Add examples for AutomatedMhaElmComparator class

examples/comparator/__init__.py

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+#!/usr/bin/env python
+# Created by "Thieu" at 23:30, 17/08/2024 ----------%                                                                               
+#       Email: nguyenthieu2102@gmail.com            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%

examples/comparator/exam_automated_mha_elm_binary_classifier_comparator.py

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+#!/usr/bin/env python
+# Created by "Thieu" at 23:30, 17/08/2024 ----------%                                                                               
+#       Email: nguyenthieu2102@gmail.com            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
+
+from intelelm import get_dataset, AutomatedMhaElmComparator
+
+
+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 optimizer dict
+optimizer_dict = {
+    'BaseGA':       {"epoch": 10, "pop_size": 20},
+    "OriginalPSO":  {"epoch": 10, "pop_size": 20},
+}
+
+# Initialize the comparator
+compartor = AutomatedMhaElmComparator(
+    optimizer_dict=optimizer_dict,
+    task="classification",
+    hidden_size=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)
+# print(results)
+
+# results = compartor.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)
+print(results)

examples/comparator/exam_automated_mha_elm_multiclass_classifier_comparator.py

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+#!/usr/bin/env python
+# Created by "Thieu" at 19:55, 19/08/2024 ----------%                                                                               
+#       Email: nguyenthieu2102@gmail.com            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
+
+from intelelm import get_dataset, AutomatedMhaElmComparator
+
+
+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 optimizer dict
+optimizer_dict = {
+    'BaseGA':       {"epoch": 10, "pop_size": 20},
+    "OriginalPSO":  {"epoch": 10, "pop_size": 20},
+}
+
+# Initialize the comparator
+compartor = AutomatedMhaElmComparator(
+    optimizer_dict=optimizer_dict,
+    task="classification",
+    hidden_size=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)
+print(results)
+
+# results = compartor.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)
+# print(results)

examples/comparator/exam_automated_mha_elm_regressor_comparator.py

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+#!/usr/bin/env python
+# Created by "Thieu" at 19:59, 19/08/2024 ----------%                                                                               
+#       Email: nguyenthieu2102@gmail.com            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
+
+import numpy as np
+from intelelm import get_dataset, AutomatedMhaElmComparator
+
+
+data = get_dataset("diabetes")
+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)
+
+data.y_train, scaler_y = data.scale(data.y_train, scaling_methods=('minmax', ))
+data.y_test = scaler_y.transform(np.reshape(data.y_test, (-1, 1)))
+
+
+# Example optimizer dict
+optimizer_dict = {
+    'BaseGA':       {"epoch": 10, "pop_size": 20},
+    "OriginalPSO":  {"epoch": 10, "pop_size": 20},
+}
+
+# Initialize the comparator
+compartor = AutomatedMhaElmComparator(
+    optimizer_dict=optimizer_dict,
+    task="regression",
+    hidden_size=10,
+    act_name="elu",
+    obj_name="R2",
+    verbose=False,
+    seed=42,
+)
+
+# Perform comparison
+# results = compartor.compare_cross_val_score(data.X_train, data.y_train, metric="RMSE", cv=4, n_trials=2, to_csv=True)
+# print(results)
+
+# results = compartor.compare_cross_validate(data.X_train, data.y_train, metrics=["MSE", "MAPE", "R2", "KGE", "NSE"],
+#                                            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)
+print(results)