Update examples of Comparator class

thieu1995 · Oct 9, 2024 · 2e25b96 · 2e25b96
1 parent 555779e
commit 2e25b96
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Showing 3 changed files with 14 additions and 14 deletions.
diff --git a/examples/comparator/exam_automated_mha_elm_binary_classifier_comparator.py b/examples/comparator/exam_automated_mha_elm_binary_classifier_comparator.py
@@ -21,24 +21,24 @@
 }
 
 # Initialize the comparator
-compartor = AutomatedMhaElmComparator(
+comparator = AutomatedMhaElmComparator(
     optimizer_dict=optimizer_dict,
     task="classification",
-    hidden_size=10,
+    layer_sizes=(10, ),
     act_name="elu",
     obj_name="F1S",
     verbose=False,
     seed=42,
 )
 
 # Perform comparison
-# results = compartor.compare_cross_val_score(data.X_train, data.y_train, metric="AS", cv=4, n_trials=2, to_csv=True)
+# results = comparator.compare_cross_val_score(data.X_train, data.y_train, metric="AS", cv=4, n_trials=2, to_csv=True)
 # print(results)
 
-# results = compartor.compare_cross_validate(data.X_train, data.y_train, metrics=["AS", "PS", "F1S", "NPV"],
+# results = comparator.compare_cross_validate(data.X_train, data.y_train, metrics=["AS", "PS", "F1S", "NPV"],
 #                                            cv=4, return_train_score=True, n_trials=2, to_csv=True)
 # print(results)
 
-results = compartor.compare_train_test(data.X_train, data.y_train, data.X_test, data.y_test, metrics=["AS", "PS", "F1S", "NPV"],
-                                       n_trials=2, to_csv=True)
+results = comparator.compare_train_test(data.X_train, data.y_train, data.X_test, data.y_test,
+                                        metrics=["AS", "PS", "F1S", "NPV"], n_trials=2, to_csv=True)
 print(results)
diff --git a/examples/comparator/exam_automated_mha_elm_multiclass_classifier_comparator.py b/examples/comparator/exam_automated_mha_elm_multiclass_classifier_comparator.py
@@ -21,24 +21,24 @@
 }
 
 # Initialize the comparator
-compartor = AutomatedMhaElmComparator(
+comparator = AutomatedMhaElmComparator(
     optimizer_dict=optimizer_dict,
     task="classification",
-    hidden_size=10,
+    layer_sizes=(10, ),
     act_name="elu",
     obj_name="F1S",
     verbose=True,
     seed=42,
 )
 
 # Perform comparison
-results = compartor.compare_cross_val_score(data.X_train, data.y_train, metric="AS", cv=4, n_trials=2, to_csv=True)
+results = comparator.compare_cross_val_score(data.X_train, data.y_train, metric="AS", cv=4, n_trials=2, to_csv=True)
 print(results)
 
-# results = compartor.compare_cross_validate(data.X_train, data.y_train, metrics=["AS", "PS", "F1S", "NPV"],
+# results = comparator.compare_cross_validate(data.X_train, data.y_train, metrics=["AS", "PS", "F1S", "NPV"],
 #                                            cv=4, return_train_score=True, n_trials=2, to_csv=True)
 # print(results)
 
-# results = compartor.compare_train_test(data.X_train, data.y_train, data.X_test, data.y_test, metrics=["AS", "PS", "F1S", "NPV"],
+# results = comparator.compare_train_test(data.X_train, data.y_train, data.X_test, data.y_test, metrics=["AS", "PS", "F1S", "NPV"],
 #                                        n_trials=2, to_csv=True)
 # print(results)
diff --git a/examples/comparator/exam_automated_mha_elm_regressor_comparator.py b/examples/comparator/exam_automated_mha_elm_regressor_comparator.py
@@ -29,7 +29,7 @@
 compartor = AutomatedMhaElmComparator(
     optimizer_dict=optimizer_dict,
     task="regression",
-    hidden_size=10,
+    layer_sizes=(10, ),
     act_name="elu",
     obj_name="R2",
     verbose=False,
@@ -44,6 +44,6 @@
 #                                            cv=4, return_train_score=True, n_trials=2, to_csv=True)
 # print(results)
 
-results = compartor.compare_train_test(data.X_train, data.y_train, data.X_test, data.y_test, metrics=["MSE", "MAPE", "R2", "KGE", "NSE"],
-                                       n_trials=2, to_csv=True)
+results = compartor.compare_train_test(data.X_train, data.y_train, data.X_test, data.y_test,
+                                       metrics=["MSE", "MAPE", "R2", "KGE", "NSE"], n_trials=2, to_csv=True)
 print(results)