def main(config, results):
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
ds = Dataset(config, tokenizer)
label_names = ds.label_names
model_config = tu.load_model_config(config)
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)
results = experiment.results
# experiment.evaluate('valid', valid_dataloader)
print(f"================== DONE ITER {i} =======================\n\n")
return results
def main(config, results):
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
ds = Dataset(config, tokenizer)
label_names = ds.label_names
dataloaders = getattr(ds, f"get_{config.dataset}_train_valid_dataloaders")(include_valid_df=True)
for i, (train_dataloader, (valid_dataloader, valid_df)) in enumerate(dataloaders):
print(f"------------------ BEGIN ITER {i} -----------------------")
# need to reload original model config, to avoid vocabulary size mismatch
# caused by custom tokens
model_config = tu.load_model_config(config)
model = tu.load_model(config, model_config)
model.resize_token_embeddings(len(tokenizer))
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)
results = experiment.results
# experiment.evaluate('valid', valid_dataloader)
print(f"================== DONE ITER {i} =======================\n\n")
return results
def main(config, results):
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
ds = Dataset(config, tokenizer)
label_names = ds.label_names
train_dataloader, valid_dataloader, test_dataloader = getattr(
ds, f"get_{config.dataset}_train_valid_test_dataloaders")()
model = tu.load_model(config, model_config)
model.resize_token_embeddings(len(tokenizer))
model.to(config.device)
util.set_seed(config)
experiment = Experiment(config,
model,
tokenizer,
label_names=label_names,
results=results)
global_step, tr_loss = experiment.train(train_dataloader,
valid_dataloader=valid_dataloader,
test_dataloader=test_dataloader)
results = experiment.results
return results
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)
backtrans_dfs = []
run_configs = RUN_CONFIGS[config.run_configs]
# Do not use CV in this setting
train_valid_df, test_df = train_test_split(ds.df,
test_size=0.2,
shuffle=True,
stratify=ds.df.label_id,
random_state=config.seed)
results = active_learn(config,
model_config,
tokenizer,
results,
ds.label_names,
test_df,
train_valid_df,
backtrans_dfs,
ds.get_dataloader,
run_configs,
balance=True)
return results
def main(config, results):
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
ds = Dataset(config, tokenizer)
label_names = ds.label_names
train_dataloader, valid_dataloader = ds.get_train_valid_dataloaders()
model = tu.load_model(config, model_config)
model.to(config.device)
util.set_seed(config)
experiment = Experiment(config,
model,
tokenizer,
label_names=label_names,
results=results)
global_step, tr_loss = experiment.train(train_dataloader,
valid_dataloader=valid_dataloader)
results = experiment.results
experiment.save_model(util.models_path('comment_code_shuffle'))
return results
def main(config, results):
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
ds = SentiDataset(config, tokenizer)
backtrans_dfs = ds.load_backtrans_dfs(ds.ALL_BACKTRANS_LANGS, 'train')
run_configs = RUN_CONFIGS[config.run_configs]
results = active_learn(config, model_config, tokenizer, results,
ds.label_names, ds.test_df, ds.train_valid_df,
backtrans_dfs, ds.get_dataloader, run_configs)
return results
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):
pd.set_option('display.max_rows', None)
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
ds = SentiDataset(config, tokenizer)
test_dataloader = ds.get_test_dataloader()
model = tu.load_model(config, model_config)
model.to(config.device)
util.set_seed(config)
train_dataloader, valid_dataloader = ds.get_train_valid_dataloaders()
test_dataloader = ds.get_test_dataloader()
test_dataloaders = {'test': ds.get_test_dataloader()}
if config.jira:
test_dataloaders['JIRA'] = (
ds.get_jira_dataloader(),
dict(pred_label_ids_func=ds.neutral_to_negative))
if config.app_reviews:
test_dataloaders['AppReviews'] = ds.get_app_reviews_dataloader()
if config.sentidata_so:
test_dataloaders[
'StackOverflow (SentiData)'] = ds.get_stack_overflow_dataloader()
experiment = Experiment(config,
model,
tokenizer,
label_names=ds.label_names,
results=results)
global_step, tr_loss = experiment.train(train_dataloader,
valid_dataloader=valid_dataloader,
test_dataloader=test_dataloaders)
# interp_df = experiment.interpret(test_dataloader, ds.test_df, label_names=ds.label_names)
# with pd.option_context('display.max_rows', None, 'display.max_columns', None):
# print(interp_df)
# if config.interp_out_file:
# interp_df.to_csv(config.interp_out_file, index=False)
return experiment.results
def main(config, results):
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
ds = Dataset(config, tokenizer)
label_names = ds.label_names
train_dataloader = ds.get_train_dataloader()
fake_valid_dataloader = ds.get_fake_valid_dataloader()
# with config:
# config.max_steps=100
model = tu.load_model(config, model_config)
model.to(config.device)
util.set_seed(config)
experiment = Experiment(config, model, tokenizer, label_names=label_names, results=results)
global_step, tr_loss = experiment.train(train_dataloader, valid_dataloader=fake_valid_dataloader) #test_dataloader=test_dataloader)
valid_dataloader = ds.get_valid_dataloader()
test_dataloader = ds.get_test_dataloader()
experiment.evaluate('test_final', test_dataloader)
experiment.evaluate('valid_final', valid_dataloader)
experiment.save_model('test_model_complexity')
with config:
config.model_path = 'test_model_complexity'
model = tu.load_model(config, model_config)
model.to(config.device)
logger.warn('#################################### =========================')
experiment = Experiment(config, model, tokenizer, label_names=label_names, results=results)
experiment.evaluate('test_final_reloaded', test_dataloader)
experiment.evaluate('valid_final_reloaded', valid_dataloader)
results = experiment.results
return results
def main(config, results):
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
ds = Dataset(config, tokenizer)
label_names = ds.label_names
train_dataloader, valid_dataloader = ds.get_train_valid_dataloaders()
test_dataloader = ds.get_test_dataloader()
# with config:
# config.max_steps=100
model = tu.load_model(config, model_config)
model.to(config.device)
util.set_seed(config)
experiment = Experiment(config, model, tokenizer, label_names=label_names, results=results)
global_step, tr_loss = experiment.train(train_dataloader, valid_dataloader=valid_dataloader, test_dataloader=test_dataloader)
results = experiment.results
return results
def main():
config = get_config(parse_args)
util.set_seed(config)
with config:
config.logging_steps = 50
config.train_epochs = 5
# config.train_head_only = True
print("model is now", config.model_path)
ds = CadoDataset(config)
label_names = ds.label_names
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
f1s = []
results = None
test_dataloader = ds.get_test_dataloader(tokenizer)
for i, (train_dataloader, (valid_dataloader, valid_df)) in enumerate(ds.get_all_train_valid_dataloaders(tokenizer, 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"{config.single_class if config.single_class else 'multi'}_{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, test_dataloader=test_dataloader)
results = experiment.results
# experiment.evaluate('valid', valid_dataloader)
print(f"================== DONE ITER {i} =======================\n\n")
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
