Add normalize_Y

jungtaekkim · Nov 21, 2021 · 1fd9b6d · 1fd9b6d
1 parent 155d55f
commit 1fd9b6d
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Showing 3 changed files with 15 additions and 2 deletions.
diff --git a/bayeso/bo/bo_w_gp.py b/bayeso/bo/bo_w_gp.py
@@ -362,8 +362,12 @@ def optimize(self, X_train: np.ndarray, Y_train: np.ndarray,
 
         time_start = time.time()
 
-        if self.normalize_Y:
+        if self.normalize_Y and str_mlm_method != 'converged':
+            if self.debug:
+                self.logger.debug('Responses are normalized.')
+
             Y_train = utils_bo.normalize_min_max(Y_train)
+            normalized_Y = True
 
         time_start_surrogate = time.time()
 
@@ -455,6 +459,7 @@ def optimize(self, X_train: np.ndarray, Y_train: np.ndarray,
         dict_info = {
             'next_points': next_points,
             'acquisitions': acquisitions,
+            'normalize_Y': normalized_Y,
             'Y_train': Y_train,
             'cov_X_X': cov_X_X,
             'inv_cov_X_X': inv_cov_X_X,

diff --git a/bayeso/bo/bo_w_tp.py b/bayeso/bo/bo_w_tp.py
@@ -348,6 +348,9 @@ def optimize(self, X_train: np.ndarray, Y_train: np.ndarray,
         time_start = time.time()
 
         if self.normalize_Y:
+            if self.debug:
+                self.logger.debug('Responses are normalized.')
+
             Y_train = utils_bo.normalize_min_max(Y_train)
 
         time_start_surrogate = time.time()
@@ -378,6 +381,7 @@ def optimize(self, X_train: np.ndarray, Y_train: np.ndarray,
         dict_info = {
             'next_points': next_points,
             'acquisitions': acquisitions,
+            'normalize_Y': self.normalize_Y,
             'Y_train': Y_train,
             'cov_X_X': cov_X_X,
             'inv_cov_X_X': inv_cov_X_X,

diff --git a/bayeso/bo/bo_w_trees.py b/bayeso/bo/bo_w_trees.py
@@ -236,6 +236,9 @@ def optimize(self, X_train: np.ndarray, Y_train: np.ndarray,
         time_start = time.time()
 
         if self.normalize_Y:
+            if self.debug:
+                self.logger.debug('Responses are normalized.')
+
             Y_train = utils_bo.normalize_min_max(Y_train)
 
         time_start_surrogate = time.time()
@@ -262,8 +265,9 @@ def optimize(self, X_train: np.ndarray, Y_train: np.ndarray,
 
         dict_info = {
             'next_points': next_points,
-            'Y_train': Y_train,
             'acquisitions': acquisitions,
+            'normalize_Y': self.normalize_Y,
+            'Y_train': Y_train,
             'trees': trees,
             'time_surrogate': time_end_surrogate - time_start_surrogate,
             'time_acq': time_end_acq - time_start_acq,