dmlc--dgl
WangYQ
f5bba284f8
* add eges example * remove csv files and add data link * Update README.md * Update main.py * Update model.py * Update sampler.py * Update utils.py * Update model.py Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
101 行
3.2 KiB
Python
101 行
3.2 KiB
Python
import dgl
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import torch as th
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import torch.optim as optim
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from torch.utils.data import DataLoader
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from sklearn import metrics
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import utils
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from model import EGES
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from sampler import Sampler
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def train(args, train_g, sku_info, num_skus, num_brands, num_shops, num_cates):
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sampler = Sampler(
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train_g,
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args.walk_length,
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args.num_walks,
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args.window_size,
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args.num_negative
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)
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# for each node in the graph, we sample pos and neg
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# pairs for it, and feed these sampled pairs into the model.
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# (nodes in the graph are of course batched before sampling)
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dataloader = DataLoader(
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th.arange(train_g.num_nodes()),
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# this is the batch_size of input nodes
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batch_size=args.batch_size,
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shuffle=True,
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collate_fn=lambda x: sampler.sample(x, sku_info)
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)
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model = EGES(args.dim, num_skus, num_brands, num_shops, num_cates)
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optimizer = optim.Adam(model.parameters(), lr=args.lr)
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for epoch in range(args.epochs):
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epoch_total_loss = 0
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for step, (srcs, dsts, labels) in enumerate(dataloader):
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# the batch size of output pairs is unfixed
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# TODO: shuffle the triples?
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srcs_embeds, dsts_embeds = model(srcs, dsts)
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loss = model.loss(srcs_embeds, dsts_embeds, labels)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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epoch_total_loss += loss.item()
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if step % args.log_every == 0:
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print('Epoch {:05d} | Step {:05d} | Step Loss {:.4f} | Epoch Avg Loss: {:.4f}'.format(
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epoch, step, loss.item(), epoch_total_loss / (step + 1)))
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eval(model, test_g, sku_info)
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return model
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def eval(model, test_graph, sku_info):
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preds, labels = [], []
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for edge in test_graph:
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src = th.tensor(sku_info[edge.src.numpy()[0]]).view(1, 4)
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dst = th.tensor(sku_info[edge.dst.numpy()[0]]).view(1, 4)
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# (1, dim)
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src = model.query_node_embed(src)
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dst = model.query_node_embed(dst)
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# (1, dim) -> (1, dim) -> (1, )
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logit = th.sigmoid(th.sum(src * dst))
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preds.append(logit.detach().numpy().tolist())
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labels.append(edge.label)
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fpr, tpr, thresholds = metrics.roc_curve(labels, preds, pos_label=1)
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print("Evaluate link prediction AUC: {:.4f}".format(metrics.auc(fpr, tpr)))
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if __name__ == "__main__":
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args = utils.init_args()
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valid_sku_raw_ids = utils.get_valid_sku_set(args.item_info_data)
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g, sku_encoder, sku_decoder = utils.construct_graph(
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args.action_data,
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args.session_interval_sec,
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valid_sku_raw_ids
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)
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train_g, test_g = utils.split_train_test_graph(g)
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sku_info_encoder, sku_info_decoder, sku_info = \
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utils.encode_sku_fields(args.item_info_data, sku_encoder, sku_decoder)
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num_skus = len(sku_encoder)
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num_brands = len(sku_info_encoder["brand"])
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num_shops = len(sku_info_encoder["shop"])
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num_cates = len(sku_info_encoder["cate"])
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print(
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"Num skus: {}, num brands: {}, num shops: {}, num cates: {}".\
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format(num_skus, num_brands, num_shops, num_cates)
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)
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model = train(args, train_g, sku_info, num_skus, num_brands, num_shops, num_cates)
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