Update examples for MhaMlp models

Author: thieu1995

examples/exam_mha_mlp_binary_classification.py

@@ -25,9 +25,10 @@
 
 ## Create model
 print(MhaMlpClassifier.SUPPORTED_CLS_OBJECTIVES)
-model = MhaMlpClassifier(hidden_layers=(100,), act_names="ELU", dropout_rates=None, act_output=None,
-                       optim="BaseGA", optim_params={"name": "WOA", "epoch": 100, "pop_size": 30},
-                         obj_name="F1S", seed=42, verbose=True)
+model = MhaMlpClassifier(hidden_layers=(100,), act_names="ReLU", dropout_rates=None, act_output=None,
+                         optim="BaseGA", optim_params={"name": "WOA", "epoch": 100, "pop_size": 30},
+                         obj_name="F1S", seed=42, verbose=True,
+                         lb=None, ub=None, mode='single', n_workers=None, termination=None)
 ## Train the model
 model.fit(X=data.X_train, y=data.y_train)
 

examples/exam_mha_mlp_multi_class_classification.py

@@ -27,9 +27,10 @@
 model = MhaMlpClassifier(hidden_layers=(50,), act_names="Tanh",
                          dropout_rates=None, act_output=None,
                          optim="BaseGA", optim_params={"name": "WOA", "epoch": 100, "pop_size": 20},
-                         obj_name="F1S", seed=42, verbose=True)
+                         obj_name="F1S", seed=42, verbose=True,
+                         lb=-2., ub=2., mode='single', n_workers=None, termination=None)
 ## Train the model
-model.fit(X=data.X_train, y=data.y_train, lb=-1., ub=1.0)
+model.fit(X=data.X_train, y=data.y_train)
 
 ## Test the model
 y_pred = model.predict(data.X_test)

examples/exam_mha_mlp_regression.py

@@ -27,7 +27,8 @@
 ## Create model
 model = MhaMlpRegressor(hidden_layers=(30, 15,), act_names="ELU", dropout_rates=0.2, act_output=None,
                         optim="BaseGA", optim_params={"name": "WOA", "epoch": 10, "pop_size": 30},
-                        obj_name="MSE", seed=42, verbose=True)
+                        obj_name="MSE", seed=42, verbose=True,
+                        lb=None, ub=None, mode='single', n_workers=None, termination=None)
 ## Train the model
 model.fit(data.X_train, data.y_train)