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 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=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)