def rnn(
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 = RecurrentNetwork(
hidden_size=128,
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 rnn(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 = RecurrentNetwork(hidden_size=128,
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 rnn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
explain=False,
static="features",
ignore_vars=None,
num_epochs=50,
early_stopping=5,
hidden_size=128,
predict_delta=False,
spatial_mask=None,
include_latlons=False,
normalize_y=True,
include_prev_y=True,
include_yearly_aggs=True,
clear_nans=True,
weight_observations=False,
):
predictor = RecurrentNetwork(
hidden_size=hidden_size,
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,
normalize_y=normalize_y,
include_prev_y=include_prev_y,
include_yearly_aggs=include_yearly_aggs,
clear_nans=clear_nans,
weight_observations=weight_observations,
)
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 rnn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
ignore_vars=None,
pretrained=True,
):
predictor = RecurrentNetwork(
hidden_size=128,
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)