From bad19884d479dd49c094726d7f82c13818094c80 Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Mon, 2 Jun 2025 19:17:00 +0200 Subject: [PATCH] refactor(qav3): align multi objective optimization metrics namespace MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../freqaimodels/QuickAdapterRegressorV3.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py index cb73f74..814b903 100644 --- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py +++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py @@ -441,10 +441,10 @@ class QuickAdapterRegressorV3(BaseRegressionModel): "harmonic_mean", "power_mean", "weighted_sum", - "knn-d1", - "knn-d2-mean", - "knn-d2-median", - "knn-d2-max", + "knn_d1", + "knn_d2_mean", + "knn_d2_median", + "knn_d2_max", } label_metric = self.ft_params.get("label_metric", "seuclidean") if label_metric not in metrics: @@ -544,7 +544,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel): ) - sp.stats.pmean(normalized_matrix, p=p, weights=np_weights, axis=1) elif metric == "weighted_sum": return np.sum(np_weights * (ideal_point - normalized_matrix), axis=1) - elif metric == "knn-d1": + elif metric == "knn_d1": if normalized_matrix.shape[0] < 2: return np.full(normalized_matrix.shape[0], np.inf) nbrs = sklearn.neighbors.NearestNeighbors( @@ -552,7 +552,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel): ).fit(normalized_matrix) distances, _ = nbrs.kneighbors(normalized_matrix) return distances[:, 1] - elif metric in {"knn-d2-mean", "knn-d2-median", "knn-d2-max"}: + elif metric in {"knn_d2_mean", "knn_d2_median", "knn_d2_max"}: if normalized_matrix.shape[0] < 2: return np.full(normalized_matrix.shape[0], np.inf) n_neighbors = ( @@ -566,11 +566,11 @@ class QuickAdapterRegressorV3(BaseRegressionModel): n_neighbors=n_neighbors, metric=label_knn_metric, **knn_kwargs ).fit(normalized_matrix) distances, _ = nbrs.kneighbors(normalized_matrix) - if metric == "knn-d2-mean": + if metric == "knn_d2_mean": return np.mean(distances[:, 1:], axis=1) - elif metric == "knn-d2-median": + elif metric == "knn_d2_median": return np.median(distances[:, 1:], axis=1) - elif metric == "knn-d2-max": + elif metric == "knn_d2_max": return np.max(distances[:, 1:], axis=1) else: raise ValueError(f"Unsupported distance metric: {metric}") -- 2.43.0