def linear_nn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
explain=False,
static="features",
ignore_vars=None,
num_epochs=50,
early_stopping=5,
layer_sizes=[100],
predict_delta=False,
spatial_mask=None,
include_latlons=False,
):
predictor = LinearNetwork(
layer_sizes=layer_sizes,
data_folder=get_data_path(),
experiment=experiment,
include_pred_month=include_pred_month,
surrounding_pixels=surrounding_pixels,
static=static,
ignore_vars=ignore_vars,
predict_delta=predict_delta,
spatial_mask=spatial_mask,
include_latlons=include_latlons,
)
predictor.train(num_epochs=num_epochs, early_stopping=early_stopping)
predictor.evaluate(save_preds=True)
predictor.save_model()
if explain:
_ = predictor.explain(save_shap_values=True)
def linear_nn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
ignore_vars=None,
include_static=True,
):
# if the working directory is alread ml_drought don't need ../data
if Path(".").absolute().as_posix().split("/")[-1] == "ml_drought":
data_path = Path("data")
else:
data_path = Path("../data")
predictor = LinearNetwork(
layer_sizes=[100],
data_folder=data_path,
experiment=experiment,
include_pred_month=include_pred_month,
surrounding_pixels=surrounding_pixels,
ignore_vars=ignore_vars,
include_static=include_static,
)
predictor.train(num_epochs=50, early_stopping=5)
predictor.evaluate(save_preds=True)
predictor.save_model()
def test_save(self, tmp_path, monkeypatch):
layer_sizes = [10]
input_layer_sizes = copy(layer_sizes)
input_size = 10
dropout = 0.25
include_pred_month = True
include_latlons = True
include_monthly_aggs = True
surrounding_pixels = 1
ignore_vars = ['precip']
include_yearly_aggs = True
def mocktrain(self):
self.model = LinearModel(input_size,
layer_sizes,
dropout,
include_pred_month,
include_latlons,
include_yearly_aggs,
include_static=True)
self.input_size = input_size
monkeypatch.setattr(LinearNetwork, 'train', mocktrain)
model = LinearNetwork(data_folder=tmp_path,
layer_sizes=layer_sizes,
dropout=dropout,
experiment='one_month_forecast',
include_pred_month=include_pred_month,
include_latlons=include_latlons,
include_monthly_aggs=include_monthly_aggs,
include_yearly_aggs=include_yearly_aggs,
surrounding_pixels=surrounding_pixels,
ignore_vars=ignore_vars)
model.train()
model.save_model()
assert (tmp_path / 'models/one_month_forecast/linear_network/model.pt'
).exists(), f'Model not saved!'
model_dict = torch.load(model.model_dir / 'model.pt',
map_location='cpu')
for key, val in model_dict['model']['state_dict'].items():
assert (model.model.state_dict()[key] == val).all()
assert model_dict['dropout'] == dropout
assert model_dict['layer_sizes'] == input_layer_sizes
assert model_dict['model']['input_size'] == input_size
assert model_dict['include_pred_month'] == include_pred_month
assert model_dict['include_latlons'] == include_latlons
assert model_dict['include_monthly_aggs'] == include_monthly_aggs
assert model_dict['include_yearly_aggs'] == include_yearly_aggs
assert model_dict['surrounding_pixels'] == surrounding_pixels
assert model_dict['ignore_vars'] == ignore_vars
def linear_nn(experiment='one_month_forecast',
include_pred_month=True,
surrounding_pixels=1):
# if the working directory is alread ml_drought don't need ../data
if Path('.').absolute().as_posix().split('/')[-1] == 'ml_drought':
data_path = Path('data')
else:
data_path = Path('../data')
predictor = LinearNetwork(layer_sizes=[100],
data_folder=data_path,
experiment=experiment,
include_pred_month=include_pred_month,
surrounding_pixels=surrounding_pixels)
predictor.train(num_epochs=50, early_stopping=5)
predictor.evaluate(save_preds=True)
predictor.save_model()
_ = predictor.explain(save_shap_values=True)
def linear_nn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
ignore_vars=None,
pretrained=False,
):
predictor = LinearNetwork(
layer_sizes=[100],
data_folder=get_data_path(),
experiment=experiment,
include_pred_month=include_pred_month,
surrounding_pixels=surrounding_pixels,
ignore_vars=ignore_vars,
)
predictor.train(num_epochs=50, early_stopping=5)
predictor.evaluate(save_preds=True)
predictor.save_model()
_ = predictor.explain(save_shap_values=True)
def test_save(self, tmp_path, monkeypatch):
layer_sizes = [10]
input_layer_sizes = copy(layer_sizes)
input_size = 10
dropout = 0.25
include_pred_month = True
include_latlons = True
include_monthly_aggs = True
surrounding_pixels = 1
ignore_vars = ["precip"]
include_yearly_aggs = True
normalize_y = False
def mocktrain(self):
self.model = LinearModel(
input_size,
layer_sizes,
dropout,
include_pred_month,
include_latlons,
include_yearly_aggs,
include_static=True,
include_prev_y=True,
)
self.input_size = input_size
monkeypatch.setattr(LinearNetwork, "train", mocktrain)
model = LinearNetwork(
data_folder=tmp_path,
layer_sizes=layer_sizes,
dropout=dropout,
experiment="one_month_forecast",
include_pred_month=include_pred_month,
include_latlons=include_latlons,
include_monthly_aggs=include_monthly_aggs,
include_yearly_aggs=include_yearly_aggs,
surrounding_pixels=surrounding_pixels,
ignore_vars=ignore_vars,
include_prev_y=True,
normalize_y=normalize_y,
)
model.train()
model.save_model()
assert (tmp_path / "models/one_month_forecast/linear_network/model.pt"
).exists(), f"Model not saved!"
model_dict = torch.load(model.model_dir / "model.pt",
map_location="cpu")
for key, val in model_dict["model"]["state_dict"].items():
assert (model.model.state_dict()[key] == val).all()
assert model_dict["dropout"] == dropout
assert model_dict["layer_sizes"] == input_layer_sizes
assert model_dict["model"]["input_size"] == input_size
assert model_dict["include_pred_month"] == include_pred_month
assert model_dict["include_latlons"] == include_latlons
assert model_dict["include_monthly_aggs"] == include_monthly_aggs
assert model_dict["include_yearly_aggs"] == include_yearly_aggs
assert model_dict["surrounding_pixels"] == surrounding_pixels
assert model_dict["ignore_vars"] == ignore_vars
assert model_dict["include_prev_y"] is True
assert model_dict["normalize_y"] == normalize_y