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Created mlp_activation_comparison.py #12918

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51 changes: 51 additions & 0 deletions machine_learning/mlp_activation_comparison.py
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import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier


# Compare different activation functions in MLPClassifier
def compare_activations():
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Please provide return type hint for the function: compare_activations. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file machine_learning/mlp_activation_comparison.py, please provide doctest for the function compare_activations

X, y = make_moons(n_samples=200, noise=0.25, random_state=3)

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machine_learning/mlp_activation_comparison.py:10:5: N806 Variable `X` in function should be lowercase
X_train, X_test, y_train, y_test = train_test_split(

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machine_learning/mlp_activation_comparison.py:11:5: N806 Variable `X_train` in function should be lowercase
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_train

Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_test

X, y, stratify=y, random_state=42
)

activations = ["identity", "logistic", "tanh", "relu"]

for activation in activations:
mlp = MLPClassifier(
hidden_layer_sizes=[50],
max_iter=1000,
activation=activation,
random_state=0,
)
mlp.fit(X_train, y_train)

print(
f"Activation: {activation}, "
f"Train Accuracy: {mlp.score(X_train, y_train):.2f}, "
f"Test Accuracy: {mlp.score(X_test, y_test):.2f}"
)

# Decision boundary
x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
xx, yy = np.meshgrid(
np.linspace(x_min, x_max, 200),
np.linspace(y_min, y_max, 200),
)
Z = mlp.predict(np.c_[xx.ravel(), yy.ravel()])

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machine_learning/mlp_activation_comparison.py:39:9: N806 Variable `Z` in function should be lowercase
Z = Z.reshape(xx.shape)

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machine_learning/mlp_activation_comparison.py:40:9: N806 Variable `Z` in function should be lowercase

plt.contourf(xx, yy, Z, alpha=0.3)
plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, marker="o", label="Train")
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, marker="s", label="Test")
plt.title(f"Activation: {activation}")
plt.legend()
plt.show()


if __name__ == "__main__":
compare_activations()
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