT+1933PzlfIN5JiE2ERs+0vc/CFYTcIa61bPsej/8HAnX/Lo8Y/vS4IFwJSP/U6vtX2/8PzOgjCZAACZCADQGKVhtatH2AQIr9QmAVjrxOz0KshECQPFAFHzUmUKR7ePKZ4Q1N/w4lq3IqP/kQIsCte19tb4yChiRAAiRAAv8JAYrW/6Sj/n83EwRiQrQiRxrFyDjT+itdne4dCCEgViFGfkZY+nHGwZXwT7p5dABJ0BOtr7a3dJ/mJEACJEACH0+AovXju+hbHMxx8gXENoadLs1xjgJs3EpkyZ8l/EM8KrTy5AB/5cJRgkz9kz/wNlscR1YC2dpfeyM/wZcO9un1yshBjuTgY/kjK38cF5sgwlmj7S7RBs4mwqWoFqt59TPyZ439k/1//kzrSPPry5doDbHYYxbXL9lPe8u7JEACJEACn0iAovUTe+VLfSrSAK5wsYs1ik3b5gJJ4EIuA8SXWuqq1eYbB3J5QNpTv0oYSblEEGc3Ydxa6NWfBbS177h4MQlnyBCuJeT62Fp4dqkWJrk+Tr0pzouK6/XWWHsetmGKZq3a5bQrufUF8kP+dxrz6pOsjGkdDQ0ZVP9q+0GFvEACJEACJPAfEKBo/Q866ZtczNMDVtcMAtMtK+ItpNxqFmel2LsCbkfFVYt9xEhQbBJIiHXUmqG1te/5aiBas+MSwg0G4hooEG9lb2ESUIpW4SJIemoc9UKmoD1P+aD/vea86rTIU4S+By9IWouwxmt7tf14zbxDAiRAAiTw6QQoWj+9h77SP5XmaoulEq+bA06Zbua1EmXecWRhT7TpZRuoww/8022WtcWuSA/YHtLWPVv7VmHqcFa0JgikwCbStQ1AdoQnlmg3rxStIzGmSSDQFeQP+t9rzrNPc9U/TYiG+nQ2OJ77YvxW66vtbzXxiARIgARI4H8lQNH6v/bcV/itFl8FWDkCzmrfTUCvYkansgmU2QYkOpOPWYi17OWBneJka98ua060lqJ0NcxNei2jWpjW3pzATrQq4WvZ3mvdv3hQ5MiSI3xXxRfrZs17vrzavlcdT0mABEiABP4fAhSt/09ffbGnGSLfLV+lX9+M16KwM1vXnrmrjwfRAMUF8XFbLdxSi562R0TppTXD2sNoa988Piday9XyK4xl+EL9yr8d52ktWpUv9/rftOO3PuuUZv244tHqX20/WjFvkAAJkAAJfCoBitZP7Zmv8+uCszYMoG5oEWMnxW1b1FIUep3X59ZIihzn0wH+0oH0tggnXk+XZdvYf4pobUOx8b/93FOOc5zj80TcarW46hbi8Gr7pzSKhZAACZAACXwQAYrWD+qMb3alnEWUwUTKo97Cojo8YPuULbKqLATmKZcM7OdE62+EB4wOmHn/n57yynBm+SpaX20/yoY3SIAESIAE/lcCFK3/a8/9b36nAaSYel1ezcTdFl7VK+Z3+ryuZfos6eOWyz5FIHsxrm1GjYi8ppmytW8X9oSFWOkei4cWYj3m/9M3F7hEWIsJ/qh2wLqGB7zavtddPCUBEiABEvj/CVC0/v99+H+0oEgQLATcINHGlxaJyuHaXU1fprxSQneQQKB+1dxZaV+Jopvo7WIpym1EuyJXbStrbt8tbz57gEoQcEfKq06bbnUOswfYtvdWljp6+kwrMhyXAnJk84gqfVmb/6vtu+3lGQmQAAmQwP9PgKL1/+/D/6YFlTCVWAYxsiZzPtSOVzt40sEm6qvT+jW3u8OptblAqBZtyfVAzFbJ9j3sovM1Mb/KiZrFAZZSCeZ2yivA1r4Dei48oDS+Y3MBY9H6oP+dxjznpNo8ou7fJrvVdXOHYf7ZV9s/p1UshQRIgARI4FMIULR+Sk/8ET/y7ISDv4Lr3LZZXfoqV2ujcvogutu4inIb1BDpSPrTS6y2cfXwI+vyhQN35eMQX+MCOhXY2l8fLrdxdXE4X6+MHNyxjaumpDSQcDqbKVRGd/uvqeMply4xDv7yxt9xsfIPGMEPvNr+KY1iISRAAiRAAp9AgKL1E3qBPpAACZAACZAACZAACUwSoGidxMObJEACJEACJEACJEACn0CAovUTeoE+kAAJkAAJkAAJkAAJTBKgaJ3Ew5skQAIkQAIkQAIkQAKfQICi9RN6gT6QAAmQAAmQAAmQAAlMEqBoncTDmyRAAiRAAiRAAiRAAp9AgKL1E3qBPpAACZAACZAACZAACUwSoGidxMObJEACJEACJEACJEACn0CAovUTeoE+kAAJkAAJkAAJkAAJTBKgaJ3Ew5skQAIkQAIkQAIkQAKfQICi9RN6gT6QAAmQAAmQAAmQAAlMEqBoncTDmy8jUMTYSReH88tq+CMFFzhHATbeD6QQEMKBu/JxiC+j7b/Ee2xcB0LZO0v4hwT5qHX/Ro6T70D6p5FnLogPPlZN+fIHS/+ACXf6FfCcBEiABEiABLQEKFq1WHjx5QQuIVZCIEjMakoDCWefmhn/GasCSeBCLgPEl6JudYE8DeG7EuswG5DIwjWk6yM81zI1T3HcOJDrEEPrweMo4m0ljlchBrK4SBB4Es7miLRRwcUFcbCElGto3BlWwCskQAIkQAIkMEKAonUEDC+/mIClaE0CAWGqcF/s+qcUX8Q7SLlF3OjVtmPZEUuxxLGtRHXX1DNKbC4k/FOjNNsFtY6LBLuFms0VEAPRqgT0AmJ51IjfSlyLTTQyO9uqg4ckQAIkQAIkMEKAonUEDC+/mMAfE61FuocnJdbRYH7ybtDJTkDsxqaqMxw90akv3S8wJhwv4QrC0wnOxr0CaeBCSB/+RiNaixhbIbDVKmgAeYRNX0Q3RfOTBEiABEiABAwIULQaQKLJCwj8MdGa7usY0sEM5b1sLwhXAqtwTARX92+z0yn2i66I7dScqXANrzsz2zZI93BFJbrLWe9+O9I9FmKFUXdQiejZ2dx2nTwmARIgARIggRYBitYWDB7+IgFD0ZpHm+p1dLnIqH41LTaIRt9k59XCpHohkPxRC43iYfxlfoLvrHA47rF0qgVJ21OGPD1g/SMhhIS3PQ1edefJAf7KhdP4I3/gbbY4JqMOlVCfP9N6QbSeeqVficRFEwd8ibAWEsFoWHCCQIiREIEMx2U1u6qiDbSiNQkgJkVrJaK9TrzCL443VkUCJEACJPDfE6Bo/e+78D9tgKFobVpnFtOqWZjULDRaHpC2Yz/r+psFSHm8w0I6cLwApf4sZx67r7sv0RpSLhHEGa5FFTnS0C9nIT9pFrFIdlgIiV3zun52JrSZuW0HwVb0MxU6IFw0+vc+0ZqVM8O3md+mZ/lJAiRAAiRAAmYEKFrNONHq2QReIFrLle3ahUkp9q6A26gu1ZZB/dVM4y0ms3qdfnv93n/d3gWSBBJiHQ1ndLtmv3KmBLgnBWTbH8OZ0IGoVDO0UsAN0qtQ14rWkqcczwZRxrRyMd2vDABWQgIkQAJfSoCi9Us79uObNRCN0x7Pz7ROv34uwwzk7rbSflB/JVpvCQqamccmZlTlJxUQ/ukq3toeF+kB28NN2LXv/d5xhmjrlSmpXL8X2nCXaFVtlhCLHZLr1PJIeAByRBsxkr+1zh6gQipugH8PC2siARIgARL4CgIUrV/Rjf9hIwaicboNs6JVxahO5X0tX/e3YjoH9c+JVgBZiLVUsa4h0ryl4qZd/5W7+fmIjYrNlR620fAVP+4QrU2IQT/sQTvTqlrZ8NmdkDV48jOinQfP32NXLhzT+PYrhFgJCZAACZDA/06AovV/78H/1f+BaJxuyKxorcsrc4g2i6Q0n9eJvkH9BqJVuaiS5R+31Y5SjovN9ogovWhnX6db9Ly7WbgpF4apTQZG14PZxrQWKQK3F2JQuzwqWtX9/Ixwu8SPVIvmJH68DfbldljVwrBbuMXz2s+SSIAESIAE/gYBita/0c+f18qBaJx20Uy0TqRs6hc/qN9QtLbLKXKcTwf4SwfS2yI8N9OLbaPXHpc7XKlMB7t4OnG/ZfaAdO9W2QA0E6OTonW0uQl2UzPho8/xBgmQAAmQAAlUBChaORLeQ2AgGqfdmBWtdXjAbSHVdHljC7GuM7Hox7ROlVfHbC72GMso9fyUV+p1vNr1Si2SSgxmem3ytNaxrJqZ6uFMtnPNKjBFSPnqiTWeuLfCZHW8SQIkQAIk8H0EKFq/r0//jxY9W7TWIlPuYq2AK9IArvRx3al0UP/cTGuKQLZiYvuUS1E2nlz/+ZsLFIh3cmTb1L5z1fnsjlgTortdon6mteJzTbHVfqDU10srX3uP85QESIAESIAEQNHKQfAeAgPROO3G7EyrCjeNt5Aqwf3glXadI7S9i9Og/nnRqnaUGkuOX5x8CNESxb3mPH2mtd42tb9Iqldt97ScmV0Od70qEgSLqY0KusXoRWsVsyq3mh8NRYKdRfnd2nhGAiRAAiRAAhUBilaOhPcQGIjGaTdSlQd1FQ52qOo+Vb+md3c4Xer40jxF6LsQct0Vs4P650QrUG0u4GEXnXFLHlAgiwMse7lMu3694Gx2YZW+zjIG1vURnusdvJrNF9ZzbG/l6UUrkJ98SOFgc0yv8bVFFiNYSjibaKbvbuXziARIgARIgAR0BChadVR47fUEihg76eJwNqyqSLBfOvWWrubbuAq1wj9QKap69VwibJz2rGyKvdOOz9Rvk3qJ1TauXr06Xq2Qd+CufBzKFfK9Ol55Wqawara1Hf902hsq1P5c4n2V/UDFrDpqm9vkKjJNXD4f3JF8rEC5ze3yp8wV22QPCJTINymYNiRAAiRAAiQwQYCidQIOb5EACZAACZAACZAACXwGAYrWz+gHekECJEACJEACJEACJDBBgKJ1Ag5vkQAJkAAJkAAJkAAJfAYBitbP6Ad6QQIkQAIkQAIkQAIkMEGAonUCDm+RAAmQAAmQAAmQAAl8BgGK1s/oB3pBAiRAAiRAAiRAAiQwQYCidQIOb5EACZAACZAACZAACXwGAYrWz+gHekECJEACJEACJEACJDBBgKJ1Ag5vkQAJkAAJkAAJkAAJfAYBitbP6Ad6QQIkQAIkQAIkQAIkMEGAonUCDm+RAAmQAAmQAAmQAAl8BgGK1s/ohz/hRZEebnveq33vB/8c7FM9iiLZwRES6+iiN/jPr6Z7R8NDw+hnDx2ib+dzf/cWOEcBNt4PZDneHLgrH4d4fBxd4v1tnDpL+IcE+YQDdvb2/kxUrb1l588F8cHHyq3Hn/zB0j9gAo+2zqmLdv70SkoCyHWE8d7q2duezpb/ej5dl3OcfAfSP02Oueszs/5fLc0O8hTH7QquU//tqcdDMvof4Lf5mDWDVt9LgKL1e/v2w1qWYr8QWB6zu/zKT9uPEq1pIOGMKey7Wth76BJiJQSCpHd95PTT+Iy4+cuXCySBC7kMEF+Kuu4CeRrCdyXW4XAsZuEa0vURnutvafUlvnEg1yGG1oCdvb0/tsCs/CkSBJ6EszkibURJcUEcLCHlGho8tu5Y8hkWnx09iFX4MtE6Wf4v8Om3uIi31Y8rwzZP+t8vfO78EmEtBVw/bI2HDKedpx8Pb+Az1wTe/34CFK3f38cf0sIEgYUI+xCnR91IAgFhqihHS5m4YSlaJ0r6s7eKeAcpt4gbvdomkR2xFEt0fkPprqln1JfzQsI/NcquLsjS3tqftr8mx1b+KAG9gFgeNWK8EtdiE5nN9o35ZuVPvxD14+JYiqjXiNa58n+Bz6DJCXaLeoZzVrTO+d8vfO68QLyTI+PhgnAlerO/b+Az1wTe/xMEKFr/RDd/QiMpWq164Y+J1iLdw5PPDf9IdgJiNzZVneHoiU64SbpfYEyoXcIVhNcVeLb2tv5YjRcAVv4UMbZCYKtV9ADyCJu+qLd0yMqfVtmDUJlZAdd62ODQqPwH+diP5wJp4EJIH/5GTM4uG/nf4zDvT/UmbDUyvZ6ffAgR4Pq/6UE+Pfd4SgLGBChajVHR8DECFK1W/P6YaL1+ET9NoFSzQ6twLBqyun+bLa++tEdjpjMVruG1ZmZt7W39sRotSrKW4TfG/qd7LMQKo3hQifrB7LKxW5b+jJRbvtF42pgYVjJa/oN8rMdzuodbx+yP+jR0H6a28/6ccXA1bxOaOpOgK1of5NMUy08SsCVA0WpLjPZ3EnhQtOYn+HJ8odbVqUuEjXRxSHMk+021oMDRL15Sz+TJAf7KhdMsCpM/8DZbHEdWHuTRRrNgaoOo9+b46s+9B7aidY6Puu+scDjusVSLLJwltqcMeXrA+kdCCAlve9K8Ks6rhUz1Qh35oxYmxU+PMZyfCbIFeUG0nvgSrkXZoolLVvF8QiLQrXIrq67G71XE2drD0h/r5lr6X4qQKdFaiWyvEz9h4ZQ1H33ZpqJM//T81dHyH+RjN54zHJfV7KqKmx71SdMcU1s7f4YVXaI1xKL1d/RBPsMaeIUEzAhQtJpxotXDBB4UraYirrZz3RX8MEWui2es26L+EEu5RBBnuJoVOdLQL2c9rgJF0/byy+KTYlrn+NT3mwVFebzDQjpwvAClPi9nEvuvi6vYxs5CpmZh0vKA9ApNA+jDL6lsCwshsWtej8/OHDUzpfVyLFv7GR4Df2bsB7dt/ZkVHVkZx3ibiR7UOH3B1p+R0kxF2cjjs5dHy381n5ZnmQo9Ee41c8qoT61nmkMb2+YZ+89qLHTeWvwiH3t/+cQ3E6Bo/ebe/ai2/a5oHYvNuiHpvx6+3VFHSSAhJlLtlF8W/6Fovblc9cctprF6ndv+YipXMmsXMqXYuwJuM0vZRffxZ0qwe1J0UynNfgn3xout/QQVrT8T9tpbtv6UP2LkeHaKMqb1gcWGtv5oG2U36zhSxOTlUdH3aj6NV82K/SC9/nAe9al5pvVpY9t6zPiwyFOEvgcv6KV9+y0+xp7S8K8QoGj9Kz399nZWImmYm7XJRTrz6r+eKbyJrpEGmdpB5UMUEP7p+mXRLlHllN0ebl8k7Xvq+FtE641nM5PYxIBW52Ovh8swCbnTr8zvw/qY8wzR1itTCrl+LxTCVmTZ2msZjPtzPq7ged78v12dz9PanxzRpr8ivHGyzh6gQmbqAfJ6f5q6u5+vFmXj5dvx6Xpteqb+BkmIxQ5J663FuE/Dcm1sh0+PXxmEQTkbHM8tJ/EbfMb9452/S4Ci9e/2/S+3/HdnWm9ibKKZWYi1VLGcKi9h+w/yxDP1rfLLwqiS+bK0Fsbiu356zn5wv98fPdGqYmCnUpSV4QRTMaDaVr3tYn4+YqNieaWHbaTJuGor+mztey2f9Sc/I47j2X9JVgdT3+NPM/53J2TN8M/PiHYePH+P3Urg+sbiN/zpMVKnrxJlTVWT5dvwaQq0+GxCQvphSJM+9cq3se09anZaQ6jI3gAAExFJREFU5MiSI3xX/d/ppY97MR8zB2n11whQtP61Hn9be/siydKRgegaed7UrnlcJVM/bqsdkBwXm+0RUXrRzr42j6jP8svim0VrzXF8ZryaIX8lgjbvR46zcFMutFOxuSPr61S+qJnV9I2ofzym1cgf2wbb+t+Un58Rbpf4kao/JX68DfbldlhV9oB2uEjziNHnvf70Cn+1KJst/1V8ihSB2wtRqds+61OLkY1t6zH7wzrmvS+w8So+9h7yiT9CgKL1j3T0+5v5oaK1DabIcT4d4C8dSG+LsPM6rG34V0RrO8VTt/3/y1m5Q5TKjLCLpxPl2652t7WvgRn7Ywv4Tn/Gq0mwm5ppH3+wuvMkf14tyu4v/zE+6d6FUCnHtJP+03la2+jv979dijrOcY7PE/9HbBfmPcan7x3PSaAhQNHakODniwn8B6L1SqCO6WuneLneqw7KL4tXTjPazhjP2Q/u9/ujmUmsY1rr8IDbQq0egCefPpqSR+tOuSOTgBskszPn1nlObfOiKget/NG2aOLic/KiXivIjvDEGlET4ny9YXrwHH+eJ8r0ft9d/gyf6fFcx7I2afYmP6dj/U39n/anfHVUiejR/q7+PjQxznqaraszfFqWPCQBKwIUrVa4aHw/gb5IsixpILpGnje1Q4pATsRkln90x/NYfr1oRfUlJXexVvAVaQBX+ujvbDrSK7OX55OfzxbRM5jalrJnWp/O7uDU+xFjZ2/vj97L8at2/lTj/5ryq1dsdlyObOnZM5w4tfNHX5CpKNM/PX91vPzH+Dwynsd9GrbH1HbWn9mZ8epHyC084DE+w5bwCgmYEaBoNeNEq4cJfJ5o3S8ExlbHF+W2heOirPyy+OaZVgBlyivtK8z6VeETdyqanQmyHX/1NpO3L1mDAsqZ0GVr16v6mSJBsNBsVGBjf48/Bi53TGz8qTdXkFvNj5IiwU7X3k5lBidW/ujLMxVl+qfnr46XX8X03svnkfE87tOwPaa28/5UGxxo29v8Lej8SH2Mz7AlvEICZgQoWs040ephAp8mWoFqcwEPu+jc2oSgQBYHWEr1Wnk85VWq8riuQs0OUg+DqgownjGu65uzH9zv90cvPKAstg6TcHc4Xerl5WXeRrVH+lobj/ek1j9ezOxCIH0VZcyp6yM816vym80U1vq+Nra/0x+9l+NXjf1RUYwnH1I42BzTayxjkcUIlhLOJnrK2LbxR9cqU1Gme9bk2lT5v8FH5+OUT317G9v+s/3z8u2JkFgG8S2bxHWzFRdBOy/XL42fvo88JwGKVo6BXyKg9raejs+adKTcptTF4Txppb6J4UvNbNnIY5dYbePq1aun1QpqB+7Kx6FcQT3ykLpcJNgvnXpL1xds41rE2KntaOfa27g4x0dtb+u0F36k2Dvt/hjbZrS7jatQGRYClSKsqfhDP8sUUE0O4PFPR7NBwiXeV9kkVKyho7at7SVW7zXZyP4Bf3rVzZ4a+VOXUm5jvPwpc9c22QMC9SNuthZzAxt/+qWq3aIcv85F27/5hPO58n+DT78Z54MLadjmOf/7Zc+eX2Ic/CabhBr/Llb+AWN/Dt/BZ7YNNPhqAhStX929bBwJkAAJkAAJkAAJfAcBitbv6Ee2ggRIgARIgARIgAS+mgBF61d3LxtHAiRAAiRAAiRAAt9BgKL1O/qRrSABEiABEiABEiCBryZA0frV3cvGkQAJkAAJkAAJkMB3EKBo/Y5+ZCtIgARIgARIgARI4KsJULR+dfeycSRAAiRAAiRAAiTwHQQoWr+jH9kKEiABEiABEiABEvhqAhStX929bBwJkAAJkAAJkAAJfAcBitbv6Ee2ggRIgARIgARIgAS+mgBF61d3LxtHAiRAAiRAAiRAAt9BgKL1O/rxP2hFjpPvYBNd/gNfH3cx3TsQau/6uX8/e6Sa6opkB0dIrP8ILw2CkUsFzlGAjfcDWbJ14K58HMY2RwdwiffYuHV/OEv4hwT5SOnqspV9nuK4XcF16r6WP1j6ByRTFUzUrbtl5Q/s+ejqHL9mWX5+RhRsOny8zR6nrBiv4pE7SQC5jtD/K3MJV/P/F4WAezg/Urvm2ervnvRPI2PugvjgY9WMz3r8TAxnTR1jly6I1hN/g5wAiaYb7MbbWN28TgKvIUDR+hquLHVA4IJwJbAK+18nA8O3XEgDCWevk49PcucSYiUEgsSsvPy0pWgdoCqQBC7kMkB8ab5tC+RpCN+VWIfZ4IksXEO6PsJzrSKVyNw4kOsQQ2vAyv4SYS0FXD9E2ojUIsNp50HKNTTuDPybu2DlD+z5zNXfvW9ZfpEgcPt8LoiDJaRwEegUU7dC67Ps6EGswoFonSuo5PwCn4p4W/240vmk+HgSzubYGj81n6eMnxT7hcDC4u+a3Xibo8r7JPB8AhStz2fKErUEPlu0JoGAMFWU2vbNXLQUrTOl/cnbRbyDlFvEjV5tU8iOWIoljm0lqrumnlFiYSHhnxqlWRdkZV8g3kmI5VEjfquxPj671nZ84tjKH8Caz0TVulu25af7BcRC9yahQLwdY6er2eSa+vFyLH9EWIvWTP2g1P/oMal51KZIsFvUM50D0ap+ACxGxk/140BsopHZ2dEauzeKGFshhuO8a3U7sxxvtwd5RAK/R4Ci9fdY//GaKFptZlr/98FSpHt48rnhDclOQOzGpqozHD3RCacoRdPIF3/5utjrCk47+2oWazUynZqffAgRYMxbk/618wew5WPiQ9vGrvzq//vYLJ+agRTCR/93Q7s+0+NBKM5AIE6VVP/AGJl5b560H88F0sCFkD78jRjO/taCcqv9BQYgj7Dp/whrnFG/u0z+f5VifAHTiVbb8dZyh4ck8GsEKFp/DfVfr4ii9S+J1quQsBIQU/9H5sZPdf82W16JytGY4PIL3WvNzNran3FwNbO1TROS4EHRauuPLZ/GUdNP2/Jn7Es+Kzw7Wqh8Y2Ix5qrX970Zeg0S6/Gc7uHWMelan9I9FmKq/dWPsMHbgNo3I39KxiNvJgZttB1vgwJ4gQR+hQBF669gZiXAzJfYGKL+Qg7HxWYfj8as5ckB/sqF0yyAkj/wNlscR1bG5NFGs0Bjg6j35njMPePrtuEB+Qm+dMZnSdR9Z4XDcY+lWgTkLLE9ZcjTA9Y/EkJIeNuT5tV1Xi1kqhd+yB+1MGmcp3H7eoZGM0G9Z6ZP1aKSCZGI6kv+OrOn4k2FRDAappwgaL86tbWfdhaXaD3yanzmwea2tT+WfJp6jD9ty6/+v1/7o1dPOdOqDR3oGVqeagXiaBkp9q5ZzKfdeM5wXFazqypaRevTrGiv+HmdeJdbQ0z80b1NuJXQO7Ieb73neUoCv0SAovWXQLOaO0RrkVYLOXYnXBcbFxecdi508YJKKEi5RBBnuIY9FjnS0C9nPcZmLVTflF8snxTTOidy6/vNgqI83mEhHTheUK1cL2cSBbqvHzULaZqFScsD0iu0/2+0qmwLCyGxa163zs5kNeOxDoK1tZ9ElD2+6PCp/qgw3h6fSf/tb+rKz47LEeGuYlqdTiiHfY36J7QCUW8KFcIh5XNCFNpVZGWmAvf6g1Pr06xorcbQ7c1BuwazY7W4VOzibjaMsewWTx5vZh7SigTsCVC02jPjE3cRaESCefaAMi5QauICsyO8wau1qvyxP/KJ+gOuSYXTNOV/Fa03nV3NHN5EavW6r52toXwVql3IVM04uabBbw20D/lUgt2TopvqaFYU9MaLrf1I24s8Reh78ILptFojj98uP8kfVaCWz62mh49Gyx/NHrDG5ni+/bB82INbAVqBeLvdOqpmQ8dmgluGdodqxlJllAjSa/u0PpU/OuV4NpEypvWRxaEq1ZZaBKbeuKjsFvUv0iKrsjfIJfbtX6lPHG92wGhNAnYEKFrteNH6bgL2orVIQ+zD2x//a9XaWcj6j7R/un5ZXO3LhQsHbA+asmqjbxGtNxHb512dj71uLMMk5E6/Mr8N8qOOM0Rbr0wp5Pq9UAjbL2Fb+x6HQZiJs8HxfJu6Ph9X8Dxv/t+uzuf5oD+Ve+N8rP3ptXeu/MZcCVq3CdVpf/ZFU/PAg59agagrs5xZnI9l1T06fk39DZIQi10n/6nepxzRRmjfGKFOXVbmeL79hx6vVnunCudQP55uo/BmmO5dCLeV2eEp4+1WPo9I4FUEKFpfRZbl9gj0RVTv9uxpgfxyRhIdsV1VieIHf8+zEGvZm1mYLbcy+HrRqmJgp/LE1ml/xmNADUH+kll+PmKjYnmlh23UznNVO2D7JWxrP9bOIkeWHOG7yrfWIpj8jDiOZ/8lWR1M/aA/s3xs/em1d7Z81DlvhQv/mNzCe1AgiwMsZY9Pr/x7T/UCcVha+eZlJLPE0NrsShMi0Q9DGvWp+XvVDn9SMfw7D56/x67Ma60Z22buTFv1sxc8ON6mK+NdEngeAYrW57FkSZME7hOtxTlCsFa7H0n8eCv4uyNO8WE8UX9xQXzcVjsgqUVb2yOi9KKdbWi7+/WitZ6dntuha/BDoA3pQ46zcFMutFObDIysrwNsY/Rs7edY1DHFfQEz99j1/gP+GPG5VmR/YFR+uZBQbSaiF11FEpQzsO3wFXtPhk+MCsS2aRFjJ/vx3m2DO47r+HvdblyTPuVnhNslfpSIL//GbbAvt8OqFhY+m8+tZdXf42t4xAPj7VYmj0jg9QQoWl/PmDWUBO4QrekBnnThh2k3ybY2PECDuchxPh3gLx1Ib4uw9bq2b/03RGs7xVOfwP9xXu1cJOHt4u6Y6Ltvuxra1h45zvF5wocHF9JY+1MBMObT52V4blp+lYe1NdM8KP9BPoPyqguTArF5Rm31KqZ8awzNP8vX7SrOXqPRjXwaVJVgN/VmZGBve6H6e3xdA3DneLOtlfYk8CgBitZHCfJ5QwK2orWKUb3OBLRrMRWt12fqHWYmUux8vWitwwNuC7WucF5yYJKSx7ricscetchFH6fXLc8276Sl/ctfp1r6oxpvxadLy+jMovwy3dLM5gqv+D9nIhDLJPp+HTts1PC5ZP51LGs7Znf0eCKNXduXcrHpGtHIutXH/39VPxpuMe53jLe2vzwmgV8iQNH6S6BZja1orVbDa1+PaUVrikBO5OXUZhy49corvkBvpQPQ+tyx6J7M2Q/uV7xur/f7vKtzuYu1oRJFGsB9Yvofo+Tn3RbPnE1tm6p/dHaHn96PGCv72ZmpSgTcHR4AFeGwwNhWnqUo7Phvz0dPbeyqXfmmM6030TRWr931edFavXa3rfeR8az3qfp7dU3R1mtmmS5Mu0VwZTjnT37aQi1OHE03XWYn6GYvsBtvPYd5SgK/RICi9ZdAs5q+iJojUtnrZlrVH2Q5eHVWiYSxL6Oi3FZzPCfj94tWtTe94qZ7hVm/qrXYSWiu9x6fCerVYLuPunrcdi91K/sqZZLcjvwIUKwf/RFg4889fHqIJ09ty69TNk3HtLo4nCdrtb6pF4itYuYWJLZM24ePjGe9T5V41o6fIsFuMbWRxtzMr/q/voMc3Qa2fvPUzh6gGmsz3tpweEwCv0iAovUXYf/tqlQKFgF5mwqcxVEm/xZL7JvVNs1GAVKWotU/dZO5VJsLeNhFZzRpCVX6mGa1cjt3Yr/yMhH3KtTsINW3vPN8MDM6U86c/eD+3Eyrqq/5strhdKnZlXlF1R7pa2083oyXv3d7dqGI3pUyBtP1EZ7rOadmM4WRveZt7MvZaSGxDOLb6vhmjAoXQdIdn3oPp68a+3Mnn+naW3fvKL+Kf61j0q8oVJy5yh4gy1y218utqh451AvEVol3tKP19F2HYz5Vf98cbI63mP0iixEsJZxN9ODfogzRxoFwVth3Nltp8rTq/78bj7e7SPAhEnicAEXr4wxZgiEBtWJYJYGfWsG+6e2fmp2CeltSlSLnB95KLag6I9qojAJi8Pr0EqttXL16Na6qy4G78nEoV+ROOFok2C+rVFpCvGAb13LFssXMUrn6esL+EmHjtGdNU+yddrzc2Lab3W1chcqwEKjk4xNsPuFWGUM6PXbUuHI0GyRc4n2VTUKNF0dtWzud+N/K/hLj4Derv1X5Llb+AXPDzQapkT8P8DHy5c7yc5X9Y+PBVenJFP9yW+UAYTISrGnkzLiR2o3KmXotfj7A7c8wjhf3lDvng34HP1V4ue30sv5bVmcPCNSP7qfUrP6v7zvbWssfD5sgQvMbTleN0XjTPchrJPALBChafwEyqyABEiABEiABEiABEniMAEXrY/z4NAmQAAmQAAmQAAmQwC8QoGj9BcisggRIgARIgARIgARI4DECFK2P8ePTJEACJEACJEACJEACv0CAovUXILMKEiABEiABEiABEiCBxwhQtD7Gj0+TAAmQAAmQAAmQAAn8AgGK1l+AzCpIgARIgARIgARIgAQeI0DR+hg/Pk0CJEACJEACJEACJPALBChafwEyqyABEiABEiABEiABEniMAEXrY/z4NAmQAAmQAAmQAAmQwC8Q+AcVYS2SrsGgpwAAAABJRU5ErkJggg=="
    },
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"
    }
   },
   "cell_type": "markdown",
   "id": "sufficient-prior",
   "metadata": {},
   "source": [
    "#### 좋은 모델 학습을 위해 data나누는 과정\n",
    "\n",
    "tr, test = split_by_date(data, n_days=90)\n",
    "\n",
    "tr, val = split_by_date(tr, n_days=90)\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "-----------------------------------------------------\n",
    "\n",
    "tr, test = split_by_date(data, n_days=120)\n",
    "\n",
    "tr, val = split_by_date(tr, n_days=120)\n",
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "\n",
    "- train set이 너무 크고 valid set이 너무 작아서 제대로 된 결과가 나오지 않을 것 같습니다\n",
    "\n",
    "\n",
    "-----------------------------------------------------\n",
    "\n",
    "\n",
    "tr, test = split_by_date(data, n_days=150)\n",
    "\n",
    "tr, val = split_by_date(tr, n_days=150)\n",
    "\n",
    "![image-3.png](attachment:image-3.png)\n",
    "\n",
    "- 위의 두 결과보다는 낫지만 아직 만족할만한 비율은 아니기에 한 번도 올려보겠습니다\n",
    "\n",
    "\n",
    "----------------------------------------------------\n",
    "\n",
    "tr, test = split_by_date(data, n_days=180)\n",
    "\n",
    "tr, val = split_by_date(tr, n_days=180)\n",
    "\n",
    "![image-4.png](attachment:image-4.png)\n",
    "\n",
    "- 이정도면 가장 이상적인 dataset나눔인 것 같아서 더 이상 진행을 하지 않겠습니다.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "##### 기본 용어 정리\n",
    "\n",
    "- Events: 데이터 수\n",
    "\n",
    "- Sessions: UserId의 값\n",
    "\n",
    "- Items: ItemId의 값\n",
    "\n",
    "- First Time: 최소 시간\n",
    "\n",
    "- Last Time: 최대 시간"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "quarterly-frederick",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train set에 없는 아이템이 val, test기간에 생길 수 있으므로 train data를 기준으로 인덱싱합니다.\n",
    "\n",
    "id2idx = {item_id : index for index, item_id in enumerate(tr['ItemId'].unique())}\n",
    "\n",
    "def indexing(df, id2idx):\n",
    "    df['item_idx'] = df['ItemId'].map(lambda x: id2idx.get(x, -1))  # id2idx에 없는 아이템은 모르는 값(-1) 처리 해줍니다.\n",
    "    return df\n",
    "\n",
    "tr = indexing(tr, id2idx)\n",
    "val = indexing(val, id2idx)\n",
    "test = indexing(test, id2idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "elder-canadian",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_path = data_path / 'processed'\n",
    "save_path.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "tr.to_pickle(save_path / 'train.pkl')\n",
    "val.to_pickle(save_path / 'valid.pkl')\n",
    "test.to_pickle(save_path / 'test.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "linear-desktop",
   "metadata": {},
   "source": [
    "#### SessionDataset\n",
    "\n",
    "- 세션이 시작되는 인덱스를 담는 값\n",
    "\n",
    "- 새로 인덱싱한 값을 갖는 클래스 만들기\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "informal-composite",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SessionDataset:\n",
    "    \"\"\"Credit to yhs-968/pyGRU4REC.\"\"\"\n",
    "\n",
    "    def __init__(self, data):\n",
    "        self.df = data\n",
    "        self.click_offsets = self.get_click_offsets()\n",
    "        self.session_idx = np.arange(self.df['UserId'].nunique())  # indexing to UserId\n",
    "\n",
    "    def get_click_offsets(self):\n",
    "        \"\"\"\n",
    "        Return the indexes of the first click of each session IDs,\n",
    "        \"\"\"\n",
    "        offsets = np.zeros(self.df['UserId'].nunique() + 1, dtype=np.int32)\n",
    "        offsets[1:] = self.df.groupby('UserId').size().cumsum()\n",
    "        return offsets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chinese-japanese",
   "metadata": {},
   "source": [
    "train데이터로 SessionDataset 객체를 만들고 인스턴스 변수들을 살펴보겠습니다.\n",
    "\n",
    "click_offsets 변수는 각 세션이 시작된 인덱스를 담고 있습니다.\n",
    "\n",
    "session_idx 변수는 각 세션을 인덱싱한 np.array 입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "daily-december",
   "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>UserId</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Time</th>\n",
       "      <th>item_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>3186</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:00:19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1270</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1721</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>1022</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2340</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:01:43</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    UserId  ItemId  Rating                Time  item_idx\n",
       "31       1    3186       4 2000-12-31 22:00:19         0\n",
       "22       1    1270       5 2000-12-31 22:00:55         1\n",
       "27       1    1721       4 2000-12-31 22:00:55         2\n",
       "37       1    1022       5 2000-12-31 22:00:55         3\n",
       "24       1    2340       3 2000-12-31 22:01:43         4"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr_dataset = SessionDataset(tr)\n",
    "tr_dataset.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "northern-terminology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0,     53,    169, ..., 818341, 818460, 818736], dtype=int32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "tr_dataset.click_offsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "going-latex",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    1,    2, ..., 6033, 6034, 6035])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr_dataset.session_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dominant-musician",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SessionDataLoader:\n",
    "    \"\"\"Credit to yhs-968/pyGRU4REC.\"\"\"\n",
    "\n",
    "    def __init__(self, dataset: SessionDataset, batch_size=50):\n",
    "        self.dataset = dataset\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\" Returns the iterator for producing session-parallel training mini-batches.\n",
    "        Yields:\n",
    "            input (B,):  Item indices that will be encoded as one-hot vectors later.\n",
    "            target (B,): a Variable that stores the target item indices\n",
    "            masks: Numpy array indicating the positions of the sessions to be terminated\n",
    "        \"\"\"\n",
    "\n",
    "        start, end, mask, last_session, finished = self.initialize()  # initialize 메소드에서 확인해주세요.\n",
    "        \"\"\"\n",
    "        start : Index Where Session Start\n",
    "        end : Index Where Session End\n",
    "        mask : indicator for the sessions to be terminated\n",
    "        \"\"\"\n",
    "\n",
    "        while not finished:\n",
    "            min_len = (end - start).min() - 1  # Shortest Length Among Sessions\n",
    "            for i in range(min_len):\n",
    "                # Build inputs & targets\n",
    "                inp = self.dataset.df['item_idx'].values[start + i]\n",
    "                target = self.dataset.df['item_idx'].values[start + i + 1]\n",
    "                yield inp, target, mask\n",
    "\n",
    "            start, end, mask, last_session, finished = self.update_status(start, end, min_len, last_session, finished)\n",
    "\n",
    "    def initialize(self):\n",
    "        first_iters = np.arange(self.batch_size)    # 첫 배치에 사용할 세션 Index를 가져옵니다.\n",
    "        last_session = self.batch_size - 1    # 마지막으로 다루고 있는 세션 Index를 저장해둡니다.\n",
    "        start = self.dataset.click_offsets[self.dataset.session_idx[first_iters]]       # data 상에서 session이 시작된 위치를 가져옵니다.\n",
    "        end = self.dataset.click_offsets[self.dataset.session_idx[first_iters] + 1]  # session이 끝난 위치 바로 다음 위치를 가져옵니다.\n",
    "        mask = np.array([])   # session의 모든 아이템을 다 돌은 경우 mask에 추가해줄 것입니다.\n",
    "        finished = False         # data를 전부 돌았는지 기록하기 위한 변수입니다.\n",
    "        return start, end, mask, last_session, finished\n",
    "\n",
    "    def update_status(self, start: np.ndarray, end: np.ndarray, min_len: int, last_session: int, finished: bool):  \n",
    "        # 다음 배치 데이터를 생성하기 위해 상태를 update합니다.\n",
    "        \n",
    "        start += min_len   # __iter__에서 min_len 만큼 for문을 돌았으므로 start를 min_len 만큼 더해줍니다.\n",
    "        mask = np.arange(self.batch_size)[(end - start) == 1]  \n",
    "        # end는 다음 세션이 시작되는 위치인데 start와 한 칸 차이난다는 것은 session이 끝났다는 뜻입니다. mask에 기록해줍니다.\n",
    "\n",
    "        for i, idx in enumerate(mask, start=1):  # mask에 추가된 세션 개수만큼 새로운 세션을 돌것입니다.\n",
    "            new_session = last_session + i  \n",
    "            if new_session > self.dataset.session_idx[-1]:  # 만약 새로운 세션이 마지막 세션 index보다 크다면 모든 학습데이터를 돈 것입니다.\n",
    "                finished = True\n",
    "                break\n",
    "            # update the next starting/ending point\n",
    "            start[idx] = self.dataset.click_offsets[self.dataset.session_idx[new_session]]     # 종료된 세션 대신 새로운 세션의 시작점을 기록합니다.\n",
    "            end[idx] = self.dataset.click_offsets[self.dataset.session_idx[new_session] + 1]\n",
    "\n",
    "        last_session += len(mask)  # 마지막 세션의 위치를 기록해둡니다.\n",
    "        return start, end, mask, last_session, finished"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "inside-preliminary",
   "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>UserId</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Time</th>\n",
       "      <th>item_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>3186</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:00:19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1270</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1721</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>1022</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2340</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:01:43</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    UserId  ItemId  Rating                Time  item_idx\n",
       "31       1    3186       4 2000-12-31 22:00:19         0\n",
       "22       1    1270       5 2000-12-31 22:00:55         1\n",
       "27       1    1721       4 2000-12-31 22:00:55         2\n",
       "37       1    1022       5 2000-12-31 22:00:55         3\n",
       "24       1    2340       3 2000-12-31 22:01:43         4"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr_data_loader = SessionDataLoader(tr_dataset, batch_size=4)\n",
    "tr_dataset.df.head(5)\n",
    "                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "numeric-steam",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "iter_ex = iter(tr_data_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "forced-threat",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Input Item Idx are : [ 0 53 64 54]\n",
      "Label Item Idx are :       [ 1 54 62 24]\n",
      "Previous Masked Input Idx are []\n"
     ]
    }
   ],
   "source": [
    "inputs, labels, mask =  next(iter_ex)\n",
    "print(f'Model Input Item Idx are : {inputs}')\n",
    "print(f'Label Item Idx are : {\"\":5} {labels}')\n",
    "print(f'Previous Masked Input Idx are {mask}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "contrary-despite",
   "metadata": {},
   "source": [
    "### Step 3. 모델 구성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ancient-accessory",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mrr_k(pred, truth: int, k: int):\n",
    "    indexing = np.where(pred[:k] == truth)[0]\n",
    "    if len(indexing) > 0:\n",
    "        return 1 / (indexing[0] + 1)\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "\n",
    "def recall_k(pred, truth: int, k: int) -> int:\n",
    "    answer = truth in pred[:k]\n",
    "    return int(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "czech-shoot",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(args):\n",
    "    inputs = Input(batch_shape=(args.batch_size, 1, args.num_items))\n",
    "    gru, _ = GRU(args.hsz, stateful=True, return_state=True, name='GRU')(inputs)\n",
    "    dropout = Dropout(args.drop_rate)(gru)\n",
    "    predictions = Dense(args.num_items, activation='softmax')(dropout)\n",
    "    model = Model(inputs=inputs, outputs=[predictions])\n",
    "    model.compile(loss=categorical_crossentropy, optimizer=Adam(args.lr), metrics=['accuracy'])\n",
    "    model.summary()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "junior-plymouth",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Args:\n",
    "    def __init__(self, tr, val, test, batch_size, hsz, drop_rate, lr, epochs, k):\n",
    "        self.tr = tr\n",
    "        self.val = val\n",
    "        self.test = test\n",
    "        self.num_items = tr['ItemId'].nunique()\n",
    "        self.num_sessions = tr['UserId'].nunique()\n",
    "        self.batch_size = batch_size\n",
    "        self.hsz = hsz\n",
    "        self.drop_rate = drop_rate\n",
    "        self.lr = lr\n",
    "        self.epochs = epochs\n",
    "        self.k = k\n",
    "#  batch_size를 기존보다 줄여서 학습 속도 줄이기\n",
    "args = Args(tr, val, test, batch_size=128, hsz=50, drop_rate=0.1, lr=0.001, epochs=3, k=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "hydraulic-insulin",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         [(128, 1, 3307)]          0         \n",
      "_________________________________________________________________\n",
      "GRU (GRU)                    [(128, 50), (128, 50)]    503850    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (128, 50)                 0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (128, 3307)               168657    \n",
      "=================================================================\n",
      "Total params: 672,507\n",
      "Trainable params: 672,507\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = create_model(args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thrown-county",
   "metadata": {},
   "source": [
    "### Step 4. 모델 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "earlier-thompson",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train 셋으로 학습하면서 valid 셋으로 검증합니다.\n",
    "def train_model(model, args):\n",
    "    train_dataset = SessionDataset(args.tr)\n",
    "    train_loader = SessionDataLoader(train_dataset, batch_size=args.batch_size)\n",
    "\n",
    "    for epoch in range(1, args.epochs + 1):\n",
    "        total_step = len(args.tr) - args.tr['UserId'].nunique()\n",
    "        tr_loader = tqdm(train_loader, total=total_step // args.batch_size, desc='Train', mininterval=1)\n",
    "        for feat, target, mask in tr_loader:\n",
    "            reset_hidden_states(model, mask)  # 종료된 session은 hidden_state를 초기화합니다. 아래 메서드에서 확인해주세요.\n",
    "\n",
    "            input_ohe = to_categorical(feat, num_classes=args.num_items)\n",
    "            input_ohe = np.expand_dims(input_ohe, axis=1)\n",
    "            target_ohe = to_categorical(target, num_classes=args.num_items)\n",
    "\n",
    "            result = model.train_on_batch(input_ohe, target_ohe)\n",
    "            tr_loader.set_postfix(train_loss=result[0], accuracy = result[1])\n",
    "\n",
    "        val_recall, val_mrr = get_metrics(args.val, model, args, args.k)  # valid set에 대해 검증합니다.\n",
    "\n",
    "        print(f\"\\t - Recall@{args.k} epoch {epoch}: {val_recall:3f}\")\n",
    "        print(f\"\\t - MRR@{args.k}    epoch {epoch}: {val_mrr:3f}\\n\")\n",
    "\n",
    "\n",
    "def reset_hidden_states(model, mask):\n",
    "    gru_layer = model.get_layer(name='GRU')  # model에서 gru layer를 가져옵니다.\n",
    "    hidden_states = gru_layer.states[0].numpy()  # gru_layer의 parameter를 가져옵니다.\n",
    "    for elt in mask:  # mask된 인덱스 즉, 종료된 세션의 인덱스를 돌면서\n",
    "        hidden_states[elt, :] = 0  # parameter를 초기화 합니다.\n",
    "    gru_layer.reset_states(states=hidden_states)\n",
    "\n",
    "\n",
    "def get_metrics(data, model, args, k: int):  # valid셋과 test셋을 평가하는 코드입니다. \n",
    "                                             # train과 거의 같지만 mrr, recall을 구하는 라인이 있습니다.\n",
    "    dataset = SessionDataset(data)\n",
    "    loader = SessionDataLoader(dataset, batch_size=args.batch_size)\n",
    "    recall_list, mrr_list = [], []\n",
    "\n",
    "    total_step = len(data) - data['UserId'].nunique()\n",
    "    for inputs, label, mask in tqdm(loader, total=total_step // args.batch_size, desc='Evaluation', mininterval=1):\n",
    "        reset_hidden_states(model, mask)\n",
    "        input_ohe = to_categorical(inputs, num_classes=args.num_items)\n",
    "        input_ohe = np.expand_dims(input_ohe, axis=1)\n",
    "\n",
    "        pred = model.predict(input_ohe, batch_size=args.batch_size)\n",
    "        pred_arg = tf.argsort(pred, direction='DESCENDING')  # softmax 값이 큰 순서대로 sorting 합니다.\n",
    "\n",
    "        length = len(inputs)\n",
    "        recall_list.extend([recall_k(pred_arg[i], label[i], k) for i in range(length)])\n",
    "        mrr_list.extend([mrr_k(pred_arg[i], label[i], k) for i in range(length)])\n",
    "\n",
    "    recall, mrr = np.mean(recall_list), np.mean(mrr_list)\n",
    "    return recall, mrr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "indian-saying",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:19<00:03, 44.60it/s, accuracy=0, train_loss=6.06]      \n",
      "Evaluation:  47%|████▋     | 34/73 [00:27<00:31,  1.25it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0391, train_loss=5.95] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 1: 0.093290\n",
      "\t - MRR@20    epoch 1: 0.023686\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:20<00:03, 44.26it/s, accuracy=0.0391, train_loss=5.71] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.28it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0469, train_loss=5.61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 2: 0.118566\n",
      "\t - MRR@20    epoch 2: 0.034174\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:19<00:03, 44.41it/s, accuracy=0.0234, train_loss=5.56] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 3: 0.125460\n",
      "\t - MRR@20    epoch 3: 0.036846\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 학습 시간이 다소 오래 소요됩니다. (예상시간 1시간)\n",
    "train_model(model, args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acting-sewing",
   "metadata": {},
   "source": [
    "### Step 5. 모델 테스트\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "developmental-bosnia",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation:  37%|███▋      | 20/54 [00:15<00:26,  1.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20: 0.135547\n",
      "\t - MRR@20: 0.037852\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def test_model(model, args, test):\n",
    "    test_recall, test_mrr = get_metrics(test, model, args, 20)\n",
    "    print(f\"\\t - Recall@{args.k}: {test_recall:3f}\")\n",
    "    print(f\"\\t - MRR@{args.k}: {test_mrr:3f}\\n\")\n",
    "\n",
    "test_model(model, args, test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "animal-raising",
   "metadata": {},
   "source": [
    "### Step 6.모델 개선\n",
    "\n",
    "1. 학습률 변경\n",
    "\n",
    "2. epochs변경\n",
    "\n",
    "3. drop_rate 변경"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intense-referral",
   "metadata": {},
   "source": [
    "1. 학습률 변경 (10배씩증가)\n",
    "\n",
    "- Ir = 0.01\n",
    "\n",
    "- Ir = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "abstract-straight",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:17<00:02, 45.15it/s, accuracy=0.0547, train_loss=5.57] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.109, train_loss=5.33] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 1: 0.129136\n",
      "\t - MRR@20    epoch 1: 0.037630\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:15<00:02, 46.03it/s, accuracy=0.0391, train_loss=5.41] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0547, train_loss=5.69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 2: 0.133042\n",
      "\t - MRR@20    epoch 2: 0.039020\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:15<00:02, 45.84it/s, accuracy=0.0469, train_loss=5.38] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 3: 0.133961\n",
      "\t - MRR@20    epoch 3: 0.039969\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "args = Args(tr, val, test, batch_size=128, hsz=50, drop_rate=0.1, lr=0.05, epochs=3, k=20)\n",
    "train_model(model, args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "alert-oxygen",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation:  37%|███▋      | 20/54 [00:15<00:26,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20: 0.147656\n",
      "\t - MRR@20: 0.044058\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "test_model(model, args, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "given-insert",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:15<00:02, 45.96it/s, accuracy=0.0547, train_loss=5.4]  \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0547, train_loss=5.28]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 1: 0.136029\n",
      "\t - MRR@20    epoch 1: 0.040405\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:14<00:02, 46.04it/s, accuracy=0.0781, train_loss=5.39] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0781, train_loss=5.3] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 2: 0.137868\n",
      "\t - MRR@20    epoch 2: 0.041125\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:18<00:02, 44.72it/s, accuracy=0.0547, train_loss=5.37] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 3: 0.138097\n",
      "\t - MRR@20    epoch 3: 0.040445\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "args = Args(tr, val, test, batch_size=128, hsz=50, drop_rate=0.1, lr=0.1, epochs=3, k=20)\n",
    "train_model(model, args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "religious-bosnia",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation:  37%|███▋      | 20/54 [00:15<00:25,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20: 0.149609\n",
      "\t - MRR@20: 0.046093\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "test_model(model, args, test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "special-family",
   "metadata": {},
   "source": [
    "\n",
    "-  cs231n에서 배운 바로 하이퍼파라미터 조정할 시 가장 먼저 하는 것은 학습률을 본다고 했기 때문에 가장 먼저 바꾸었지만 눈에 띄게 큰 상향을 보진 못했습니다.\n",
    "\n",
    "- 정확도와 매트리스 그리고 수치가 전보다 좋지 않게 나왔다\n",
    "\n",
    "- 배치사이즈  너무 크면 안돌아가고 작으면 텐서가 맞지 않는다고 나온다.\n",
    "\n",
    "-  위에 시도한 것보다 약간 나아졌다.\n",
    "\n",
    "-  학습률 0.001, 0.01, 0.1을 비교해봤을 때 0.1의 수치가 가장 좋음. 학습률 증가 시 이득은 있다는 것을 알 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "angry-ambassador",
   "metadata": {},
   "source": [
    "2. epoch 변경\n",
    "\n",
    "- epochs = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "occupational-tampa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:20<00:03, 44.15it/s, accuracy=0.0625, train_loss=5.34] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0781, train_loss=5.25]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 1: 0.139476\n",
      "\t - MRR@20    epoch 1: 0.041073\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:21<00:03, 43.93it/s, accuracy=0.0625, train_loss=5.33] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0859, train_loss=5.23] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 2: 0.141774\n",
      "\t - MRR@20    epoch 2: 0.040890\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:21<00:03, 43.90it/s, accuracy=0.0703, train_loss=5.33] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.29it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0469, train_loss=5.5] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 3: 0.141544\n",
      "\t - MRR@20    epoch 3: 0.042072\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:20<00:03, 44.27it/s, accuracy=0.0625, train_loss=5.22] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0703, train_loss=5.22]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 4: 0.137638\n",
      "\t - MRR@20    epoch 4: 0.042069\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:19<00:03, 44.45it/s, accuracy=0.0781, train_loss=5.3]  \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0938, train_loss=5.25]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 5: 0.140395\n",
      "\t - MRR@20    epoch 5: 0.041158\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:21<00:03, 43.86it/s, accuracy=0.0547, train_loss=5.21] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.109, train_loss=5.24]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 6: 0.139706\n",
      "\t - MRR@20    epoch 6: 0.040527\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:17<00:02, 45.09it/s, accuracy=0.0547, train_loss=5.26] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0781, train_loss=5.5]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 7: 0.140395\n",
      "\t - MRR@20    epoch 7: 0.040854\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:20<00:03, 44.16it/s, accuracy=0.0625, train_loss=5.23] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.117, train_loss=5.17] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 8: 0.140855\n",
      "\t - MRR@20    epoch 8: 0.040297\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:19<00:03, 44.40it/s, accuracy=0.0625, train_loss=5.25] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0547, train_loss=5.5] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 9: 0.142233\n",
      "\t - MRR@20    epoch 9: 0.041352\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:19<00:03, 44.41it/s, accuracy=0.0703, train_loss=5.22] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0938, train_loss=5.25]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 10: 0.139936\n",
      "\t - MRR@20    epoch 10: 0.041126\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:21<00:03, 43.93it/s, accuracy=0.0703, train_loss=5.15] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0781, train_loss=5.19] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 11: 0.141314\n",
      "\t - MRR@20    epoch 11: 0.040201\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:21<00:03, 44.05it/s, accuracy=0.0625, train_loss=5.25] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.31it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.109, train_loss=5.22] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 12: 0.140395\n",
      "\t - MRR@20    epoch 12: 0.040900\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:21<00:03, 43.98it/s, accuracy=0.0703, train_loss=5.15] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:27<00:31,  1.23it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0938, train_loss=5.19] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 13: 0.140165\n",
      "\t - MRR@20    epoch 13: 0.040655\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:22<00:03, 43.50it/s, accuracy=0.0703, train_loss=5.14] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0938, train_loss=5.23] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 14: 0.141085\n",
      "\t - MRR@20    epoch 14: 0.040604\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:22<00:03, 43.64it/s, accuracy=0.0469, train_loss=5.18] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:30,  1.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 15: 0.141314\n",
      "\t - MRR@20    epoch 15: 0.040241\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "args = Args(tr, val, test, batch_size=128, hsz=50, drop_rate=0.1, lr=0.1, epochs=15, k=20)\n",
    "train_model(model, args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "meaning-orchestra",
   "metadata": {},
   "source": [
    "epoch을 늘려도 성능 향상 크게 되지 않은 것을 보아 성능에 큰 영향을 주지는 못하는 것 같습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "satellite-stadium",
   "metadata": {},
   "source": [
    "3. drop_rate 변경\n",
    "\n",
    "- drop_rate = 0.5\n",
    "\n",
    "- drop_rate = 0.9\n",
    "\n",
    "- drop_rate = 1.5\n",
    "\n",
    "- drop_rate = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "flying-forest",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:23<00:03, 43.36it/s, accuracy=0.0703, train_loss=5.13] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.31it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0938, train_loss=5.2]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 1: 0.139936\n",
      "\t - MRR@20    epoch 1: 0.040907\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:20<00:03, 44.31it/s, accuracy=0.0703, train_loss=5.16] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.109, train_loss=5.23] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 2: 0.139476\n",
      "\t - MRR@20    epoch 2: 0.041133\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:20<00:03, 44.34it/s, accuracy=0.0703, train_loss=5.24] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 3: 0.140395\n",
      "\t - MRR@20    epoch 3: 0.041048\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "args = Args(tr, val, test, batch_size=128, hsz=50, drop_rate=0.5, lr=0.1, epochs=3, k=20)\n",
    "train_model(model, args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "hourly-thousand",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation:  37%|███▋      | 20/54 [00:15<00:26,  1.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20: 0.142578\n",
      "\t - MRR@20: 0.042832\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "test_model(model, args, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "close-bunch",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:23<00:03, 43.41it/s, accuracy=0.0703, train_loss=5.16] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.102, train_loss=5.22]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 1: 0.141544\n",
      "\t - MRR@20    epoch 1: 0.041142\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:20<00:03, 44.25it/s, accuracy=0.0859, train_loss=5.21] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0781, train_loss=5.23]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 2: 0.139476\n",
      "\t - MRR@20    epoch 2: 0.040483\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:18<00:02, 44.98it/s, accuracy=0.0391, train_loss=5.16] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 3: 0.139936\n",
      "\t - MRR@20    epoch 3: 0.040962\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "args = Args(tr, val, test, batch_size=128, hsz=50, drop_rate=0.9, lr=0.1, epochs=3, k=20)\n",
    "train_model(model, args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "tropical-bonus",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation:  37%|███▋      | 20/54 [00:15<00:25,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20: 0.147266\n",
      "\t - MRR@20: 0.042663\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "test_model(model, args, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "sustainable-replacement",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:14<00:02, 46.05it/s, accuracy=0.0547, train_loss=5.27] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.109, train_loss=5.17] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 1: 0.140625\n",
      "\t - MRR@20    epoch 1: 0.041108\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:22<00:03, 43.53it/s, accuracy=0.0859, train_loss=5.19] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0859, train_loss=5.23]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 2: 0.140855\n",
      "\t - MRR@20    epoch 2: 0.041304\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:20<00:03, 44.22it/s, accuracy=0.0625, train_loss=5.2]  \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 3: 0.139936\n",
      "\t - MRR@20    epoch 3: 0.041557\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "args = Args(tr, val, test, batch_size=128, hsz=50, drop_rate=1.5, lr=0.1, epochs=3, k=20)\n",
    "train_model(model, args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "extensive-vancouver",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation:  37%|███▋      | 20/54 [00:15<00:25,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20: 0.145313\n",
      "\t - MRR@20: 0.042283\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "test_model(model, args, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "certified-boring",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:19<00:03, 44.43it/s, accuracy=0.0469, train_loss=5.16] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0938, train_loss=5.45] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 1: 0.140855\n",
      "\t - MRR@20    epoch 1: 0.041166\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:21<00:03, 43.99it/s, accuracy=0.0547, train_loss=5.19] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.30it/s]\n",
      "Train:   0%|          | 0/6349 [00:00<?, ?it/s, accuracy=0.0859, train_loss=5.45] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 2: 0.139936\n",
      "\t - MRR@20    epoch 2: 0.040666\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train:  98%|█████████▊| 6215/6349 [02:20<00:03, 44.38it/s, accuracy=0.0547, train_loss=5.19] \n",
      "Evaluation:  47%|████▋     | 34/73 [00:26<00:29,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20 epoch 3: 0.137408\n",
      "\t - MRR@20    epoch 3: 0.040590\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "args = Args(tr, val, test, batch_size=128, hsz=50, drop_rate=0, lr=0.1, epochs=3, k=20)\n",
    "train_model(model, args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "differential-vision",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation:  37%|███▋      | 20/54 [00:15<00:26,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t - Recall@20: 0.145313\n",
      "\t - MRR@20: 0.042264\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "test_model(model, args, test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ultimate-concern",
   "metadata": {},
   "source": [
    "- 드롭아웃 여부가 크게 반영이 된 것 같지 않지만 그 이유는 epoch수가 너무 적어서 인 것 같습니다.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "built-welding",
   "metadata": {},
   "source": [
    "# 프로젝트 회고\n",
    "\n",
    "- 학습을 위해서 데이터를 나눠야해서 split_by_date를 여러 번 수정을 했습니다. 근데 경우에 따라 인덱스가 축 방향을 벗어났다는 에러가 떴습니다.\n",
    "\n",
    "- 구글링 및 질문을  통해서 에러가 떴던 이유는 배치사이즈를 조절하지 않았다는 것을 알게 되어서 수정해보니 더이상 에러가 뜨지 않았습니다.\n",
    "\n",
    "- 그래서, 제가 배운 것은 배치사이즈가 크면 인덱스가 축 범위를 넘어갈 수 있다 입니다.\n",
    "\n",
    "- 반대로 너무 작다면 사이즈가 맞지 않게 됩니다.\n",
    "\n",
    "- 사실 프로젝트는 했지만 Session-based recommendation개념은 여전히 이해가 잘 안 된 것 같습니다. 왜냐하면, 실제 이론적인 부분을 증명하는 코드를 이용했을 때 코드 자체가 이해가 안된 부분이 많았기 때문입니다.\n",
    "\n",
    "- 그래서, 어느 부분을 수정하고 왜 실행이 안된거지 에러 코드를 이해하는 것과 수정하는 것이 어렵습니다.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "electric-absolute",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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