DGL学习(八): HAN官方教程代码实现

import torch
from sklearn.metrics import f1_score

from utils import load_data, EarlyStopping

def score(logits, labels): #  micro_f1 和 macro_f1
    _, indices = torch.max(logits, dim=1)
    prediction = indices.long().cpu().numpy()
    labels = labels.cpu().numpy()

    accuracy = (prediction == labels).sum() / len(prediction)
    micro_f1 = f1_score(labels, prediction, average=‘micro‘)
    macro_f1 = f1_score(labels, prediction, average=‘macro‘)

    return accuracy, micro_f1, macro_f1

# 评估
def evaluate(model, g, features, labels, mask, loss_func):
    model.eval()
    with torch.no_grad():
        logits = model(g, features)
    loss = loss_func(logits[mask], labels[mask])
    accuracy, micro_f1, macro_f1 = score(logits[mask], labels[mask])

    return loss, accuracy, micro_f1, macro_f1

def main(args):
    # If args[‘hetero‘] is True, g would be a heterogeneous graph.
    # Otherwise, it will be a list of homogeneous graphs.
    g, features, labels, num_classes, train_idx, val_idx, test_idx, train_mask,     val_mask, test_mask = load_data(args[‘dataset‘])

    if hasattr(torch, ‘BoolTensor‘):
        train_mask = train_mask.bool()
        val_mask = val_mask.bool()
        test_mask = test_mask.bool()

    features = features.to(args[‘device‘])
    labels = labels.to(args[‘device‘])
    train_mask = train_mask.to(args[‘device‘])
    val_mask = val_mask.to(args[‘device‘])
    test_mask = test_mask.to(args[‘device‘])

    if args[‘hetero‘]:
        from model_hetero import HAN
        model = HAN(meta_paths=[[‘pa‘, ‘ap‘], [‘pf‘, ‘fp‘]],
                    in_size=features.shape[1],
                    hidden_size=args[‘hidden_units‘],
                    out_size=num_classes,
                    num_heads=args[‘num_heads‘],
                    dropout=args[‘dropout‘]).to(args[‘device‘])
        g = g.to(args[‘device‘])
    else:
        from model import HAN
        model = HAN(num_meta_paths=len(g),
                    in_size=features.shape[1],
                    hidden_size=args[‘hidden_units‘],
                    out_size=num_classes,
                    num_heads=args[‘num_heads‘],
                    dropout=args[‘dropout‘]).to(args[‘device‘])
        g = [graph.to(args[‘device‘]) for graph in g] #异质图

    stopper = EarlyStopping(patience=args[‘patience‘])
    loss_fcn = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=args[‘lr‘],
                                 weight_decay=args[‘weight_decay‘])

    for epoch in range(args[‘num_epochs‘]):
        model.train()
        logits = model(g, features)
        loss = loss_fcn(logits[train_mask], labels[train_mask])

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        train_acc, train_micro_f1, train_macro_f1 = score(logits[train_mask], labels[train_mask])
        val_loss, val_acc, val_micro_f1, val_macro_f1 = evaluate(model, g, features, labels, val_mask, loss_fcn)
        early_stop = stopper.step(val_loss.data.item(), val_acc, model)

        print(‘Epoch {:d} | Train Loss {:.4f} | Train Micro f1 {:.4f} | Train Macro f1 {:.4f} | ‘
              ‘Val Loss {:.4f} | Val Micro f1 {:.4f} | Val Macro f1 {:.4f}‘.format(
            epoch + 1, loss.item(), train_micro_f1, train_macro_f1, val_loss.item(), val_micro_f1, val_macro_f1))

        if early_stop:
            break

    stopper.load_checkpoint(model)
    test_loss, test_acc, test_micro_f1, test_macro_f1 = evaluate(model, g, features, labels, test_mask, loss_fcn)
    print(‘Test loss {:.4f} | Test Micro f1 {:.4f} | Test Macro f1 {:.4f}‘.format(
        test_loss.item(), test_micro_f1, test_macro_f1))

if __name__ == ‘__main__‘:
    import argparse

    from utils import setup

    parser = argparse.ArgumentParser(‘HAN‘)
    parser.add_argument(‘-s‘, ‘--seed‘, type=int, default=1,
                        help=‘Random seed‘)
    parser.add_argument(‘-ld‘, ‘--log-dir‘, type=str, default=‘results‘,
                        help=‘Dir for saving training results‘)
    parser.add_argument(‘--hetero‘, type=bool ,default=True,
                        help=‘Use metapath coalescing with DGL\‘s own dataset‘)
    args = parser.parse_args().__dict__

    args = setup(args)

    main(args)

import datetime
import dgl
import errno
import numpy as np
import os
import pickle
import random
import torch

from dgl.data.utils import download, get_download_dir, _get_dgl_url
from pprint import pprint
from scipy import sparse
from scipy import io as sio

def set_random_seed(seed=0):
    """Set random seed.
    Parameters
    ----------
    seed : int
        Random seed to use
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(seed)

def mkdir_p(path, log=True):
    """Create a directory for the specified path.
    Parameters
    ----------
    path : str
        Path name
    log : bool
        Whether to print result for directory creation
    """
    try:
        os.makedirs(path)
        if log:
            print(‘Created directory {}‘.format(path))
    except OSError as exc:
        if exc.errno == errno.EEXIST and os.path.isdir(path) and log:
            print(‘Directory {} already exists.‘.format(path))
        else:
            raise

def get_date_postfix():
    """Get a date based postfix for directory name.
    Returns
    -------
    post_fix : str
    """
    dt = datetime.datetime.now()
    post_fix = ‘{}_{:02d}-{:02d}-{:02d}‘.format(
        dt.date(), dt.hour, dt.minute, dt.second)

    return post_fix

def setup_log_dir(args, sampling=False):
    """Name and create directory for logging.
    Parameters
    ----------
    args : dict
        Configuration
    Returns
    -------
    log_dir : str
        Path for logging directory
    sampling : bool
        Whether we are using sampling based training
    """
    date_postfix = get_date_postfix()
    log_dir = os.path.join(
        args[‘log_dir‘],
        ‘{}_{}‘.format(args[‘dataset‘], date_postfix))

    if sampling:
        log_dir = log_dir + ‘_sampling‘

    mkdir_p(log_dir)
    return log_dir

# The configuration below is from the paper.
default_configure = {
    ‘lr‘: 0.005,             # Learning rate
    ‘num_heads‘: [8],        # Number of attention heads for node-level attention
    ‘hidden_units‘: 8,
    ‘dropout‘: 0.6,
    ‘weight_decay‘: 0.001,
    ‘num_epochs‘: 200,
    ‘patience‘: 100
}

sampling_configure = {
    ‘batch_size‘: 20
}

def setup(args):
    args.update(default_configure)
    set_random_seed(args[‘seed‘])
    args[‘dataset‘] = ‘ACMRaw‘ if args[‘hetero‘] else ‘ACM‘
    args[‘device‘] = ‘cuda: 0‘ if torch.cuda.is_available() else ‘cpu‘
    args[‘log_dir‘] = setup_log_dir(args)
    return args

def setup_for_sampling(args):
    args.update(default_configure)
    args.update(sampling_configure)
    set_random_seed()
    args[‘device‘] = ‘cuda: 0‘ if torch.cuda.is_available() else ‘cpu‘
    args[‘log_dir‘] = setup_log_dir(args, sampling=True)
    return args

def get_binary_mask(total_size, indices):
    mask = torch.zeros(total_size)
    mask[indices] = 1
    return mask.byte()

def load_acm(remove_self_loop):
    url = ‘dataset/ACM3025.pkl‘
    data_path = get_download_dir() + ‘/ACM3025.pkl‘
    download(_get_dgl_url(url), path=data_path)

    with open(data_path, ‘rb‘) as f:
        data = pickle.load(f)

    labels, features = torch.from_numpy(data[‘label‘].todense()).long(),                        torch.from_numpy(data[‘feature‘].todense()).float()
    num_classes = labels.shape[1]
    labels = labels.nonzero()[:, 1]

    if remove_self_loop:
        num_nodes = data[‘label‘].shape[0]
        data[‘PAP‘] = sparse.csr_matrix(data[‘PAP‘] - np.eye(num_nodes))
        data[‘PLP‘] = sparse.csr_matrix(data[‘PLP‘] - np.eye(num_nodes))

    # Adjacency matrices for meta path based neighbors
    # (Mufei): I verified both of them are binary adjacency matrices with self loops
    author_g = dgl.graph(data[‘PAP‘], ntype=‘paper‘, etype=‘author‘)
    subject_g = dgl.graph(data[‘PLP‘], ntype=‘paper‘, etype=‘subject‘)
    gs = [author_g, subject_g]

    train_idx = torch.from_numpy(data[‘train_idx‘]).long().squeeze(0)
    val_idx = torch.from_numpy(data[‘val_idx‘]).long().squeeze(0)
    test_idx = torch.from_numpy(data[‘test_idx‘]).long().squeeze(0)

    num_nodes = author_g.number_of_nodes()
    train_mask = get_binary_mask(num_nodes, train_idx)
    val_mask = get_binary_mask(num_nodes, val_idx)
    test_mask = get_binary_mask(num_nodes, test_idx)

    print(‘dataset loaded‘)
    pprint({
        ‘dataset‘: ‘ACM‘,
        ‘train‘: train_mask.sum().item() / num_nodes,
        ‘val‘: val_mask.sum().item() / num_nodes,
        ‘test‘: test_mask.sum().item() / num_nodes
    })

    return gs, features, labels, num_classes, train_idx, val_idx, test_idx,            train_mask, val_mask, test_mask

def load_acm_raw(remove_self_loop):
    assert not remove_self_loop
    url = ‘dataset/ACM.mat‘
    data_path = get_download_dir() + ‘/ACM.mat‘
    download(_get_dgl_url(url), path=data_path)

    data = sio.loadmat(data_path)
    p_vs_l = data[‘PvsL‘]       # paper-field?
    p_vs_a = data[‘PvsA‘]       # paper-author
    p_vs_t = data[‘PvsT‘]       # paper-term, bag of words
    p_vs_c = data[‘PvsC‘]       # paper-conference, labels come from that


    # We assign
    # (1) KDD papers as class 0 (data mining),
    # (2) SIGMOD and VLDB papers as class 1 (database),
    # (3) SIGCOMM and MOBICOMM papers as class 2 (communication)
    conf_ids = [0, 1, 9, 10, 13]
    label_ids = [0, 1, 2, 2, 1]

    p_vs_c_filter = p_vs_c[:, conf_ids] ## 过滤出上述五个会议的数据

    ‘‘‘
    首先对跨列(axis=1)进行求和，每一篇paper会对应一个数num, 如果num!=0，那么这篇paper就在五大会议之一中发表过,否则它就没发表过。
    .A1是将上述 papernum*1的二维矩阵转为 1D矩阵。
    .nonzero 是当使用布尔数组直接作为下标对象或者元组下标对象中有布尔数组时，都相当于用nonzero()将布尔数组转换成一组整数数组，然后使用整数数组进行下标运算。
    [0] 是取出一个list
    
    这一步等于是筛选出所有在上述5个会议发表过的论文。
    ‘‘‘
    p_selected = (p_vs_c_filter.sum(1) != 0).A1.nonzero()[0]

    p_vs_l = p_vs_l[p_selected]
    p_vs_a = p_vs_a[p_selected]
    p_vs_t = p_vs_t[p_selected]
    p_vs_c = p_vs_c[p_selected]

    # 构造多个二分图
    pa = dgl.bipartite(p_vs_a, ‘paper‘, ‘pa‘, ‘author‘)
    ap = dgl.bipartite(p_vs_a.transpose(), ‘author‘, ‘ap‘, ‘paper‘)
    pl = dgl.bipartite(p_vs_l, ‘paper‘, ‘pf‘, ‘field‘)
    lp = dgl.bipartite(p_vs_l.transpose(), ‘field‘, ‘fp‘, ‘paper‘)

    # 构造异质图
    hg = dgl.hetero_from_relations([pa, ap, pl, lp])

    features = torch.FloatTensor(p_vs_t.toarray())

    pc_p, pc_c = p_vs_c.nonzero() # 返回包含矩阵非零元素索引的数组（row，col）元组。 row指的是 paper , col是会议

    labels = np.zeros(len(p_selected), dtype=np.int64) ## label数量为paper数量

    for conf_id, label_id in zip(conf_ids, label_ids):
        labels[pc_p[pc_c == conf_id]] = label_id # 为每一个会议打上标记
    labels = torch.LongTensor(labels) # 转为tensor

    num_classes = 3

    float_mask = np.zeros(len(pc_p))
    for conf_id in conf_ids:
        pc_c_mask = (pc_c == conf_id)
        float_mask[pc_c_mask] = np.random.permutation(np.linspace(0, 1, pc_c_mask.sum()))
    train_idx = np.where(float_mask <= 0.2)[0]
    val_idx = np.where((float_mask > 0.2) & (float_mask <= 0.3))[0]
    test_idx = np.where(float_mask > 0.3)[0]

    num_nodes = hg.number_of_nodes(‘paper‘) # 图中节点数

    print(num_nodes)
    train_mask = get_binary_mask(num_nodes, train_idx)
    val_mask = get_binary_mask(num_nodes, val_idx)
    test_mask = get_binary_mask(num_nodes, test_idx)

    return hg, features, labels, num_classes, train_idx, val_idx, test_idx,            train_mask, val_mask, test_mask

def load_data(dataset, remove_self_loop=False):
    if dataset == ‘ACM‘:
        return load_acm(remove_self_loop)
    elif dataset == ‘ACMRaw‘:
        return load_acm_raw(remove_self_loop)
    else:
        return NotImplementedError(‘Unsupported dataset {}‘.format(dataset))

class EarlyStopping(object):
    def __init__(self, patience=10):
        dt = datetime.datetime.now()
        self.filename = ‘early_stop_{}_{:02d}-{:02d}-{:02d}.pth‘.format(
            dt.date(), dt.hour, dt.minute, dt.second)
        self.patience = patience
        self.counter = 0
        self.best_acc = None
        self.best_loss = None
        self.early_stop = False

    def step(self, loss, acc, model):
        if self.best_loss is None:
            self.best_acc = acc
            self.best_loss = loss
            self.save_checkpoint(model)
        elif (loss > self.best_loss) and (acc < self.best_acc):
            self.counter += 1
            print(f‘EarlyStopping counter: {self.counter} out of {self.patience}‘)
            if self.counter >= self.patience:
                self.early_stop = True
        else:
            if (loss <= self.best_loss) and (acc >= self.best_acc):
                self.save_checkpoint(model)
            self.best_loss = np.min((loss, self.best_loss))
            self.best_acc = np.max((acc, self.best_acc))
            self.counter = 0
        return self.early_stop

    def save_checkpoint(self, model):
        """Saves model when validation loss decreases."""
        torch.save(model.state_dict(), self.filename)

    def load_checkpoint(self, model):
        """Load the latest checkpoint."""
        model.load_state_dict(torch.load(self.filename))

import torch
import torch.nn as nn
import torch.nn.functional as F

from dgl.nn.pytorch import GATConv

class SemanticAttention(nn.Module):
    def __init__(self, in_size, hidden_size=128):
        super(SemanticAttention, self).__init__()

        self.project = nn.Sequential(
            nn.Linear(in_size, hidden_size),
            nn.Tanh(),
            nn.Linear(hidden_size, 1, bias=False)
        )

    def forward(self, z):
        w = self.project(z).mean(0)                    # (M, 1)
        beta = torch.softmax(w, dim=0)                 # (M, 1)
        beta = beta.expand((z.shape[0],) + beta.shape) # (N, M, 1)

        return (beta * z).sum(1)                       # (N, D * K)

class HANLayer(nn.Module):
    """
    HAN layer.
    Arguments
    ---------
    num_meta_paths : number of homogeneous graphs generated from the metapaths.
    in_size : input feature dimension
    out_size : output feature dimension
    layer_num_heads : number of attention heads
    dropout : Dropout probability
    Inputs
    ------
    g : list[DGLGraph]
        List of graphs
    h : tensor
        Input features
    Outputs
    -------
    tensor
        The output feature
    """
    def __init__(self, num_meta_paths, in_size, out_size, layer_num_heads, dropout):
        super(HANLayer, self).__init__()

        # One GAT layer for each meta path based adjacency matrix
        self.gat_layers = nn.ModuleList()
        for i in range(num_meta_paths):
            self.gat_layers.append(GATConv(in_size, out_size, layer_num_heads,
                                           dropout, dropout, activation=F.elu))
        self.semantic_attention = SemanticAttention(in_size=out_size * layer_num_heads)
        self.num_meta_paths = num_meta_paths

    def forward(self, gs, h):
        semantic_embeddings = []

        for i, g in enumerate(gs):
            semantic_embeddings.append(self.gat_layers[i](g, h).flatten(1))
        semantic_embeddings = torch.stack(semantic_embeddings, dim=1)                  # (N, M, D * K)

        return self.semantic_attention(semantic_embeddings)                            # (N, D * K)

class HAN(nn.Module):
    def __init__(self, num_meta_paths, in_size, hidden_size, out_size, num_heads, dropout):
        super(HAN, self).__init__()

        self.layers = nn.ModuleList()
        self.layers.append(HANLayer(num_meta_paths, in_size, hidden_size, num_heads[0], dropout))
        for l in range(1, len(num_heads)):
            self.layers.append(HANLayer(num_meta_paths, hidden_size * num_heads[l-1],
                                        hidden_size, num_heads[l], dropout))
        self.predict = nn.Linear(hidden_size * num_heads[-1], out_size)

    def forward(self, g, h):
        for gnn in self.layers:
            h = gnn(g, h)

        return self.predict(h)

"""This model shows an example of using dgl.metapath_reachable_graph on the original heterogeneous
graph.
Because the original HAN implementation only gives the preprocessed homogeneous graph, this model
could not reproduce the result in HAN as they did not provide the preprocessing code, and we
constructed another dataset from ACM with a different set of papers, connections, features and
labels.
"""

import torch
import torch.nn as nn
import torch.nn.functional as F

import dgl
from dgl.nn.pytorch import GATConv

class SemanticAttention(nn.Module):
    def __init__(self, in_size, hidden_size=128):
        super(SemanticAttention, self).__init__()

        self.project = nn.Sequential(
            nn.Linear(in_size, hidden_size),
            nn.Tanh(),
            nn.Linear(hidden_size, 1, bias=False)
        )

    def forward(self, z):
        w = self.project(z).mean(0)                    # (M, 1) 输入维度为(N, M, D * K) 经过project层变为 (N, M, 1) , 对节点求mean之后变为 (M,1)
        beta = torch.softmax(w, dim=0)                 # (M, 1) 使用softmax归一化
        beta = beta.expand((z.shape[0],) + beta.shape) # (N, M, 1) 扩展 N 份
        return (beta * z).sum(1)                       # (N, D * K) (N, M, 1)* (N, M, D*K) = (N , M , D*K) sum(1) -> (N, D*K)

class HANLayer(nn.Module):
    """
    HAN layer.
    Arguments
    ---------
    meta_paths : list of metapaths, each as a list of edge types
    in_size : input feature dimension
    out_size : output feature dimension
    layer_num_heads : number of attention heads
    dropout : Dropout probability
    Inputs
    ------
    g : DGLHeteroGraph
        The heterogeneous graph
    h : tensor
        Input features
    Outputs
    -------
    tensor
        The output feature
    """
    def __init__(self, meta_paths, in_size, out_size, layer_num_heads, dropout):
        super(HANLayer, self).__init__()

        # One GAT layer for each meta path based adjacency matrix
        self.gat_layers = nn.ModuleList()
        for i in range(len(meta_paths)):
            self.gat_layers.append(GATConv(in_size, out_size, layer_num_heads,
                                           dropout, dropout, activation=F.elu))
        self.semantic_attention = SemanticAttention(in_size=out_size * layer_num_heads)
        self.meta_paths = list(tuple(meta_path) for meta_path in meta_paths)

        self._cached_graph = None
        self._cached_coalesced_graph = {}

    def forward(self, g, h):
        semantic_embeddings = []

        if self._cached_graph is None or self._cached_graph is not g:
            self._cached_graph = g
            self._cached_coalesced_graph.clear()
            for meta_path in self.meta_paths:
                self._cached_coalesced_graph[meta_path] = dgl.metapath_reachable_graph(g, meta_path)

        for i, meta_path in enumerate(self.meta_paths):
            new_g = self._cached_coalesced_graph[meta_path] # 通过metapath得到的同质图
            semantic_embeddings.append(self.gat_layers[i](new_g, h).flatten(1))        # (N, D*K) N是节点数， D是输出的维度out_size, K是注意力头数

        semantic_embeddings = torch.stack(semantic_embeddings, dim=1)                  # (N, M, D * K) 堆叠M个元路径的结果

        return self.semantic_attention(semantic_embeddings)                            # (N, D * K)

class HAN(nn.Module):
    def __init__(self, meta_paths, in_size, hidden_size, out_size, num_heads, dropout):
        super(HAN, self).__init__()

        self.layers = nn.ModuleList()
        self.layers.append(HANLayer(meta_paths, in_size, hidden_size, num_heads[0], dropout))
        for l in range(1, len(num_heads)):
            self.layers.append(HANLayer(meta_paths, hidden_size * num_heads[l-1],
                                        hidden_size, num_heads[l], dropout))
        self.predict = nn.Linear(hidden_size * num_heads[-1], out_size)

    def forward(self, g, h):
        for gnn in self.layers:
            h = gnn(g, h) #

        return self.predict(h)