1-4,时间序列数据建模流程范例.ipynb

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   "id": "520bca22",
   "metadata": {},
   "source": [
    "# 1-4,时间序列数据建模流程范例"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "914d0791",
   "metadata": {},
   "source": [
    "2020年发生的新冠肺炎疫情灾难给各国人民的生活造成了诸多方面的影响。\n",
    "\n",
    "有的同学是收入上的，有的同学是感情上的，有的同学是心理上的，还有的同学是体重上的。\n",
    "\n",
    "本文基于中国2020年3月之前的疫情数据，建立时间序列RNN模型，对中国的新冠肺炎疫情结束时间进行预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fd11a67",
   "metadata": {},
   "source": [
    "![](./data/疫情前后对比.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "306f3ae5",
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.__version__ =  2.0.1\n"
     ]
    }
   ],
   "source": [
    "import torch \n",
    "print(\"torch.__version__ = \", torch.__version__)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73b65b4b",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<font color=\"red\">\n",
    " \n",
    "公众号 **算法美食屋** 回复关键词：**pytorch**， 获取本项目源码和所用数据集百度云盘下载链接。\n",
    "    \n",
    "</font> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19e680ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "#mac系统上pytorch和matplotlib在jupyter中同时跑需要更改环境变量\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\" \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dbb69fd",
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   "outputs": [],
   "source": []
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  {
   "cell_type": "markdown",
   "id": "4d46f9f9",
   "metadata": {},
   "source": [
    "## 一，准备数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a5acccb",
   "metadata": {},
   "source": [
    "本文的数据集取自tushare，获取该数据集的方法参考了以下文章。\n",
    "\n",
    "《https://zhuanlan.zhihu.com/p/109556102》\n",
    "\n",
    "![](./data/1-4-新增人数.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "738b171a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "903d0408",
   "metadata": {},
   "outputs": [
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   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "df = pd.read_csv(\"./eat_pytorch_datasets/covid-19.csv\",sep = \"\\t\")\n",
    "df.plot(x = \"date\",y = [\"confirmed_num\",\"cured_num\",\"dead_num\"],figsize=(10,6))\n",
    "plt.xticks(rotation=60);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5a55006",
   "metadata": {},
   "outputs": [
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   "source": [
    "dfdata = df.set_index(\"date\")\n",
    "dfdiff = dfdata.diff(periods=1).dropna()\n",
    "dfdiff = dfdiff.reset_index(\"date\")\n",
    "\n",
    "dfdiff.plot(x = \"date\",y = [\"confirmed_num\",\"cured_num\",\"dead_num\"],figsize=(10,6))\n",
    "plt.xticks(rotation=60)\n",
    "dfdiff = dfdiff.drop(\"date\",axis = 1).astype(\"float32\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8cb89487",
   "metadata": {},
   "outputs": [
    {
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       "      <td>457.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>688.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>769.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1771.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1459.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   confirmed_num  cured_num  dead_num\n",
       "0          457.0        4.0      16.0\n",
       "1          688.0       11.0      15.0\n",
       "2          769.0        2.0      24.0\n",
       "3         1771.0        9.0      26.0\n",
       "4         1459.0       43.0      26.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfdiff.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15dc30a0",
   "metadata": {},
   "source": [
    "下面我们通过继承torch.utils.data.Dataset实现自定义时间序列数据集。\n",
    "\n",
    "torch.utils.data.Dataset是一个抽象类，用户想要加载自定义的数据只需要继承这个类，并且覆写其中的两个方法即可：\n",
    "\n",
    "* `__len__`:实现len(dataset)返回整个数据集的大小。\n",
    "* `__getitem__`:用来获取一些索引的数据，使`dataset[i]`返回数据集中第i个样本。\n",
    "\n",
    "不覆写这两个方法会直接返回错误。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d654bf5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "from torch import nn \n",
    "from torch.utils.data import Dataset,DataLoader,TensorDataset\n",
    "\n",
    "\n",
    "#用某日前8天窗口数据作为输入预测该日数据\n",
    "WINDOW_SIZE = 8\n",
    "\n",
    "class Covid19Dataset(Dataset):\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(dfdiff) - WINDOW_SIZE\n",
    "    \n",
    "    def __getitem__(self,i):\n",
    "        x = dfdiff.loc[i:i+WINDOW_SIZE-1,:]\n",
    "        feature = torch.tensor(x.values)\n",
    "        y = dfdiff.loc[i+WINDOW_SIZE,:]\n",
    "        label = torch.tensor(y.values)\n",
    "        return (feature,label)\n",
    "    \n",
    "ds_train = Covid19Dataset()\n",
    "\n",
    "#数据较小，可以将全部训练数据放入到一个batch中，提升性能\n",
    "dl_train = DataLoader(ds_train,batch_size = 38)\n",
    "\n",
    "for features,labels in dl_train:\n",
    "    break \n",
    "    \n",
    "#dl_train同时作为验证集\n",
    "dl_val = dl_train\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f1410eb",
   "metadata": {},
   "source": [
    "## 二，定义模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b39abea",
   "metadata": {},
   "source": [
    "使用Pytorch通常有三种方式构建模型：使用nn.Sequential按层顺序构建模型，继承nn.Module基类构建自定义模型，继承nn.Module基类构建模型并辅助应用模型容器进行封装。\n",
    "\n",
    "此处选择第二种方式构建模型。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "42466f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (lstm): LSTM(3, 3, num_layers=5, batch_first=True)\n",
      "  (linear): Linear(in_features=3, out_features=3, bias=True)\n",
      "  (block): Block()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn \n",
    "import importlib \n",
    "import torchkeras \n",
    "\n",
    "torch.random.seed()\n",
    "\n",
    "class Block(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Block,self).__init__()\n",
    "    \n",
    "    def forward(self,x,x_input):\n",
    "        x_out = torch.max((1+x)*x_input[:,-1,:],torch.tensor(0.0))\n",
    "        return x_out\n",
    "    \n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        # 3层lstm\n",
    "        self.lstm = nn.LSTM(input_size = 3,hidden_size = 3,num_layers = 5,batch_first = True)\n",
    "        self.linear = nn.Linear(3,3)\n",
    "        self.block = Block()\n",
    "        \n",
    "    def forward(self,x_input):\n",
    "        x = self.lstm(x_input)[0][:,-1,:]\n",
    "        x = self.linear(x)\n",
    "        y = self.block(x,x_input)\n",
    "        return y\n",
    "        \n",
    "net = Net()\n",
    "print(net)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05e00cd",
   "metadata": {},
   "source": [
    "```\n",
    "Net(\n",
    "  (lstm): LSTM(3, 3, num_layers=5, batch_first=True)\n",
    "  (linear): Linear(in_features=3, out_features=3, bias=True)\n",
    "  (block): Block()\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e02fe265",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n",
      "Layer (type)                            Output Shape              Param #\n",
      "==========================================================================\n",
      "LSTM-1                                    [-1, 8, 3]                  480\n",
      "Linear-2                                     [-1, 3]                   12\n",
      "Block-3                                      [-1, 3]                    0\n",
      "==========================================================================\n",
      "Total params: 492\n",
      "Trainable params: 492\n",
      "Non-trainable params: 0\n",
      "--------------------------------------------------------------------------\n",
      "Input size (MB): 0.000069\n",
      "Forward/backward pass size (MB): 0.000229\n",
      "Params size (MB): 0.001877\n",
      "Estimated Total Size (MB): 0.002174\n",
      "--------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from torchkeras import summary\n",
    "summary(net,input_data=features);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4649f9f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4d8d90bc",
   "metadata": {},
   "source": [
    "### 三，训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39722b11",
   "metadata": {},
   "source": [
    "训练Pytorch通常需要用户编写自定义训练循环，训练循环的代码风格因人而异。\n",
    "\n",
    "有3类典型的训练循环代码风格：脚本形式训练循环，函数形式训练循环，类形式训练循环。\n",
    "\n",
    "此处我们通过引入torchkeras库中的KerasModel工具来训练模型，无需编写自定义循环。\n",
    "\n",
    "torchkeras详情:  https://github.com/lyhue1991/torchkeras \n",
    "\n",
    "注：循环神经网络调试较为困难，需要设置多个不同的学习率多次尝试，以取得较好的效果。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd846cf3-8a65-4cbf-8bc9-ba28c6eed841",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchmetrics.regression import MeanAbsolutePercentageError\n",
    "\n",
    "def mspe(y_pred,y_true):\n",
    "    err_percent = (y_true - y_pred)**2/(torch.max(y_true**2,torch.tensor(1e-7)))\n",
    "    return torch.mean(err_percent)\n",
    "\n",
    "net = Net() \n",
    "loss_fn = mspe\n",
    "metric_dict = {\"mape\":MeanAbsolutePercentageError()}\n",
    "\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.03)\n",
    "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9bfa7af9-ab5f-4fa5-bc61-3e1cd77cc087",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchkeras import KerasModel \n",
    "model = KerasModel(net,\n",
    "       loss_fn = loss_fn,\n",
    "       metrics_dict= metric_dict,\n",
    "       optimizer = optimizer,\n",
    "       lr_scheduler = lr_scheduler) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d8185438",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31m<<<<<< 🐌 cpu is used >>>>>>\u001b[0m\n"
     ]
    },
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       "      18.00% [18/100] [00:02<00:10]\n",
       "      <br>\n",
       "      ████████████████████100.00% [1/1] [val_loss=0.4363, val_mape=0.5570]\n",
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     "text": [
      "\u001b[0;31m<<<<<< val_loss without improvement in 10 epoch,early stopping >>>>>> \n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "dfhistory = model.fit(train_data=dl_train,\n",
    "            val_data=dl_val,\n",
    "            epochs=100,\n",
    "            ckpt_path='checkpoint',\n",
    "            patience=10,\n",
    "            monitor='val_loss',\n",
    "            mode='min',\n",
    "            callbacks=None,\n",
    "            plot=True,\n",
    "            cpu=True\n",
    "            )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "792e4f1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ed66a496",
   "metadata": {},
   "source": [
    "### 四，评估模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b51cbde5",
   "metadata": {},
   "source": [
    "评估模型一般要设置验证集或者测试集，由于此例数据较少，我们仅仅可视化损失函数在训练集上的迭代情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "46963c00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████| 1/1 [00:00<00:00, 63.91it/s, val_loss=0.384, val_mape=0.505]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'val_loss': 0.38373321294784546, 'val_mape': 0.5048269033432007}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(dl_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdc717dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2687ac02",
   "metadata": {},
   "source": [
    "### 五，使用模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e0db6a",
   "metadata": {},
   "source": [
    "此处我们使用模型预测疫情结束时间，即 新增确诊病例为0 的时间。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4164d6eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed_num</th>\n",
       "      <th>cured_num</th>\n",
       "      <th>dead_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>143.0</td>\n",
       "      <td>1681.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>99.0</td>\n",
       "      <td>1678.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1661.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>40.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    confirmed_num  cured_num  dead_num\n",
       "41          143.0     1681.0      30.0\n",
       "42           99.0     1678.0      28.0\n",
       "43           44.0     1661.0      27.0\n",
       "44           40.0     1535.0      22.0\n",
       "45           19.0     1297.0      17.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用dfresult记录现有数据以及此后预测的疫情数据\n",
    "dfresult = dfdiff[[\"confirmed_num\",\"cured_num\",\"dead_num\"]].copy()\n",
    "dfresult.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "727b7d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#预测此后1000天的新增走势,将其结果添加到dfresult中\n",
    "for i in range(1000):\n",
    "    arr_input = torch.unsqueeze(torch.from_numpy(dfresult.values[-38:,:]),axis=0)\n",
    "    arr_predict = model.forward(arr_input)\n",
    "\n",
    "    dfpredict = pd.DataFrame(torch.floor(arr_predict).data.numpy(),\n",
    "                columns = dfresult.columns)\n",
    "    dfresult = pd.concat([dfresult,dfpredict],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d081b013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed_num</th>\n",
       "      <th>cured_num</th>\n",
       "      <th>dead_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.0</td>\n",
       "      <td>948.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.0</td>\n",
       "      <td>854.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    confirmed_num  cured_num  dead_num\n",
       "50            0.0      999.0       0.0\n",
       "51            0.0      948.0       0.0\n",
       "52            0.0      900.0       0.0\n",
       "53            0.0      854.0       0.0\n",
       "54            0.0      810.0       0.0"
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfresult.query(\"confirmed_num==0\").head()\n",
    "\n",
    "# 第50天开始新增确诊降为0，第45天对应3月10日，也就是5天后，即预计3月15日新增确诊降为0\n",
    "# 注：该预测偏乐观\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad96908f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cc5477ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>confirmed_num</th>\n",
       "      <th>cured_num</th>\n",
       "      <th>dead_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     confirmed_num  cured_num  dead_num\n",
       "137            0.0        0.0       0.0\n",
       "138            0.0        0.0       0.0\n",
       "139            0.0        0.0       0.0\n",
       "140            0.0        0.0       0.0\n",
       "141            0.0        0.0       0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfresult.query(\"cured_num==0\").head()\n",
    "# 第137天开始新增治愈降为0，第45天对应3月10日，也就是大概3个月后，即6月12日左右全部治愈。\n",
    "# 注: 该预测偏悲观，并且存在问题，如果将每天新增治愈人数加起来，将超过累计确诊人数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da13d833",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ec4f9500",
   "metadata": {},
   "source": [
    "### 六，保存模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a78086b",
   "metadata": {},
   "source": [
    "模型权重保存在了model.ckpt_path路径。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "859daed0-91c7-4ad6-9789-2c238ebb9990",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checkpoint\n"
     ]
    }
   ],
   "source": [
    "print(model.ckpt_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b3ad034a-63a5-42df-b65e-1cd4d7c543bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_ckpt('checkpoint') #可以加载权重"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39cb303a",
   "metadata": {},
   "source": [
    "**如果本书对你有所帮助，想鼓励一下作者，记得给本项目加一颗星星star⭐️，并分享给你的朋友们喔😊!** \n",
    "\n",
    "如果对本书内容理解上有需要进一步和作者交流的地方，欢迎在公众号\"算法美食屋\"下留言。作者时间和精力有限，会酌情予以回复。\n",
    "\n",
    "也可以在公众号后台回复关键字：**加群**，加入读者交流群和大家讨论。\n",
    "\n",
    "![算法美食屋logo.png](https://tva1.sinaimg.cn/large/e6c9d24egy1h41m2zugguj20k00b9q46.jpg)"
   ]
  }
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