Fix attribute errors: #185 #183

comet/models/base.py

@@ -651,7 +651,7 @@ def predict(
 
  scores = torch.cat([pred["scores"] for pred in predictions], dim=0).tolist()
  if "metadata" in predictions[0]:
- metadata = flatten_metadata([pred.metadata for pred in predictions])
+ metadata = flatten_metadata([pred["metadata"] for pred in predictions])
  else:
  metadata = []
 

tests/integration/models/test_ranking_metric.py

@@ -81,7 +81,7 @@ def test_training(self):
  predictions = trainer.predict(
  ckpt_path="best", dataloaders=dataloader, return_predictions=True
  )
- y_pos = torch.cat([p.scores for p in predictions], dim=0)
+ y_pos = torch.cat([p["scores"] for p in predictions], dim=0)
 
  # Scores for "worse" translations
  neg_translations = [
@@ -96,7 +96,7 @@ def test_training(self):
  predictions = trainer.predict(
  ckpt_path="best", dataloaders=dataloader, return_predictions=True
  )
- y_neg = torch.cat([p.scores for p in predictions], dim=0)
+ y_neg = torch.cat([p["scores"] for p in predictions], dim=0)
  ## This shouldn't break!
  pearsonr(y_pos, y_neg)[0]
 

tests/integration/models/test_referenceless_regression.py

@@ -84,5 +84,5 @@ def test_training(self):
  predictions = trainer.predict(
  ckpt_path="best", dataloaders=dataloader, return_predictions=True
  )
- y_hat = torch.cat([p.scores for p in predictions], dim=0).tolist()
+ y_hat = torch.cat([p["scores"] for p in predictions], dim=0).tolist()
  assert pearsonr(y_hat, y)[0] > 0.85

tests/integration/models/test_regression_metric.py

@@ -84,5 +84,6 @@ def test_training(self):
  predictions = trainer.predict(
  ckpt_path="best", dataloaders=dataloader, return_predictions=True
  )
- y_hat = torch.cat([p.scores for p in predictions], dim=0).tolist()
+ breakpoint()
+ y_hat = torch.cat([p["scores"] for p in predictions], dim=0).tolist()
  assert pearsonr(y_hat, y)[0] > 0.85

tests/integration/models/test_unified_metric.py

@@ -87,7 +87,7 @@ def test_regression_with_references(self):
  predictions = trainer.predict(
  ckpt_path="best", dataloaders=dataloader, return_predictions=True
  )
- y_hat = torch.cat([p.scores for p in predictions], dim=0).tolist()
+ y_hat = torch.cat([p["scores"] for p in predictions], dim=0).tolist()
  assert pearsonr(y_hat, y)[0] > 0.9
 
  def test_regression_without_references(self):
@@ -153,6 +153,6 @@ def test_regression_without_references(self):
  predictions = trainer.predict(
  ckpt_path="best", dataloaders=dataloader, return_predictions=True
  )
- y_hat = torch.cat([p.scores for p in predictions], dim=0).tolist()
+ y_hat = torch.cat([p["scores"] for p in predictions], dim=0).tolist()
  assert pearsonr(y_hat, y)[0] > 0.9