diff --git a/EIVPackage/EIVData/linear.py b/EIVPackage/EIVData/linear.py
index b1825905acafc69332d1c01418eb7db00e0f28eb..89dd7a18a26f72293de31b39b1eb5f01ae88d0fe 100644
--- a/EIVPackage/EIVData/linear.py
+++ b/EIVPackage/EIVData/linear.py
@@ -31,11 +31,12 @@ def load_data(seed=0, splitting_part=0.8, normalize=True,
:param return_ground_truth: Boolean. If True, the unnoisy ground truth will
also be returned. Defaults to False.
:returns: linear_trainset, linear_testset if return_ground_truth is False,
- else linear_trainset, linear_testset, (true_x, true_y)
+ else linear_trainset, linear_testset, (true_train_x, true_train_y),
+ (true_test_x, true_test_y)
"""
random_generator = torch.Generator().manual_seed(seed)
# draw different seeds for noise and splitting
- seeds = torch.randint(0,sys.maxsize,(4,), generator=random_generator)
+ seeds = torch.randint(0,sys.maxsize,(3,), generator=random_generator)
# create new generators from tensor seeds
create_generator = lambda tensor_seed:\
torch.Generator().manual_seed(tensor_seed.item())
@@ -55,14 +56,18 @@ def load_data(seed=0, splitting_part=0.8, normalize=True,
true_x = (true_x-normalization_x[0])/normalization_x[1]
noisy_y = (noisy_y-normalization_y[0])/normalization_y[1]
true_y = (true_y-normalization_y[0])/normalization_y[1]
- linear_dataset = TensorDataset(noisy_x, noisy_y)
- dataset_len = len(linear_dataset)
+ dataset_len = noisy_x.shape[0]
train_len = int(dataset_len*splitting_part)
test_len = dataset_len - train_len
- linear_trainset, linear_testset = random_split(linear_dataset,
- lengths=[train_len, test_len],
- generator=create_generator(seeds[3]))
+ true_train_x, true_test_x = torch.split(true_x, [train_len, test_len])
+ true_train_y, true_test_y = torch.split(true_y, [train_len, test_len])
+ noisy_train_x, noisy_test_x = torch.split(noisy_x, [train_len, test_len])
+ noisy_train_y, noisy_test_y = torch.split(noisy_y, [train_len, test_len])
+ linear_trainset = TensorDataset(noisy_train_x, noisy_train_y)
+ linear_testset = TensorDataset(noisy_test_x, noisy_test_y)
if not return_ground_truth:
return linear_trainset, linear_testset
else:
- return linear_trainset, linear_testset, (true_x, true_y)
+ return linear_trainset, linear_testset, (true_train_x, true_train_y),\
+ (true_test_x, true_test_y)
+
diff --git a/EIVPackage/EIVData/quadratic.py b/EIVPackage/EIVData/quadratic.py
index 9b817a5445b4790417e4dd07a2cc15d44950eb94..eab8b6c6810371573d7885f13561d5c74e84fddb 100644
--- a/EIVPackage/EIVData/quadratic.py
+++ b/EIVPackage/EIVData/quadratic.py
@@ -31,11 +31,12 @@ def load_data(seed=0, splitting_part=0.8, normalize=True,
:param return_ground_truth: Boolean. If True, the unnoisy ground truth will
also be returned. Defaults to False.
:returns: linear_trainset, linear_testset if return_ground_truth is False,
- else linear_trainset, linear_testset, (true_x, true_y)
+ else linear_trainset, linear_testset, (true_train_x, true_train_y),
+ (true_test_x, true_test_y)
"""
random_generator = torch.Generator().manual_seed(seed)
# draw different seeds for noise and splitting
- seeds = torch.randint(0,sys.maxsize,(4,), generator=random_generator)
+ seeds = torch.randint(0,sys.maxsize,(3,), generator=random_generator)
# create new generators from tensor seeds
create_generator = lambda tensor_seed:\
torch.Generator().manual_seed(tensor_seed.item())
@@ -55,14 +56,18 @@ def load_data(seed=0, splitting_part=0.8, normalize=True,
true_x = (true_x-normalization_x[0])/normalization_x[1]
noisy_y = (noisy_y-normalization_y[0])/normalization_y[1]
true_y = (true_y-normalization_y[0])/normalization_y[1]
- linear_dataset = TensorDataset(noisy_x, noisy_y)
- dataset_len = len(linear_dataset)
+ dataset_len = noisy_x.shape[0]
train_len = int(dataset_len*splitting_part)
test_len = dataset_len - train_len
- linear_trainset, linear_testset = random_split(linear_dataset,
- lengths=[train_len, test_len],
- generator=create_generator(seeds[3]))
+ true_train_x, true_test_x = torch.split(true_x, [train_len, test_len])
+ true_train_y, true_test_y = torch.split(true_y, [train_len, test_len])
+ noisy_train_x, noisy_test_x = torch.split(noisy_x, [train_len, test_len])
+ noisy_train_y, noisy_test_y = torch.split(noisy_y, [train_len, test_len])
+ linear_trainset = TensorDataset(noisy_train_x, noisy_train_y)
+ linear_testset = TensorDataset(noisy_test_x, noisy_test_y)
if not return_ground_truth:
return linear_trainset, linear_testset
else:
- return linear_trainset, linear_testset, (true_x, true_y)
+ return linear_trainset, linear_testset, (true_train_x, true_train_y),\
+ (true_test_x, true_test_y)
+
diff --git a/Experiments/evaluate_metrics.py b/Experiments/evaluate_metrics.py
index d694558ba35333831660c26edab47bc0d3d014f1..31af058a19075c616231689acacdba39c053f14c 100644
--- a/Experiments/evaluate_metrics.py
+++ b/Experiments/evaluate_metrics.py
@@ -124,7 +124,8 @@ def collect_metrics(x,y, seed=0,
noneiv_metrics['coverage_normalized'],_ =\
epistemic_coverage(not_averaged_predictions, y, normalize_errors=True)
noneiv_metrics['res_std'] = normalized_std(not_averaged_predictions, y)
-
+
+ # metrics that need a ground truth
# NLL
@@ -221,7 +222,14 @@ seed_list = range(noneiv_conf_dict["seed_range"][0],
noneiv_conf_dict["seed_range"][1])
max_batch_number = 2
for seed in tqdm(seed_list):
- train_data, test_data = load_data(seed=seed)
+ try:
+ train_data, test_data, (true_train_x, true_train_y),\
+ (true_test_x, true_test_y) \
+ = load_data(seed=seed, return_ground_truth=True)
+ except TypeError:
+ train_data, test_data = load_data(seed=seed)
+ (true_train_x, true_train_y), (true_test_x, true_test_y)\
+ = (None,None), (None,None)
test_dataloader = DataLoader(test_data,
batch_size=int(np.min((len(test_data),
800))), shuffle=True)