From 0ac085c07fb7410fb362204b87f3d4ebe41f8801 Mon Sep 17 00:00:00 2001 From: bxdd Date: Tue, 25 Apr 2023 14:11:52 +0800 Subject: [PATCH] [MNT] black format --- examples/example_m5/main.py | 10 +++++++--- examples/example_pfs/main.py | 8 ++++++-- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/examples/example_m5/main.py b/examples/example_m5/main.py index 7e30c2a..473cfc8 100644 --- a/examples/example_m5/main.py +++ b/examples/example_m5/main.py @@ -171,7 +171,7 @@ class M5DatasetWorkflow: job_selector_score = m5.score(test_y, job_selector_predict_y) print(f"mixture reuse loss (job selector): {job_selector_score}") - reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode='vote') + reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="vote") ensemble_predict_y = reuse_ensemble.predict(user_data=test_x) ensemble_score = m5.score(test_y, ensemble_predict_y) print(f"mixture reuse loss (ensemble): {ensemble_score}\n") @@ -185,8 +185,12 @@ class M5DatasetWorkflow: logger.info("Single search score %.3f +/- %.3f" % (np.mean(single_score_list), np.std(single_score_list))) logger.info("Random search score: %.3f +/- %.3f" % (np.mean(random_score_list), np.std(random_score_list))) logger.info("Average score improvement: %.3f" % (np.mean(improve_list))) - logger.info("Job selector score: %.3f +/- %.3f" % (np.mean(job_selector_score_list), np.std(job_selector_score_list))) - logger.info("Average ensemble score: %.3f +/- %.3f" % (np.mean(ensemble_score_list), np.std(ensemble_score_list))) + logger.info( + "Job selector score: %.3f +/- %.3f" % (np.mean(job_selector_score_list), np.std(job_selector_score_list)) + ) + logger.info( + "Average ensemble score: %.3f +/- %.3f" % (np.mean(ensemble_score_list), np.std(ensemble_score_list)) + ) if __name__ == "__main__": diff --git a/examples/example_pfs/main.py b/examples/example_pfs/main.py index 5d31633..2ca972a 100644 --- a/examples/example_pfs/main.py +++ b/examples/example_pfs/main.py @@ -183,8 +183,12 @@ class PFSDatasetWorkflow: logger.info("Single search score %.3f +/- %.3f" % (np.mean(single_score_list), np.std(single_score_list))) logger.info("Random search score: %.3f +/- %.3f" % (np.mean(random_score_list), np.std(random_score_list))) logger.info("Average score improvement: %.3f" % (np.mean(improve_list))) - logger.info("Job selector score: %.3f +/- %.3f" % (np.mean(job_selector_score_list), np.std(job_selector_score_list))) - logger.info("Average ensemble score: %.3f +/- %.3f" % (np.mean(ensemble_score_list), np.std(ensemble_score_list))) + logger.info( + "Job selector score: %.3f +/- %.3f" % (np.mean(job_selector_score_list), np.std(job_selector_score_list)) + ) + logger.info( + "Average ensemble score: %.3f +/- %.3f" % (np.mean(ensemble_score_list), np.std(ensemble_score_list)) + ) if __name__ == "__main__":