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from gensim.test.utils import common_texts
from gensim.corpora.dictionary import Dictionary
from gensim.models import LdaModel

# Create a corpus from a list of texts
common_dictionary = Dictionary(common_texts)
common_corpus = [common_dictionary.doc2bow(text) for text in common_texts]

# Train the model on the corpus.
lda = LdaModel(common_corpus, num_topics=10)

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# efficiently compute the aggregation of feature interactions 
emb_list = [emb_item, pooled_interaction, emb_price_rank, emb_city, emb_last_item, emb_impression_index, emb_star]
emb_concat = torch.cat(emb_list, dim=1)
sum_squared = torch.pow( torch.sum( emb_concat, dim=1) , 2).unsqueeze(1)
squared_sum = torch.sum( torch.pow( emb_concat, 2) , dim=1).unsqueeze(1)
second_order = 0.5 * (squared_sum - sum_squared)

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for epoch in range(1):
    loss = train()
    train_acc = evaluate(train_loader)
    val_acc = evaluate(val_loader)    
    test_acc = evaluate(test_loader)
    print('Epoch: {:03d}, Loss: {:.5f}, Train Auc: {:.5f}, Val Auc: {:.5f}, Test Auc: {:.5f}'.
          format(epoch, loss, train_acc, val_acc, test_acc))

khuangaf

embed_dim = 128
from torch_geometric.nn import TopKPooling
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
import torch.nn.functional as F
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()

        self.conv1 = SAGEConv(embed_dim, 128)
        self.pool1 = TopKPooling(128, ratio=0.8)

khuangaf

embed_dim = 128
from torch_geometric.nn import GraphConv, TopKPooling, GatedGraphConv
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
import torch.nn.functional as F
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()

        self.conv1 = SAGEConv(embed_dim, 128)
        self.pool1 = TopKPooling(128, ratio=0.8)

khuangaf

import torch
from torch.nn import Sequential as Seq, Linear, ReLU
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import remove_self_loops, add_self_loops
class SAGEConv(MessagePassing):
    def __init__(self, in_channels, out_channels):
        super(SAGEConv, self).__init__(aggr='max') #  "Max" aggregation.
        self.lin = torch.nn.Linear(in_channels, out_channels)
        self.act = torch.nn.ReLU()
        self.update_lin = torch.nn.Linear(in_channels + out_channels, in_channels, bias=False)

khuangaf

embed_dim = 128
from torch_geometric.nn import GraphConv, TopKPooling, GatedGraphConv
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
import torch.nn.functional as F
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()

        self.conv1 = SAGEConv(embed_dim, 128)
        self.pool1 = TopKPooling(128, ratio=0.8)

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def evaluate(loader):
    model.eval()

    predictions = []
    labels = []

    with torch.no_grad():
        for data in loader:

            data = data.to(device)

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def train():
    model.train()

    loss_all = 0
    for data in train_loader:
        data = data.to(device)
        optimizer.zero_grad()
        output = model(data)
        label = data.y.to(device)
        loss = crit(output, label)

khuangaf

embed_dim = 128
from torch_geometric.nn import GraphConv, TopKPooling, GatedGraphConv
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
import torch.nn.functional as F
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()

        self.conv1 = GraphConv(embed_dim, 128, aggr='max')
        self.pool1 = TopKPooling(128, ratio=0.8)