def make_mode_holdout(data, seed, configuration, num_run, output_dir):
global evaluator
evaluator = HoldoutEvaluator(data, output_dir, configuration,
seed=seed,
num_run=num_run,
all_scoring_functions=False,
**_get_base_dict())
loss, opt_pred, valid_pred, test_pred = evaluator.fit_predict_and_loss()
evaluator.finish_up(loss, opt_pred, valid_pred, test_pred)
def make_mode_holdout_iterative_fit(data, seed, configuration, num_run):
global evaluator
evaluator = HoldoutEvaluator(data, configuration, seed=seed, num_run=num_run, **_get_base_dict())
evaluator.iterative_fit()
signal.signal(15, empty_signal_handler)
evaluator.finish_up()
backend = Backend(None, os.getcwd())
if os.path.exists(backend.get_model_dir()):
backend.save_model(evaluator.model, num_run, seed)
def make_mode_holdout_iterative_fit(data, seed, configuration, num_run,
output_dir):
global evaluator
evaluator = HoldoutEvaluator(data, output_dir, configuration,
seed=seed,
num_run=num_run,
all_scoring_functions=False,
**_get_base_dict())
evaluator.iterative_fit()
signal.signal(15, empty_signal_handler)
evaluator.finish_up()
def make_mode_holdout(data, seed, configuration, num_run, output_dir):
global evaluator
evaluator = HoldoutEvaluator(data,
output_dir,
configuration,
seed=seed,
num_run=num_run,
all_scoring_functions=False,
**_get_base_dict())
loss, opt_pred, valid_pred, test_pred = evaluator.fit_predict_and_loss()
evaluator.finish_up(loss, opt_pred, valid_pred, test_pred)
def make_mode_holdout_iterative_fit(data, seed, configuration, num_run,
output_dir):
global evaluator
evaluator = HoldoutEvaluator(data,
output_dir,
configuration,
seed=seed,
num_run=num_run,
all_scoring_functions=False,
**_get_base_dict())
evaluator.iterative_fit()
signal.signal(15, empty_signal_handler)
evaluator.finish_up()
def make_mode_holdout_iterative_fit(data, seed, configuration, num_run):
global evaluator
evaluator = HoldoutEvaluator(data,
configuration,
seed=seed,
num_run=num_run,
**_get_base_dict())
evaluator.iterative_fit()
signal.signal(15, empty_signal_handler)
evaluator.finish_up()
backend = Backend(None, os.getcwd())
if os.path.exists(backend.get_model_dir()):
backend.save_model(evaluator.model, num_run, seed)
