def main_source_training(source_dataset, target_dataset, target_subject, model, params, weights_exp, eval_mode):
hist_f = params["hist"] // freq
save_file = compute_weights_file(model, source_dataset, target_dataset, target_subject, weights_exp)
train, valid, test, scalers = preprocessing_source_multi(source_dataset, target_dataset, target_subject, ph_f,
hist_f, day_len_f)
make_predictions_tl(target_subject, model, params, ph_f, train, valid, test,
eval_mode=eval_mode, fit=True, save_model_file=save_file)
def main_target_training(source_dataset, target_dataset, target_subject, model, params, eval_mode, exp, plot):
hist_f = params["hist"] // freq
train, valid, test, scalers = preprocessing(target_dataset, target_subject, ph_f, hist_f, day_len_f)
raw_results = make_predictions_tl(target_subject, model, params, ph_f, train, valid, test,
eval_mode=eval_mode, fit=True, save_model_file=None)
return evaluation(raw_results, scalers, source_dataset, target_dataset, target_subject, model, params, exp, plot,
"target_training")
def main_target_global(source_dataset, target_dataset, target_subject, model, params, weights_exp, eval_mode, exp,
plot):
hist_f = params["hist"] // freq
weights_file = compute_weights_file(model, source_dataset, target_dataset, target_subject, weights_exp)
train, valid, test, scalers = preprocessing(target_dataset, target_subject, ph_f, hist_f, day_len_f)
raw_results = make_predictions_tl(target_subject, model, params, ph_f, train, valid, test,
weights_file=weights_file, eval_mode=eval_mode, fit=False, save_model_file=None)
return evaluation(raw_results, scalers, source_dataset, target_dataset, target_subject, model, params, exp, plot,
"target_global")
def end_to_end(source_dataset, target_dataset, target_subject, model, params, weights_exp, eval_mode, exp,
plot):
hist_f = params["hist"] // freq
save_file = compute_weights_file(model, source_dataset, target_dataset, target_subject, weights_exp)
train_m, valid_m, test_m, scalers_m = preprocessing_source_multi(source_dataset, target_dataset, target_subject,
ph_f, hist_f, day_len_f)
make_predictions_tl(target_subject, model, params, ph_f, train_m, valid_m, test_m,
eval_mode=eval_mode, fit=True, save_model_file=save_file)
train, valid, test, scalers = preprocessing(target_dataset, target_subject, ph_f, hist_f, day_len_f)
raw_results = make_predictions_tl(target_subject, model, params, ph_f, train, valid, test,
weights_file=save_file, eval_mode=eval_mode, fit=False, save_model_file=None)
evaluation(raw_results, scalers, source_dataset, target_dataset, target_subject, model, params, exp, plot,
"target_global")
raw_results_2 = make_predictions_tl(target_subject, model, params, ph_f, train, valid, test,
weights_file=save_file, eval_mode=eval_mode, fit=True,
save_model_file=None)
return evaluation(raw_results_2, scalers, source_dataset, target_dataset, target_subject, model, params, exp, plot,
"target_finetuning")
def main_target_finetuning(source_dataset, target_dataset, target_subject,
Model, params, weights_exp, eval_mode, exp, plot):
hist_f = params["hist"] // freq
weights_file = compute_weights_file(Model, source_dataset, target_dataset,
target_subject, weights_exp)
train, valid, test, scalers = preprocessing(target_dataset, target_subject,
ph_f, hist_f, day_len_f)
raw_results = make_predictions_tl(target_subject,
Model,
params,
ph_f,
train,
valid,
test,
weights_file=weights_file,
tl_mode="target_finetuning",
eval_mode=eval_mode)
evaluation(raw_results, scalers, source_dataset, target_dataset,
target_subject, Model, params, exp, plot, "target_finetuning")