improve speed (middle of the work)

predictor/cross_validation.py

     epoch_loss = 0
 
     ddi_graph.n_id = torch.arange(ddi_graph.num_nodes)
-    
+    start = time.time()
     # print("len of batch loader: ",len(loader))
     for i, batch in enumerate(loader):
         # print(f"this is batch {i} ------------------")
         # print(type(sampled_data))
         # print("Sampled_data: ")
         # print(sampled_data.batch_size)
+
+        start_inner = time.time()
           
         for subgraph in neighbor_loader:
             # print("this is subgraph in cross_validation:")
             loss.backward()
             optimizer.step()
             epoch_loss += loss.item()
+        print("for on subgraphs time: ", time.time() - start_inner)
     
     # print("epoch_loss: ", epoch_loss)
+    print("train epoch time: ", time.time() - start)
     return epoch_loss
 
 
             if angry >= patience:
                 break
         end_time = time.time()
-        print("epoch time (min): ", (end_time - start_time)/60)
+        print("epoch time: ", (end_time - start_time))
     if sl:
         model.load_state_dict(torch.load(find_best_model(mdl_dir)))
     return min_loss

predictor/model/models.py

 import os
 import sys 
 import pandas as pd
-
+import time
 PROJ_DIR = os.path.dirname(os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..')))
 
 sys.path.insert(0, PROJ_DIR)
 from drug.models import GCN
 from drug.datasets import DDInteractionDataset
-from model.utils import get_FP_by_negative_index
+from model.utils import get_FP_by_negative_index, get_FP_by_negative_indices
 from const import Drug2FP_FILE
 
 
         #drug2_feat = x[drug2_idx]
         drug1_feat = torch.empty((len(drug1_idx), len(x[0])))
         drug2_feat = torch.empty((len(drug2_idx), len(x[0])))
-        for index, element in enumerate(drug1_idx):
-            x_element = element
-            if element >= 0:
-                x_element = (node_indices == element).nonzero().squeeze()
-                drug1_feat[index] = (x[x_element])
-        for index, element in enumerate(drug2_idx):
-            x_element = element
-            if element >= 0:
-                x_element = (node_indices == element).nonzero().squeeze()
-                drug2_feat[index] = (x[x_element])
+
+        print("x shape: ", x.size())
+        print("node_indices: ", node_indices.size())
+
+        start_time = time.time()
+        # for index, element in enumerate(drug1_idx):
+        #     x_element = element
+        #     if element >= 0:
+        #         x_element = (node_indices == element).nonzero().squeeze()
+        #         drug1_feat[index] = (x[x_element])
+        # for index, element in enumerate(drug2_idx):
+        #     x_element = element
+        #     if element >= 0:
+        #         x_element = (node_indices == element).nonzero().squeeze()
+        #         drug2_feat[index] = (x[x_element])
+
+        mask_positive = (drug1_idx >= 0)
+        x_elements_positive = (node_indices.unsqueeze(-1) == drug1_idx[mask_positive]).nonzero(as_tuple=True)[0]
+        drug1_feat[mask_positive] = x[x_elements_positive]
+
+        mask_negative = ~mask_positive
+        drug1_feat[mask_negative] = get_FP_by_negative_indices(drug1_idx[mask_negative], self.drug2FP_df)
+
+        mask_positive = (drug2_idx >= 0)
+        x_elements_positive = (node_indices.unsqueeze(-1) == drug2_idx[mask_positive]).nonzero(as_tuple=True)[0]
+        drug2_feat[mask_positive] = x[x_elements_positive]
+        
         if self.gpu_id is not None:
             drug1_feat = drug1_feat.cuda(self.gpu_id)
             drug2_feat = drug2_feat.cuda(self.gpu_id)
+
+        print("first: ", time.time() - start_time)
+        start_time = time.time()
+
         for i, x in enumerate(drug1_idx):
             if x < 0:
                 drug1_feat[i] = get_FP_by_negative_index(x,self.drug2FP_df)
         for i, x in enumerate(drug2_idx):
             if x < 0:
                 drug2_feat[i] = get_FP_by_negative_index(x,self.drug2FP_df)
+        print("second: ", time.time() - start_time)
+        
         feat = torch.cat([drug1_feat, drug2_feat, cell_feat], 1)
         return feat
 
     
     # prev input: self, drug1_feat: torch.Tensor, drug2_feat: torch.Tensor, cell_feat: torch.Tensor, subgraph: related subgraph for the batch
     def forward(self, drug1_idx, drug2_idx, cell_feat, subgraph):
+        start = time.time()
         feat = self.connector(drug1_idx, drug2_idx, cell_feat, subgraph)
+        print("Connector forward time: ", time.time() - start)
         out = self.layers(feat)
         return out
 

predictor/model/utils.py

 
     array = np.array(list(drug2FP_df.iloc[-index])[1:])
     return torch.tensor(array, dtype=torch.float32)
+
+
+def get_FP_by_negative_indices(indices, drug2FP_df = None):
+    # converting negative indices to real indices
+    indices *= -1
+    print(indices)
+    print(type(drug2FP_df))
+
+    if drug2FP_df is None or drug2FP_df.empty:
+        print("load Drug2FP_FILE")
+        drug2FP_df = pd.read_csv(Drug2FP_FILE)
+
+    # array = np.array(list(drug2FP_df.iloc[[indices]])[1:])
+    print(drug2FP_df.iloc[[indices]])
+    # return torch.tensor(array, dtype=torch.float32)