PoliBa-DeepLearning/NanoSocrates
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Fixed evaluation

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This commit is contained in:
Christian Risi 2025-10-16 20:05:35 +02:00
parent 892f91aad7
commit fe62b1edd5
2 changed files with 6 additions and 5 deletions
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8
Playgrounds/evaluation.py
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@ -11,13 +11,13 @@ DEVICE = torch_shims.get_default_device()
torch.set_default_device(DEVICE) torch.set_default_device(DEVICE)
# Get paths # Get paths
MODEL_DIR = "Assets/Model/curated" # MODEL_DIR = "Assets/Model/curated"
# MODEL_DIR= "Assets/Dataset/Tmp" MODEL_DIR= "Assets/Dataset/Tmp"
VOCABULARY_PATH = Path("Assets/Model/small/bpe-small-16.json") VOCABULARY_PATH = Path("Assets/Model/small/bpe-small-16.json")
TRAIN_DATASET_PATH = Path("Assets/Dataset/1-hop/small/holdout/train.csv") TRAIN_DATASET_PATH = Path("Assets/Dataset/1-hop/small/holdout/train.csv")
VALIDATION_DATASET_PATH = Path("Assets/Dataset/1-hop/small/holdout/evaluation.csv") VALIDATION_DATASET_PATH = Path("Assets/Dataset/1-hop/small/holdout/evaluation.csv")
TEST_DATASET_PATH = Path("Assets/Dataset/1-hop/small/holdout/test.csv") TEST_DATASET_PATH = Path("Assets/Dataset/1-hop/small/holdout/test.csv")
# TEST_DATASET_PATH = Path("Assets/Dataset/1-hop/toy/rdf_text.csv") TEST_DATASET_PATH = Path("Assets/Dataset/1-hop/toy/rdf_text.csv")
MODEL_PATH = Path(f"{MODEL_DIR}/NanoSocrates.zip") MODEL_PATH = Path(f"{MODEL_DIR}/NanoSocrates.zip")
@ -162,7 +162,7 @@ with torch.no_grad():
if tasktype == Batch.TaskType.TEXT2RDF: if tasktype == Batch.TaskType.TEXT2RDF:
ref = TUtils.remove_padding(exp_tokens, PAD_TOKEN, END_TOKEN) ref = TUtils.remove_padding(exp_tokens, PAD_TOKEN, END_TOKEN)
pred = TUtils.remove_padding(tokens, PAD_TOKEN, END_TOKEN) pred = TUtils.remove_padding(tokens[1:], PAD_TOKEN, END_TOKEN)
ref, pred = TUtils.balance_paddings(ref, pred, PAD_TOKEN) ref, pred = TUtils.balance_paddings(ref, pred, PAD_TOKEN)
precision, recall = TUtils.txt2rdf(ref, pred) precision, recall = TUtils.txt2rdf(ref, pred)

3
Project_Model/Libs/TransformerUtils/metrics.py
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@ -35,7 +35,8 @@ def rouge(ref: list[str], pred: list[str]):
def f1(precision: float, recall: float): def f1(precision: float, recall: float):
return (2 * recall * precision) / (precision + recall) divisor = max((precision + recall), 1E-5)
return (2 * recall * precision) / divisor
def average(array: list[float]): def average(array: list[float]):