27 lines
980 B
Python
27 lines
980 B
Python
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import matplotlib.pyplot as plt
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import joblib
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import numpy as np
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from services.ml.modelBuilder import X_train
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# Загрузка модели и признаков
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model_rf = joblib.load('laptop_price_model.pkl')
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feature_columns = joblib.load('feature_columns.pkl')
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# Получение важности признаков
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importances = model_rf.feature_importances_
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indices = np.argsort(importances)[::-1]
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# Вывод наиболее важных признаков
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print("Важность признаков:")
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for f in range(X_train.shape[1]):
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print(f"{f + 1}. {feature_columns[indices[f]]} ({importances[indices[f]]})")
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# Визуализация важности признаков
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plt.figure(figsize=(12, 8))
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plt.title("Важность признаков (Random Forest)")
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plt.bar(range(X_train.shape[1]), importances[indices], align='center')
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plt.xticks(range(X_train.shape[1]), [feature_columns[i] for i in indices], rotation=90)
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plt.tight_layout()
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plt.show()
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