Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "9f9471e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import Dataset, DataLoader,TensorDataset\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from torch import tensor\n",
    "from pandas.core.frame import DataFrame\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error\n",
    "from sklearn.utils import shuffle\n",
    "import scipy as sp\n",
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "outputs": [],
   "source": [
    "def normalization(data,label):   # data[n,10,4] label[n,2]\n",
    "    mm_x = MinMaxScaler()\n",
    "    mm_y = MinMaxScaler()\n",
    "    seq_len = data.shape[1]\n",
    "    feature_num = data.shape[2]\n",
    "    data = data.reshape(-1,feature_num)\n",
    "    data = mm_x.fit_transform(data)\n",
    "    data = data.reshape(-1,seq_len,feature_num)\n",
    "    label = mm_y.fit_transform(label)\n",
    "    return data,label,mm_y"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "outputs": [],
   "source": [
    "def dbscan_predict(dbscan_model, X_new, metric=sp.spatial.distance.euclidean):\n",
    "    # Result is noise by default\n",
    "    y_new = np.ones(shape=len(X_new), dtype=int) * -1\n",
    "    # Iterate all input samples for a label\n",
    "    for j, x_new in enumerate(X_new):\n",
    "        # Find a core sample closer than EPS\n",
    "        for i, x_core in enumerate(dbscan_model.components_):\n",
    "            if metric(x_new, x_core) < dbscan_model.eps:\n",
    "                # Assign label of x_core to x_new\n",
    "                y_new[j] = dbscan_model.labels_[dbscan_model.core_sample_indices_[i]]\n",
    "                break\n",
    "\n",
    "    return y_new"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "6b686adb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self, feature_size, size_hidden,out_size,num_layers=1,dropout=0):\n",
    "        super().__init__()\n",
    "        self.feature_size = feature_size\n",
    "        self.size_hidden = size_hidden\n",
    "        self.num_layers = num_layers\n",
    "        self.num_direction = 1  # 单向lstm\n",
    "        self.rnn = nn.LSTM(input_size=feature_size, hidden_size=size_hidden, num_layers=num_layers,dropout=dropout, batch_first=True)\n",
    "        self.out = nn.Linear(size_hidden, out_size)\n",
    "        self.dropout=nn.Dropout(p=dropout)\n",
    "    def forward(self, input):\n",
    "        batch_size,seq_len=input.size()[0],input.size()[1]\n",
    "        # print(f\"batch_size:{batch_size}\",f\"seq_len:{seq_len}\")\n",
    "        h_0 = torch.rand(self.num_direction*self.num_layers,input.size(0),self.size_hidden)\n",
    "        h_0=h_0.cuda()\n",
    "        c_0 = torch.rand(self.num_direction*self.num_layers,input.size(0),self.size_hidden)\n",
    "        c_0=c_0.cuda()\n",
    "        # print(f\"h_0 shape:{h_0.shape}\")\n",
    "        output, _ = self.rnn(input,(h_0,c_0))\n",
    "        # output=self.dropout(output)    # 不加dropout效果会好不少\n",
    "        pred = self.out(output)   # [32,10,2]\n",
    "        pred = pred[:,-1,:]   # -> [32,2]?\n",
    "        # print(f\"pred_shape:{pred.shape}\")\n",
    "        return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "03752325",
   "metadata": {},
   "outputs": [],
   "source": [
    "# useful_tag = ['X', 'Y', 'SOG', 'COG', 'Heading']\n",
    "useful_tag = ['MMSI','LAT', 'LON','SOG']\n",
    "# predict_tag = ['X', 'Y']\n",
    "# predict_tag = ['LAT', 'LON']\n",
    "predict_tag = ['LAT', 'LON','SOG']\n",
    "len_topredict = 5\n",
    "# features = ['MMSI', 'BaseDateTime', 'X', 'Y', 'SOG', 'COG', 'Heading', 'Status']\n",
    "features = ['MMSI', 'BaseDateTime', 'LAT', 'LON', 'SOG', 'COG', 'Heading', 'Status']\n",
    "data_file_root_path = './data/path_data/'\n",
    "ratio = 0.8\n",
    "feature_size=len(useful_tag)\n",
    "batch_size=32 # 最好是2的次幂\n",
    "hidden_size = 15\n",
    "output_size=len(predict_tag)\n",
    "drop_out=0.3\n",
    "lr = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "eb814964",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyDataSet(Dataset):\n",
    "    def __init__(self, df: DataFrame):\n",
    "        self.x_data,self.y_data = df[useful_tag].astype('float32').values,df[predict_tag].astype('float32').values\n",
    "        self.length = len(self.x_data)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.x_data[index], self.y_data[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "outputs": [],
   "source": [
    "def get_cluster(trajectorys):\n",
    "    lst = list()\n",
    "    cnt = 0\n",
    "    for trajectory in trajectorys:\n",
    "        if  cnt < 5:\n",
    "            print(trajectory)\n",
    "        cnt += 1\n",
    "        init_point = trajectory[0]\n",
    "        lst.append(init_point)\n",
    "    cluster = DBSCAN(eps=0.02,min_samples=7)\n",
    "    cluster.fit(np.array(lst))\n",
    "    labels = cluster.labels_\n",
    "    print(len(set(labels)))\n",
    "    print(labels[:100])\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9ada6570",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "504\n",
      "(50026, 5, 4)\n",
      "(50026, 3)\n"
     ]
    }
   ],
   "source": [
    "X = []\n",
    "y = []\n",
    "num = 0\n",
    "for i in range(1000):\n",
    "    data_file_path = data_file_root_path + 'id' + str(i) + '/'\n",
    "    \"\"\"\n",
    "        还要做一步归一化\n",
    "    \"\"\"\n",
    "    file_lst = os.listdir(data_file_path)\n",
    "    for filename in file_lst:\n",
    "        path = data_file_path + filename\n",
    "        df = pd.read_csv(path)\n",
    "        if len(y) < 50000:\n",
    "            num += 1\n",
    "            dataset = MyDataSet(df)\n",
    "            for j in range(len_topredict, len(dataset)):\n",
    "                t1, t2 = dataset.x_data[j - len_topredict:j], dataset.y_data[j]   # 改预测的feature的话要改这里\n",
    "                X.append(t1)\n",
    "                y.append(t2)\n",
    "            # train_loader = DataLoader(dataset=dataset, batch_size=3)  # 这里batch_size就是一次从一个轨迹中取几个点\n",
    "# 109条轨迹\n",
    "print(num)\n",
    "X,y = np.array(X),np.array(y)\n",
    "print(X.shape)\n",
    "print(y.shape)\n",
    "X,y,mm_y = normalization(X,y)\n",
    "# get_cluster(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "bb6ecfc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data(X,y,split_ratio):\n",
    "    train_size=int(len(y)*split_ratio)\n",
    "    test_size=len(y)-train_size\n",
    "    X_data = torch.Tensor(np.array(X))\n",
    "    y_data = torch.Tensor(np.array(y))\n",
    "    X_train = torch.Tensor(np.array(X[0:train_size]))\n",
    "    y_train = torch.Tensor(np.array(y[0:train_size]))\n",
    "    X_test = torch.Tensor(np.array(X[train_size:]))\n",
    "    y_test = torch.Tensor(np.array(y[train_size:]))\n",
    "    return X_train,y_train,X_test,y_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "bad12100",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataset(X_train,y_train,X_test,y_test):\n",
    "    train_dataset = TensorDataset(X_train,y_train)\n",
    "    test_dataset = TensorDataset(X_test,y_test)\n",
    "    return train_dataset,test_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "outputs": [],
   "source": [
    "def train(net, train_iter, loss, epochs, lr):\n",
    "    trainer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "    iter = 0\n",
    "    los = []\n",
    "    for epoch in range(epochs):\n",
    "        for i,(X, y) in enumerate(train_iter):\n",
    "            X,y = shuffle(X,y,random_state=131)\n",
    "            X = X.cuda()\n",
    "            y = y.cuda()\n",
    "            # print(X.shape,y.shape)  #  X [32,10,5]  y [32,2]\n",
    "            trainer.zero_grad()\n",
    "            l = loss(net(X), y)\n",
    "            iter += 1\n",
    "            if iter % 100 == 0:\n",
    "                los.append(l.sum())\n",
    "                # print(f'iter: {iter}', f'loss：{l.sum()}')\n",
    "            l.sum().backward()\n",
    "            trainer.step()\n",
    "    los=torch.stack(los)\n",
    "    plt.plot(range(len(los)), los.data.cpu().numpy())\n",
    "    plt.xlabel('step')\n",
    "    plt.ylabel('loss')\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "outputs": [],
   "source": [
    "def test(X,y):\n",
    "    X = X.cuda()\n",
    "    y = y.cuda()\n",
    "    net.eval()\n",
    "    predict = net(X)\n",
    "    predict = predict.data.cpu().numpy()\n",
    "    # y_data_plot = np.reshape(y_data_plot,(-1,1))\n",
    "    predict = mm_y.inverse_transform(predict)\n",
    "    y_data_plot = mm_y.inverse_transform(y.data.cpu().numpy())\n",
    "    # print(y_data_plot[:20])\n",
    "    # print(predict[:20])\n",
    "    plt.plot(y_data_plot)\n",
    "    plt.plot(predict)\n",
    "    plt.legend(('real','predict'), fontsize='15')\n",
    "    plt.show()\n",
    "    print(f'LAT:  mean_absolute_error: {mean_absolute_error(y_data_plot[:,0],predict[:,0])}, mean_squared_error: {mean_squared_error(y_data_plot[:,0],predict[:,0])}')\n",
    "    print(f'LON:  mean_absolute_error: {mean_absolute_error(y_data_plot[:,1],predict[:,1])}, mean_squared_error: {mean_squared_error(y_data_plot[:,1],predict[:,1])}')\n",
    "    print(f'SOG:  mean_absolute_error: {mean_absolute_error(y_data_plot[:,2],predict[:,2])}, mean_squared_error: {mean_squared_error(y_data_plot[:,2],predict[:,2])}')\n",
    "    # print(f'COG:  mean_absolute_error: {mean_absolute_error(y_data_plot[:,3],predict[:,3])}, mean_squared_error: {mean_squared_error(y_data_plot[:,3],predict[:,3])}')\n",
    "    # print(f'Heading:  mean_absolute_error: {mean_absolute_error(y_data_plot[:,4],predict[:,4])}, mean_squared_error: {mean_squared_error(y_data_plot[:,4],predict[:,4])}')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (rnn): LSTM(4, 15, batch_first=True, dropout=0.3)\n",
      "  (out): Linear(in_features=15, out_features=3, bias=True)\n",
      "  (dropout): Dropout(p=0.3, inplace=False)\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ashdr\\miniconda3\\lib\\site-packages\\torch\\nn\\modules\\rnn.py:67: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.3 and num_layers=1\n",
      "  warnings.warn(\"dropout option adds dropout after all but last \"\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = 20\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "loss = nn.MSELoss(reduction='none').to(device)\n",
    "# loss=nn.L1Loss(reduction='sum')\n",
    "net = Net(feature_size=feature_size,size_hidden=hidden_size,out_size=output_size,dropout=drop_out,num_layers=1)\n",
    "net = net.to(device)\n",
    "print(net)\n",
    "X,y = shuffle(X,y,random_state=711)\n",
    "X_train,y_train,X_test,y_test = split_data(X,y,ratio)\n",
    "train_dataset,test_dataset = get_dataset(X_train,y_train,X_test, y_test)\n",
    "train_loader = DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True,drop_last=True)\n",
    "test_loader = DataLoader(dataset=test_dataset,batch_size=batch_size,shuffle=True,drop_last=True)  #batch_size=32   [32,10,5]\n",
    "train(net=net,train_iter=train_loader,loss=loss,epochs=epochs,lr=lr)\n",
    "    # 每个epoch 选择不同的train和test?"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LAT:  mean_absolute_error: 0.03195713460445404, mean_squared_error: 0.002108186949044466\n",
      "LON:  mean_absolute_error: 0.07556184381246567, mean_squared_error: 0.007771079894155264\n",
      "SOG:  mean_absolute_error: 0.45257309079170227, mean_squared_error: 1.8113151788711548\n"
     ]
    }
   ],
   "source": [
    "# X,y = shuffle(X,y)\n",
    "# X_train,y_train,X_test,y_test = split_data(X,y,ratio)\n",
    "# print(y_test.shape)\n",
    "test(X_test,y_test)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}