Added tuning subset size and seed to data config

EventStream/data/config.py

@@ -803,6 +803,10 @@ class PytorchDatasetConfig(JSONableMixin):
             training subset. If `None` or "FULL", then the full training data is used.
         train_subset_seed: If the training data should be subsampled randomly, this specifies the seed for
             that random subsampling.
+        tuning_subset_size: If the tuning data should be subsampled randomly, this specifies the size of the
+            tuning subset. If `None` or "FULL", then the full tuning data is used.
+        tuning_subset_seed: If the tuning data should be subsampled randomly, this specifies the seed for
+            that random subsampling.
         task_df_name: If the raw dataset should be limited to a task dataframe view, this specifies the name
             of the task dataframe, and indirectly the path on disk from where that task dataframe will be
             read (save_dir / "task_dfs" / f"{task_df_name}.parquet").
@@ -873,6 +877,8 @@ class PytorchDatasetConfig(JSONableMixin):
 
     train_subset_size: int | float | str = "FULL"
     train_subset_seed: int | None = None
+    tuning_subset_size: int | float | str = "FULL"
+    tuning_subset_seed: int | None = None
 
     task_df_name: str | None = None
 
@@ -907,6 +913,22 @@ def __post_init__(self):
                 pass
             case _:
                 raise TypeError(f"train_subset_size is of unrecognized type {type(self.train_subset_size)}.")
+
+        match self.tuning_subset_size:
+            case int() as n if n < 0:
+                raise ValueError(f"If integral, tuning_subset_size must be positive! Got {n}")
+            case float() as frac if frac <= 0 or frac >= 1:
+                raise ValueError(f"If float, tuning_subset_size must be in (0, 1)! Got {frac}")
+            case int() | float() if (self.tuning_subset_seed is None):
+                seed = int(random.randint(1, int(1e6)))
+                print(f"WARNING! tuning_subset_size is set, but tuning_subset_seed is not. Setting to {seed}")
+                self.tuning_subset_seed = seed
+            case None | "FULL" | int() | float():
+                pass
+            case _:
+                raise TypeError(
+                    f"tuning_subset_size is of unrecognized type {type(self.tuning_subset_size)}."
+                )
 
     def to_dict(self) -> dict:
         """Represents this configuration object as a plain dictionary."""