COVID19_Timeseries_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COVID-19 대한민국 해외 유입 확진자 예측 모델 : LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "UCsQrKfDxdsL",
    "outputId": "e705daae-a726-48a2-bbb3-539bcd92c75e"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "from torch import nn, optim\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
    "\n",
    "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#93D30C\", \"#8F00FF\"]\n",
    "\n",
    "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
    "\n",
    "rcParams['figure.figsize'] = 14, 10\n",
    "register_matplotlib_converters()\n",
    "\n",
    "RANDOM_SEED = 42\n",
    "np.random.seed(RANDOM_SEED)\n",
    "torch.manual_seed(RANDOM_SEED)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from matplotlib import font_manager, rc\n",
    "font_name = font_manager.FontProperties(fname=\"c:/Windows/Fonts/malgun.ttf\").get_name()\n",
    "rc('font', family=font_name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HVemqBmdxsAF"
   },
   "source": [
    "# 1. Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O5uBqe3VFeO2"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/final_0509.csv')\n",
    "# 한국 일일확진자 변수 제거\n",
    "df.drop(['Unnamed: 0'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## lag 변수 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Date = pd.to_datetime(df.Date)\n",
    "#df['dayofweek'] = df['Date'].dt.dayofweek\n",
    "df.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "lag_col= list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "lag_amount = 3\n",
    "\n",
    "for col in lag_col:\n",
    "    for i in range(lag_amount):\n",
    "        df['{0}_lag{1}'.format(col,i+1)] = df['{}'.format(col)].shift(i+1)\n",
    "    \n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\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>MM_conf</th>\n",
       "      <th>LA_conf</th>\n",
       "      <th>NL_conf</th>\n",
       "      <th>PH_conf</th>\n",
       "      <th>RU_conf</th>\n",
       "      <th>KR</th>\n",
       "      <th>TW_conf</th>\n",
       "      <th>US_conf</th>\n",
       "      <th>AE_conf</th>\n",
       "      <th>GB_conf</th>\n",
       "      <th>...</th>\n",
       "      <th>news_lag3</th>\n",
       "      <th>covid_tr_lag1</th>\n",
       "      <th>covid_tr_lag2</th>\n",
       "      <th>covid_tr_lag3</th>\n",
       "      <th>coro_tr_lag1</th>\n",
       "      <th>coro_tr_lag2</th>\n",
       "      <th>coro_tr_lag3</th>\n",
       "      <th>target_lag1</th>\n",
       "      <th>target_lag2</th>\n",
       "      <th>target_lag3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>476</td>\n",
       "      <td>284</td>\n",
       "      <td>7933</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>34037</td>\n",
       "      <td>557</td>\n",
       "      <td>6204</td>\n",
       "      <td>...</td>\n",
       "      <td>1763.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>445</td>\n",
       "      <td>156</td>\n",
       "      <td>9623</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>29078</td>\n",
       "      <td>561</td>\n",
       "      <td>4815</td>\n",
       "      <td>...</td>\n",
       "      <td>1117.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-03</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>295</td>\n",
       "      <td>10633</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>25501</td>\n",
       "      <td>564</td>\n",
       "      <td>4342</td>\n",
       "      <td>...</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-04</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "      <td>262</td>\n",
       "      <td>10581</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>22335</td>\n",
       "      <td>567</td>\n",
       "      <td>3990</td>\n",
       "      <td>...</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>318</td>\n",
       "      <td>199</td>\n",
       "      <td>10102</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23976</td>\n",
       "      <td>462</td>\n",
       "      <td>4411</td>\n",
       "      <td>...</td>\n",
       "      <td>732.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 332 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            MM_conf  LA_conf  NL_conf  PH_conf  RU_conf  KR  TW_conf  US_conf  \\\n",
       "Date                                                                            \n",
       "2020-05-01        0        0      476      284     7933   6        0    34037   \n",
       "2020-05-02        0        0      445      156     9623  13        3    29078   \n",
       "2020-05-03        4        0      335      295    10633   8        4    25501   \n",
       "2020-05-04        6        0      199      262    10581   3        2    22335   \n",
       "2020-05-05        0        0      318      199    10102   2        0    23976   \n",
       "\n",
       "            AE_conf  GB_conf  ...  news_lag3  covid_tr_lag1  covid_tr_lag2  \\\n",
       "Date                          ...                                            \n",
       "2020-05-01      557     6204  ...     1763.0           43.0           63.0   \n",
       "2020-05-02      561     4815  ...     1117.0           48.0           43.0   \n",
       "2020-05-03      564     4342  ...     1266.0           43.0           48.0   \n",
       "2020-05-04      567     3990  ...     1104.0           55.0           43.0   \n",
       "2020-05-05      462     4411  ...      732.0           30.0           55.0   \n",
       "\n",
       "            covid_tr_lag3  coro_tr_lag1  coro_tr_lag2  coro_tr_lag3  \\\n",
       "Date                                                                  \n",
       "2020-05-01           59.0          18.0          18.0          18.0   \n",
       "2020-05-02           63.0          18.0          18.0          18.0   \n",
       "2020-05-03           43.0          18.0          18.0          18.0   \n",
       "2020-05-04           48.0          18.0          18.0          18.0   \n",
       "2020-05-05           43.0          17.0          18.0          18.0   \n",
       "\n",
       "            target_lag1  target_lag2  target_lag3  \n",
       "Date                                               \n",
       "2020-05-01          4.0          5.0         12.0  \n",
       "2020-05-02          8.0          4.0          5.0  \n",
       "2020-05-03          8.0          8.0          4.0  \n",
       "2020-05-04         10.0          8.0          8.0  \n",
       "2020-05-05          8.0         10.0          8.0  \n",
       "\n",
       "[5 rows x 332 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BuOyxbF4x9lN"
   },
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 305
    },
    "colab_type": "code",
    "id": "uwmrhvJvy9hM",
    "outputId": "1afb84b0-98ee-49ca-f068-82226680a724"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결측치 확인\n",
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "전체 데이터 shape: (102, 332)\n",
      "target feature shape: (102,)\n"
     ]
    }
   ],
   "source": [
    "# 데이터 프레임 형태 확인\n",
    "print(\"전체 데이터 shape: {}\".format(df.shape))\n",
    "print(\"target feature shape: {}\".format(df['target'].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "colab_type": "code",
    "id": "3m5ixBmvzKiH",
    "outputId": "6da353a9-1fb2-4aef-b54b-e97b94a7ab17"
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 381,
       "width": 1433
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,5))\n",
    "plt.plot(df['target'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Oversea Inflow Cofirmed\")\n",
    "plt.grid(axis='x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cols = list(df.columns)\n",
    "X_cols.remove('target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 테스트 데이터 수\n",
    "test_data_size = 14\n",
    "# X변수들과 y변수 구분\n",
    "X = df[X_cols]\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MinMaxScaler을 통한 스케일링\n",
    "scaler = MinMaxScaler()\n",
    "# X scaler 생성\n",
    "Xscaler = scaler.fit(X)\n",
    "# Y scaler 생성\n",
    "yscaler = scaler.fit(y.values.reshape(-1,1))\n",
    "\n",
    "# 스케일링 적용\n",
    "X = Xscaler.transform(X)\n",
    "y = yscaler.transform(y.values.reshape(-1,1))\n",
    "\n",
    "# Train, Test set split\n",
    "X_train, X_test = X[:-test_data_size], X[-test_data_size:]\n",
    "y_train, y_test = y[:-test_data_size].flatten(), y[-test_data_size:].flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set :  (88, 331)\n",
      "test set :  (14, 331)\n"
     ]
    }
   ],
   "source": [
    "print(\"train set : \", X_train.shape)\n",
    "print(\"test set : \", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LSTM 사용을 위한 시퀀스 데이터 형성 함수\n",
    "\n",
    "def create_sequences1(array, seq_length):\n",
    "    res = []\n",
    "    if seq_length == 1:\n",
    "        for i in range(len(array)):\n",
    "            tmp=array[i:(i+seq_length)]\n",
    "            res.append(tmp)\n",
    "    else:\n",
    "        for i in range(len(array)-seq_length-1):\n",
    "            tmp = array[i:(i+seq_length)]\n",
    "            res.append(tmp)\n",
    "        \n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5TEKufKA0c_Z"
   },
   "outputs": [],
   "source": [
    "# 시퀀스 길이\n",
    "seq_length = 1\n",
    "\n",
    "X_train = create_sequences1(X_train, seq_length)\n",
    "y_train = create_sequences1(y_train, seq_length)\n",
    "X_test = create_sequences1(X_test, seq_length)\n",
    "y_test = create_sequences1(y_test, seq_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy -> tensor 변환\n",
    "X_train = torch.tensor(X_train).float()\n",
    "y_train = torch.tensor(y_train).float()\n",
    "X_test = torch.tensor(X_test).float()\n",
    "y_test = torch.tensor(y_test).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "kemyzEgk0eqT",
    "outputId": "fef29dbf-513d-4685-af23-aae387649839"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train : torch.Size([88, 1, 331])\n",
      "X_test : torch.Size([14, 1, 331])\n",
      "y_train : torch.Size([88, 1])\n",
      "y_test : torch.Size([14, 1])\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train :\",(X_train.shape))\n",
    "print(\"X_test :\",(X_test.shape))\n",
    "print(\"y_train :\",(y_train.shape))\n",
    "print(\"y_test :\",(y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AnA9qqth1XtG"
   },
   "source": [
    "# Building a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Class 생성\n",
    "\n",
    "class CoronaVirusPredictor(nn.Module):\n",
    "    \n",
    "    def __init__(self, n_features, n_hidden, seq_len, n_layers=2):\n",
    "        super(CoronaVirusPredictor, self).__init__()\n",
    "        self.n_hidden = n_hidden\n",
    "        self.seq_len = seq_len\n",
    "        self.n_layers = n_layers\n",
    "\n",
    "        self.lstm = nn.LSTM(\n",
    "        input_size = n_features,\n",
    "        hidden_size = n_hidden,\n",
    "        num_layers = n_layers,\n",
    "        #dropout=0.1\n",
    "        )\n",
    "        \n",
    "        self.linear = nn.Linear(in_features=n_hidden, out_features=1)\n",
    "        \n",
    "    def reset_hidden_state(self):\n",
    "            self.hidden = (\n",
    "                torch.zeros(self.n_layers, self.seq_len, self.n_hidden),\n",
    "                torch.zeros(self.n_layers, self.seq_len, self.n_hidden))\n",
    "            \n",
    "    def forward(self, sequences):\n",
    "        lstm_out, self.hidden = self.lstm(sequences.view(len(sequences), self.seq_len, -1), self.hidden)\n",
    "        last_time_step = lstm_out.view(self.seq_len, len(sequences), self.n_hidden)[-1]\n",
    "        y_pred = self.linear(last_time_step)\n",
    "    \n",
    "        return y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "m5IGRO_bcJVA"
   },
   "source": [
    "## __Training__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, train_data, train_labels, test_data=None, test_labels=None, num_epochs=250, lr=1e-3):\n",
    "    loss_fn = torch.nn.MSELoss()\n",
    "    \n",
    "    optimiser = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=1e-4)\n",
    "    num_epochs = num_epochs\n",
    "    \n",
    "    train_hist = np.zeros(num_epochs)\n",
    "    test_hist = np.zeros(num_epochs)\n",
    "    \n",
    "    for t in range(num_epochs):\n",
    "        model.reset_hidden_state()\n",
    "        y_pred = model(X_train)\n",
    "        loss = loss_fn(y_pred.float(), y_train)\n",
    "        \n",
    "        if test_data is not None:\n",
    "            with torch.no_grad():\n",
    "                y_test_pred = model(X_test)\n",
    "                test_loss = loss_fn(y_test_pred.float(), y_test)\n",
    "            test_hist[t] = test_loss.item()\n",
    "            \n",
    "            if t % 10 == 0:\n",
    "                print(f'Epoch {t} train loss: {round(loss.item(),4)} test loss: {round(test_loss.item(),4)}')\n",
    "        elif t % 10 == 0:\n",
    "            print(f'Epoch {t} train loss: {loss.item()}')\n",
    "            \n",
    "        train_hist[t] = loss.item()\n",
    "        optimiser.zero_grad()\n",
    "        loss.backward()\n",
    "        optimiser.step()\n",
    "        \n",
    "    return model.eval(), train_hist, test_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyper-parameter 설정\n",
    "n_features=X_train.shape[-1]\n",
    "n_hidden=64\n",
    "n_layers=4\n",
    "lr=1e-4\n",
    "num_epochs=200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 125
    },
    "colab_type": "code",
    "id": "6TjwDYRCdpjT",
    "outputId": "5b9e6ced-0e77-4ef3-df6e-709e4e3d4960"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 train loss: 0.1072 test loss: 0.016\n",
      "Epoch 10 train loss: 0.0925 test loss: 0.0091\n",
      "Epoch 20 train loss: 0.0793 test loss: 0.0044\n",
      "Epoch 30 train loss: 0.0656 test loss: 0.0023\n",
      "Epoch 40 train loss: 0.0502 test loss: 0.0051\n",
      "Epoch 50 train loss: 0.0343 test loss: 0.0166\n",
      "Epoch 60 train loss: 0.0227 test loss: 0.0386\n",
      "Epoch 70 train loss: 0.0178 test loss: 0.0486\n",
      "Epoch 80 train loss: 0.0134 test loss: 0.0225\n",
      "Epoch 90 train loss: 0.0102 test loss: 0.0147\n",
      "Epoch 100 train loss: 0.0078 test loss: 0.0083\n",
      "Epoch 110 train loss: 0.0072 test loss: 0.0047\n",
      "Epoch 120 train loss: 0.007 test loss: 0.003\n",
      "Epoch 130 train loss: 0.0069 test loss: 0.0027\n",
      "Epoch 140 train loss: 0.0068 test loss: 0.0024\n",
      "Epoch 150 train loss: 0.0068 test loss: 0.0023\n",
      "Epoch 160 train loss: 0.0067 test loss: 0.0022\n",
      "Epoch 170 train loss: 0.0067 test loss: 0.0022\n",
      "Epoch 180 train loss: 0.0067 test loss: 0.0022\n",
      "Epoch 190 train loss: 0.0066 test loss: 0.0021\n"
     ]
    }
   ],
   "source": [
    "# Training Model\n",
    "model = CoronaVirusPredictor(n_features=n_features, n_hidden=n_hidden, seq_len=seq_length, n_layers=n_layers)\n",
    "model, train_hist, test_hist = train_model(model, X_train, y_train, X_test, y_test, num_epochs=num_epochs, lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2038491fe48>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 597,
       "width": 829
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting Loss\n",
    "plt.plot(train_hist, label=\"Training loss\")\n",
    "plt.plot(test_hist, label=\"Test loss\")\n",
    "plt.title('n_features:{0}, n_hidden:{1}, n_layers:{2}, lr:{3}, seq_length:{4}, num_epochs:{5}'.format(n_features,n_hidden,n_layers,lr,seq_length,num_epochs))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wvooIV0Sd_zc"
   },
   "source": [
    "# Predicting daily cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ui_DygrTd9YL"
   },
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    \n",
    "    preds = []\n",
    "    for i in range(len(X_test)):\n",
    "        test_seq = X_test[i:i+1]\n",
    "        y_test_pred = model(test_seq)\n",
    "        pred = torch.flatten(y_test_pred).item()\n",
    "        preds.append(pred)\n",
    "        new_seq = test_seq.numpy().flatten()\n",
    "        new_seq = np.append(new_seq, pred)\n",
    "        new_seq = new_seq[1:]\n",
    "        test_seq = torch.as_tensor(new_seq).view(n_features, seq_length, 1).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.11690759658813477,\n",
       " 0.11724621057510376,\n",
       " 0.11685064435005188,\n",
       " 0.11522848904132843,\n",
       " 0.11415790021419525,\n",
       " 0.11189968883991241,\n",
       " 0.11054246127605438,\n",
       " 0.11027385294437408,\n",
       " 0.10908515751361847,\n",
       " 0.11051195859909058,\n",
       " 0.11194856464862823,\n",
       " 0.11333797872066498,\n",
       " 0.11357825994491577,\n",
       " 0.11508245766162872]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction value 스케일링 역변환\n",
    "pred_values = yscaler.inverse_transform(np.array(preds).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.48208618],\n",
       "       [7.50375748],\n",
       "       [7.47844124],\n",
       "       [7.3746233 ],\n",
       "       [7.30610561],\n",
       "       [7.16158009],\n",
       "       [7.07471752],\n",
       "       [7.05752659],\n",
       "       [6.98145008],\n",
       "       [7.07276535],\n",
       "       [7.16470814],\n",
       "       [7.25363064],\n",
       "       [7.26900864],\n",
       "       [7.36527729]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_values_ceiled  = list(pred_values.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# True value 스케일링 역변환\n",
    "true_values = yscaler.inverse_transform(y_test)[:, [-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 실제값 예측값 데이터 프레임 생성\n",
    "score_table = pd.DataFrame({'True':true_values.flatten(),\n",
    "                            'Pred':pred_values_ceiled})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>True</th>\n",
       "      <th>Pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.482086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.503757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.478441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.374623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.0</td>\n",
       "      <td>7.306106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.161580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.0</td>\n",
       "      <td>7.074718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.057527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.981450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.072765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.164708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.0</td>\n",
       "      <td>7.253631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.269009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.365277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    True      Pred\n",
       "0    6.0  7.482086\n",
       "1    4.0  7.503757\n",
       "2    2.0  7.478441\n",
       "3    4.0  7.374623\n",
       "4   10.0  7.306106\n",
       "5    7.0  7.161580\n",
       "6   12.0  7.074718\n",
       "7    5.0  7.057527\n",
       "8    4.0  6.981450\n",
       "9    8.0  7.072765\n",
       "10   8.0  7.164708\n",
       "11  10.0  7.253631\n",
       "12   8.0  7.269009\n",
       "13   3.0  7.365277"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# validation score\n",
    "MSE = mean_squared_error(score_table['True'], score_table['Pred'])\n",
    "RMSE = np.sqrt(MSE)\n",
    "score = 100*(1-(((score_table['Pred'] -score_table['True'])**2).sum())/((score_table['True']**2).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE : 9.230596785813825, RMSE : 3.03818972182677, SCORE : 81.72159052314095\n"
     ]
    }
   ],
   "source": [
    "print(\"MSE : {0}, RMSE : {1}, SCORE : {2}\".format(MSE, RMSE, score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x203851779b0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 308,
       "width": 596
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(range(y_train.__len__()),yscaler.inverse_transform(y_train)[:, [-1]])\n",
    "plt.plot(range(y_train.__len__(), y_train.__len__()+y_test.__len__()),true_values, label='Real')\n",
    "plt.plot(range(y_train.__len__(), y_train.__len__()+y_test.__len__()),pred_values_ceiled, label='Pred')\n",
    "#plt.xlim(70)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 저장\n",
    "#PATH = './models/LSTM_seq1_n_features_{0}_n_hidden_{1}_n_layers_{2}_lr_{3}_seq_length_{4}_num_epochs_{5}.pth'.format(n_features,n_hidden,n_layers,lr,seq_length,num_epochs)\n",
    "PATH = './models/{6}_n_features_{0}_n_hidden_{1}_n_layers_{2}_lr_{3}_seq_length_{4}_num_epochs_{5}.pth'.format(n_features,n_hidden,n_layers,lr,seq_length,num_epochs, score.round(2))\n",
    "\n",
    "torch.save(model, PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 불러오기\n",
    "#PATH = './models/score84_n_features_10_n_hidden_64_n_layers_4_lr_0.0001_seq_length_1_num_epochs_250.pth'\n",
    "model = torch.load(PATH)\n",
    "#model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BRrFV77HeURz"
   },
   "source": [
    "# Use all data for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X변수들과 y변수 구분\n",
    "X_all = df[X_cols]\n",
    "y_all = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MinMaxScaler을 통한 스케일링\n",
    "scaler = MinMaxScaler()\n",
    "# X scaler 생성\n",
    "Xscaler = scaler.fit(X_all)\n",
    "# Y scaler 생성\n",
    "yscaler = scaler.fit(y_all.values.reshape(-1,1))\n",
    "\n",
    "# 스케일링 적용\n",
    "X_all = Xscaler.transform(X_all)\n",
    "y_all = yscaler.transform(y_all.values.reshape(-1,1))\n",
    "y_all = y_all.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.      , 0.015625, 0.015625, 0.      , 0.      , 0.03125 ,\n",
       "       0.015625, 0.015625, 0.03125 , 0.      , 0.015625, 0.03125 ,\n",
       "       0.03125 , 0.      , 0.      , 0.      , 0.      , 0.015625,\n",
       "       0.      , 0.      , 0.      , 0.      , 0.      , 0.      ,\n",
       "       0.      , 0.      , 0.      , 0.      , 0.      , 0.265625,\n",
       "       0.      , 0.      , 0.      , 0.      , 0.      , 0.      ,\n",
       "       0.      , 0.      , 0.      , 0.015625, 0.      , 0.      ,\n",
       "       0.015625, 0.015625, 0.015625, 0.03125 , 0.03125 , 0.0625  ,\n",
       "       0.046875, 0.03125 , 0.      , 0.015625, 0.0625  , 0.15625 ,\n",
       "       0.21875 , 0.109375, 0.34375 , 0.234375, 0.328125, 0.421875,\n",
       "       0.875   , 0.890625, 0.390625, 0.84375 , 0.765625, 1.      ,\n",
       "       0.65625 , 0.65625 , 0.640625, 0.71875 , 0.640625, 0.828125,\n",
       "       0.4375  , 0.515625, 0.46875 , 0.453125, 0.125   , 0.265625,\n",
       "       0.40625 , 0.265625, 0.171875, 0.234375, 0.1875  , 0.25    ,\n",
       "       0.15625 , 0.078125, 0.125   , 0.078125, 0.09375 , 0.0625  ,\n",
       "       0.03125 , 0.0625  , 0.15625 , 0.109375, 0.1875  , 0.078125,\n",
       "       0.0625  , 0.125   , 0.125   , 0.15625 , 0.125   , 0.046875])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_all :  (102, 331)\n",
      "y_all :  (102,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_all : \", X_all.shape)\n",
    "print(\"y_all : \", y_all.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5TEKufKA0c_Z"
   },
   "outputs": [],
   "source": [
    "X_all = create_sequences1(X_all, seq_length)\n",
    "y_all = create_sequences1(y_all, seq_length)\n",
    "X_all = torch.from_numpy(np.array(X_all)).float()\n",
    "y_all = torch.from_numpy(np.array(y_all)).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 125
    },
    "colab_type": "code",
    "id": "5UpCLNUQeYfJ",
    "outputId": "61cf07ab-d749-48b8-b76b-e6bfc8e2a09d"
   },
   "outputs": [],
   "source": [
    "# model = CoronaVirusPredictor(\n",
    "#   n_features=n_features,\n",
    "#   n_hidden=n_hidden,\n",
    "#   seq_len=seq_length,\n",
    "#   n_layers=n_layers\n",
    "# )\n",
    "# model, train_hist, _ = train_model(model, X_all, y_all, num_epochs=200, lr=lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MSsGbvZAecw3"
   },
   "source": [
    "# Predicting future cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "DAYS_TO_PREDICT = 14\n",
    "with torch.no_grad():\n",
    "    test_seq = X_all[:1]\n",
    "    preds = []\n",
    "    for _ in range(DAYS_TO_PREDICT):\n",
    "        y_test_pred = model(test_seq)\n",
    "        pred = torch.flatten(y_test_pred).item()\n",
    "        preds.append(pred)\n",
    "        new_seq = test_seq.numpy().flatten()\n",
    "        new_seq = np.append(new_seq, [pred])\n",
    "        new_seq = new_seq[1:]\n",
    "        #test_seq = torch.as_tensor(new_seq).view(1, seq_length, 1).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction value 스케일링 역변환\n",
    "pred_values = yscaler.inverse_transform(np.array(preds).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7.176301956176758,\n",
       " 6.552890777587891,\n",
       " 5.6265869140625,\n",
       " 4.590296268463135,\n",
       " 3.5957493782043457,\n",
       " 2.7302017211914062,\n",
       " 2.0314455032348633,\n",
       " 1.5143073797225952,\n",
       " 1.1622556447982788,\n",
       " 0.9331351518630981,\n",
       " 0.7861291170120239,\n",
       " 0.6909612417221069,\n",
       " 0.6278258562088013,\n",
       " 0.5846792459487915]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 예측값 반올림\n",
    "import math\n",
    "\n",
    "pred_values_ceiled = list(pred_values.flatten())\n",
    "predicted_cases=pred_values_ceiled\n",
    "predicted_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 597
    },
    "colab_type": "code",
    "id": "6kkEHP_weoOc",
    "outputId": "6a596ff3-dea0-4f18-8329-f7e718e767d5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 580,
       "width": 812
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_index = pd.date_range(\n",
    "  start=df.index[-1],\n",
    "  periods=DAYS_TO_PREDICT + 1,\n",
    "  closed='right'\n",
    ")\n",
    "predicted_index = pd.to_datetime(predicted_index, format='%Y%m%d')\n",
    "\n",
    "predicted_cases = pd.Series(\n",
    "  data=predicted_cases,\n",
    "  index=predicted_index\n",
    ")\n",
    "\n",
    "plt.plot(predicted_cases, label='Predicted Daily Cases')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_ = pd.DataFrame(predicted_cases)\n",
    "df.index = pd.to_datetime(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-06</th>\n",
       "      <td>7.176302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07</th>\n",
       "      <td>6.552891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-08</th>\n",
       "      <td>5.626587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-09</th>\n",
       "      <td>4.590296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-10</th>\n",
       "      <td>3.595749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-11</th>\n",
       "      <td>2.730202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-12</th>\n",
       "      <td>2.031446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-13</th>\n",
       "      <td>1.514307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>1.162256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-15</th>\n",
       "      <td>0.933135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-16</th>\n",
       "      <td>0.786129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-17</th>\n",
       "      <td>0.690961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-18</th>\n",
       "      <td>0.627826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-19</th>\n",
       "      <td>0.584679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "2020-05-06  7.176302\n",
       "2020-05-07  6.552891\n",
       "2020-05-08  5.626587\n",
       "2020-05-09  4.590296\n",
       "2020-05-10  3.595749\n",
       "2020-05-11  2.730202\n",
       "2020-05-12  2.031446\n",
       "2020-05-13  1.514307\n",
       "2020-05-14  1.162256\n",
       "2020-05-15  0.933135\n",
       "2020-05-16  0.786129\n",
       "2020-05-17  0.690961\n",
       "2020-05-18  0.627826\n",
       "2020-05-19  0.584679"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 597
    },
    "colab_type": "code",
    "id": "JosxYymUep4z",
    "outputId": "99cece78-cee5-45fc-e426-0cb81f1e78fa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 381,
       "width": 1433
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,5))\n",
    "plt.plot(df['target'].astype(int), label='Historical Daily Cases')\n",
    "plt.plot(preds_, label='Predicted Daily Cases')\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Oversea Inflow Cofirmed\")\n",
    "plt.grid(axis='x')\n",
    "plt.legend();"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Timeseries_LSTM_prac.ipynb의 사본",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}