Added test for PTuningScorer

Author: nikiduki

tests/modules/scoring/test_ptuning.py

@@ -0,0 +1,69 @@
+import numpy as np
+import pytest
+
+from autointent.context.data_handler import DataHandler
+from autointent.modules import PTuningScorer
+
+
+def test_ptuning_prediction(dataset):
+    """Test that the transformer model can fit and make predictions."""
+    data_handler = DataHandler(dataset)
+
+    scorer = PTuningScorer(
+        base_model_config="prajjwal1/bert-tiny",
+        num_train_epochs=1,
+        batch_size=8,
+        task_type="SEQ_CLS",
+        num_virtual_tokens=10,
+    )
+
+    scorer.fit(data_handler.train_utterances(0), data_handler.train_labels(0))
+
+    test_data = [
+        "why is there a hold on my american saving bank account",
+        "i am nost sure why my account is blocked",
+        "why is there a hold on my capital one checking account",
+        "i think my account is blocked but i do not know the reason",
+        "can you tell me why is my bank account frozen",
+    ]
+
+    predictions = scorer.predict(test_data)
+
+    assert predictions.shape[0] == len(test_data)
+    assert predictions.shape[1] == len(set(data_handler.train_labels(0)))
+
+    assert 0.0 <= np.min(predictions) <= np.max(predictions) <= 1.0
+
+    if not scorer._multilabel:
+        for pred_row in predictions:
+            np.testing.assert_almost_equal(np.sum(pred_row), 1.0, decimal=5)
+
+    if hasattr(scorer, "predict_with_metadata"):
+        predictions, metadata = scorer.predict_with_metadata(test_data)
+        assert len(predictions) == len(test_data)
+        assert metadata is None
+
+
+def test_ptuning_cache_clearing(dataset):
+    """Test that the transformer model properly handles cache clearing."""
+    data_handler = DataHandler(dataset)
+
+    scorer = PTuningScorer(
+        base_model_config="prajjwal1/bert-tiny",
+        num_train_epochs=1,
+        batch_size=8,
+        task_type="SEQ_CLS",
+        num_virtual_tokens=20,
+    )
+
+    scorer.fit(data_handler.train_utterances(0), data_handler.train_labels(0))
+
+    test_data = ["test text"]
+    scorer.predict(test_data)
+    scorer.clear_cache()
+
+    assert not hasattr(scorer, "_model") or scorer._model is None
+    assert not hasattr(scorer, "_tokenizer") or scorer._tokenizer is None
+
+    with pytest.raises(RuntimeError):
+        scorer.predict(test_data)