Update examples for mha-based MLP models

Author: anh9895

examples/exam_mha_mlp_binary_classification.py

@@ -4,8 +4,8 @@
 #       Github: https://github.com/thieu1995        %                         
 # --------------------------------------------------%
 
-from metaperceptron import Data, MhaMlpClassifier
 from sklearn.datasets import load_breast_cancer
+from metaperceptron import Data, MhaMlpClassifier
 
 
 ## Load data object
@@ -28,7 +28,7 @@
 print(MhaMlpClassifier.SUPPORTED_CLS_OBJECTIVES)
 
 model = MhaMlpClassifier(hidden_layers=(100,), act_names="ELU", dropout_rates=None, act_output=None,
-                       optim="BaseGA", optim_paras=opt_paras, obj_name="F1S", seed=42, verbose=True)
+                       optim="BaseGA", optim_params=opt_paras, obj_name="F1S", seed=42, verbose=True)
 ## Train the model
 model.fit(X=data.X_train, y=data.y_train)
 

examples/exam_mha_mlp_multi_class_classification.py

@@ -4,8 +4,8 @@
 #       Github: https://github.com/thieu1995        %                         
 # --------------------------------------------------%
 
-from metaperceptron import Data, MhaMlpClassifier
 from sklearn.datasets import load_iris
+from metaperceptron import Data, MhaMlpClassifier
 
 
 ## Load data object
@@ -26,7 +26,7 @@
 ## Create model
 opt_paras = {"name": "WOA", "epoch": 100, "pop_size": 20}
 model = MhaMlpClassifier(hidden_layers=(50,), act_names="Tanh", dropout_rates=None, act_output=None,
-                       optim="BaseGA", optim_paras=opt_paras, obj_name="F1S", seed=42, verbose=True)
+                       optim="BaseGA", optim_params=opt_paras, obj_name="F1S", seed=42, verbose=True)
 ## Train the model
 model.fit(X=data.X_train, y=data.y_train, lb=-1., ub=1.0)
 

examples/exam_mha_mlp_regression.py

@@ -5,8 +5,8 @@
 # --------------------------------------------------%
 
 import numpy as np
-from metaperceptron import Data, MhaMlpRegressor
 from sklearn.datasets import load_diabetes
+from metaperceptron import Data, MhaMlpRegressor
 
 
 ## Load data object
@@ -27,7 +27,7 @@
 ## Create model
 opt_paras = {"name": "WOA", "epoch": 250, "pop_size": 30}
 model = MhaMlpRegressor(hidden_layers=(30, 15,), act_names="ELU", dropout_rates=0.2, act_output=None,
-                      optim="BaseGA", optim_paras=opt_paras, obj_name="MSE", seed=42, verbose=True)
+                      optim="BaseGA", optim_params=opt_paras, obj_name="MSE", seed=42, verbose=True)
 ## Train the model
 model.fit(data.X_train, data.y_train)