feat: allow passing validation set explicitly

model2vec/train/classifier.py

@@ -135,6 +135,8 @@ def fit(
         early_stopping_patience: int | None = 5,
         test_size: float = 0.1,
         device: str = "auto",
+        X_val: list[str] | None = None,
+        y_val: LabelType | None = None,
     ) -> StaticModelForClassification:
         """
         Fit a model.
@@ -146,6 +148,9 @@ def fit(
         This function seeds everything with a seed of 42, so the results are reproducible.
         It also splits the data into a train and validation set, again with a random seed.
 
+        If `X_val` and `y_val` are not provided, the function will automatically
+        split the training data into a train and validation set using `test_size`.
+
         :param X: The texts to train on.
         :param y: The labels to train on. If the first element is a list, multi-label classification is assumed.
         :param learning_rate: The learning rate.
@@ -157,7 +162,10 @@ def fit(
             If this is None, early stopping is disabled.
         :param test_size: The test size for the train-test split.
         :param device: The device to train on. If this is "auto", the device is chosen automatically.
+        :param X_val: The texts to be used for validation.
+        :param y_val: The labels to be used for validation.
         :return: The fitted model.
+        :raises ValueError: If either X_val or y_val are provided, but not both.
         """
         pl.seed_everything(_RANDOM_SEED)
         logger.info("Re-initializing model.")
@@ -166,11 +174,24 @@ def fit(
 
         self._initialize(y)
 
-        train_texts, validation_texts, train_labels, validation_labels = self._train_test_split(
-            X,
-            y,
-            test_size=test_size,
-        )
+        if (X_val is not None) != (y_val is not None):
+            raise ValueError("Both X_val and y_val must be provided together, or neither.")
+
+        if X_val is not None and y_val is not None:
+            # Additional check to ensure y_val is of the same type as y
+            if type(y_val[0]) != type(y[0]):
+                raise ValueError("X_val and y_val must be of the same type as X and y.")
+
+            train_texts = X
+            train_labels = y
+            validation_texts = X_val
+            validation_labels = y_val
+        else:
+            train_texts, validation_texts, train_labels, validation_labels = self._train_test_split(
+                X,
+                y,
+                test_size=test_size,
+            )
 
         if batch_size is None:
             # Set to a multiple of 32

tests/test_trainable.py

@@ -4,6 +4,7 @@
 import pytest
 import torch
 from tokenizers import Tokenizer
+from transformers import AutoTokenizer
 
 from model2vec.model import StaticModel
 from model2vec.train import StaticModelForClassification
@@ -154,6 +155,50 @@ def test_train_test_split(mock_trained_pipeline: StaticModelForClassification) -
     assert len(d) == len(b)
 
 
+def test_y_val_none() -> None:
+    """Test the y_val function."""
+    tokenizer = AutoTokenizer.from_pretrained("tests/data/test_tokenizer").backend_tokenizer
+    torch.random.manual_seed(42)
+    vectors_torched = torch.randn(len(tokenizer.get_vocab()), 12)
+    model = StaticModelForClassification(vectors=vectors_torched, tokenizer=tokenizer, hidden_dim=12).to("cpu")
+
+    X = ["dog", "cat"]
+    y = ["0", "1"]
+
+    X_val = ["dog", "cat"]
+    y_val = ["0", "1"]
+
+    with pytest.raises(ValueError):
+        model.fit(X, y, X_val=X_val, y_val=None)
+    with pytest.raises(ValueError):
+        model.fit(X, y, X_val=None, y_val=y_val)
+    model.fit(X, y, X_val=None, y_val=None)
+
+
+@pytest.mark.parametrize(
+    "y_multi,y_val_multi,should_crash",
+    [[True, True, False], [False, False, False], [True, False, True], [False, True, True]],
+)
+def test_y_val(y_multi: bool, y_val_multi: bool, should_crash: bool) -> None:
+    """Test the y_val function."""
+    tokenizer = AutoTokenizer.from_pretrained("tests/data/test_tokenizer").backend_tokenizer
+    torch.random.manual_seed(42)
+    vectors_torched = torch.randn(len(tokenizer.get_vocab()), 12)
+    model = StaticModelForClassification(vectors=vectors_torched, tokenizer=tokenizer, hidden_dim=12).to("cpu")
+
+    X = ["dog", "cat"]
+    y = [["0", "1"], ["0"]] if y_multi else ["0", "1"]  # type: ignore
+
+    X_val = ["dog", "cat"]
+    y_val = [["0", "1"], ["0"]] if y_val_multi else ["0", "1"]  # type: ignore
+
+    if should_crash:
+        with pytest.raises(ValueError):
+            model.fit(X, y, X_val=X_val, y_val=y_val)
+    else:
+        model.fit(X, y, X_val=X_val, y_val=y_val)
+
+
 def test_evaluate(mock_trained_pipeline: StaticModelForClassification) -> None:
     """Test the evaluate function."""
     if mock_trained_pipeline.multilabel: