def simple_accuracy(preds, labels):
# works also with nested lists of different lengths (needed for masked LM task)
if type(preds) == type(labels) == list:
preds = np.array(list(flatten_list(preds)))
labels = np.array(list(flatten_list(labels)))
assert type(preds) == type(labels) == np.ndarray
correct = preds == labels
return {"acc": correct.mean()}
def simple_accuracy(preds, labels):
try:
# works also with nested lists of different lengths (needed for masked LM task)
flat_preds = np.array(list(flatten_list(preds)))
flat_labels = np.array(list(flatten_list(labels)))
correct = flat_preds == flat_labels
return {"acc": correct.mean()}
except TypeError:
# TODO: THIS HACKY TRY CATCH IS FOR GNAD
return {"acc": (preds == labels.numpy()).mean()}
def convert_features_to_dataset(features):
"""
Converts a list of feature dictionaries (one for each sample) into a PyTorch Dataset.
:param features: A list of dictionaries. Each dictionary corresponds to one sample. Its keys are the
names of the type of feature and the keys are the features themselves.
:Return: a Pytorch dataset and a list of tensor names.
"""
# features can be an empty list in cases where down sampling occurs (e.g. Natural Questions downsamples instances of is_impossible)
if len(features) == 0:
return None, None
tensor_names = list(features[0].keys())
all_tensors = []
for t_name in tensor_names:
# Conversion of floats
if t_name == 'regression_label_ids':
cur_tensor = torch.tensor([sample[t_name] for sample in features],
dtype=torch.float32)
else:
try:
# Checking weather a non-integer will be silently converted to torch.long
check = features[0][t_name]
if isinstance(check, numbers.Number):
base = check
# extract a base variable from a nested lists or tuples
elif isinstance(check, list):
base = list(flatten_list(check))[0]
# extract a base variable from numpy arrays
else:
base = check.ravel()[0]
if not np.issubdtype(type(base), np.integer):
logger.warning(
f"Problem during conversion to torch tensors:\n"
f"A non-integer value for feature '{t_name}' with a value of: "
f"'{base}' will be converted to a torch tensor of dtype long."
)
except:
logger.warning(
f"Could not determine type for feature '{t_name}'. Converting now to a tensor of default type long."
)
# Convert all remaining python objects to torch long tensors
cur_tensor = torch.tensor([sample[t_name] for sample in features],
dtype=torch.long)
all_tensors.append(cur_tensor)
dataset = TensorDataset(*all_tensors)
return dataset, tensor_names