LSTM/LSTM_airplane_forcast.ipynb

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    "### 参考： [源代码](https://github.com/L1aoXingyu/code-of-learn-deep-learning-with-pytorch/tree/master/chapter5_RNN/time-series)\n",
    "## 1. 读取数据集以及预处理"
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data_csv = pd.read_csv('../dataset/airplane_data.csv',usecols=[1])\n",
    "plt.plot(data_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先我们进行预处理，将数据中 na 的数据去掉，然后将数据标准化到 0 ~ 1 之间。\n",
    "data_csv = data_csv.dropna()\n",
    "dataset = data_csv.values\n",
    "dataset = dataset.astype('float32')\n",
    "max_value = np.max(dataset)\n",
    "min_value = np.min(dataset)\n",
    "scalar = max_value - min_value\n",
    "dataset = list(map(lambda x: x / scalar, dataset))  # dataset.shape = (144, )"
   ]
  },
  {
   "source": [
    "## 2. 创建数据集\n",
    "接着我们进行数据集的创建，我们想通过前面几个月的流量来预测当月的流量：  \n",
    "- 比如我们希望通过前两个月的流量来预测当月的流量，我们可以将前两个月的流量当做输入，当月的流量当做输出。  \n",
    "- 同时我们需要将我们的数据集分为训练集和测试集，通过测试集的效果来测试模型的性能。   \n",
    "- 这里我们简单的将前面几年的数据作为训练集，后面两年的数据作为测试集。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "data_length: 142 142\n"
     ]
    }
   ],
   "source": [
    "# 这里的lookback表示用前两个月的流量预测当月流量\n",
    "def create_dataset(dataset, look_back=2):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - look_back):\n",
    "        a = dataset[i: (i + look_back)]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back])\n",
    "    return np.array(dataX), np.array(dataY)\n",
    "\n",
    "# 创建数据集以及打好标签\n",
    "data_X, data_Y = create_dataset(dataset)\n",
    "\n",
    "print(\"data_length:\", len(data_X), len(data_Y))\n",
    "\n",
    "train_size = int(len(data_X) * 0.7)\n",
    "test_size = len(data_X) - train_size\n",
    "train_X = data_X[:train_size]\n",
    "train_Y = data_Y[:train_size]\n",
    "test_X = data_X[train_size:]\n",
    "test_Y = data_Y[train_size:]"
   ]
  },
  {
   "source": [
    "## 3. 改变维度\n",
    "1. RNN 读入的数据维度是 (seq, batch, feature)，所以要重新改变一下数据的维度\n",
    "2. 这里只有一个序列，所以 batch 是 1，而输入的 feature 就是我们希望依据的几个月份，这里我们定的是两个月份，所以 feature 就是 2.\n",
    "3. 另外lstm结构中`input_size = 2`, `hidden_size = 4`"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "train_x shape: torch.Size([99, 1, 2])\ntrain_y shape: torch.Size([99, 1, 1])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "train_X = train_X.reshape(-1, 1, 2)\n",
    "train_Y = train_Y.reshape(-1, 1, 1)\n",
    "\n",
    "train_x = torch.from_numpy(train_X)\n",
    "train_y = torch.from_numpy(train_Y)\n",
    "print(\"train_x shape:\", train_x.shape)\n",
    "print(\"train_y shape:\", train_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "class lstm_reg(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size=1, num_layers=2):\n",
    "        super(lstm_reg, self).__init__()\n",
    "\n",
    "        self.rnn = nn.LSTM(input_size, hidden_size, num_layers) # LSTM\n",
    "        self.reg = nn.Linear(hidden_size, output_size)          # regression\n",
    "\n",
    "    def forward(self, x):\n",
    "        x, _ = self.rnn(x)\n",
    "        s, b, hidden = x.shape\n",
    "        x = x.view(-1, hidden)\n",
    "        x = self.reg(x)\n",
    "        x = x.view(s, b, -1)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "model: lstm_reg(\n",
      "  (rnn): LSTM(2, 4, num_layers=2)\n",
      "  (reg): Linear(in_features=4, out_features=1, bias=True)\n",
      ")\n",
      "Epoch: 100, Loss: 0.00460\n",
      "Epoch: 200, Loss: 0.00369\n",
      "Epoch: 300, Loss: 0.00325\n",
      "Epoch: 400, Loss: 0.00264\n",
      "Epoch: 500, Loss: 0.00181\n",
      "Epoch: 600, Loss: 0.00158\n",
      "Epoch: 700, Loss: 0.00154\n",
      "Epoch: 800, Loss: 0.00402\n",
      "Epoch: 900, Loss: 0.00148\n",
      "Epoch: 1000, Loss: 0.00143\n"
     ]
    }
   ],
   "source": [
    "net = lstm_reg(2, 4)\n",
    "print(\"model:\", net)\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)\n",
    "\n",
    "for e in range(1000):\n",
    "    # 前向传播\n",
    "    out = net(train_x)\n",
    "    loss = criterion(out, train_y)\n",
    "    # 反向传播\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    if (e + 1) % 100 == 0: # 每 100 次输出结果\n",
    "        print('Epoch: {}, Loss: {:.5f}'.format(e + 1, loss.item()))"
   ]
  },
  {
   "source": [
    "## 4.1 转换成测试模式\n",
    "- 用训练好的模型预测测试集数据\n",
    "- 画图表示，红线表示预测数据，蓝线表示测试集真实的数据"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe76c7f6ac8>"
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     "metadata": {},
     "execution_count": 21
    },
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# net = net.eval() \n",
    "test_X = torch.Tensor(test_X.reshape(-1, 1, 2))\n",
    "pred_test_X = net(test_X)\n",
    "pred_test_X = pred_test_X.view(-1).data.numpy()\n",
    "\n",
    "plt.plot(pred_test_X, 'r', label='prediction')\n",
    "plt.plot(test_Y, 'b', label='real')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "source": [
    "## 4.2 直接用整体数据预测看结果"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe76b7c6828>"
      ]
     },
     "metadata": {},
     "execution_count": 17
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "net = net.eval()\n",
    "data_X = data_X.reshape(-1, 1, 2)\n",
    "pred_test = net(data_X) # 测试集的预测结果\n",
    "\n",
    "pred_test = pred_test.view(-1).data.numpy()\n",
    "# 画出实际结果和预测的结果\n",
    "plt.plot(pred_test, 'r', label='prediction')\n",
    "plt.plot(dataset, 'b', label='real')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "source": [
    "## 5. 总结\n",
    "直接用75%的数据做训练预测后面25%的流量的结果图可以看出：不是那么准确，可能原因就是数据太少，毕竟只有144个月的数据"
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}