基于Cora数据集的GCN节点分类-老年痴呆自我回忆手册

技术文章 1个月前 完美者
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标签:ict   flat   方差   make   val   构建   elf   不能   orm   

Data的格式

Data = namedtuple(‘Data‘, [‘x‘, ‘y‘, ‘adjacency‘, ‘train_mask‘, ‘val_mask‘, ‘test_mask‘])

Cora数据,包括数据下载,处理,加载等功能。当数据的缓存文件存在时,将使用缓存文件,否则将下载、进行处理,并缓存到磁盘

class CoraData(object):
    download_url = "https://github.com/kimiyoung/planetoid/raw/master/data"
    filenames = ["ind.cora.{}".format(name) for name in
                 [‘x‘, ‘tx‘, ‘allx‘, ‘y‘, ‘ty‘, ‘ally‘, ‘graph‘, ‘test.index‘]]

    def __init__(self, data_root="cora", rebuild=False):
        self.data_root = data_root
        save_file = osp.join(self.data_root, "processed_cora.pkl") #Python join() 方法用于将序列中的元素以指定的字符连接生成一个新的字符串。
        if osp.exists(save_file) and not rebuild:
            print("Using Cached file: {}".format(save_file))
            self._data = pickle.load(open(save_file, "rb"))
        else:
            self.maybe_download()
            self._data = self.process_data()
            with open(save_file, "wb") as f:
                pickle.dump(self.data, f)
            print("Cached file: {}".format(save_file))  #Cached file缓存文件
    def data(self):
        """返回Data数据对象,包括x, y, adjacency, train_mask, val_mask, test_mask"""
        return self._data

    def process_data(self):
        """
        处理数据,得到节点特征和标签,邻接矩阵,训练集、验证集以及测试集
        引用自:https://github.com/rusty1s/pytorch_geometric
        """
        print("Process data ...")
        _, tx, allx, y, ty, ally, graph, test_index = [self.read_data(
            osp.join(self.data_root, "raw", name)) for name in self.filenames]
        train_index = np.arange(y.shape[0])
        val_index = np.arange(y.shape[0], y.shape[0] + 500)
        sorted_test_index = sorted(test_index)

        x = np.concatenate((allx, tx), axis=0)
        y = np.concatenate((ally, ty), axis=0).argmax(axis=1)

        x[test_index] = x[sorted_test_index]
        y[test_index] = y[sorted_test_index]
        num_nodes = x.shape[0]

        train_mask = np.zeros(num_nodes, dtype=np.bool)
        val_mask = np.zeros(num_nodes, dtype=np.bool)
        test_mask = np.zeros(num_nodes, dtype=np.bool)
        train_mask[train_index] = True
        val_mask[val_index] = True
        test_mask[test_index] = True
        adjacency = self.build_adjacency(graph)
        print("Node‘s feature shape: ", x.shape)
        print("Node‘s label shape: ", y.shape)
        print("Adjacency‘s shape: ", adjacency.shape)
        print("Number of training nodes: ", train_mask.sum())
        print("Number of validation nodes: ", val_mask.sum())
        print("Number of test nodes: ", test_mask.sum())

        return Data(x=x, y=y, adjacency=adjacency,
                    train_mask=train_mask, val_mask=val_mask, test_mask=test_mask)

    def maybe_download(self):
        save_path = os.path.join(self.data_root, "raw")
        for name in self.filenames:
            if not osp.exists(osp.join(save_path, name)):
                self.download_data(
                    "{}/{}".format(self.download_url, name), save_path)

    @staticmethod
    def build_adjacency(adj_dict):
        """根据邻接表创建邻接矩阵"""
        edge_index = []
        num_nodes = len(adj_dict)
        for src, dst in adj_dict.items():
            edge_index.extend([src, v] for v in dst)
            edge_index.extend([v, src] for v in dst)
        # 去除重复的边
        edge_index = list(k for k, _ in itertools.groupby(sorted(edge_index)))
        edge_index = np.asarray(edge_index)
        adjacency = sp.coo_matrix((np.ones(len(edge_index)), 
                                   (edge_index[:, 0], edge_index[:, 1])),
                    shape=(num_nodes, num_nodes), dtype="float32")
        return adjacency

    @staticmethod
    def read_data(path):
        """使用不同的方式读取原始数据以进一步处理"""
        name = osp.basename(path)
        if name == "ind.cora.test.index":
            out = np.genfromtxt(path, dtype="int64")
            return out
        else:
            out = pickle.load(open(path, "rb"), encoding="latin1")
            out = out.toarray() if hasattr(out, "toarray") else out
            return out

    @staticmethod
    def download_data(url, save_path):
        """数据下载工具,当原始数据不存在时将会进行下载"""
        if not os.path.exists(save_path):
            os.makedirs(save_path)
        data = urllib.request.urlopen(url)
        filename = os.path.split(url)[-1]

        with open(os.path.join(save_path, filename), ‘wb‘) as f:
            f.write(data.read())

        return True

    @staticmethod
    def normalization(adjacency):
        """计算 L=D^-0.5 * (A+I) * D^-0.5"""
        adjacency += sp.eye(adjacency.shape[0])    # 增加自连接
        degree = np.array(adjacency.sum(1))
        d_hat = sp.diags(np.power(degree, -0.5).flatten())
        return d_hat.dot(adjacency).dot(d_hat).tocoo()


# # 基于Cora数据集的GCN节点分类

# In[1]:


import itertools   #循环器每次返回的对象将赋予给i,直到循环结束
import os
import os.path as osp
import pickle      #可以将对象以文件的形式存放在磁盘上。
import urllib      #urllib是python内置的http请求库
from collections import namedtuple  #具名元组

import numpy as np
import scipy.sparse as sp
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torch.optim as optim
import matplotlib.pyplot as plt


# ## 数据准备

# In[2]:


Data = namedtuple(‘Data‘, [‘x‘, ‘y‘, ‘adjacency‘,
                           ‘train_mask‘, ‘val_mask‘, ‘test_mask‘])


class CoraData(object):
    download_url = "https://github.com/kimiyoung/planetoid/raw/master/data"
    filenames = ["ind.cora.{}".format(name) for name in
                 [‘x‘, ‘tx‘, ‘allx‘, ‘y‘, ‘ty‘, ‘ally‘, ‘graph‘, ‘test.index‘]]

    def __init__(self, data_root="cora", rebuild=False):
        """Cora数据,包括数据下载,处理,加载等功能
        当数据的缓存文件存在时,将使用缓存文件,否则将下载、进行处理,并缓存到磁盘

        处理之后的数据可以通过属性 .data 获得,它将返回一个数据对象,包括如下几部分:
            * x: 节点的特征,维度为 2708 * 1433,类型为 np.ndarray
            * y: 节点的标签,总共包括7个类别,类型为 np.ndarray
            * adjacency: 邻接矩阵,维度为 2708 * 2708,类型为 scipy.sparse.coo.coo_matrix
            * train_mask: 训练集掩码向量,维度为 2708,当节点属于训练集时,相应位置为True,否则False
            * val_mask: 验证集掩码向量,维度为 2708,当节点属于验证集时,相应位置为True,否则False
            * test_mask: 测试集掩码向量,维度为 2708,当节点属于测试集时,相应位置为True,否则False

        Args:
        -------
            data_root: string, optional
                存放数据的目录,原始数据路径: {data_root}/raw
                缓存数据路径: {data_root}/processed_cora.pkl
            rebuild: boolean, optional
                是否需要重新构建数据集,当设为True时,如果存在缓存数据也会重建数据

        """
        self.data_root = data_root
        save_file = osp.join(self.data_root, "processed_cora.pkl") #Python join() 方法用于将序列中的元素以指定的字符连接生成一个新的字符串。
        if osp.exists(save_file) and not rebuild:
            print("Using Cached file: {}".format(save_file))
            self._data = pickle.load(open(save_file, "rb"))
        else:
            self.maybe_download()
            self._data = self.process_data()
            with open(save_file, "wb") as f:
                pickle.dump(self.data, f)
            print("Cached file: {}".format(save_file))
    
    @property
    def data(self):
        """返回Data数据对象,包括x, y, adjacency, train_mask, val_mask, test_mask"""
        return self._data

    def process_data(self):
        """
        处理数据,得到节点特征和标签,邻接矩阵,训练集、验证集以及测试集
        引用自:https://github.com/rusty1s/pytorch_geometric
        """
        print("Process data ...")
        _, tx, allx, y, ty, ally, graph, test_index = [self.read_data(
            osp.join(self.data_root, "raw", name)) for name in self.filenames]
        train_index = np.arange(y.shape[0])
        val_index = np.arange(y.shape[0], y.shape[0] + 500)
        sorted_test_index = sorted(test_index)

        x = np.concatenate((allx, tx), axis=0)
        y = np.concatenate((ally, ty), axis=0).argmax(axis=1)

        x[test_index] = x[sorted_test_index]
        y[test_index] = y[sorted_test_index]
        num_nodes = x.shape[0]

        train_mask = np.zeros(num_nodes, dtype=np.bool)
        val_mask = np.zeros(num_nodes, dtype=np.bool)
        test_mask = np.zeros(num_nodes, dtype=np.bool)
        train_mask[train_index] = True
        val_mask[val_index] = True
        test_mask[test_index] = True
        adjacency = self.build_adjacency(graph)
        print("Node‘s feature shape: ", x.shape)
        print("Node‘s label shape: ", y.shape)
        print("Adjacency‘s shape: ", adjacency.shape)
        print("Number of training nodes: ", train_mask.sum())
        print("Number of validation nodes: ", val_mask.sum())
        print("Number of test nodes: ", test_mask.sum())

        return Data(x=x, y=y, adjacency=adjacency,
                    train_mask=train_mask, val_mask=val_mask, test_mask=test_mask)

    def maybe_download(self):
        save_path = os.path.join(self.data_root, "raw")
        for name in self.filenames:
            if not osp.exists(osp.join(save_path, name)):
                self.download_data(
                    "{}/{}".format(self.download_url, name), save_path)

    @staticmethod
    def build_adjacency(adj_dict):
        """根据邻接表创建邻接矩阵"""
        edge_index = []
        num_nodes = len(adj_dict)
        for src, dst in adj_dict.items():
            edge_index.extend([src, v] for v in dst)
            edge_index.extend([v, src] for v in dst)
        # 去除重复的边
        edge_index = list(k for k, _ in itertools.groupby(sorted(edge_index)))
        edge_index = np.asarray(edge_index)
        adjacency = sp.coo_matrix((np.ones(len(edge_index)), 
                                   (edge_index[:, 0], edge_index[:, 1])),
                    shape=(num_nodes, num_nodes), dtype="float32")
        return adjacency

    @staticmethod
    def read_data(path):
        """使用不同的方式读取原始数据以进一步处理"""
        name = osp.basename(path)
        if name == "ind.cora.test.index":
            out = np.genfromtxt(path, dtype="int64")
            return out
        else:
            out = pickle.load(open(path, "rb"), encoding="latin1")
            out = out.toarray() if hasattr(out, "toarray") else out
            return out

    @staticmethod
    def download_data(url, save_path):
        """数据下载工具,当原始数据不存在时将会进行下载"""
        if not os.path.exists(save_path):
            os.makedirs(save_path)
        data = urllib.request.urlopen(url)
        filename = os.path.split(url)[-1]

        with open(os.path.join(save_path, filename), ‘wb‘) as f:
            f.write(data.read())

        return True

    @staticmethod
    def normalization(adjacency):
        """计算 L=D^-0.5 * (A+I) * D^-0.5"""
        adjacency += sp.eye(adjacency.shape[0])    # 增加自连接
        degree = np.array(adjacency.sum(1))
        d_hat = sp.diags(np.power(degree, -0.5).flatten())
        return d_hat.dot(adjacency).dot(d_hat).tocoo()


# ## 图卷积层定义

# In[3]:


class GraphConvolution(nn.Module):
    def __init__(self, input_dim, output_dim, use_bias=True):
        """图卷积:L*X*\theta

        Args:
        ----------
            input_dim: int
                节点输入特征的维度
            output_dim: int
                输出特征维度
            use_bias : bool, optional
                是否使用偏置
        """
        super(GraphConvolution, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.use_bias = use_bias
        self.weight = nn.Parameter(torch.Tensor(input_dim, output_dim))
        if self.use_bias:
            self.bias = nn.Parameter(torch.Tensor(output_dim))
        else:
            self.register_parameter(‘bias‘, None)
        self.reset_parameters()

    def reset_parameters(self):
        init.kaiming_uniform_(self.weight)
        if self.use_bias:
            init.zeros_(self.bias)

    def forward(self, adjacency, input_feature):
        """邻接矩阵是稀疏矩阵,因此在计算时使用稀疏矩阵乘法
    
        Args: 
        -------
            adjacency: torch.sparse.FloatTensor
                邻接矩阵
            input_feature: torch.Tensor
                输入特征
        """
        support = torch.mm(input_feature, self.weight)
        output = torch.sparse.mm(adjacency, support)
        if self.use_bias:
            output += self.bias
        return output

    def __repr__(self):
        return self.__class__.__name__ + ‘ (‘             + str(self.in_features) + ‘ -> ‘             + str(self.out_features) + ‘)‘


# ## 模型定义

# In[4]:


class GcnNet(nn.Module):
    """
    定义一个包含两层GraphConvolution的模型
    """
    def __init__(self, input_dim=1433):
        super(GcnNet, self).__init__()
        self.gcn1 = GraphConvolution(input_dim, 16)
        self.gcn2 = GraphConvolution(16, 7)
    
    def forward(self, adjacency, feature):
        h = F.relu(self.gcn1(adjacency, feature))
        logits = self.gcn2(adjacency, h)
        return logits


# ## 模型训练

# In[5]:


# 超参数定义
learning_rate = 0.1   #学习率或步长因子
weight_decay = 5e-4   #权重衰减(L2惩罚)
epochs = 200


# In[6]:


# 模型定义:Model, Loss, Optimizer
device = "cuda" if torch.cuda.is_available() else "cpu"
print(device)
model = GcnNet().to(device)
criterion = nn.CrossEntropyLoss().to(device)   #交叉熵的值越小,两个概率分布就越接近(实际输出(概率)与期望输出(概率))
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)


# In[7]:


# 加载数据,并转换为torch.Tensor
dataset = CoraData().data
x = dataset.x / dataset.x.sum(1, keepdims=True)  # 归一化数据,使得每一行和为1
tensor_x = torch.from_numpy(x).to(device)
tensor_y = torch.from_numpy(dataset.y).to(device)
tensor_train_mask = torch.from_numpy(dataset.train_mask).to(device)
tensor_val_mask = torch.from_numpy(dataset.val_mask).to(device)
tensor_test_mask = torch.from_numpy(dataset.test_mask).to(device)
normalize_adjacency = CoraData.normalization(dataset.adjacency)   # 规范化邻接矩阵
"""计算 L=D^-0.5 * (A+I) * D^-0.5"""
indices = torch.from_numpy(np.asarray([normalize_adjacency.row, 
                                       normalize_adjacency.col]).astype(‘int64‘)).long()
#索引
values = torch.from_numpy(normalize_adjacency.data.astype(np.float32))
#数值
tensor_adjacency = torch.sparse.FloatTensor(indices, values, 
                                            (2708, 2708)).to(device)
#传到GPU


# In[8]:


# 训练主体函数
def train():
    loss_history = []
    val_acc_history = []
    model.train()   #保证BN层用每一批数据的均值和方差
    train_y = tensor_y[tensor_train_mask]  #取出训练集
    for epoch in range(epochs): #1到200
        logits = model(tensor_adjacency, tensor_x)  # 前向传播
        train_mask_logits = logits[tensor_train_mask]   # 只选择训练节点进行监督
        loss = criterion(train_mask_logits, train_y)    # 计算损失值
        optimizer.zero_grad() #是把梯度置零,也就是把loss关于weight的导数变成0.
        loss.backward()     # 反向传播计算参数的梯度
        optimizer.step()    # 使用优化方法进行梯度更新
        train_acc, _, _ = test(tensor_train_mask)     # 计算当前模型训练集上的准确率
        val_acc, _, _ = test(tensor_val_mask)     # 计算当前模型在验证集上的准确率
        # 记录训练过程中损失值和准确率的变化,用于画图
        loss_history.append(loss.item())
        val_acc_history.append(val_acc.item())
        print("Epoch {:03d}: Loss {:.4f}, TrainAcc {:.4}, ValAcc {:.4f}".format(
            epoch, loss.item(), train_acc.item(), val_acc.item()))
    
    return loss_history, val_acc_history


# In[9]:


# 测试函数
def test(mask):
    model.eval()  #不启用 BatchNormalization 和 Dropout
    with torch.no_grad():             #不能进行梯度计算的上下文管理器。
        logits = model(tensor_adjacency, tensor_x)
        test_mask_logits = logits[mask]
        predict_y = test_mask_logits.max(1)[1]
        accuarcy = torch.eq(predict_y, tensor_y[mask]).float().mean()
    return accuarcy, test_mask_logits.cpu().numpy(), tensor_y[mask].cpu().numpy()


# In[13]:


def plot_loss_with_acc(loss_history, val_acc_history):
    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    ax1.plot(range(len(loss_history)), loss_history,
             c=np.array([255, 71, 90]) / 255.)
    plt.ylabel(‘Loss‘)
    
    ax2 = fig.add_subplot(111, sharex=ax1, frameon=False)
    ax2.plot(range(len(val_acc_history)), val_acc_history,
             c=np.array([79, 179, 255]) / 255.)
    ax2.yaxis.tick_right()
    ax2.yaxis.set_label_position("right")
    plt.ylabel(‘ValAcc‘)
    
    plt.xlabel(‘Epoch‘)
    plt.title(‘Training Loss & Validation Accuracy‘)
    plt.show()


# In[ ]:


loss, val_acc = train()
test_acc, test_logits, test_label = test(tensor_test_mask)
print("Test accuarcy: ", test_acc.item())


# In[14]:


plot_loss_with_acc(loss, val_acc)


# In[ ]:










基于Cora数据集的GCN节点分类-老年痴呆自我回忆手册

标签:ict   flat   方差   make   val   构建   elf   不能   orm   

原文地址:https://www.cnblogs.com/aluckystone/p/14163591.html

版权声明:完美者 发表于 2020-12-25 11:55:55。
转载请注明:基于Cora数据集的GCN节点分类-老年痴呆自我回忆手册 | 完美导航

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