From aeb67bc07840816e274838ca0f227031acdc7f69 Mon Sep 17 00:00:00 2001 From: Hannes Schulz Date: Mon, 25 Nov 2024 15:59:43 +0100 Subject: [PATCH] fix(sweeps): actually use minimum_improvement value --- src/sweeps/bayes_search.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/sweeps/bayes_search.py b/src/sweeps/bayes_search.py index b019d7d5..3ae0fdd3 100644 --- a/src/sweeps/bayes_search.py +++ b/src/sweeps/bayes_search.py @@ -200,7 +200,7 @@ def next_sample( maximum samples to consider (since algo is O(n^3)) for performance, but also adds some randomness. this number of samples will be chosen randomly from the sample_X and used to train the GP. - improvement: floating, optional, default 0.1 + improvement: floating, optional, default 0.01 amount of improvement to optimize for -- higher means take more exploratory risks num_points_to_try: integer, optional, default 1000 number of X values to try when looking for value with highest expected probability @@ -288,7 +288,7 @@ def next_sample( min_norm_y = (min_unnorm_y - y_mean) / y_stddev - improvement else: """ - min_norm_y = (min_unnorm_y - y_mean) / y_stddev + min_norm_y = (min_unnorm_y - y_mean) / y_stddev - improvement Z = -(y_pred - min_norm_y) / (y_pred_std + STD_NUMERICAL_STABILITY_EPSILON) prob_of_improve: np.ndarray = scipy_stats.norm.cdf(Z)