{
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 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.6.10-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "cells": [
  {
   "source": [
    "# 使用PaddlePaddle完成新冠疫情病例数预测\n",
    "\n",
    "2019年12月以来，新冠疫情在全球肆虐，呈现大流行的特征。新型冠状病毒肺炎以发热、干咳、乏力等为主要表现，重症病例多在1周后出现呼吸困难，严重者快速进展为急性呼吸窘迫综合征、脓毒症休克、难以纠正的代谢性酸中毒和出凝血功能障碍及多器官功能衰竭等，对人们的健康造成了极其严重的威胁。同时，为抵御新冠病毒的扩散，不少国家和地区采取了封锁性防疫举措，全球经济复苏的进程因此受阻，政府债务不断上升。\n",
    "\n",
    "在这种背景下，各国人民都期盼着疫情的结束，早日恢复往常的生产、生活方式。本文关注到这一问题，结合约翰斯·霍普金斯大学发布的全球新冠肺炎实时统计数据，通过时间卷积神经网络对时间序列建模，实现预测未来病例数的目的。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 准备环境\n",
    "\n",
    "在开始建模之前，我们需要导入必要的包，同时为了更好地展示数据结果，我们在这里配置画图功能。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "/mnt/qiujinxuan/PaddleNLP/paddlenlp/seq2vec/encoder.py:683: DeprecationWarning: invalid escape sequence \\s\n  \"\"\"\n/mnt/qiujinxuan/PaddleNLP/paddlenlp/seq2vec/encoder.py:740: DeprecationWarning: invalid escape sequence \\s\n  \"\"\"\n"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import paddle\n",
    "import paddle.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\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\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "\n",
    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \"../..\")))\n",
    "from paddlenlp.seq2vec import TCNEncoder\n",
    "\n",
    "\n",
    "# config matplotlib\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
    "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#93D30C\", \"#8F00FF\"]\n",
    "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
    "rcParams['figure.figsize'] = 14, 10\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "source": [
    "## 数据下载\n",
    "\n",
    "数据集由约翰·霍普金斯大学系统科学与工程中心提供，每日最新数据可以从https://github.com/CSSEGISandData/COVID-19 仓库中获取，我们在本例中提供了2020年11月24日下载的病例数据。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !wget https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
   ]
  },
  {
   "source": [
    "数据集中包含了国家、省份、纬度、经度以及从2020年1月22日至今的病例数等信息。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 数据预览\n",
    "\n",
    "数据集中包含了国家/地区、省份/州、纬度、经度、日期、病例数等信息。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n0            NaN    Afghanistan  33.93911  67.709953        0        0   \n1            NaN        Albania  41.15330  20.168300        0        0   \n2            NaN        Algeria  28.03390   1.659600        0        0   \n3            NaN        Andorra  42.50630   1.521800        0        0   \n4            NaN         Angola -11.20270  17.873900        0        0   \n\n   1/24/20  1/25/20  1/26/20  1/27/20  ...  11/13/20  11/14/20  11/15/20  \\\n0        0        0        0        0  ...     42969     43035     43240   \n1        0        0        0        0  ...     26701     27233     27830   \n2        0        0        0        0  ...     65975     66819     67679   \n3        0        0        0        0  ...      5725      5725      5872   \n4        0        0        0        0  ...     13228     13374     13451   \n\n   11/16/20  11/17/20  11/18/20  11/19/20  11/20/20  11/21/20  11/22/20  \n0     43403     43628     43851     44228     44443     44503     44706  \n1     28432     29126     29837     30623     31459     32196     32761  \n2     68589     69591     70629     71652     72755     73774     74862  \n3      5914      5951      6018      6066      6142      6207      6256  \n4     13615     13818     13922     14134     14267     14413     14493  \n\n[5 rows x 310 columns]",
      "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>Province/State</th>\n      <th>Country/Region</th>\n      <th>Lat</th>\n      <th>Long</th>\n      <th>1/22/20</th>\n      <th>1/23/20</th>\n      <th>1/24/20</th>\n      <th>1/25/20</th>\n      <th>1/26/20</th>\n      <th>1/27/20</th>\n      <th>...</th>\n      <th>11/13/20</th>\n      <th>11/14/20</th>\n      <th>11/15/20</th>\n      <th>11/16/20</th>\n      <th>11/17/20</th>\n      <th>11/18/20</th>\n      <th>11/19/20</th>\n      <th>11/20/20</th>\n      <th>11/21/20</th>\n      <th>11/22/20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>Afghanistan</td>\n      <td>33.93911</td>\n      <td>67.709953</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>42969</td>\n      <td>43035</td>\n      <td>43240</td>\n      <td>43403</td>\n      <td>43628</td>\n      <td>43851</td>\n      <td>44228</td>\n      <td>44443</td>\n      <td>44503</td>\n      <td>44706</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>Albania</td>\n      <td>41.15330</td>\n      <td>20.168300</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>26701</td>\n      <td>27233</td>\n      <td>27830</td>\n      <td>28432</td>\n      <td>29126</td>\n      <td>29837</td>\n      <td>30623</td>\n      <td>31459</td>\n      <td>32196</td>\n      <td>32761</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>Algeria</td>\n      <td>28.03390</td>\n      <td>1.659600</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>65975</td>\n      <td>66819</td>\n      <td>67679</td>\n      <td>68589</td>\n      <td>69591</td>\n      <td>70629</td>\n      <td>71652</td>\n      <td>72755</td>\n      <td>73774</td>\n      <td>74862</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>Andorra</td>\n      <td>42.50630</td>\n      <td>1.521800</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>5725</td>\n      <td>5725</td>\n      <td>5872</td>\n      <td>5914</td>\n      <td>5951</td>\n      <td>6018</td>\n      <td>6066</td>\n      <td>6142</td>\n      <td>6207</td>\n      <td>6256</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Angola</td>\n      <td>-11.20270</td>\n      <td>17.873900</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>13228</td>\n      <td>13374</td>\n      <td>13451</td>\n      <td>13615</td>\n      <td>13818</td>\n      <td>13922</td>\n      <td>14134</td>\n      <td>14267</td>\n      <td>14413</td>\n      <td>14493</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 310 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "df_all = pd.read_csv('time_series_covid19_confirmed_global.csv')\n",
    "df_all.head()"
   ]
  },
  {
   "source": [
    "我们将对全世界的病例数进行预测，因此我们不需要关心具体国家的经纬度等信息，只需关注具体日期下的全球病例数即可。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "2020-01-22     555\n2020-01-23     654\n2020-01-24     941\n2020-01-25    1434\n2020-01-26    2118\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "df = df_all.iloc[:, 4:]\n",
    "daily_cases = df.sum(axis=0)\n",
    "daily_cases.index = pd.to_datetime(daily_cases.index)\n",
    "daily_cases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1008x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 826,
       "height": 597
      }
     }
    }
   ],
   "source": [
    "plt.plot(daily_cases)\n",
    "plt.title(\"Cumulative daily cases\");"
   ]
  },
  {
   "source": [
    "为了提高样本时间序列的平稳性，我们继续取一阶差分。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "2020-01-22    555\n2020-01-23     99\n2020-01-24    287\n2020-01-25    493\n2020-01-26    684\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "daily_cases = daily_cases.diff().fillna(daily_cases[0]).astype(np.int64)\n",
    "daily_cases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1008x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 868,
       "height": 597
      }
     }
    }
   ],
   "source": [
    "plt.plot(daily_cases)\n",
    "plt.title(\"Daily cases\");"
   ]
  },
  {
   "source": [
    "## 数据预处理\n",
    "\n",
    "首先划分数据集为训练集与验证集，我们取最后30天的数据作为测试集，其余作为训练集。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The number of the samples in train set is : 276\n"
    }
   ],
   "source": [
    "TEST_DATA_SIZE = 30\n",
    "\n",
    "train_data = daily_cases[:-TEST_DATA_SIZE]\n",
    "test_data = daily_cases[-TEST_DATA_SIZE:]\n",
    "\n",
    "print(\"The number of the samples in train set is : %i\"%train_data.shape[0])"
   ]
  },
  {
   "source": [
    "为了提升模型收敛速度与性能，我们使用scikit-learn进行数据归一化。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "train_data = scaler.fit_transform(np.expand_dims(train_data, axis=1)).astype('float32')\n",
    "test_data = scaler.transform(np.expand_dims(test_data, axis=1)).astype('float32')"
   ]
  },
  {
   "source": [
    "现在开始组建时间序列，可以用前10天的病例数预测当天的病例数。为了让测试集中的所有数据都能参与预测，我们将向测试集补充少量数据，这部分数据只会作为模型的输入。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The shape of x_train is: (267, 9, 1)\nThe shape of y_train is: (267, 1)\nThe shape of x_test is: (30, 9, 1)\nThe shape of y_test is: (30, 1)\n"
    }
   ],
   "source": [
    "SEQ_LEN = 10\n",
    "\n",
    "def create_sequences(data, seq_length):\n",
    "    xs = []\n",
    "    ys = []\n",
    "\n",
    "    for i in range(len(data)-seq_length+1):\n",
    "        x = data[i:i+seq_length-1]\n",
    "        y = data[i+seq_length-1]\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "x_train, y_train = create_sequences(train_data, SEQ_LEN)\n",
    "test_data = np.concatenate((train_data[-SEQ_LEN+1:],test_data),axis=0)\n",
    "x_test, y_test = create_sequences(test_data, SEQ_LEN)\n",
    "\n",
    "print(\"The shape of x_train is: %s\"%str(x_train.shape))\n",
    "print(\"The shape of y_train is: %s\"%str(y_train.shape))\n",
    "print(\"The shape of x_test is: %s\"%str(x_test.shape))\n",
    "print(\"The shape of y_test is: %s\"%str(y_test.shape))"
   ]
  },
  {
   "source": [
    "数据集处理完毕，将数据集封装到CovidDataset，以便模型训练、预测时调用。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CovidDataset(paddle.io.Dataset):\n",
    "    def __init__(self, feature, label):\n",
    "        self.feature = feature\n",
    "        self.label = label\n",
    "        super(CovidDataset, self).__init__()\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.label)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return [self.feature[index], self.label[index]]\n",
    "\n",
    "train_dataset = CovidDataset(x_train, y_train)\n",
    "test_dataset = CovidDataset(x_test, y_test)\n"
   ]
  },
  {
   "source": [
    "## 组网\n",
    "\n",
    "现在开始组建模型网络，我们采用时间卷积网络TCN作为特征提取器，将提取到的时序信息传送给全连接层获得最终的预测结果。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TimeSeriesNetwork(nn.Layer):\n",
    "\n",
    "  def __init__(self, input_size, next_k=1, num_channels=[64,128,256]):\n",
    "    super(TimeSeriesNetwork, self).__init__()\n",
    "\n",
    "    self.last_num_channel = num_channels[-1]\n",
    "\n",
    "    self.tcn = TCNEncoder(\n",
    "      input_size=input_size,\n",
    "      num_channels=num_channels,\n",
    "      kernel_size=2, \n",
    "      dropout=0.2\n",
    "    )\n",
    "\n",
    "    self.linear = nn.Linear(in_features= self.last_num_channel, out_features=next_k)\n",
    "\n",
    "  def forward(self, x):\n",
    "    tcn_out = self.tcn(x)\n",
    "    y_pred = self.linear(tcn_out)\n",
    "    return y_pred\n",
    "\n",
    "network = TimeSeriesNetwork(input_size=1)"
   ]
  },
  {
   "source": [
    "## 定义优化器、损失函数\n",
    "\n",
    "在这里我们使用Adam优化器、均方差损失函数，为启动训练做最后的准备。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR = 1e-3\n",
    "\n",
    "model = paddle.Model(network)\n",
    "\n",
    "optimizer = paddle.optimizer.Adam(\n",
    "        learning_rate=LR, parameters=model.parameters())\n",
    "\n",
    "loss = paddle.nn.MSELoss(reduction='sum')\n",
    "\n",
    "model.prepare(optimizer, loss)"
   ]
  },
  {
   "source": [
    "## 训练\n",
    "\n",
    "配置必要的超参数，启动训练。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Epoch 1/100\nstep 8/8 [==============================] - loss: 0.3619 - 101ms/step\nsave checkpoint at /mnt/qiujinxuan/PaddleNLP/examples/time_series/save_dir/0\nEpoch 2/100\nstep 8/8 [==============================] - loss: 0.2746 - 53ms/step\nEpoch 3/100\nstep 8/8 [==============================] - loss: 0.0907 - 62ms/step\nEpoch 4/100\nstep 8/8 [==============================] - loss: 0.1740 - 55ms/step\nEpoch 5/100\nstep 8/8 [==============================] - loss: 0.1825 - 56ms/step\nEpoch 6/100\nstep 8/8 [==============================] - loss: 0.1334 - 56ms/step\nEpoch 7/100\nstep 8/8 [==============================] - loss: 0.1089 - 53ms/step\nEpoch 8/100\nstep 8/8 [==============================] - loss: 0.0948 - 57ms/step\nEpoch 9/100\nstep 8/8 [==============================] - loss: 0.1400 - 61ms/step\nEpoch 10/100\nstep 8/8 [==============================] - loss: 0.2118 - 52ms/step\nEpoch 11/100\nstep 8/8 [==============================] - loss: 0.1471 - 56ms/step\nsave checkpoint at /mnt/qiujinxuan/PaddleNLP/examples/time_series/save_dir/10\nEpoch 12/100\nstep 8/8 [==============================] - loss: 0.1984 - 54ms/step\nEpoch 13/100\nstep 8/8 [==============================] - loss: 0.1015 - 55ms/step\nEpoch 14/100\nstep 8/8 [==============================] - loss: 0.0796 - 58ms/step\nEpoch 15/100\nstep 8/8 [==============================] - loss: 0.0735 - 64ms/step\nEpoch 16/100\nstep 8/8 [==============================] - loss: 0.0608 - 62ms/step\nEpoch 17/100\nstep 8/8 [==============================] - loss: 0.1182 - 61ms/step\nEpoch 18/100\nstep 8/8 [==============================] - loss: 0.0943 - 58ms/step\nEpoch 19/100\nstep 8/8 [==============================] - loss: 0.0635 - 59ms/step\nEpoch 20/100\nstep 8/8 [==============================] - loss: 0.0990 - 53ms/step\nEpoch 21/100\nstep 8/8 [==============================] - loss: 0.0657 - 55ms/step\nsave checkpoint at /mnt/qiujinxuan/PaddleNLP/examples/time_series/save_dir/20\nEpoch 22/100\nstep 8/8 [==============================] - loss: 0.1203 - 56ms/step\nEpoch 23/100\nstep 8/8 [==============================] - loss: 0.0538 - 54ms/step\nEpoch 24/100\nstep 8/8 [==============================] - loss: 0.0576 - 57ms/step\nEpoch 25/100\nstep 8/8 [==============================] - loss: 0.1379 - 55ms/step\nEpoch 26/100\nstep 8/8 [==============================] - loss: 0.0858 - 54ms/step\nEpoch 27/100\nstep 8/8 [==============================] - loss: 0.0320 - 61ms/step\nEpoch 28/100\nstep 8/8 [==============================] - loss: 0.1156 - 58ms/step\nEpoch 29/100\nstep 8/8 [==============================] - loss: 0.1335 - 57ms/step\nEpoch 30/100\nstep 8/8 [==============================] - loss: 0.0953 - 58ms/step\nEpoch 31/100\nstep 8/8 [==============================] - loss: 0.0760 - 52ms/step\nsave checkpoint at /mnt/qiujinxuan/PaddleNLP/examples/time_series/save_dir/30\nEpoch 32/100\nstep 8/8 [==============================] - loss: 0.1796 - 54ms/step\nEpoch 33/100\nstep 8/8 [==============================] - loss: 0.1689 - 56ms/step\nEpoch 34/100\nstep 8/8 [==============================] - loss: 0.1669 - 52ms/step\nEpoch 35/100\nstep 8/8 [==============================] - loss: 0.0614 - 53ms/step\nEpoch 36/100\nstep 8/8 [==============================] - loss: 0.0823 - 56ms/step\nEpoch 37/100\nstep 8/8 [==============================] - loss: 0.0829 - 53ms/step\nEpoch 38/100\nstep 8/8 [==============================] - loss: 0.0622 - 55ms/step\nEpoch 39/100\nstep 8/8 [==============================] - loss: 0.0662 - 57ms/step\nEpoch 40/100\nstep 8/8 [==============================] - loss: 0.0642 - 52ms/step\nEpoch 41/100\nstep 8/8 [==============================] - loss: 0.0459 - 56ms/step\nsave checkpoint at /mnt/qiujinxuan/PaddleNLP/examples/time_series/save_dir/40\nEpoch 42/100\nstep 8/8 [==============================] - loss: 0.0460 - 56ms/step\nEpoch 43/100\nstep 8/8 [==============================] - loss: 0.1416 - 54ms/step\nEpoch 44/100\nstep 8/8 [==============================] - loss: 0.0700 - 57ms/step\nEpoch 45/100\nstep 8/8 [==============================] - loss: 0.0840 - 52ms/step\nEpoch 46/100\nstep 8/8 [==============================] - loss: 0.0784 - 54ms/step\nEpoch 47/100\nstep 8/8 [==============================] - loss: 0.1296 - 58ms/step\nEpoch 48/100\nstep 8/8 [==============================] - loss: 0.0487 - 53ms/step\nEpoch 49/100\nstep 8/8 [==============================] - loss: 0.0421 - 56ms/step\nEpoch 50/100\nstep 8/8 [==============================] - loss: 0.0534 - 57ms/step\nEpoch 51/100\nstep 8/8 [==============================] - loss: 0.0244 - 54ms/step\nsave checkpoint at /mnt/qiujinxuan/PaddleNLP/examples/time_series/save_dir/50\nEpoch 52/100\nstep 8/8 [==============================] - loss: 0.0725 - 57ms/step\nEpoch 53/100\nstep 8/8 [==============================] - loss: 0.1687 - 55ms/step\nEpoch 54/100\nstep 8/8 [==============================] - loss: 0.0693 - 55ms/step\nEpoch 55/100\nstep 8/8 [==============================] - loss: 0.0488 - 60ms/step\nEpoch 56/100\nstep 8/8 [==============================] - loss: 0.0737 - 54ms/step\nEpoch 57/100\nstep 8/8 [==============================] - loss: 0.0303 - 53ms/step\nEpoch 58/100\nstep 8/8 [==============================] - loss: 0.0457 - 60ms/step\nEpoch 59/100\nstep 8/8 [==============================] - loss: 0.0773 - 52ms/step\nEpoch 60/100\nstep 8/8 [==============================] - loss: 0.0385 - 54ms/step\nEpoch 61/100\nstep 8/8 [==============================] - loss: 0.1311 - 57ms/step\nsave checkpoint at /mnt/qiujinxuan/PaddleNLP/examples/time_series/save_dir/60\nEpoch 62/100\nstep 8/8 [==============================] - loss: 0.1149 - 53ms/step\nEpoch 63/100\nstep 8/8 [==============================] - loss: 0.1044 - 57ms/step\nEpoch 64/100\nstep 8/8 [==============================] - loss: 0.0670 - 55ms/step\nEpoch 65/100\nstep 8/8 [==============================] - loss: 0.0946 - 53ms/step\nEpoch 66/100\nstep 8/8 [==============================] - loss: 0.0565 - 57ms/step\nEpoch 67/100\nstep 8/8 [==============================] - loss: 0.1452 - 55ms/step\nEpoch 68/100\nstep 8/8 [==============================] - loss: 0.1813 - 53ms/step\nEpoch 69/100\nstep 8/8 [==============================] - loss: 0.0444 - 57ms/step\nEpoch 70/100\nstep 8/8 [==============================] - loss: 0.0626 - 55ms/step\nEpoch 71/100\nstep 8/8 [==============================] - loss: 0.0323 - 54ms/step\nsave checkpoint at /mnt/qiujinxuan/PaddleNLP/examples/time_series/save_dir/70\nEpoch 72/100\nstep 8/8 [==============================] - loss: 0.1469 - 59ms/step\nEpoch 73/100\nstep 8/8 [==============================] - loss: 0.0329 - 53ms/step\nEpoch 74/100\nstep 8/8 [==============================] - loss: 0.0430 - 54ms/step\nEpoch 75/100\nstep 8/8 [==============================] - loss: 0.0493 - 57ms/step\nEpoch 76/100\nstep 8/8 [==============================] - loss: 0.0693 - 52ms/step\nEpoch 77/100\nstep 8/8 [==============================] - loss: 0.0862 - 54ms/step\nEpoch 78/100\nstep 8/8 [==============================] - loss: 0.0316 - 57ms/step\nEpoch 79/100\nstep 8/8 [==============================] - loss: 0.0455 - 53ms/step\nEpoch 80/100\nstep 8/8 [==============================] - loss: 0.0341 - 54ms/step\nEpoch 81/100\nstep 8/8 [==============================] - loss: 0.1113 - 56ms/step\nsave checkpoint at /mnt/qiujinxuan/PaddleNLP/examples/time_series/save_dir/80\nEpoch 82/100\nstep 8/8 [==============================] - loss: 0.0367 - 52ms/step\nEpoch 83/100\nstep 8/8 [==============================] - loss: 0.0866 - 56ms/step\nEpoch 84/100\nstep 8/8 [==============================] - loss: 0.1026 - 54ms/step\nEpoch 85/100\nstep 8/8 [==============================] - loss: 0.0860 - 54ms/step\nEpoch 86/100\nstep 8/8 [==============================] - loss: 0.0386 - 57ms/step\nEpoch 87/100\nstep 8/8 [==============================] - loss: 0.0929 - 54ms/step\nEpoch 88/100\nstep 8/8 [==============================] - loss: 0.0560 - 54ms/step\nEpoch 89/100\nstep 8/8 [==============================] - loss: 0.0727 - 58ms/step\nEpoch 90/100\nstep 8/8 [==============================] - loss: 0.0682 - 54ms/step\nEpoch 91/100\nstep 8/8 [==============================] - loss: 0.0492 - 56ms/step\nsave checkpoint at /mnt/qiujinxuan/PaddleNLP/examples/time_series/save_dir/90\nEpoch 92/100\nstep 8/8 [==============================] - loss: 0.0604 - 56ms/step\nEpoch 93/100\nstep 8/8 [==============================] - loss: 0.0158 - 53ms/step\nEpoch 94/100\nstep 8/8 [==============================] - loss: 0.0412 - 57ms/step\nEpoch 95/100\nstep 8/8 [==============================] - loss: 0.0598 - 56ms/step\nEpoch 96/100\nstep 8/8 [==============================] - loss: 0.0185 - 54ms/step\nEpoch 97/100\nstep 8/8 [==============================] - loss: 0.0352 - 59ms/step\nEpoch 98/100\nstep 8/8 [==============================] - loss: 0.0192 - 58ms/step\nEpoch 99/100\nstep 8/8 [==============================] - loss: 0.0939 - 54ms/step\nEpoch 100/100\nstep 8/8 [==============================] - loss: 0.0525 - 61ms/step\nsave checkpoint at /mnt/qiujinxuan/PaddleNLP/examples/time_series/save_dir/final\n"
    }
   ],
   "source": [
    "USE_GPU = False\n",
    "TRAIN_EPOCH = 100\n",
    "LOG_FREQ = 10\n",
    "SAVE_DIR = os.path.join(os.getcwd(),\"save_dir\")\n",
    "SAVE_FREQ = 10\n",
    "\n",
    "if USE_GPU:\n",
    "    paddle.set_device(\"gpu\")\n",
    "else:\n",
    "    paddle.set_device(\"cpu\")\n",
    "\n",
    "model.fit(train_dataset, \n",
    "    batch_size=32,\n",
    "    drop_last=True,\n",
    "    epochs=TRAIN_EPOCH,\n",
    "    log_freq=LOG_FREQ,\n",
    "    save_dir=SAVE_DIR,\n",
    "    save_freq=SAVE_FREQ,\n",
    "    verbose=1\n",
    "    )"
   ]
  },
  {
   "source": [
    "## 预测\n",
    "\n",
    "使用训练完毕的模型，对测试集中的日期对应的病例数进行预测。\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Predict begin...\nstep 30/30 [==============================] - 12ms/step          \nPredict samples: 30\n"
    }
   ],
   "source": [
    "preds = model.predict(\n",
    "        test_data=test_dataset\n",
    "        )"
   ]
  },
  {
   "source": [
    "## 数据后处理\n",
    "\n",
    "将归一化的数据转换为原始数据，画出真实值对应的曲线和预测值对应的曲线。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_cases = scaler.inverse_transform(\n",
    "    np.expand_dims(y_test.flatten(), axis=0)\n",
    ").flatten()\n",
    "\n",
    "predicted_cases = scaler.inverse_transform(\n",
    "  np.expand_dims(np.array(preds).flatten(), axis=0)\n",
    ").flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1008x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 868,
       "height": 580
      }
     }
    }
   ],
   "source": [
    "plt.plot(\n",
    "  daily_cases.index[:len(train_data)], \n",
    "  scaler.inverse_transform(train_data).flatten(),\n",
    "  label='Historical Daily Cases'\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "  daily_cases.index[len(train_data):len(train_data) + len(true_cases)], \n",
    "  true_cases,\n",
    "  label='Real Daily Cases'\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "  daily_cases.index[len(train_data):len(train_data) + len(true_cases)], \n",
    "  predicted_cases, \n",
    "  label='Predicted Daily Cases'\n",
    ")\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "source": [
    "## 进一步优化模型\n",
    "\n",
    "从上图中，我们可以看到模型大体上预测到了病例数涨幅的升降情况，在具体数值上则出现了一些误差。读者可以发挥创造力，进一步提升模型的精度与功能，例如：\n",
    "\n",
    "1. 预测未来n天\n",
    "\n",
    "我们现在是用已知的9天病例数，预测第10天的病例数，我们可以将第10天的预测结果与前8天的真实病例数拼接，预测第11天的病例数，以此类推即可预测未来n天的病例数。\n",
    "\n",
    "2. 优化模型网络\n",
    "\n",
    "本文采用的是TCN模型，如果不考虑模型的速度性能，可以尝试LSTM, GRU, transformer等模型，进一步提升模型的拟合能力。\n",
    "\n",
    "3. 优化模型超参数\n",
    "\n",
    "本文没有对超参设置进行探索，读者可以探索设置更加合理的学习率，训练轮次，TCN通道数等。\n",
    "\n",
    "4. 考虑更多的数据特征\n",
    "\n",
    "本文只考虑了病例的日期，没有考虑政策、疫苗研制情况等具体环境的影响，读者可以搜集更多的新闻资料，加入更多的数据特征。\n",
    "\n",
    "我们欢迎您对本任务做出进一步的探索，如果您可以修复某个 issue 或者增加一个新功能，欢迎给我们提交 PR。如果对应的 PR 被接受了，我们将根据贡献的质量和难度 进行打分（0-5 分，越高越好）。如果你累计获得了 10 分，可以联系我们获得面试机会或为你写推荐信。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}