Merge pull request #298 from WenjieDu/dev

Fixing the issue in time-decay matrix calculation and simplify the code

pypots/data/base.py

@@ -257,9 +257,12 @@ def _open_file_handle(self) -> h5py.File:
             raise ImportError(
                 "h5py is missing and cannot be imported. Please install it first."
             )
+        except FileNotFoundError as e:
+            raise FileNotFoundError(f"{e}")
         except OSError as e:
             raise TypeError(
-                f"{e} This probably is caused by file type error. "
+                f"{e}\n"
+                f"Check out the above error log. This probably is caused by file type error. "
                 f"Please confirm that the given file {data_file_path} is an h5 file."
             )
         except Exception as e:

pypots/data/utils.py

@@ -56,14 +56,13 @@ def _parse_delta_torch(missing_mask: torch.Tensor) -> torch.Tensor:
 
     def cal_delta_for_single_sample(mask: torch.Tensor) -> torch.Tensor:
         """calculate single sample's delta. The sample's shape is [n_steps, n_features]."""
-        d = []
-        for step in range(n_steps):
-            if step == 0:
-                d.append(torch.zeros(1, n_features, device=device))
-            else:
-                d.append(
-                    torch.ones(1, n_features, device=device) + (1 - mask[step]) * d[-1]
-                )
+        # the first step in the delta matrix is all 0
+        d = [torch.zeros(1, n_features, device=device)]
+
+        for step in range(1, n_steps):
+            d.append(
+                torch.ones(1, n_features, device=device) + (1 - mask[step - 1]) * d[-1]
+            )
         d = torch.concat(d, dim=0)
         return d
 
@@ -108,12 +107,11 @@ def _parse_delta_numpy(missing_mask: np.ndarray) -> np.ndarray:
 
     def cal_delta_for_single_sample(mask: np.ndarray) -> np.ndarray:
         """calculate single sample's delta. The sample's shape is [n_steps, n_features]."""
-        d = []
-        for step in range(seq_len):
-            if step == 0:
-                d.append(np.zeros(n_features))
-            else:
-                d.append(np.ones(n_features) + (1 - mask[step]) * d[-1])
+        # the first step in the delta matrix is all 0
+        d = [np.zeros(n_features)]
+
+        for step in range(1, seq_len):
+            d.append(np.ones(n_features) + (1 - mask[step - 1]) * d[-1])
         d = np.asarray(d)
         return d
 

pypots/imputation/mrnn/data.py

@@ -36,14 +36,13 @@ def mrnn_parse_delta_torch(missing_mask: torch.Tensor) -> torch.Tensor:
 
     def cal_delta_for_single_sample(mask: torch.Tensor) -> torch.Tensor:
         """calculate single sample's delta. The sample's shape is [n_steps, n_features]."""
-        d = []
-        for step in range(n_steps):
-            if step == 0:
-                d.append(torch.ones(1, n_features, device=device))
-            else:
-                d.append(
-                    torch.ones(1, n_features, device=device) + (1 - mask[step]) * d[-1]
-                )
+        # the first step in the delta matrix is all 0
+        d = [torch.ones(1, n_features, device=device)]
+
+        for step in range(1, n_steps):
+            d.append(
+                torch.ones(1, n_features, device=device) + (1 - mask[step - 1]) * d[-1]
+            )
         d = torch.concat(d, dim=0)
         return d
 

pypots/nn/modules/transformer/attention.py

@@ -177,7 +177,7 @@ def forward(
 
         # keep useful variables
         batch_size, n_steps = q.size(0), q.size(1)
-        residual = q
+        residual = v
 
         # now separate the last dimension of q, k, v into different heads -> [batch_size, n_steps, n_heads, d_k or d_v]
         q = self.w_qs(q).view(batch_size, n_steps, self.n_heads, self.d_k)