新增管理员页面和用户申诉、迁移审核页面，推荐系统
Change-Id: Ief5646321feb98fadb17da4b4e91caeaacdbacc5
diff --git a/recommend/model/base_model.py b/recommend/model/base_model.py
new file mode 100644
index 0000000..819442a
--- /dev/null
+++ b/recommend/model/base_model.py
@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+from utils.parse_args import args
+from scipy.sparse import csr_matrix
+import scipy.sparse as sp
+import numpy as np
+import torch.nn.functional as F
+
+
+class BaseModel(nn.Module):
+ def __init__(self, dataloader):
+ super(BaseModel, self).__init__()
+ self.num_users = dataloader.num_users
+ self.num_items = dataloader.num_items
+ self.emb_size = args.emb_size
+
+ def forward(self):
+ pass
+
+ def cal_loss(self, batch_data):
+ pass
+
+ def _check_inf(self, loss, pos_score, neg_score, edge_weight):
+ # find inf idx
+ inf_idx = torch.isinf(loss) | torch.isnan(loss)
+ if inf_idx.any():
+ print("find inf in loss")
+ if type(edge_weight) != int:
+ print(edge_weight[inf_idx])
+ print(f"pos_score: {pos_score[inf_idx]}")
+ print(f"neg_score: {neg_score[inf_idx]}")
+ raise ValueError("find inf in loss")
+
+ def _make_binorm_adj(self, mat):
+ a = csr_matrix((self.num_users, self.num_users))
+ b = csr_matrix((self.num_items, self.num_items))
+ mat = sp.vstack(
+ [sp.hstack([a, mat]), sp.hstack([mat.transpose(), b])])
+ mat = (mat != 0) * 1.0
+ # mat = (mat + sp.eye(mat.shape[0])) * 1.0# MARK
+ degree = np.array(mat.sum(axis=-1))
+ d_inv_sqrt = np.reshape(np.power(degree, -0.5), [-1])
+ d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.0
+ d_inv_sqrt_mat = sp.diags(d_inv_sqrt)
+ mat = mat.dot(d_inv_sqrt_mat).transpose().dot(
+ d_inv_sqrt_mat).tocoo()
+
+ # make torch tensor
+ idxs = torch.from_numpy(np.vstack([mat.row, mat.col]).astype(np.int64))
+ vals = torch.from_numpy(mat.data.astype(np.float32))
+ shape = torch.Size(mat.shape)
+ return torch.sparse.FloatTensor(idxs, vals, shape).to(args.device)
+
+ def _make_binorm_adj_self_loop(self, mat):
+ a = csr_matrix((self.num_users, self.num_users))
+ b = csr_matrix((self.num_items, self.num_items))
+ mat = sp.vstack(
+ [sp.hstack([a, mat]), sp.hstack([mat.transpose(), b])])
+ mat = (mat != 0) * 1.0
+ mat = (mat + sp.eye(mat.shape[0])) * 1.0 # self loop
+ degree = np.array(mat.sum(axis=-1))
+ d_inv_sqrt = np.reshape(np.power(degree, -0.5), [-1])
+ d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.0
+ d_inv_sqrt_mat = sp.diags(d_inv_sqrt)
+ mat = mat.dot(d_inv_sqrt_mat).transpose().dot(
+ d_inv_sqrt_mat).tocoo()
+
+ # make torch tensor
+ idxs = torch.from_numpy(np.vstack([mat.row, mat.col]).astype(np.int64))
+ vals = torch.from_numpy(mat.data.astype(np.float32))
+ shape = torch.Size(mat.shape)
+ return torch.sparse.FloatTensor(idxs, vals, shape).to(args.device)
+
+
+ def _sp_matrix_to_sp_tensor(self, sp_matrix):
+ coo = sp_matrix.tocoo()
+ indices = torch.LongTensor([coo.row, coo.col])
+ values = torch.FloatTensor(coo.data)
+ return torch.sparse.FloatTensor(indices, values, coo.shape).coalesce().to(args.device)
+
+ def _bpr_loss(self, user_emb, pos_item_emb, neg_item_emb):
+ pos_score = (user_emb * pos_item_emb).sum(dim=1)
+ neg_score = (user_emb * neg_item_emb).sum(dim=1)
+ loss = -torch.log(1e-10 + torch.sigmoid((pos_score - neg_score)))
+ self._check_inf(loss, pos_score, neg_score, 0)
+ return loss.mean()
+
+ def _nce_loss(self, pos_score, neg_score, edge_weight=1):
+ numerator = torch.exp(pos_score)
+ denominator = torch.exp(pos_score) + torch.exp(neg_score).sum(dim=1)
+ loss = -torch.log(numerator/denominator) * edge_weight
+ self._check_inf(loss, pos_score, neg_score, edge_weight)
+ return loss.mean()
+
+ def _infonce_loss(self, pos_1, pos_2, negs, tau):
+ pos_1 = self.cl_mlp(pos_1)
+ pos_2 = self.cl_mlp(pos_2)
+ negs = self.cl_mlp(negs)
+ pos_1 = F.normalize(pos_1, dim=-1)
+ pos_2 = F.normalize(pos_2, dim=-1)
+ negs = F.normalize(negs, dim=-1)
+ pos_score = torch.mul(pos_1, pos_2).sum(dim=1)
+ # B, 1, E * B, E, N -> B, N
+ neg_score = torch.bmm(pos_1.unsqueeze(1), negs.transpose(1, 2)).squeeze(1)
+ # infonce loss
+ numerator = torch.exp(pos_score / tau)
+ denominator = torch.exp(pos_score / tau) + torch.exp(neg_score / tau).sum(dim=1)
+ loss = -torch.log(numerator/denominator)
+ self._check_inf(loss, pos_score, neg_score, 0)
+ return loss.mean()
+
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