def run_task(task_name, fit_args=None):
task_dict = task_loader.get_task_dict()
task_id = task_dict[task_name]['openml_task_id'] # openml task id
n_folds = 1 # do 5 folds of train/val split
if fit_args is None:
fit_args = dict()
predictors, scores = task_utils.run_task(task_id,
n_folds=n_folds,
fit_args=fit_args,
print_leaderboard=True)
score = float(np.mean(scores))
num_models = float(
np.mean([len(predictor.get_model_names())
for predictor in predictors]))
if len(scores) > 1:
score_std = np.std(scores, ddof=1)
else:
score_std = 0.0 # Should this be np.inf?
print(f'{task_name} score: {round(score, 5)} (+- {round(score_std, 5)})')
return score, num_models
import numpy as np
from autogluon_benchmark.tasks import task_loader, task_utils
if __name__ == "__main__":
task_dict = task_loader.get_task_dict()
task_name = 'Australian' # task name in yaml config
task_id = task_dict[task_name]['openml_task_id'] # openml task id
n_folds = 5 # do 5 folds of train/val split
fit_args = {
'eval_metric': 'roc_auc',
}
predictors, scores = task_utils.run_task(task_id, n_folds=n_folds, fit_args=fit_args)
score = float(np.mean(scores))
if len(scores) > 1:
score_std = np.std(scores, ddof=1)
else:
score_std = 0.0 # Should this be np.inf?
print(f'{task_name} score: {round(score, 5)} (+- {round(score_std, 5)})')