Merge pull request #3 from anh9895/main

Add MhaMlpTuner and MhaMlpComparator classes.

.gitignore

@@ -1,4 +1,5 @@
-
+examples/comparator/history/
+examples/core/history/
 examples/helpers/history/
 metaperceptron/data/
 # Pycharm

examples/helpers/__init__.py → examples/comparator/__init__.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-# Created by "Thieu" at 22:28, 14/08/2023 ----------%
+# Created by "Thieu" at 23:30, 17/08/2024 ----------%                                                                               
 #       Email: nguyenthieu2102@gmail.com            %                                                    
 #       Github: https://github.com/thieu1995        %                         
 # --------------------------------------------------%

examples/comparator/exam_mha_mlp_binary_classifier_comparator.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python
+# Created by "Thieu" at 23:30, 17/08/2024 ----------%                                                                               
+#       Email: nguyenthieu2102@gmail.com            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.datasets import load_breast_cancer
+from metaperceptron import MhaMlpComparator
+
+
+## Load data object
+X, y = load_breast_cancer(return_X_y=True)
+
+# Split data into train and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Standardize the features
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+print(X_train.shape, X_test.shape)
+
+# Here is the list of optimizers you want to compare
+optim_dict = {
+    'BaseGA':       {"epoch": 10, "pop_size": 20},
+    "OriginalPSO":  {"epoch": 10, "pop_size": 20},
+}
+
+# Initialize the comparator
+compartor = MhaMlpComparator(
+    optim_dict=optim_dict,
+    task="classification",
+    hidden_layers=(10, ),
+    act_names="ReLU",
+    dropout_rates=None,
+    act_output=None,
+    obj_name="F1S",
+    verbose=True,
+    seed=42,
+)
+
+### Perform comparison
+# results = compartor.compare_cross_val_score(X_train, y_train, metric="AS", cv=4, n_trials=2, to_csv=True)
+# print(results)
+
+# results = compartor.compare_cross_validate(X_train, 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(X_train, y_train, X_test, y_test,
+                                       metrics=["AS", "PS", "F1S", "NPV"], n_trials=2, to_csv=True)
+print(results)

examples/comparator/exam_mha_mlp_multiclass_classifier_comparator.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python
+# Created by "Thieu" at 19:55, 19/08/2024 ----------%                                                                               
+#       Email: nguyenthieu2102@gmail.com            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.datasets import load_iris
+from metaperceptron import MhaMlpComparator
+
+
+## Load data object
+X, y = load_iris(return_X_y=True)
+
+# Split data into train and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Standardize the features
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+print(X_train.shape, X_test.shape)
+
+# Here is the list of optimizers you want to compare
+optim_dict = {
+    'BaseGA':       {"epoch": 10, "pop_size": 20},
+    "OriginalPSO":  {"epoch": 10, "pop_size": 20},
+}
+
+# Initialize the comparator
+compartor = MhaMlpComparator(
+    optim_dict=optim_dict,
+    task="classification",
+    hidden_layers=(10, ),
+    act_names="ReLU",
+    dropout_rates=None,
+    act_output=None,
+    obj_name="F1S",
+    verbose=True,
+    seed=42,
+)
+
+## Perform comparison
+# results = compartor.compare_cross_val_score(X_train, y_train, metric="AS", cv=4, n_trials=2, to_csv=True)
+# print(results)
+
+# results = compartor.compare_cross_validate(X_train, 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(X_train, y_train, X_test, y_test,
+                                       metrics=["AS", "PS", "F1S", "NPV"], n_trials=2, to_csv=True)
+print(results)

examples/comparator/exam_mha_mlp_regressor_comparator.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+# Created by "Thieu" at 19:59, 19/08/2024 ----------%                                                                               
+#       Email: nguyenthieu2102@gmail.com            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, MinMaxScaler
+from sklearn.datasets import load_diabetes
+from metaperceptron import MhaMlpComparator
+
+
+## Load data object
+X, y = load_diabetes(return_X_y=True)
+
+# Split data into train and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Standardize the features
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+print(X_train.shape, X_test.shape)
+
+# Min-max the output
+y_scaler = MinMaxScaler(feature_range=(0, 1))
+y_train = y_scaler.fit_transform(y_train.reshape(-1, 1))
+y_test = y_scaler.transform(y_test.reshape(-1, 1))
+
+
+# Here is the list of optimizers you want to compare
+optim_dict = {
+    'BaseGA':       {"epoch": 10, "pop_size": 20},
+    "OriginalPSO":  {"epoch": 10, "pop_size": 20},
+}
+
+# Initialize the comparator
+compartor = MhaMlpComparator(
+    optim_dict=optim_dict,
+    task="regression",
+    hidden_layers=(10, ),
+    act_names="ELU",
+    dropout_rates=None,
+    act_output=None,
+    obj_name="R2",
+    verbose=True,
+    seed=42,
+)
+
+### Perform comparison
+# results = compartor.compare_cross_val_score(X_train, y_train, metric="RMSE", cv=4, n_trials=2, to_csv=True)
+# print(results)
+
+# results = compartor.compare_cross_validate(X_train, 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(X_train, y_train, X_test, y_test,
+                                       metrics=["MSE", "MAPE", "R2", "KGE", "NSE"], n_trials=2, to_csv=True)
+print(results)