Refine mapping print.

wit/meaning_dataset.py

@@ -140,7 +140,7 @@ class MeaningDataset(Dataset):
         for m in meanings:
             sq = mm.get_sequence(m)
             if len(sq) >= min_seq_len:
-                self.mapping.append(mm.get_mapping(m))
+                self.mapping.append({m: mm.get_mapping(m)})
                 self.data.append(sq)
                 self.length.append(len(sq))
 
@@ -175,9 +175,41 @@ class MeaningDataset(Dataset):
         output["token_type_ids"] = torch.zeros(data.shape)
         return output
 
+    def get_token_batch(self, index_list):  # must equal sequence length
+        return [self.data[i] for i in index_list]
+
+    def print_token_batch(self, index_list):  # must equal sequence length
+        data = [self.data[i] for i in index_list]
+        output = {}
+        data = torch.tensor(np.stack(data, axis=0)).long()
+        output["input_ids"] = data
+        output["labels"] = data.clone()
+        output["token_type_ids"] = torch.zeros(data.shape)
+        return output
+
     def get_mapping_batch(self, index_list):
         return [self.mapping[i] for i in index_list]
 
+    def __get_mapping_str__(map, prefix):
+        if isinstance(map, dict):
+            base = ""
+            for key, value in map.items():
+                base += prefix + str(key) + "\n"
+                base += MeaningDataset.__get_mapping_str__(value, prefix + "    ")
+            return base
+        else:
+            return ""
+
+    def print_mapping_batch(self, index_list):
+        tokens = self.get_token_batch(index_list)
+        map = self.get_mapping_batch(index_list)
+        s = "--------------------------------------------------------\n"
+        for i, m in enumerate(map):
+            s += str(tokens[i]) + "\n"
+            s += MeaningDataset.__get_mapping_str__(m, "")
+            s += "--------------------------------------------------------\n"
+        return s
+
     def split(self, ratio):
         l = len(self.data)
         middle = int(l * ratio)
@@ -238,22 +270,7 @@ class BatchGroupMeaningDataloader(Dataset):
         return self.dataset.get_mapping_batch(self.indexBatch[idx])
 
     def print_mapping(self, idx):
-        map = self.mapping(idx)
+        return self.dataset.print_mapping_batch(self.indexBatch[idx])
-        s = ""
-        for m in map:
-            s += BatchGroupMeaningDataloader.get_mapping_str(m, "")
-            s += "--------\n"
-        return s
-
-    def get_mapping_str(map, prefix):
-        if isinstance(map, dict):
-            base = ""
-            for key, value in map.items():
-                base += prefix + str(key) + "\n"
-                base += BatchGroupMeaningDataloader.get_mapping_str(value, prefix + "    ")
-            return base
-        else:
-            return ""
 
 
 if __name__ == "__main__":