def main(config, results):
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
if config.clap:
ds = ClapDataset(config, tokenizer)
else:
ds = Dataset(config, tokenizer)
label_names = ds.label_names
model_config = tu.load_model_config(config)
interp_out_file = Path(config.interp_out_file) if config.interp_out_file else None
for i, (train_dataloader, (valid_dataloader, valid_df)) in enumerate(ds.get_train_valid_dataloaders(include_valid_df=True)):
print(f"------------------ BEGIN ITER {i} -----------------------")
model = tu.load_model(config, model_config)
model.to(config.device)
util.set_seed(config)
run_name = f"fold{i}"
experiment = Experiment(config, model, tokenizer, label_names=label_names, run_name=run_name, results=results)
global_step, tr_loss = experiment.train(
train_dataloader, valid_dataloader=valid_dataloader)
if interp_out_file:
interp_df = experiment.interpret(valid_dataloader, valid_df, label_names=label_names)
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
print(interp_df)
interp_df.to_csv(interp_out_file.with_name(f"{interp_out_file.name}_iter{i}"), index=False)
results = experiment.results
# experiment.evaluate('valid', valid_dataloader)
print(f"================== DONE ITER {i} =======================\n\n")
return results
def main(config, results):
logger.warning('Unclassified threshold: %s', config.self_train_thresh)
ds = TDDataset(config,
binary=True,
self_train_thresh=config.self_train_thresh,
keyword_masking_frac=config.keyword_masking_frac)
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
label_names = ds.label_names
#project_name = 'emf-2.4.1'
project_name = config.single_project
iter_obj = [
(project_name, *ds.get_train_valid_dataloaders(
tokenizer, project_name, include_valid_df=True))
] if project_name else ds.get_fold_dataloaders(tokenizer,
include_valid_df=True)
interp_out_file = Path(
config.interp_out_file) if config.interp_out_file else None
# for train_dataloader, valid_dataloader in [ds.get_train_valid_dataloaders(tokenizer, project_name)]:
for project_name, train_dataloader, (valid_dataloader,
valid_df) in iter_obj:
print(
f"------------------ BEGIN PROJECT {project_name} -----------------------"
)
model = tu.load_model(config, model_config)
model.to(config.device)
util.set_seed(config)
experiment = Experiment(config,
model,
tokenizer,
label_names=label_names,
run_name=project_name,
results=results)
global_step, tr_loss = experiment.train(
train_dataloader, valid_dataloader=valid_dataloader)
if interp_out_file:
interp_df = experiment.interpret(valid_dataloader, valid_df)
with pd.option_context('display.max_rows', None,
'display.max_columns', None):
print(interp_df)
interp_df.to_csv(interp_out_file.with_name(
f"{project_name}_{interp_out_file.name}"),
index=False)
results = experiment.results
# experiment.evaluate('valid', valid_dataloader)
print(
f"================== DONE PROJECT {project_name} =======================\n\n"
)
return results