def test_big_mean(self, tmp_path, monkeypatch):
def mockiter(self):
class MockIterator:
def __init__(self):
self.idx = 0
self.max_idx = 10
def __iter__(self):
return self
def __next__(self):
if self.idx < self.max_idx:
# batch_size = 10, timesteps = 2, num_features = 1
self.idx += 1
return (np.ones(
(10, 2, 1)), np.ones(
(10, ), dtype=np.int8), np.ones(
(10, 2)), np.ones((10, 2)), np.ones(
(10, 2)), np.ones((10, 2))), None
else:
raise StopIteration()
return MockIterator()
def do_nothing(self, data_path, batch_file_size, shuffle_data, mode,
pred_months, surrounding_pixels, ignore_vars):
pass
monkeypatch.setattr(DataLoader, '__iter__', mockiter)
monkeypatch.setattr(DataLoader, '__init__', do_nothing)
model = LinearRegression(tmp_path)
calculated_mean = model._calculate_big_mean()
# 1 for the 2 features and for the first month, 0 for the rest
expected_mean = np.array([
1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.,
1.
])
# np.isclose because of rounding
assert np.isclose(calculated_mean, expected_mean).all()
def test_big_mean(self, tmp_path, monkeypatch):
def mockiter(self):
class MockIterator:
def __init__(self):
self.idx = 0
self.max_idx = 10
def __iter__(self):
return self
def __next__(self):
if self.idx < self.max_idx:
# batch_size = 10, timesteps = 2, num_features = 1
self.idx += 1
return (
(
np.ones((10, 2, 1)),
np.ones((10,), dtype=np.int8),
np.ones((10, 2)),
np.ones((10, 2)),
np.ones((10, 2)),
np.ones((10, 2)),
np.ones((10, 1)),
),
None,
)
else:
raise StopIteration()
return MockIterator()
def do_nothing(
self,
data_path,
batch_file_size,
mode,
shuffle_data,
clear_nans,
normalize,
experiment,
mask,
pred_months,
to_tensor,
surrounding_pixels,
ignore_vars,
monthly_aggs,
static,
device,
predict_delta,
spatial_mask,
normalize_y,
incl_yearly_aggs,
):
pass
monkeypatch.setattr(DataLoader, "__iter__", mockiter)
monkeypatch.setattr(DataLoader, "__init__", do_nothing)
model = LinearRegression(tmp_path, normalize_y=False)
calculated_mean = model._calculate_big_mean()
# 1 for the 2 features and for the first month, 0 for the rest
expected_mean = np.array(
[
1.0,
1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
1.0,
1.0,
1.0,
1.0,
]
)
# np.isclose because of rounding
assert np.isclose(calculated_mean, expected_mean).all()