{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a136c37d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['일자', '계(명)', '국내발생(명)', '해외유입(명)', '사망', 'Unnamed: 5', 'Unnamed: 6',\n",
      "       'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# !pip install keras\n",
    "# !pip install tensorflow\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.family'] = 'Malgun Gothic'\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")\n",
    "\n",
    "fdir = 'C:/Users/windy/Desktop/수업관련/비교과/'\n",
    "covid = pd.read_csv(fdir+'코로나바이러스감염증-19_확진환자_발생현황_230904_최종v2.csv', encoding='utf-8')\n",
    "print(covid.columns)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2450198e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = covid['국내발생(명)'][1:]\n",
    "y2 = covid['해외유입(명)'][1:]\n",
    "x = covid['일자'][1:]\n",
    "\n",
    "y1 = [x.encode('utf-8') if isinstance(x, str) else x for x in y1]\n",
    "y2 = [x.encode('utf-8') if isinstance(x, str) else x for x in y2]\n",
    "\n",
    "\n",
    "import decimal\n",
    "import datetime\n",
    "plt.rcParams['axes.labelsize'] = 15\n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rcParams['font.weight']='bold'\n",
    "plt.rcParams['font.size'] = 15\n",
    "\n",
    "xval, yval1, yval2 = [], [], []\n",
    "for i in range(len(x)):    \n",
    "    tmp=x[i+1].split('-')\n",
    "    xval.append(datetime.datetime(int(tmp[0]),int(tmp[1]),int(tmp[2])))    \n",
    "    ytmp1 = str(y1[i], 'utf-8')\n",
    "    ytmp1= ytmp1.replace(\",\",\"\")\n",
    "    yval1.append(int(ytmp1))\n",
    "\n",
    "    ytmp2= str(y2[i],'utf-8')  \n",
    "    ytmp2= ytmp2.replace(\",\",\"\")    \n",
    "    if ytmp2=='-':\n",
    "        yval2.append(0)\n",
    "    else:\n",
    "        yval2.append(int(ytmp2))\n",
    "\n",
    "xval = np.flip(xval)\n",
    "import matplotlib.dates as mdates\n",
    "fig, ax1 = plt.subplots(figsize=(9,6))\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(xval, yval1, 'g-')\n",
    "ax2.plot(xval, yval2, 'b-')\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# ax1.set_xlabel('X data')\n",
    "ax1.set_ylabel('Confirmed new cases originated in Korea ', color='g')\n",
    "ax2.set_ylabel('Confirmed new cases coming from abroad', color='b')\n",
    "dateFmt = mdates.DateFormatter('%Y-%m-%d')\n",
    "# # 3. x축 레이블을 포맷팅한다.\n",
    "ax1.xaxis.set_major_formatter(dateFmt)\n",
    "plt.xticks(rotation=45)\n",
    "ax1.tick_params(axis='x', labelrotation=45)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85425341",
   "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>국내발생(명)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.081248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.068563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.069233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.021173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.053078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1320 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       국내발생(명)\n",
       "0     0.081248\n",
       "1     0.068563\n",
       "2     0.069233\n",
       "3     0.021173\n",
       "4     0.053078\n",
       "...        ...\n",
       "1315  0.000000\n",
       "1316  0.000000\n",
       "1317  0.000000\n",
       "1318  0.000000\n",
       "1319  0.000000\n",
       "\n",
       "[1320 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "max_infected=np.max(yval1)\n",
    "\n",
    "covid.sort_index(ascending=False).reset_index(drop=True)\n",
    "scaler = MinMaxScaler()\n",
    "scale_cols = ['국내발생(명)']\n",
    "df_scaled = scaler.fit_transform(np.flip(np.array(yval1).reshape(-1,1)))\n",
    "df_scaled = np.nan_to_num(df_scaled, copy=False)\n",
    "df_scaled = pd.DataFrame(df_scaled)\n",
    "df_scaled.columns = scale_cols\n",
    "df_scaled\n",
    "# pd.set_option('display.max_rows', None)\n",
    "# print (covid['서울시 추가 확진'])\n",
    "# print (df_scaled['서울시 추가 확진'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "45442827",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       국내발생(명)\n",
      "0     0.081248\n",
      "1     0.068563\n",
      "2     0.069233\n",
      "3     0.021173\n",
      "4     0.053078\n",
      "...        ...\n",
      "1215  0.000005\n",
      "1216  0.000000\n",
      "1217  0.000002\n",
      "1218  0.000000\n",
      "1219  0.000006\n",
      "\n",
      "[1220 rows x 1 columns]\n",
      "       국내발생(명)\n",
      "1220  0.000003\n",
      "1221  0.000005\n",
      "1222  0.000002\n",
      "1223  0.000008\n",
      "1224  0.000006\n",
      "...        ...\n",
      "1315  0.000000\n",
      "1316  0.000000\n",
      "1317  0.000000\n",
      "1318  0.000000\n",
      "1319  0.000000\n",
      "\n",
      "[100 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "def make_dataset(data, label, window_size):\n",
    "    feature_list =[]\n",
    "    label_list = []\n",
    "    for i in range(len(data)-window_size):\n",
    "        feature_list.append(np.array(data.iloc[i:i+window_size]))\n",
    "        label_list.append(np.array(label.iloc[i+window_size]))\n",
    "    return np.array(feature_list), np.array(label_list)\n",
    "\n",
    "TEST_SIZE = 100\n",
    "WINDOW_SIZE = 100\n",
    "\n",
    "train = df_scaled[:-TEST_SIZE]\n",
    "test = df_scaled[-TEST_SIZE:]\n",
    "\n",
    "print (train)\n",
    "print (test)\n",
    "feature_cols = ['국내발생(명)']\n",
    "label_cols = ['국내발생(명)']\n",
    "train_feature = train[feature_cols]\n",
    "train_label = train[label_cols]\n",
    "train_feature, train_label = make_dataset(train_feature, train_label, WINDOW_SIZE)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(train_feature, train_label, test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "11307625",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ lstm_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">66,560</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ lstm_9 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │          \u001b[38;5;34m66,560\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m129\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">66,689</span> (260.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m66,689\u001b[0m (260.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">66,689</span> (260.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m66,689\u001b[0m (260.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m895/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 373616.1250\n",
      "Epoch 1: val_loss improved from inf to 0.00226, saving model to ./tmp_checkpoint.keras\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 35ms/step - loss: 373408.0625 - val_loss: 0.0023\n",
      "Epoch 2/20\n",
      "\u001b[1m895/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 0.0021\n",
      "Epoch 2: val_loss improved from 0.00226 to 0.00166, saving model to ./tmp_checkpoint.keras\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 35ms/step - loss: 0.0021 - val_loss: 0.0017\n",
      "Epoch 3/20\n",
      "\u001b[1m895/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 7.7430e-04\n",
      "Epoch 3: val_loss improved from 0.00166 to 0.00145, saving model to ./tmp_checkpoint.keras\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 34ms/step - loss: 7.7493e-04 - val_loss: 0.0015\n",
      "Epoch 4/20\n",
      "\u001b[1m895/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 8.4666e-04\n",
      "Epoch 4: val_loss did not improve from 0.00145\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 33ms/step - loss: 8.4688e-04 - val_loss: 0.0015\n",
      "Epoch 5/20\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 0.0013\n",
      "Epoch 5: val_loss did not improve from 0.00145\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 33ms/step - loss: 0.0013 - val_loss: 0.0015\n",
      "Epoch 6/20\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 7.6661e-04\n",
      "Epoch 6: val_loss did not improve from 0.00145\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 33ms/step - loss: 7.6679e-04 - val_loss: 0.0020\n",
      "Epoch 7/20\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 9.7312e-04\n",
      "Epoch 7: val_loss improved from 0.00145 to 0.00144, saving model to ./tmp_checkpoint.keras\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 33ms/step - loss: 9.7318e-04 - val_loss: 0.0014\n",
      "Epoch 8/20\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 8.6334e-04\n",
      "Epoch 8: val_loss did not improve from 0.00144\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 35ms/step - loss: 8.6338e-04 - val_loss: 0.0015\n",
      "Epoch 9/20\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.8432e-04\n",
      "Epoch 9: val_loss improved from 0.00144 to 0.00141, saving model to ./tmp_checkpoint.keras\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 36ms/step - loss: 8.8444e-04 - val_loss: 0.0014\n",
      "Epoch 10/20\n",
      "\u001b[1m895/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.0093e-04\n",
      "Epoch 10: val_loss did not improve from 0.00141\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 38ms/step - loss: 7.0143e-04 - val_loss: 0.0017\n",
      "Epoch 11/20\n",
      "\u001b[1m895/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 7.9906e-04\n",
      "Epoch 11: val_loss did not improve from 0.00141\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 36ms/step - loss: 7.9929e-04 - val_loss: 0.0018\n",
      "Epoch 12/20\n",
      "\u001b[1m895/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 6.5006e-04\n",
      "Epoch 12: val_loss did not improve from 0.00141\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 33ms/step - loss: 6.5069e-04 - val_loss: 0.0018\n",
      "Epoch 13/20\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 5.6750e-04\n",
      "Epoch 13: val_loss did not improve from 0.00141\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 34ms/step - loss: 5.6782e-04 - val_loss: 0.0020\n",
      "Epoch 14/20\n",
      "\u001b[1m895/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.7726e-04\n",
      "Epoch 14: val_loss improved from 0.00141 to 0.00138, saving model to ./tmp_checkpoint.keras\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 37ms/step - loss: 8.7733e-04 - val_loss: 0.0014\n",
      "Epoch 15/20\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.0040e-04\n",
      "Epoch 15: val_loss did not improve from 0.00138\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 36ms/step - loss: 8.0051e-04 - val_loss: 0.0019\n",
      "Epoch 16/20\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 6.3967e-04\n",
      "Epoch 16: val_loss did not improve from 0.00138\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 37ms/step - loss: 6.4001e-04 - val_loss: 0.0015\n",
      "Epoch 17/20\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 6.2941e-04\n",
      "Epoch 17: val_loss did not improve from 0.00138\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 37ms/step - loss: 6.2965e-04 - val_loss: 0.0014\n",
      "Epoch 18/20\n",
      "\u001b[1m895/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 7.5605e-04\n",
      "Epoch 18: val_loss did not improve from 0.00138\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 37ms/step - loss: 7.5647e-04 - val_loss: 0.0018\n",
      "Epoch 19/20\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 8.3673e-04\n",
      "Epoch 19: val_loss did not improve from 0.00138\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 37ms/step - loss: 8.3672e-04 - val_loss: 0.0015\n",
      "Epoch 20/20\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 9.4866e-04\n",
      "Epoch 20: val_loss improved from 0.00138 to 0.00137, saving model to ./tmp_checkpoint.keras\n",
      "\u001b[1m896/896\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 35ms/step - loss: 9.4857e-04 - val_loss: 0.0014\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.layers import LSTM\n",
    "import os\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(128,\n",
    "              input_shape=(train_feature.shape[1],train_feature.shape[2]),\n",
    "              activation='relu',\n",
    "              return_sequences = False)\n",
    "         )\n",
    "model.add(Dense(1))\n",
    "model.summary()\n",
    "\n",
    "# model.add(LSTM(128,                  # 유닛 수\n",
    "#                activation='relu',\n",
    "#                return_sequences=True,\n",
    "#                input_shape=(train_feature.shape[1], train_feature.shape[2])))\n",
    "# model.add(LSTM(64,\n",
    "#                activation='relu',\n",
    "#                return_sequences=False))   # 마지막 LSTM이면 False(또는 생략)\n",
    "# model.add(Dense(32, activation='relu'))\n",
    "# model.add(Dense(1))\n",
    "# model.summary()\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "early_stop = EarlyStopping(monitor='val_loss', patience=10)\n",
    "model_path='./'\n",
    "filename=os.path.join(model_path, 'tmp_checkpoint.keras')\n",
    "checkpoint = ModelCheckpoint(filename, monitor='val_loss', verbose=1, save_best_only =True, mode='auto')\n",
    "\n",
    "history = model.fit(x_train,y_train,\n",
    "                   epochs=20,\n",
    "                   batch_size=1,\n",
    "                   validation_data=(x_valid, y_valid),\n",
    "                   callbacks=[early_stop,checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8225939a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print (history.history)\n",
    "# plt.figure()\n",
    "# plt.plot(history.history['accuracy'])\n",
    "# plt.plot(history.history['val_accuracy'])\n",
    "# plt.title('model accuracy')\n",
    "# plt.ylabel('accuracy')\n",
    "# plt.xlabel('epoch')\n",
    "# plt.legend(['train', 'val'], loc='upper left')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.ylim(0,0.006)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "51ed6807",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       국내발생(명)\n",
      "1220  0.000003\n",
      "1221  0.000005\n",
      "1222  0.000002\n",
      "1223  0.000008\n",
      "1224  0.000006\n",
      "...        ...\n",
      "1315  0.000000\n",
      "1316  0.000000\n",
      "1317  0.000000\n",
      "1318  0.000000\n",
      "1319  0.000000\n",
      "\n",
      "[100 rows x 1 columns]\n",
      "\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 54ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = 1\n",
    "end = len(df_scaled)\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "new_model = tf.keras.models.load_model('tmp_checkpoint.keras')\n",
    "p_data = df_scaled[start:end]\n",
    "X_tmp = p_data[feature_cols]\n",
    "Y_tmp = p_data[label_cols]\n",
    "X_test, Y_test = make_dataset(X_tmp, Y_tmp, WINDOW_SIZE)\n",
    "\n",
    "print (X_tmp[-100:])\n",
    "yp = new_model.predict(X_test)\n",
    "# yp = new_model.predict(X_tmp[-100:])\n",
    "numdays=len(yp)\n",
    "\n",
    "xx = [xval[-start] + datetime.timedelta(days=x) for x in range(len(X_test[:,0]))]\n",
    "# xx2 = [xval[start] - datetime.timedelta(days=WINDOW_SIZE) + datetime.timedelta(days=x) for x in range(numdays)]\n",
    "xx2 = [xval[-start] + datetime.timedelta(days=WINDOW_SIZE) + datetime.timedelta(days=x) for x in range(numdays)]\n",
    "\n",
    "real_data = yval1[:100]\n",
    "xx3 = [xx[-1] + datetime.timedelta(days=x) for x in range(len(real_data))]\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.plot(xx,X_test[:,0]*max_infected, color = 'b', label='Original')\n",
    "plt.plot(xx2,np.abs(yp)*max_infected, color='r', label='Prediction')\n",
    "plt.plot(xx3,real_data, color='g', label='Hidden')\n",
    "plt.legend(loc=0)\n",
    "# plt.xlabel('days')\n",
    "plt.ylabel('Confirmed cases')\n",
    "plt.title('Validation Results')\n",
    "# plt.xlim(xx[-1]-datetime.timedelta(days=WINDOW_SIZE),xx[-1]+ datetime.timedelta(days=WINDOW_SIZE))\n",
    "# plt.ylim(0,2000)\n",
    "plt.tick_params(axis='x', labelrotation=45)\n",
    "plt.show()\n",
    "        \n",
    "# train_label = train[label_cols]\n",
    "# plt.figure()\n",
    "# plt.plot(train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2339c4ee-4df2-4581-936d-e44289e6b38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.plot(xx,X_test[:,0]*max_infected, color = 'b', label='Original')\n",
    "plt.plot(xx2,np.abs(yp-np.min(yp))*max_infected, color='r', label='Prediction')\n",
    "plt.plot(xx3,real_data, color='g', label='Hidden')\n",
    "plt.legend(loc=0)\n",
    "plt.ylabel('Confirmed cases')\n",
    "plt.title('Validation Results')\n",
    "plt.xlim(xx[-1]-datetime.timedelta(days=WINDOW_SIZE),xx[-1]+ datetime.timedelta(days=WINDOW_SIZE))\n",
    "plt.ylim(0,2000)\n",
    "plt.tick_params(axis='x', labelrotation=45)\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8e02d8a-d242-4b26-a913-80c923b33d6d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}