model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch_geometric.data import Data\n",
    "from torch_geometric.nn import GATConv\n",
    "\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (145, 145, 200)\n",
      "Labels shape: (145, 145)\n"
     ]
    }
   ],
   "source": [
    "data_mat = sio.loadmat('Indian_pines_corrected.mat')\n",
    "labels_mat = sio.loadmat('Indian_pines_gt.mat')\n",
    "data = data_mat['indian_pines_corrected']\n",
    "labels = labels_mat['indian_pines_gt']  #一共十六个类别,0-16,0代表背景,应该去除\n",
    "\n",
    "# 数据形状\n",
    "print('Data shape:', data.shape)\n",
    "print('Labels shape:', labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masked data shape: (10249, 200)\n",
      "Masked labels shape: (10249,)\n",
      "Number of classes: 16\n"
     ]
    }
   ],
   "source": [
    "# 重塑数据\n",
    "height, width, bands = data.shape\n",
    "data_reshaped = data.reshape(-1, bands)\n",
    "labels_reshaped = labels.reshape(-1)\n",
    "\n",
    "\n",
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "data_reshaped = scaler.fit_transform(data_reshaped)\n",
    "\n",
    "# 去除为0的标签（未标记的像素）\n",
    "masked_data = data_reshaped[labels_reshaped > 0]\n",
    "print('Masked data shape:', masked_data.shape)\n",
    "masked_labels = labels_reshaped[labels_reshaped > 0] - 1  # 标签从0开始\n",
    "print('Masked labels shape:', masked_labels.shape)\n",
    "# 查看类别数量\n",
    "num_classes = np.max(masked_labels) + 1\n",
    "print('Number of classes:', num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "class HSI_Dataset(Dataset):\n",
    "    def __init__(self, data, labels):\n",
    "        self.data = torch.from_numpy(data).float()\n",
    "        self.labels = torch.from_numpy(labels).long()\n",
    "        self.indices = np.arange(len(self.labels))\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        # 将光谱数据视为“图像”的通道\n",
    "        sample = self.data[idx]\n",
    "        sample = sample.unsqueeze(1).unsqueeze(2)  # shape: [bands, 1, 1]\n",
    "        label = self.labels[idx]\n",
    "        index = self.indices[idx]  # 获取样本索引\n",
    "        return sample, label, index\n",
    "\n",
    "# 划分训练和测试集\n",
    "train_data, test_data, train_labels, test_labels = train_test_split(\n",
    "    masked_data, masked_labels, test_size=0.98, random_state=42, stratify=masked_labels)  #每种类别的比例保持一致\n",
    "\n",
    "# 创建数据集\n",
    "train_dataset = HSI_Dataset(train_data, train_labels)\n",
    "test_dataset = HSI_Dataset(test_data, test_labels)\n",
    "\n",
    "# 创建数据加载器\n",
    "batch_size = 64\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CNN编码器，用于提取光谱特征\n",
    "class CNNEncoder(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels):\n",
    "        super(CNNEncoder, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)\n",
    "        self.conv2 = nn.Conv2d(64, out_channels, kernel_size=3, padding=1)\n",
    "        self.pool = nn.AdaptiveAvgPool2d(1)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x形状: [batch_size, in_channels, height, width]\n",
    "        x = self.relu(self.conv1(x))\n",
    "        x = self.relu(self.conv2(x))\n",
    "        x = self.pool(x)\n",
    "        x = x.view(x.size(0), -1)  # 展平\n",
    "        return x  # 返回形状: [batch_size, out_channels]\n",
    "\n",
    "# GAT编码器，用于捕获空间关系\n",
    "class GATEncoder(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, heads=8):\n",
    "        super(GATEncoder, self).__init__()\n",
    "        self.gat1 = GATConv(in_channels, 64, heads=heads, concat=True)\n",
    "        self.gat2 = GATConv(64 * heads, out_channels, heads=1, concat=False)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        # x形状: [num_nodes, in_channels]\n",
    "        x = self.relu(self.gat1(x, edge_index))\n",
    "        x = self.relu(self.gat2(x, edge_index))\n",
    "        return x  # 返回形状: [num_nodes, out_channels]\n",
    "\n",
    "# 加权特征融合模型\n",
    "class WeightedFeatureFusion(nn.Module):\n",
    "    def __init__(self, cnn_in_channels, gat_in_channels, num_classes):\n",
    "        super(WeightedFeatureFusion, self).__init__()\n",
    "        self.cnn_encoder = CNNEncoder(cnn_in_channels, 128)\n",
    "        self.gat_encoder = GATEncoder(gat_in_channels, 128)\n",
    "        self.cnn_weight = nn.Parameter(torch.tensor(0.5))\n",
    "        self.gat_weight = nn.Parameter(torch.tensor(0.5))\n",
    "        self.classifier = nn.Linear(128, num_classes)\n",
    "\n",
    "    def forward(self, cnn_x, data):\n",
    "        # cnn_x 形状：[num_nodes, in_channels, 1, 1]\n",
    "        cnn_feat = self.cnn_encoder(cnn_x)  # 形状：[num_nodes, 128]\n",
    "        gat_feat = self.gat_encoder(data.x, data.edge_index)  # 形状：[num_nodes, 128]\n",
    "\n",
    "        # 融合特征\n",
    "        fused_feat = self.cnn_weight * cnn_feat + self.gat_weight * gat_feat\n",
    "        out = self.classifier(fused_feat)  # 形状：[num_nodes, num_classes]\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cpu')\n",
    "\n",
    "model = WeightedFeatureFusion(cnn_in_channels=bands, gat_in_channels=bands, num_classes=num_classes)\n",
    "model=model.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss(ignore_index=255)\n",
    "optimizer = optim.AdamW(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All labels: tensor([  2,   2,   2,  ..., 255, 255, 255])\n"
     ]
    }
   ],
   "source": [
    "# 创建图数据\n",
    "from torch_geometric.utils import grid\n",
    "\n",
    "# 获取图的边索引\n",
    "edge_index = grid(height, width)[0]\n",
    "\n",
    "# 所有节点的特征\n",
    "node_features = torch.from_numpy(data_reshaped).float()  # 形状：[num_nodes, feature_dim]\n",
    "\n",
    "# 节点标签（包括未标记的节点）\n",
    "#labels_reshaped = labels_reshaped.astype(np.int32)\n",
    "labels_reshaped[labels_reshaped == 0] = 255\n",
    "labels_reshaped[labels_reshaped != 255] -= 1\n",
    "all_labels = torch.from_numpy(labels_reshaped).long()  # 减 1 是让标签从 0 开始\n",
    "print('All labels:', all_labels)\n",
    "# 创建图数据对象\n",
    "graph_data = Data(x=node_features.to(device), edge_index=edge_index.to(device))\n",
    "\n",
    "# 划分训练集和测试集\n",
    "labeled_indices = torch.where(all_labels != 255)[0]\n",
    "\n",
    "train_indices, test_indices = train_test_split(\n",
    "    labeled_indices.cpu().numpy(),\n",
    "    test_size=0.98,\n",
    "    random_state=42,\n",
    "    stratify=all_labels[labeled_indices].cpu()\n",
    ")\n",
    "\n",
    "train_mask = torch.zeros(all_labels.size(0), dtype=torch.bool)\n",
    "test_mask = torch.zeros(all_labels.size(0), dtype=torch.bool)\n",
    "train_mask[train_indices] = True\n",
    "test_mask[test_indices] = True\n",
    "\n",
    "masked_labels = all_labels  # 包含未标记的节点，值为 -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_labels = np.unique(masked_labels.cpu().numpy())\n",
    "#print(\"Unique labels:\", unique_labels[16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/100], Loss: 2.7172\n",
      "Test Accuracy: 36.36%\n",
      "Epoch [2/100], Loss: 2.4372\n",
      "Test Accuracy: 36.49%\n",
      "Epoch [3/100], Loss: 2.2133\n",
      "Test Accuracy: 36.33%\n",
      "Epoch [4/100], Loss: 2.0350\n",
      "Test Accuracy: 36.30%\n",
      "Epoch [5/100], Loss: 1.9099\n",
      "Test Accuracy: 36.39%\n",
      "Epoch [6/100], Loss: 1.8262\n",
      "Test Accuracy: 38.94%\n",
      "Epoch [7/100], Loss: 1.7594\n",
      "Test Accuracy: 40.68%\n",
      "Epoch [8/100], Loss: 1.7050\n",
      "Test Accuracy: 41.23%\n",
      "Epoch [9/100], Loss: 1.6639\n",
      "Test Accuracy: 41.71%\n",
      "Epoch [10/100], Loss: 1.6175\n",
      "Test Accuracy: 42.20%\n",
      "Epoch [11/100], Loss: 1.5672\n",
      "Test Accuracy: 43.57%\n",
      "Epoch [12/100], Loss: 1.5288\n",
      "Test Accuracy: 45.80%\n",
      "Epoch [13/100], Loss: 1.5015\n",
      "Test Accuracy: 46.90%\n",
      "Epoch [14/100], Loss: 1.4693\n",
      "Test Accuracy: 48.47%\n",
      "Epoch [15/100], Loss: 1.4264\n",
      "Test Accuracy: 49.16%\n",
      "Epoch [16/100], Loss: 1.3835\n",
      "Test Accuracy: 49.53%\n",
      "Epoch [17/100], Loss: 1.3516\n",
      "Test Accuracy: 49.36%\n",
      "Epoch [18/100], Loss: 1.3255\n",
      "Test Accuracy: 49.45%\n",
      "Epoch [19/100], Loss: 1.2934\n",
      "Test Accuracy: 49.72%\n",
      "Epoch [20/100], Loss: 1.2590\n",
      "Test Accuracy: 50.13%\n",
      "Epoch [21/100], Loss: 1.2295\n",
      "Test Accuracy: 51.16%\n",
      "Epoch [22/100], Loss: 1.2040\n",
      "Test Accuracy: 51.01%\n",
      "Epoch [23/100], Loss: 1.1798\n",
      "Test Accuracy: 51.40%\n",
      "Epoch [24/100], Loss: 1.1528\n",
      "Test Accuracy: 52.03%\n",
      "Epoch [25/100], Loss: 1.1237\n",
      "Test Accuracy: 52.73%\n",
      "Epoch [26/100], Loss: 1.0987\n",
      "Test Accuracy: 53.41%\n",
      "Epoch [27/100], Loss: 1.0772\n",
      "Test Accuracy: 54.68%\n",
      "Epoch [28/100], Loss: 1.0547\n",
      "Test Accuracy: 55.98%\n",
      "Epoch [29/100], Loss: 1.0317\n",
      "Test Accuracy: 56.87%\n",
      "Epoch [30/100], Loss: 1.0116\n",
      "Test Accuracy: 57.45%\n",
      "Epoch [31/100], Loss: 0.9901\n",
      "Test Accuracy: 57.85%\n",
      "Epoch [32/100], Loss: 0.9646\n",
      "Test Accuracy: 57.80%\n",
      "Epoch [33/100], Loss: 0.9408\n",
      "Test Accuracy: 57.68%\n",
      "Epoch [34/100], Loss: 0.9200\n",
      "Test Accuracy: 57.96%\n",
      "Epoch [35/100], Loss: 0.8993\n",
      "Test Accuracy: 58.58%\n",
      "Epoch [36/100], Loss: 0.8777\n",
      "Test Accuracy: 59.44%\n",
      "Epoch [37/100], Loss: 0.8566\n",
      "Test Accuracy: 60.47%\n",
      "Epoch [38/100], Loss: 0.8356\n",
      "Test Accuracy: 61.50%\n",
      "Epoch [39/100], Loss: 0.8127\n",
      "Test Accuracy: 62.01%\n",
      "Epoch [40/100], Loss: 0.7909\n",
      "Test Accuracy: 62.38%\n",
      "Epoch [41/100], Loss: 0.7703\n",
      "Test Accuracy: 62.71%\n",
      "Epoch [42/100], Loss: 0.7489\n",
      "Test Accuracy: 62.71%\n",
      "Epoch [43/100], Loss: 0.7273\n",
      "Test Accuracy: 62.55%\n",
      "Epoch [44/100], Loss: 0.7071\n",
      "Test Accuracy: 63.25%\n",
      "Epoch [45/100], Loss: 0.6858\n",
      "Test Accuracy: 64.58%\n",
      "Epoch [46/100], Loss: 0.6659\n",
      "Test Accuracy: 65.18%\n",
      "Epoch [47/100], Loss: 0.6466\n",
      "Test Accuracy: 65.30%\n",
      "Epoch [48/100], Loss: 0.6266\n",
      "Test Accuracy: 65.51%\n",
      "Epoch [49/100], Loss: 0.6066\n",
      "Test Accuracy: 65.45%\n",
      "Epoch [50/100], Loss: 0.5890\n",
      "Test Accuracy: 65.81%\n",
      "Epoch [51/100], Loss: 0.5683\n",
      "Test Accuracy: 66.59%\n",
      "Epoch [52/100], Loss: 0.5517\n",
      "Test Accuracy: 67.33%\n",
      "Epoch [53/100], Loss: 0.5357\n",
      "Test Accuracy: 67.58%\n",
      "Epoch [54/100], Loss: 0.5175\n",
      "Test Accuracy: 67.88%\n",
      "Epoch [55/100], Loss: 0.5015\n",
      "Test Accuracy: 68.18%\n",
      "Epoch [56/100], Loss: 0.4860\n",
      "Test Accuracy: 68.51%\n",
      "Epoch [57/100], Loss: 0.4692\n",
      "Test Accuracy: 69.09%\n",
      "Epoch [58/100], Loss: 0.4533\n",
      "Test Accuracy: 69.43%\n",
      "Epoch [59/100], Loss: 0.4385\n",
      "Test Accuracy: 69.27%\n",
      "Epoch [60/100], Loss: 0.4235\n",
      "Test Accuracy: 69.83%\n",
      "Epoch [61/100], Loss: 0.4091\n",
      "Test Accuracy: 69.99%\n",
      "Epoch [62/100], Loss: 0.3952\n",
      "Test Accuracy: 70.58%\n",
      "Epoch [63/100], Loss: 0.3816\n",
      "Test Accuracy: 70.53%\n",
      "Epoch [64/100], Loss: 0.3682\n",
      "Test Accuracy: 70.65%\n",
      "Epoch [65/100], Loss: 0.3556\n",
      "Test Accuracy: 70.79%\n",
      "Epoch [66/100], Loss: 0.3432\n",
      "Test Accuracy: 71.12%\n",
      "Epoch [67/100], Loss: 0.3313\n",
      "Test Accuracy: 71.20%\n",
      "Epoch [68/100], Loss: 0.3198\n",
      "Test Accuracy: 71.02%\n",
      "Epoch [69/100], Loss: 0.3086\n",
      "Test Accuracy: 71.32%\n",
      "Epoch [70/100], Loss: 0.2978\n",
      "Test Accuracy: 70.78%\n",
      "Epoch [71/100], Loss: 0.2894\n",
      "Test Accuracy: 71.34%\n",
      "Epoch [72/100], Loss: 0.2860\n",
      "Test Accuracy: 69.88%\n",
      "Epoch [73/100], Loss: 0.2783\n",
      "Test Accuracy: 71.39%\n",
      "Epoch [74/100], Loss: 0.2621\n",
      "Test Accuracy: 71.55%\n",
      "Epoch [75/100], Loss: 0.2500\n",
      "Test Accuracy: 70.27%\n",
      "Epoch [76/100], Loss: 0.2482\n",
      "Test Accuracy: 71.54%\n",
      "Epoch [77/100], Loss: 0.2376\n",
      "Test Accuracy: 71.34%\n",
      "Epoch [78/100], Loss: 0.2260\n",
      "Test Accuracy: 70.92%\n",
      "Epoch [79/100], Loss: 0.2219\n",
      "Test Accuracy: 71.67%\n",
      "Epoch [80/100], Loss: 0.2157\n",
      "Test Accuracy: 71.64%\n",
      "Epoch [81/100], Loss: 0.2064\n",
      "Test Accuracy: 70.85%\n",
      "Epoch [82/100], Loss: 0.2001\n",
      "Test Accuracy: 71.41%\n",
      "Epoch [83/100], Loss: 0.1933\n",
      "Test Accuracy: 71.39%\n",
      "Epoch [84/100], Loss: 0.1884\n",
      "Test Accuracy: 70.64%\n",
      "Epoch [85/100], Loss: 0.1831\n",
      "Test Accuracy: 71.22%\n",
      "Epoch [86/100], Loss: 0.1737\n",
      "Test Accuracy: 71.76%\n",
      "Epoch [87/100], Loss: 0.1700\n",
      "Test Accuracy: 71.46%\n",
      "Epoch [88/100], Loss: 0.1616\n",
      "Test Accuracy: 70.95%\n",
      "Epoch [89/100], Loss: 0.1585\n",
      "Test Accuracy: 71.47%\n",
      "Epoch [90/100], Loss: 0.1522\n",
      "Test Accuracy: 71.45%\n",
      "Epoch [91/100], Loss: 0.1457\n",
      "Test Accuracy: 70.84%\n",
      "Epoch [92/100], Loss: 0.1440\n",
      "Test Accuracy: 71.06%\n",
      "Epoch [93/100], Loss: 0.1376\n",
      "Test Accuracy: 71.00%\n",
      "Epoch [94/100], Loss: 0.1348\n",
      "Test Accuracy: 70.90%\n",
      "Epoch [95/100], Loss: 0.1282\n",
      "Test Accuracy: 70.91%\n",
      "Epoch [96/100], Loss: 0.1231\n",
      "Test Accuracy: 70.97%\n",
      "Epoch [97/100], Loss: 0.1207\n",
      "Test Accuracy: 70.92%\n",
      "Epoch [98/100], Loss: 0.1143\n",
      "Test Accuracy: 71.37%\n",
      "Epoch [99/100], Loss: 0.1100\n",
      "Test Accuracy: 71.60%\n",
      "Epoch [100/100], Loss: 0.1068\n",
      "Test Accuracy: 71.35%\n",
      "Confusion Matrix:\n",
      "[[   464    390      0     80     62      0      0   2943      0      0\n",
      "     224     11      0     54     30    242]\n",
      " [     0  69231   1387    473      0    378      0     95      0  18545\n",
      "   44847   4229      1     43    183    588]\n",
      " [     0   8294  22476   2148     28    226      0      0      0   3793\n",
      "   40456   3883     10      0      0     86]\n",
      " [     0   6714    824   9549      0    175      0     99      0    273\n",
      "    4382   1159     22      0      0      3]\n",
      " [   599    700    129    458  25163   2723     88    543      0    315\n",
      "     925    711     93  14616    225     12]\n",
      " [     0      0      0     52   1499  52845      0      0      0    338\n",
      "     621      0    207  15710    228      0]\n",
      " [    95     10      0      0    671     47    244    999      0      0\n",
      "     548      0     15     23     48      0]\n",
      " [   223   3338      0      0     23    212      0  39877      0      0\n",
      "    3041      0     35    102     49      0]\n",
      " [     0      2     12    146      0    651      0      0      0      0\n",
      "     179      0    915     53     42      0]\n",
      " [     0   6307   1436    329      0    762      0      0      0  41813\n",
      "   44095    426     29     18     85      0]\n",
      " [     0  18944   5364   1051     16   1118      0      0      0  17115\n",
      "  188083   8621     20      5      3    260]\n",
      " [     0  14117   2632   2741      0    824      0      0      0   6986\n",
      "   13654  16263     35      4    802     42]\n",
      " [     3      2      0     70      0   3285      0      0      0     29\n",
      "     208      0  14736   1723     44      0]\n",
      " [     0      0      0      0   1819    163      0      0      0      0\n",
      "       0      0      0 120107   1910      1]\n",
      " [     1      0      0     29      0   5299      0      0      0      2\n",
      "      23      0    612  15387  16447      0]\n",
      " [     0    197      0      0      0      0      0      0      0     43\n",
      "     988   5309      0      0      0   2563]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/huasheng/miniconda3/envs/IndianPines/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.34      0.10      0.16      4500\n",
      "           1       0.54      0.49      0.52    140000\n",
      "           2       0.66      0.28      0.39     81400\n",
      "           3       0.56      0.41      0.47     23200\n",
      "           4       0.86      0.53      0.66     47300\n",
      "           5       0.77      0.74      0.75     71500\n",
      "           6       0.73      0.09      0.16      2700\n",
      "           7       0.89      0.85      0.87     46900\n",
      "           8       0.00      0.00      0.00      2000\n",
      "           9       0.47      0.44      0.45     95300\n",
      "          10       0.55      0.78      0.65    240600\n",
      "          11       0.40      0.28      0.33     58100\n",
      "          12       0.88      0.73      0.80     20100\n",
      "          13       0.72      0.97      0.82    124000\n",
      "          14       0.82      0.44      0.57     37800\n",
      "          15       0.68      0.28      0.40      9100\n",
      "\n",
      "    accuracy                           0.62   1004500\n",
      "   macro avg       0.62      0.46      0.50   1004500\n",
      "weighted avg       0.62      0.62      0.60   1004500\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/huasheng/miniconda3/envs/IndianPines/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/huasheng/miniconda3/envs/IndianPines/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 100\n",
    "\n",
    "# 将节点特征调整为 CNN 输入的形状\n",
    "cnn_x = node_features.unsqueeze(2).unsqueeze(3).to(device)  # 形状：[num_nodes, in_channels, 1, 1]\n",
    "\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    outputs = model(cnn_x, graph_data)  # 输出形状：[num_nodes, num_classes]\n",
    "\n",
    "    # 只计算训练集上的损失\n",
    "    loss = criterion(outputs[train_mask], masked_labels[train_mask].to(device))\n",
    "    #loss = criterion(outputs[train_mask], all_labels[train_mask].to(device))\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n",
    "\n",
    "    # 验证\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        outputs = model(cnn_x, graph_data)\n",
    "        _, predicted = torch.max(outputs[test_mask], 1)\n",
    "        total = test_mask.sum().item()\n",
    "        correct = (predicted.cpu() == masked_labels[test_mask]).sum().item()\n",
    "        accuracy = 100 * correct / total\n",
    "        print('Test Accuracy: {:.2f}%'.format(accuracy))\n",
    "        \n",
    "        all_preds.extend(predicted.cpu().numpy())\n",
    "        all_labels.extend(masked_labels[test_mask].cpu().numpy())\n",
    "\n",
    "# 计算混淆矩阵\n",
    "cm = confusion_matrix(all_labels, all_preds)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)\n",
    "\n",
    "# 可视化混淆矩阵\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "# 打印分类报告\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(all_labels, all_preds))"
   ]
  }
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