def test_predict(self, tmp_path, use_pred_months, use_latlons, experiment):
x, _, _ = _make_dataset(size=(5, 5), const=True)
y = x.isel(time=[-1])
train_features = tmp_path / f"features/{experiment}/train/1980_1"
train_features.mkdir(parents=True)
test_features = tmp_path / f"features/{experiment}/test/1980_1"
test_features.mkdir(parents=True)
# 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)
# if nowcast we need another x feature
if experiment == "nowcast":
x_add1, _, _ = _make_dataset(size=(5, 5),
const=True,
variable_name="precip")
x_add2, _, _ = _make_dataset(size=(5, 5),
const=True,
variable_name="temp")
x = xr.merge([x, x_add1, x_add2])
norm_dict = {
"VHI": {
"mean": 0,
"std": 1
},
"precip": {
"mean": 0,
"std": 1
},
"temp": {
"mean": 0,
"std": 1
},
}
else:
norm_dict = {"VHI": {"mean": 0, "std": 1}}
with (tmp_path /
f"features/{experiment}/normalizing_dict.pkl").open("wb") as f:
pickle.dump(norm_dict, f)
x.to_netcdf(test_features / "x.nc")
y.to_netcdf(test_features / "y.nc")
x.to_netcdf(train_features / "x.nc")
y.to_netcdf(train_features / "y.nc")
layer_sizes = [10]
dropout = 0.25
model = LinearNetwork(
data_folder=tmp_path,
layer_sizes=layer_sizes,
dropout=dropout,
experiment=experiment,
include_pred_month=use_pred_months,
include_latlons=use_latlons,
)
model.train()
test_arrays_dict, pred_dict = model.predict()
# the foldername "1980_1" is the only one which should be in the dictionaries
assert ("1980_1" in test_arrays_dict.keys()) and (len(test_arrays_dict)
== 1)
assert ("1980_1" in pred_dict.keys()) and (len(pred_dict) == 1)
# _make_dataset with const=True returns all ones
assert (test_arrays_dict["1980_1"]["y"] == 1).all()
def test_predict(self, tmp_path, use_pred_months, use_latlons, experiment):
x, _, _ = _make_dataset(size=(5, 5), const=True)
y = x.isel(time=[-1])
train_features = tmp_path / f'features/{experiment}/train/hello'
train_features.mkdir(parents=True)
test_features = tmp_path / f'features/{experiment}/test/hello'
test_features.mkdir(parents=True)
# 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)
# if nowcast we need another x feature
if experiment == 'nowcast':
x_add1, _, _ = _make_dataset(size=(5, 5),
const=True,
variable_name='precip')
x_add2, _, _ = _make_dataset(size=(5, 5),
const=True,
variable_name='temp')
x = xr.merge([x, x_add1, x_add2])
norm_dict = {
'VHI': {
'mean': 0,
'std': 1
},
'precip': {
'mean': 0,
'std': 1
},
'temp': {
'mean': 0,
'std': 1
}
}
else:
norm_dict = {'VHI': {'mean': 0, 'std': 1}}
with (tmp_path /
f'features/{experiment}/normalizing_dict.pkl').open('wb') as f:
pickle.dump(norm_dict, f)
x.to_netcdf(test_features / 'x.nc')
y.to_netcdf(test_features / 'y.nc')
x.to_netcdf(train_features / 'x.nc')
y.to_netcdf(train_features / 'y.nc')
layer_sizes = [10]
dropout = 0.25
model = LinearNetwork(data_folder=tmp_path,
layer_sizes=layer_sizes,
dropout=dropout,
experiment=experiment,
include_pred_month=use_pred_months,
include_latlons=use_latlons)
model.train()
test_arrays_dict, pred_dict = model.predict()
# the foldername "hello" is the only one which should be in the dictionaries
assert ('hello' in test_arrays_dict.keys()) and (len(test_arrays_dict)
== 1)
assert ('hello' in pred_dict.keys()) and (len(pred_dict) == 1)
# _make_dataset with const=True returns all ones
assert (test_arrays_dict['hello']['y'] == 1).all()