fix: empty dataset splits (#73)

If a dataset is small enough or the values for the train/val/test splits
is very small, it may be possible that no piece of data is assigned to
that split. This change enforces that no split is empty, while it may
have size 1 and thus not really useful, but it won't break the execution
and will leave the responsibility to the user to take action in order to
fix the disparity caused by his/her actions or the very small dataset
size.

tti_eval/cli/main.py

@@ -58,7 +58,7 @@ def build_command(
                 embd_defn.save_embeddings(embeddings=embeddings, split=split, overwrite=True)
                 print(f"Embeddings saved successfully to file at `{embd_defn.embedding_path(split)}`")
             except Exception as e:
-                print(f"Failed to build embeddings for this bastard: {embd_defn}")
+                print(f"Failed to build embeddings for {embd_defn} on the specified split {split}")
                 print(e)
                 import traceback
 

tti_eval/dataset/types/encord_ds.py

@@ -108,8 +108,8 @@ def _setup(
         ssh_key_path = ssh_key_path or os.getenv("ENCORD_SSH_KEY_PATH")
         if ssh_key_path is None:
             raise ValueError(
-                "The `ssh_key_path` parameter and the `ENCORD_SSH_KEY_PATH` environment variable are both missing."
-                "Please set one of them to proceed"
+                "The `ssh_key_path` parameter and the `ENCORD_SSH_KEY_PATH` environment variable are both missing. "
+                "Please set one of them to proceed."
             )
         client = EncordUserClient.create_with_ssh_private_key(ssh_private_key_path=ssh_key_path)
         self._project = client.get_project(project_hash)

tti_eval/dataset/utils.py

@@ -95,16 +95,25 @@ def simple_random_split(
     :raises ValueError: If the sum of `train_split` and `validation_split` is greater than 1,
         or if `train_split` or `validation_split` are less than 0.
     """
+    if dataset_size < 3:
+        raise ValueError(f"Expected a dataset with size at least 3, got {dataset_size}")
+
     if train_split < 0 or validation_split < 0:
         raise ValueError(f"Expected positive splits, got ({train_split=}, {validation_split=})")
-    if train_split + validation_split > 1:
+    if train_split + validation_split >= 1:
         raise ValueError(
             f"Expected `train_split` and `validation_split` sum between 0 and 1, got {train_split + validation_split}"
         )
     rng = np.random.default_rng(seed)
     selection = rng.permutation(dataset_size)
-    train_size = int(dataset_size * train_split)
-    validation_size = int(dataset_size * validation_split)
+    train_size = max(1, int(dataset_size * train_split))
+    validation_size = max(1, int(dataset_size * validation_split))
+    # Ensure that the TEST split has at least an element
+    if train_size + validation_size == dataset_size:
+        if train_size > 1:
+            train_size -= 1
+        if validation_size > 1:
+            validation_size -= 1
     return {
         Split.TRAIN: selection[:train_size],
         Split.VALIDATION: selection[train_size : train_size + validation_size],