def test_get_background(self, tmp_path):
x, _, _ = _make_dataset(size=(5, 5), const=True)
y = x.isel(time=[-1])
train_features = tmp_path / "features/one_month_forecast/train/1980_1"
train_features.mkdir(parents=True)
x.to_netcdf(train_features / "x.nc")
y.to_netcdf(train_features / "y.nc")
norm_dict = {"VHI": {"mean": 0, "std": 1}}
with (tmp_path / "features/one_month_forecast/normalizing_dict.pkl"
).open("wb") as f:
pickle.dump(norm_dict, f)
# static
x_static, _, _ = _make_dataset(size=(5, 5), add_times=False)
static_features = tmp_path / f"features/static"
static_features.mkdir(parents=True)
x_static.to_netcdf(static_features / "data.nc")
static_norm_dict = {"VHI": {"mean": 0.0, "std": 1.0}}
with (tmp_path /
f"features/static/normalizing_dict.pkl").open("wb") as f:
pickle.dump(static_norm_dict, f)
model = LinearNetwork(
data_folder=tmp_path,
layer_sizes=[100],
dropout=0.25,
include_pred_month=True,
)
background = model._get_background(sample_size=3)
assert (background[0].shape[0] == 3
), f"Got {background[0].shape[0]} samples back, expected 3"
assert (background[1].shape[0] == 3
), f"Got {background[1].shape[0]} samples back, expected 3"
assert (len(background[1].shape) == 2
), f"Expected 2 dimensions, got {len(background[1].shape)}"
def test_get_background(self, tmp_path):
x, _, _ = _make_dataset(size=(5, 5), const=True)
y = x.isel(time=[-1])
train_features = tmp_path / 'features/one_month_forecast/train/hello'
train_features.mkdir(parents=True)
x.to_netcdf(train_features / 'x.nc')
y.to_netcdf(train_features / 'y.nc')
norm_dict = {'VHI': {'mean': 0, 'std': 1}}
with (tmp_path / 'features/one_month_forecast/normalizing_dict.pkl'
).open('wb') as f:
pickle.dump(norm_dict, f)
# static
x_static, _, _ = _make_dataset(size=(5, 5), add_times=False)
static_features = tmp_path / f'features/static'
static_features.mkdir(parents=True)
x_static.to_netcdf(static_features / 'data.nc')
static_norm_dict = {'VHI': {'mean': 0.0, 'std': 1.0}}
with (tmp_path /
f'features/static/normalizing_dict.pkl').open('wb') as f:
pickle.dump(static_norm_dict, f)
model = LinearNetwork(data_folder=tmp_path,
layer_sizes=[100],
dropout=0.25,
include_pred_month=True)
background = model._get_background(sample_size=3)
assert background[0].shape[0] == 3, \
f'Got {background[0].shape[0]} samples back, expected 3'
assert background[1].shape[0] == 3, \
f'Got {background[1].shape[0]} samples back, expected 3'
assert len(background[1].shape) == 2, \
f'Expected 2 dimensions, got {len(background[1].shape)}'