47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
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import pandas as pd
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.model_selection import train_test_split, GridSearchCV
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from sklearn.neural_network import MLPRegressor
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# Загрузка данных
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data = pd.read_csv("smoking_drinking_dataset.csv")
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data = data.sample(frac=0.3, random_state=42)
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# # Подготовка данных
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data = pd.get_dummies(data, columns=['sex', 'DRK_YN'], drop_first=True)
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# Разделение данных на признаки (X) и целевую переменную (y)
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X = data.drop(columns=['SMK_stat_type_cd'])
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y = data['SMK_stat_type_cd']
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# Разделение данных
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Создание и обучение модели
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model = MLPRegressor(hidden_layer_sizes=(100,1), max_iter=500, learning_rate_init=0.001, random_state=42, alpha=0.0001, activation='tanh', solver='adam')
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#model = MLPRegressor(random_state=42)
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model.fit(X_train, y_train)
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# Предсказание на тестовом наборе
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predictions = model.predict(X_test)
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# Оценка модели
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mse = mean_squared_error(y_test, predictions)
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r2 = r2_score(y_test, predictions)
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print(f'Mean Squared Error: {mse}')
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print(f'R-squared: {r2}')
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# Выбор оптимальных параметров c помощью GridSearchCV
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# param_grid = {'hidden_layer_sizes': [(50,50,50), (50,100,50), (100,1), (200,100)],
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# 'activation': ['relu','tanh','logistic'],
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# 'alpha': [0.0001, 0.05],
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# 'max_iter': [50, 100, 500, 1000],
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# 'learning_rate': ['constant','adaptive'],
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# 'solver': ['adam', 'sgd']}
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# estimator=MLPRegressor(max_iter=10000, random_state=42)
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# grid = GridSearchCV(estimator, param_grid, n_jobs= -1, cv=5)
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# grid.fit(X_train, y_train)
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#
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# print(grid.best_params_)
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