From bd89d11b1fd00e4130650a016da28087e4cf7379 Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Tue, 3 Jun 2025 19:56:07 +0200 Subject: [PATCH] refactor(qav3): remove duplicate logic in kmeans MO trial selection MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../freqaimodels/QuickAdapterRegressorV3.py | 30 +------------------ 1 file changed, 1 insertion(+), 29 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py index 45386a2..c0eb21a 100644 --- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py +++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py @@ -585,35 +585,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel): metric=label_kmeans_metric, **cdist_kwargs, ).flatten() - best_cluster = np.argmin(cluster_distances_to_ideal) - best_center = cluster_centers[best_cluster].reshape(1, -1) - distances_to_best_cluster = sp.spatial.distance.cdist( - normalized_matrix, - best_center, - metric=label_kmeans_metric, - **cdist_kwargs, - ).flatten() - penalty_value = ( - np.clip( - ( - np.mean(np.delete(cluster_distances_to_ideal, best_cluster)) - / cluster_distances_to_ideal[best_cluster] - if cluster_distances_to_ideal[best_cluster] > 0 - else np.std( - np.delete(cluster_distances_to_ideal, best_cluster) - ) - if len(cluster_distances_to_ideal) > 1 - else 1.0 - ) - - 1.0, - 0.5, - 3.0, - ) - if len(cluster_distances_to_ideal) > 1 - else 1.0 - ) - penalties = np.where(cluster_labels == best_cluster, 0.0, penalty_value) - return distances_to_best_cluster + penalties + return cluster_distances_to_ideal[cluster_labels] elif metric == "knn_d1": if n_samples < 2: return np.full(n_samples, np.inf) -- 2.43.0