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Add examples for MhaMlp-based models
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#!/usr/bin/env python | ||
# Created by "Thieu" at 21:35, 02/11/2023 ----------% | ||
# Email: nguyenthieu2102@gmail.com % | ||
# Github: https://github.com/thieu1995 % | ||
# --------------------------------------------------% | ||
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from metaperceptron import Data, MhaMlpClassifier | ||
from sklearn.datasets import load_breast_cancer | ||
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## Load data object | ||
X, y = load_breast_cancer(return_X_y=True) | ||
data = Data(X, y) | ||
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## 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) | ||
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## 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) | ||
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data.y_train, scaler_y = data.encode_label(data.y_train) | ||
data.y_test = scaler_y.transform(data.y_test) | ||
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## Create model | ||
opt_paras = {"name": "WOA", "epoch": 100, "pop_size": 30} | ||
print(MhaMlpClassifier.SUPPORTED_CLS_OBJECTIVES) | ||
model = MhaMlpClassifier(hidden_size=50, act1_name="tanh", act2_name="sigmoid", | ||
obj_name="NPV", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True) | ||
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## Train the model | ||
model.fit(X=data.X_train, y=data.y_train) | ||
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## Test the model | ||
y_pred = model.predict(data.X_test, return_prob=True) | ||
print(y_pred) | ||
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## 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"])) |
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#!/usr/bin/env python | ||
# Created by "Thieu" at 21:35, 02/11/2023 ----------% | ||
# Email: nguyenthieu2102@gmail.com % | ||
# Github: https://github.com/thieu1995 % | ||
# --------------------------------------------------% | ||
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from metaperceptron import Data, MhaMlpClassifier | ||
from sklearn.datasets import load_iris | ||
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## Load data object | ||
X, y = load_iris(return_X_y=True) | ||
data = Data(X, y) | ||
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## 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) | ||
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## 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) | ||
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data.y_train, scaler_y = data.encode_label(data.y_train) | ||
data.y_test = scaler_y.transform(data.y_test) | ||
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## Create model | ||
opt_paras = {"name": "WOA", "epoch": 100, "pop_size": 20} | ||
model = MhaMlpClassifier(hidden_size=50, act1_name="tanh", act2_name="softmax", | ||
obj_name="CEL", optimizer="OriginalWOA", optimizer_paras=None, verbose=True) | ||
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## Train the model | ||
model.fit(X=data.X_train, y=data.y_train, lb=-1., ub=1.0) | ||
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## Test the model | ||
y_pred = model.predict(data.X_test, return_prob=True) | ||
print(y_pred) | ||
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## 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"])) |
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,42 @@ | ||
#!/usr/bin/env python | ||
# Created by "Thieu" at 21:35, 02/11/2023 ----------% | ||
# Email: nguyenthieu2102@gmail.com % | ||
# Github: https://github.com/thieu1995 % | ||
# --------------------------------------------------% | ||
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import numpy as np | ||
from metaperceptron import Data, MhaMlpRegressor | ||
from sklearn.datasets import load_diabetes | ||
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## Load data object | ||
X, y = load_diabetes(return_X_y=True) | ||
data = Data(X, y) | ||
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## Split train and test | ||
data.split_train_test(test_size=0.2, random_state=2) | ||
print(data.X_train.shape, data.X_test.shape) | ||
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## Scaling dataset | ||
data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("standard")) | ||
data.X_test = scaler_X.transform(data.X_test) | ||
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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))) | ||
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## Create model | ||
opt_paras = {"name": "WOA", "epoch": 250, "pop_size": 30} | ||
model = MhaMlpRegressor(hidden_size=15, act1_name="relu", act2_name="sigmoid", | ||
obj_name="MSE", optimizer="BaseGA", optimizer_paras=None, verbose=True) | ||
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## Train the model | ||
model.fit(data.X_train, data.y_train) | ||
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## Test the model | ||
y_pred = model.predict(data.X_test) | ||
print(y_pred) | ||
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## Calculate some metrics | ||
print(model.score(X=data.X_test, y=data.y_test, method="RMSE")) | ||
print(model.scores(X=data.X_test, y=data.y_test, list_methods=["R2", "NSE", "MAPE"])) | ||
print(model.evaluate(y_true=data.y_test, y_pred=y_pred, list_metrics=["R2", "NSE", "MAPE", "NNSE"])) |