def test_emmental_dataset(caplog):
"""Unit test of emmental dataset"""
caplog.set_level(logging.INFO)
x1 = [
torch.Tensor([1]),
torch.Tensor([1, 2]),
torch.Tensor([1, 2, 3]),
torch.Tensor([1, 2, 3, 4]),
torch.Tensor([1, 2, 3, 4, 5]),
]
y1 = torch.Tensor([0, 0, 0, 0, 0])
dataset = EmmentalDataset(X_dict={"data1": x1},
Y_dict={"label1": y1},
name="new_data")
# Check if the dataset is correctly constructed
assert torch.equal(dataset[0][0]["data1"], x1[0])
assert torch.equal(dataset[0][1]["label1"], y1[0])
x2 = [
torch.Tensor([1, 2, 3, 4, 5]),
torch.Tensor([1, 2, 3, 4]),
torch.Tensor([1, 2, 3]),
torch.Tensor([1, 2]),
torch.Tensor([1]),
]
dataset.add_features(X_dict={"data2": x2})
# Check add one more feature to dataset
assert torch.equal(dataset[0][0]["data2"], x2[0])
y2 = torch.Tensor([1, 1, 1, 1, 1])
dataset.add_labels(Y_dict={"label2": y2})
# Check add one more label to dataset
assert torch.equal(dataset[0][1]["label2"], y2[0])
dataset.remove_label(label_name="label1")
# Check remove one more label to dataset
assert "label1" not in dataset.Y_dict
def test_emmental_dataset(caplog):
"""Unit test of emmental dataset."""
caplog.set_level(logging.INFO)
dirpath = "temp_test_data"
Meta.reset()
emmental.init(dirpath)
x1 = [
torch.Tensor([1]),
torch.Tensor([1, 2]),
torch.Tensor([1, 2, 3]),
torch.Tensor([1, 2, 3, 4]),
torch.Tensor([1, 2, 3, 4, 5]),
]
y1 = torch.Tensor([0, 0, 0, 0, 0])
dataset = EmmentalDataset(X_dict={"data1": x1},
Y_dict={"label1": y1},
name="new_data")
# Check if the dataset is correctly constructed
assert torch.equal(dataset[0][0]["data1"], x1[0])
assert torch.equal(dataset[0][1]["label1"], y1[0])
x2 = [
torch.Tensor([1, 2, 3, 4, 5]),
torch.Tensor([1, 2, 3, 4]),
torch.Tensor([1, 2, 3]),
torch.Tensor([1, 2]),
torch.Tensor([1]),
]
dataset.add_features(X_dict={"data2": x2})
dataset.remove_feature("data2")
assert "data2" not in dataset.X_dict
dataset.add_features(X_dict={"data2": x2})
# Check add one more feature to dataset
assert torch.equal(dataset[0][0]["data2"], x2[0])
y2 = torch.Tensor([1, 1, 1, 1, 1])
dataset.add_labels(Y_dict={"label2": y2})
with pytest.raises(ValueError):
dataset.add_labels(Y_dict={"label2": x2})
# Check add one more label to dataset
assert torch.equal(dataset[0][1]["label2"], y2[0])
dataset.remove_label(label_name="label1")
# Check remove one more label to dataset
assert "label1" not in dataset.Y_dict
with pytest.raises(ValueError):
dataset = EmmentalDataset(X_dict={"data1": x1},
Y_dict={"label1": y1},
name="new_data",
uid="ids")
dataset = EmmentalDataset(X_dict={"_uids_": x1},
Y_dict={"label1": y1},
name="new_data")
dataset = EmmentalDataset(X_dict={"data1": x1}, name="new_data")
# Check if the dataset is correctly constructed
assert torch.equal(dataset[0]["data1"], x1[0])
dataset.add_features(X_dict={"data2": x2})
dataset.remove_feature("data2")
assert "data2" not in dataset.X_dict
dataset.add_features(X_dict={"data2": x2})
# Check add one more feature to dataset
assert torch.equal(dataset[0]["data2"], x2[0])
y2 = torch.Tensor([1, 1, 1, 1, 1])
dataset.add_labels(Y_dict={"label2": y2})
# Check add one more label to dataset
assert torch.equal(dataset[0][1]["label2"], y2[0])
shutil.rmtree(dirpath)
