46 lines
1.9 KiB
Python
46 lines
1.9 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.datasets import make_classification
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from sklearn.linear_model import Perceptron
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from sklearn.neural_network import MLPClassifier
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from sklearn.model_selection import train_test_split, learning_curve
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from sklearn.metrics import accuracy_score
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# Наполнение искусственными данными
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rs = np.random.RandomState(42)
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X, y = make_classification(n_samples=500, n_features=2, n_redundant=0, n_informative=2, random_state=rs,
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n_clusters_per_class=1)
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# Обучающие и тестовые наборы данных
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=rs)
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# Список моделей для обучения
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models = [
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('Персептрон', Perceptron()),
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('MLP (10 нейронов)', MLPClassifier(hidden_layer_sizes=(10,), alpha=0.01, random_state=rs)),
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('MLP (100 нейронов)', MLPClassifier(hidden_layer_sizes=(100,), alpha=0.01, random_state=rs))
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]
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fig, axs = plt.subplots(1, len(models), figsize=(12, 4))
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# Визуализация графиков
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for i, (name, model) in enumerate(models):
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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# Построение кривых обуч ения
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train_sizes, train_scores, valid_scores = learning_curve(
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model, X, y, train_sizes=[50, 80, 110], cv=5)
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axs[i].plot(train_sizes, train_scores.mean(axis=1), 'o-', color="r",
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label="Оценка обучения")
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axs[i].plot(train_sizes, valid_scores.mean(axis=1), 'o-', color="g",
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label="Оценка кросс-валидации")
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axs[i].set_title(f'{name} (Точность: {accuracy:.2f})')
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axs[i].set_xlabel("Training examples")
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axs[i].set_ylabel("Score")
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axs[i].legend(loc="best")
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axs[i].grid()
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plt.show()
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