[Feature] Time-based split in evaluation

recbole/data/dataset/dataset.py

@@ -1729,6 +1729,62 @@ def leave_one_out(self, group_by, leave_one_mode):
         next_ds = [self.copy(_) for _ in next_df]
         return next_ds
 
+    def time_based_split(self, ratios, group_by):
+        """Split interaction records by time-based strategy that combines global temporal and leave-one-out constraints.
+
+        Args:
+            ratios (list): List of split ratios.
+            group_by (str): Field name that interaction records should be grouped by before splitting.
+
+        Returns:
+            list: List of :class:`~Dataset`, whose interaction features have been split.
+        """
+        self.logger.debug(f"time based split, group_by=[{group_by}]")
+        if group_by is None:
+            raise ValueError("Time-based split strategy requires a group field")
+
+        if self.time_field not in self.inter_feat:
+            raise ValueError(f"Field [{self.time_field}] is not in inter_feat.")
+
+        self.logger.debug(f"time-based split with ratios [{ratios}], group_by=[{group_by}]")
+        tot_ratio = sum(ratios)
+        ratios = [_ / tot_ratio for _ in ratios]
+
+        # Determine the global temporal boundary (e.g., 90th percentile)
+        all_times = self.inter_feat[self.time_field].numpy()
+        global_temporal_boundary = np.percentile(all_times, 100 * (1 - ratios[-1]))
+
+        train_index, valid_index, test_index = [], [], []
+        grouped_inter_feat_index = self._grouped_index(self.inter_feat[group_by].numpy())
+
+        for grouped_index in grouped_inter_feat_index:
+            grouped_index = np.array(grouped_index)
+            grouped_inter_feat = self.inter_feat[grouped_index]
+            grouped_inter_feat.sort(by=self.time_field)
+
+            # Split into training/validation and test sets based on the global temporal boundary
+            times = grouped_inter_feat[self.time_field].numpy()
+            train_valid_mask = times <= global_temporal_boundary
+            test_mask = ~train_valid_mask
+
+            train_valid_index = grouped_index[train_valid_mask]
+            test_user_indices = grouped_index[test_mask]
+
+            split_point = int(len(train_valid_index) * (ratios[0] / (1 - ratios[2])))
+            train_index.extend(train_valid_index[:split_point])
+            valid_index.extend(train_valid_index[split_point:])
+
+            test_index.extend(test_user_indices)
+
+        self._drop_unused_col()
+        next_df = [
+            self.inter_feat[train_index],
+            self.inter_feat[valid_index],
+            self.inter_feat[test_index],
+        ]
+        next_ds = [self.copy(_) for _ in next_df]
+        return next_ds
+
     def shuffle(self):
         """Shuffle the interaction records inplace."""
         self.inter_feat.shuffle()
@@ -1799,6 +1855,13 @@ def build(self):
             datasets = self.leave_one_out(
                 group_by=self.uid_field, leave_one_mode=split_args["LS"]
             )
+        elif split_mode == "TS":
+            if not isinstance(split_args["TS"], list):
+                raise ValueError(f'The value of "TS" [{split_args}] should be a list.')
+            datasets = self.time_based_split(
+                ratios=split_args["TS"],
+                group_by=self.uid_field
+            )
         else:
             raise NotImplementedError(
                 f"The splitting_method [{split_mode}] has not been implemented."