66 lines
3.1 KiB
Python
66 lines
3.1 KiB
Python
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import pandas as pd
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import numpy as np
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from matplotlib import pyplot as plt
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from sklearn import metrics
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from sklearn.linear_model import Ridge
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filein = "P:\\ULSTU\\ИИС\\Datasets\\heart_2020_norm.csv"
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# Метод решения задачи предсказания на всех признаках данных
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def ridge_all():
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df = pd.read_csv(filein, sep=',')
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x_train = df.drop("HeartDisease", axis=1).iloc[0:round(len(df) / 100 * 99)]
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y_train = df["HeartDisease"].iloc[0:round(len(df) / 100 * 99)]
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x_test = df.drop("HeartDisease", axis=1).iloc[round(len(df) / 100 * 99):len(df)]
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y_test = df["HeartDisease"].iloc[round(len(df) / 100 * 99):len(df)]
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rid = Ridge(alpha=1.0)
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rid.fit(x_train.values, y_train.values)
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y_predict = rid.predict(x_test.values)
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err = pred_errors(y_predict, y_test.values)
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make_plots(y_test.values, y_predict, err[0], err[1], "Гребневая регрессия (все признаки)")
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# Метод решения задачи предсказания на значимых признаках данных
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def ridge_valuable():
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df = pd.read_csv(filein, sep=',')
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x_train = df[["BMI", "PhysicalHealth", "MentalHealth", "AgeCategory", "Race",
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"PhysicalActivity", "GenHealth", "SleepTime", ]].iloc[0:round(len(df) / 100 * 99)]
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y_train = df["HeartDisease"].iloc[0:round(len(df) / 100 * 99)]
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x_test = df[["BMI", "PhysicalHealth", "MentalHealth", "AgeCategory", "Race",
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"PhysicalActivity", "GenHealth", "SleepTime", ]].iloc[round(len(df) / 100 * 99):len(df)]
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y_test = df["HeartDisease"].iloc[round(len(df) / 100 * 99):len(df)]
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rid = Ridge(alpha=1.0)
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rid.fit(x_train.values, y_train.values)
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y_predict = rid.predict(x_test.values)
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err = pred_errors(y_predict, y_test.values)
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make_plots(y_test.values, y_predict, err[0], err[1], "Гребневая регрессия (значимые признаки)")
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# Метод рассчёта ошибок
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def pred_errors(y_predict, y_test):
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mid_square = np.round(np.sqrt(metrics.mean_squared_error(y_test, y_predict)),3) # Рассчёт среднеквадратичной ошибки модели
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det_kp = np.round(metrics.r2_score (y_test, y_predict), 2) # Рассчёт коэфициента детерминации модели
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return mid_square, det_kp
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# Метод отрисовки графиков
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def make_plots(y_test, y_predict, mid_sqrt, det_kp, title):
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plt.plot(y_test, c="red", label="\"y\" исходная") # Создание графика исходной функции
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plt.plot(y_predict, c="green", label="\"y\" предсказанная \n"
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"Ср^2 = " + str(mid_sqrt) + "\n"
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"Кд = " + str(det_kp)) # Создание графика предсказанной функции
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plt.legend(loc='lower left')
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plt.title(title)
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plt.savefig('static/' + title + '.png')
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plt.close()
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if __name__ == '__main__':
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ridge_all()
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ridge_valuable()
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