chore: import upstream snapshot with attribution

2026-07-13 12:36:30 +08:00
commit 55ab4e4a73
473 changed files with 72932 additions and 0 deletions
@@ -0,0 +1,95 @@
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from easygraph.classes import Hypergraph
+
+
+class DHCF(nn.Module):
+    r"""The DHCF model proposed in `Dual Channel Hypergraph Collaborative Filtering <https://dl.acm.org/doi/10.1145/3394486.3403253>`_ paper (KDD 2020).
+
+    .. note::
+
+        The user and item embeddings and trainable parameters are initialized with xavier_uniform distribution.
+
+    Parameters:
+        ``num_users`` (``int``): The Number of users.
+        ``num_items`` (``int``): The Number of items.
+        ``emb_dim`` (``int``): Embedding dimension.
+        ``num_layers`` (``int``): The Number of layers. Defaults to ``3``.
+        ``drop_rate`` (``float``): The dropout probability. Defaults to ``0.5``.
+    """
+
+    def __init__(
+        self,
+        num_users: int,
+        num_items: int,
+        emb_dim: int,
+        num_layers: int = 3,
+        drop_rate: float = 0.5,
+    ) -> None:
+        super().__init__()
+        self.num_users, self.num_items = num_users, num_items
+        self.num_layers = num_layers
+        self.drop_rate = drop_rate
+        self.u_embedding = nn.Embedding(num_users, emb_dim)
+        self.i_embedding = nn.Embedding(num_items, emb_dim)
+        self.W_gc, self.W_bi = nn.ModuleList(), nn.ModuleList()
+        for _ in range(self.num_layers):
+            self.W_gc.append(nn.Linear(emb_dim, emb_dim))
+            self.W_bi.append(nn.Linear(emb_dim, emb_dim))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        r"""Initialize learnable parameters."""
+        nn.init.xavier_uniform_(self.u_embedding.weight)
+        nn.init.xavier_uniform_(self.i_embedding.weight)
+        for W_gc, W_bi in zip(self.W_gc, self.W_bi):
+            nn.init.xavier_uniform_(W_gc.weight)
+            nn.init.xavier_uniform_(W_bi.weight)
+            nn.init.constant_(W_gc.bias, 0)
+            nn.init.constant_(W_bi.bias, 0)
+
+    def forward(
+        self, hg_ui: Hypergraph, hg_iu: Hypergraph
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        r"""The forward function.
+
+        Parameters:
+            ``hg_ui`` (``eg.Hypergraph``): The hypergraph structure that users as vertices.
+            ``hg_iu`` (``eg.Hypergraph``): The hypergraph structure that items as vertices.
+        """
+        u_embs = self.u_embedding.weight
+        i_embs = self.i_embedding.weight
+        all_embs = torch.cat([u_embs, i_embs], dim=0)
+
+        embs_list = [all_embs]
+        for _idx in range(self.num_layers):
+            u_embs, i_embs = torch.split(
+                all_embs, [self.num_users, self.num_items], dim=0
+            )
+            # ==========================================================================================
+            # Two JHConv Layers for users and items, respectively.
+            u_embs = hg_ui.smoothing_with_HGNN(u_embs)
+            i_embs = hg_iu.smoothing_with_HGNN(i_embs)
+            g_embs = torch.cat([u_embs, i_embs], dim=0)
+            sum_embs = F.leaky_relu(
+                self.W_gc[_idx](g_embs) + g_embs, negative_slope=0.2
+            )
+            # ==========================================================================================
+
+            bi_embs = all_embs * g_embs
+            bi_embs = F.leaky_relu(self.W_bi[_idx](bi_embs), negative_slope=0.2)
+
+            all_embs = sum_embs + bi_embs
+            all_embs = F.dropout(all_embs, p=self.drop_rate, training=self.training)
+            all_embs = F.normalize(all_embs, p=2, dim=1)
+
+            embs_list.append(all_embs)
+        embs = torch.stack(embs_list, dim=1)
+        embs = torch.mean(embs, dim=1)
+
+        u_embs, i_embs = torch.split(embs, [self.num_users, self.num_items], dim=0)
+        return u_embs, i_embs