Add examples for InaRbfClassifier

examples/exam_ina_rbf_binary_classifier.py

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+#!/usr/bin/env python
+# Created by "Thieu" at 16:12, 10/05/2024 ----------%                                                                               
+#       Email: [email protected]            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
+
+from evorbf import Data, InaRbfClassifier
+from sklearn.datasets import load_breast_cancer
+
+
+## Load data object
+# total classes = 2, total samples = 569, total features = 30
+X, y = load_breast_cancer(return_X_y=True)
+data = Data(X, y)
+
+## Split train and test
+data.split_train_test(test_size=0.2, random_state=2, inplace=True, shuffle=True)
+print(data.X_train.shape, data.X_test.shape)
+
+## Scaling dataset
+data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("standard", "minmax"))
+data.X_test = scaler_X.transform(data.X_test)
+
+data.y_train, scaler_y = data.encode_label(data.y_train)
+data.y_test = scaler_y.transform(data.y_test)
+
+## Create model
+opt_paras = {"name": "WOA", "epoch": 100, "pop_size": 30}
+print(InaRbfClassifier.SUPPORTED_CLS_OBJECTIVES)
+model = InaRbfClassifier(size_hidden=25, center_finder="kmean", regularization=False, lamda=0.5, obj_name="AS",
+                        optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True, seed=42)
+
+## Train the model
+model.fit(X=data.X_train, y=data.y_train)
+
+## Test the model
+y_pred = model.predict(data.X_test, return_prob=False)
+print(y_pred)
+
+y_pred = model.predict(data.X_test, return_prob=True)
+print(y_pred)
+
+## Calculate some metrics
+print(model.score(X=data.X_test, y=data.y_test, method="AS"))
+print(model.scores(X=data.X_test, y=data.y_test, list_methods=["PS", "RS", "NPV", "F1S", "F2S"]))
+print(model.evaluate(y_true=data.y_test, y_pred=y_pred, list_metrics=["F2S", "CKS", "FBS"]))

examples/exam_ina_rbf_multi_class_classifier.py

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+#!/usr/bin/env python
+# Created by "Thieu" at 16:58, 10/05/2024 ----------%                                                                               
+#       Email: [email protected]            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
+
+from evorbf import Data, InaRbfClassifier
+from sklearn.datasets import load_iris
+
+
+## Load data object
+# total classes = 3, total samples = 150, total features = 4
+X, y = load_iris(return_X_y=True)
+data = Data(X, y)
+
+## Split train and test
+data.split_train_test(test_size=0.2, random_state=2, inplace=True, shuffle=True)
+print(data.X_train.shape, data.X_test.shape)
+
+## Scaling dataset
+data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("standard", "minmax"))
+data.X_test = scaler_X.transform(data.X_test)
+
+data.y_train, scaler_y = data.encode_label(data.y_train)
+data.y_test = scaler_y.transform(data.y_test)
+
+## Create model
+opt_paras = {"name": "WOA", "epoch": 100, "pop_size": 30}
+model = InaRbfClassifier(size_hidden=15, center_finder="kmean", regularization=False, lamda=0.5, obj_name="NPV",
+                        optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True, seed=42)
+
+## Train the model
+model.fit(X=data.X_train, y=data.y_train, lb=-1., ub=1.0)
+
+## Test the model
+y_pred = model.predict(data.X_test, return_prob=True)
+print(y_pred)
+
+## Calculate some metrics
+print(model.score(X=data.X_test, y=data.y_test, method="AS"))
+print(model.scores(X=data.X_test, y=data.y_test, list_methods=["PS", "RS", "NPV", "F1S", "F2S"]))
+print(model.evaluate(y_true=data.y_test, y_pred=y_pred, list_metrics=["F2S", "CKS", "FBS"]))