Add examples for MhaElmTuner class

examples/tuner/exam_mha_elm_binary_classifier_tuner.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+# Created by "Thieu" at 06:36, 17/08/2024 ----------%                                                                               
+#       Email: nguyenthieu2102@gmail.com            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
+
+import numpy as np
+from intelelm import get_dataset, MhaElmTuner
+
+
+data = get_dataset("circles")
+data.split_train_test(test_size=0.2, random_state=2)
+print(data.X_train.shape, data.X_test.shape)
+
+data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=('minmax', ))
+data.X_test = scaler_X.transform(data.X_test)
+
+# Example parameter grid
+param_dict = {
+    'hidden_size': [10, 20],
+    'act_name': ['relu', 'elu'],
+    "obj_name": ["BSL", "KLDL", "F1S"],
+    'optimizer': ['BaseGA', "OriginalPSO"],
+    'optimizer_paras__epoch': [10, 20],
+    'optimizer_paras__pop_size': [20],
+    'seed': [42],
+    "verbose": [False],
+}
+
+# Initialize the tuner
+tuner = MhaElmTuner(
+    task="classification",
+    param_dict=param_dict,
+    search_method="randomsearch",  # or "randomsearch"
+    cv=3,  # Example additional argument
+    scoring='accuracy',  # Example additional argument
+    verbose=2
+)
+
+# Perform tuning
+tuner.fit(data.X_train, data.y_train)
+
+print("Best Parameters: ", tuner.best_params_)
+print("Best Estimator: ", tuner.best_estimator_)
+
+pred = tuner.predict(data.X_test)
+# print(pred)
+
+print(tuner.best_estimator_.score(data.X_test, data.y_test, method="AS"))
+print(tuner.best_estimator_.score(data.X_test, data.y_test, method="PS"))
+print(tuner.best_estimator_.score(data.X_test, data.y_test, method="RS"))
+print(tuner.best_estimator_.scores(data.X_test, data.y_test, list_methods=["AS", "PS", "RS", "F1S", "NPV"]))

examples/tuner/exam_mha_elm_multiclass_classifier_tuner.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+# Created by "Thieu" at 06:50, 17/08/2024 ----------%                                                                               
+#       Email: nguyenthieu2102@gmail.com            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
+
+import numpy as np
+from intelelm import get_dataset, MhaElmTuner
+
+
+data = get_dataset("blobs")
+data.split_train_test(test_size=0.2, random_state=2)
+print(data.X_train.shape, data.X_test.shape)
+
+data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=('minmax', ))
+data.X_test = scaler_X.transform(data.X_test)
+
+# Example parameter grid
+param_dict = {
+    'hidden_size': [10, 20],
+    'act_name': ['relu', 'elu'],
+    "obj_name": ["BSL", "KLDL", "F1S"],
+    'optimizer': ['BaseGA', "OriginalPSO"],
+    'optimizer_paras__epoch': [10, 20],
+    'optimizer_paras__pop_size': [20],
+    'seed': [42],
+    "verbose": [False],
+}
+
+# Initialize the tuner
+tuner = MhaElmTuner(
+    task="classification",
+    param_dict=param_dict,
+    search_method="randomsearch",  # or "randomsearch"
+    cv=3,  # Example additional argument
+    scoring='accuracy',  # Example additional argument
+    verbose=2
+)
+
+# Perform tuning
+tuner.fit(data.X_train, data.y_train)
+
+print("Best Parameters: ", tuner.best_params_)
+print("Best Estimator: ", tuner.best_estimator_)
+
+pred = tuner.predict(data.X_test)
+# print(pred)
+
+print(tuner.best_estimator_.score(data.X_test, data.y_test, method="AS"))
+print(tuner.best_estimator_.score(data.X_test, data.y_test, method="PS"))
+print(tuner.best_estimator_.score(data.X_test, data.y_test, method="RS"))
+print(tuner.best_estimator_.scores(data.X_test, data.y_test, list_methods=["AS", "PS", "RS", "F1S", "NPV"]))

examples/tuner/exam_mha_elm_regressor_tuner.py

@@ -5,7 +5,7 @@
 # --------------------------------------------------%
 
 import numpy as np
-from intelelm import get_dataset, MhaElmRegressorTuner
+from intelelm import get_dataset, MhaElmTuner
 
 
 data = get_dataset("diabetes")
@@ -23,15 +23,15 @@
     'hidden_size': [10, 20],
     'act_name': ['relu', 'elu'],
     "obj_name": ["RMSE", "MAE"],
-    'optimizer': ['BaseGA'],
-    'optimizer_paras__epoch': [10,],
-    'optimizer_paras__pop_size': [20],
+    'optimizer': ['BaseGA', "OriginalPSO"],
+    'optimizer_paras__epoch': [10, 20],
+    'optimizer_paras__pop_size': [20, 30],
     'seed': [42],
     "verbose": [False],
 }
 
 # Initialize the tuner
-tuner = MhaElmRegressorTuner(
+tuner = MhaElmTuner(
     task="regression",
     param_dict=param_dict,
     search_method="randomsearch",  # or "randomsearch"