added check for min/max

franneck94 · Sep 12, 2022 · c8d077d · c8d077d
1 parent 4f82c35
commit c8d077d
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Showing 3 changed files with 12 additions and 10 deletions.
diff --git a/setup.cfg b/setup.cfg
@@ -44,13 +44,6 @@ strict_optional = true
 no_implicit_optional = true
 warn_no_return = true
 warn_unreachable = true
-plugins = pydantic.mypy
-
-[pydantic-mypy]
-init_forbid_extra = True
-init_typed = True
-warn_required_dynamic_aliases = True
-warn_untyped_fields = True
 
 
 [pylint.config]

diff --git a/tensorcross/model_selection/search.py b/tensorcross/model_selection/search.py
@@ -68,7 +68,7 @@ def _run_search(
             kwargs (Any): Keyword arguments for the fit method of the
                 tf.keras.models.Model or tf.keras.models.Sequential model.
         """
-
+        maximize = True
         tensorboard_callback = None
         tensorboard_log_dir = ""
 
@@ -101,12 +101,16 @@ def _run_search(
             if len(model.metrics) > 1:
                 val_score = model.evaluate(val_dataset, verbose=0)[-1]
             else:
+                maximize = False
                 val_score = model.evaluate(val_dataset, verbose=0)
             self.results_["val_scores"].append(val_score)
             self.results_["params"].append(grid_combination)
 
         logger.setLevel(tf_log_level)  # Issue 30
-        best_run_idx = np.argmax(self.results_["val_scores"])
+        if maximize:
+            best_run_idx = np.argmax(self.results_["val_scores"])
+        else:
+            best_run_idx = np.argmin(self.results_["val_scores"])
         self.results_["best_score"] = self.results_["val_scores"][best_run_idx]
         self.results_["best_params"] = self.results_["params"][best_run_idx]
 

diff --git a/tensorcross/model_selection/search_cv.py b/tensorcross/model_selection/search_cv.py
@@ -69,6 +69,7 @@ def _run_search(
             kwargs (Any): Keyword arguments for the fit method of the
                 tf.keras.models.Model or tf.keras.models.Sequential model.
         """
+        maximize = True
         tensorboard_callback = None
         tensorboard_log_dir = ""
 
@@ -118,6 +119,7 @@ def _run_search(
                 if len(model.metrics) > 1:
                     val_score = model.evaluate(val_dataset, verbose=0)[-1]
                 else:
+                    maximize = False
                     val_score = model.evaluate(val_dataset, verbose=0)
                 val_scores[fold] = val_score
 
@@ -127,7 +129,10 @@ def _run_search(
         logger.setLevel(tf_log_level)  # Issue 30
 
         mean_val_scores = np.mean(self.results_["val_scores"], axis=0)
-        best_run_idx = np.argmax(mean_val_scores)
+        if maximize:
+            best_run_idx = np.argmax(mean_val_scores)
+        else:
+            best_run_idx = np.argmin(mean_val_scores)
         self.results_["best_score"] = self.results_["val_scores"][best_run_idx]
         self.results_["best_params"] = self.results_["params"][best_run_idx]