def earnn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
pretrained=True,
ignore_vars=None,
):
data_path = get_data_path()
if not pretrained:
predictor = EARecurrentNetwork(
hidden_size=128,
data_folder=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()
else:
predictor = load_model(data_path /
f"models/{experiment}/ealstm/model.pt")
test_file = data_path / f"features/{experiment}/test/2018_3"
assert test_file.exists()
all_explanations_for_file(test_file, predictor, batch_size=100)
def earnn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
pretrained=False,
explain=False,
static="features",
ignore_vars=None,
num_epochs=50,
early_stopping=5,
static_embedding_size=10,
hidden_size=128,
predict_delta=False,
spatial_mask=None,
include_latlons=False,
normalize_y=True,
include_prev_y=True,
include_yearly_aggs=True, # new
clear_nans=True,
weight_observations=False,
pred_month_static=False,
):
data_path = get_data_path()
if not pretrained:
predictor = EARecurrentNetwork(
hidden_size=hidden_size,
data_folder=data_path,
experiment=experiment,
include_pred_month=include_pred_month,
surrounding_pixels=surrounding_pixels,
static=static,
static_embedding_size=static_embedding_size,
ignore_vars=ignore_vars,
predict_delta=predict_delta,
spatial_mask=spatial_mask,
include_latlons=include_latlons,
normalize_y=normalize_y,
include_prev_y=include_prev_y,
include_yearly_aggs=include_yearly_aggs,
clear_nans=clear_nans,
weight_observations=weight_observations,
pred_month_static=pred_month_static,
)
predictor.train(num_epochs=num_epochs, early_stopping=early_stopping)
predictor.evaluate(save_preds=True)
predictor.save_model()
else:
predictor = load_model(data_path /
f"models/{experiment}/ealstm/model.pt")
if explain:
test_file = data_path / f"features/{experiment}/test/2018_3"
assert test_file.exists()
all_explanations_for_file(test_file, predictor, batch_size=100)