106 lines
4.3 KiB
Python
106 lines
4.3 KiB
Python
import numpy as np
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.feature_selection import f_regression
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from sklearn.preprocessing import MinMaxScaler
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# загрузка dataset
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data = pd.read_csv('dataset.csv')
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# разделение dataset на тренировочную и тестовую выборки
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X = data.drop(['Target'], axis=1)
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y = data['Target']
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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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# Тренировка моделей
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# Линейная регрессия
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lr = LinearRegression()
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lr.fit(X_train, y_train)
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# Сокращение признаков случайными деревьями с помощью Random Forest Regressor
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rf = RandomForestRegressor()
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rf.fit(X_train, y_train)
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# Ранжирование признаков использую каждую модель/метод
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# Получение абсолютных значений коэффициентов в качестве оценок важности признаков
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lr_scores = abs(lr.coef_)
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# Получение оценок важности объектов из модели Random Forest Regressor
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rf_scores = rf.feature_importances_
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# Отображение итоговых оценок по каждой колонке
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feature_names = X.columns.tolist()
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# показать оценки рангов по модели линейной регрессии
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print("оценки линейной регрессии:")
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for feature, score in zip(feature_names, lr_scores):
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print(f"{feature}: {round(score, 4)}")
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# оценки метода рандомных лесов
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print("\nоценки Random Forest:")
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for feature, score in zip(feature_names, rf_scores):
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print(f"{feature}: {round(score, 4)}")
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# вычисление значений оценки для f_regression
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f_scores, p_values = f_regression(X, y)
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# оценки f_regression
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print("\nоценки f_regression:")
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for feature, score in zip(feature_names, f_scores):
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print(f"{feature}: {round(score, 4)}")
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# использую MinMaxScaler для точных средних значений рангов
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scaler = MinMaxScaler()
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lr_scores_scaled = scaler.fit_transform(lr_scores.reshape(-1, 1)).flatten()
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rf_scores_scaled = scaler.fit_transform(rf_scores.reshape(-1, 1)).flatten()
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f_scores_scaled = scaler.fit_transform(f_scores.reshape(-1, 1)).flatten()
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# вычисление средних оценок для каждого признака
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average_scores = {}
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for feature in feature_names:
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average_scores[feature] = (lr_scores_scaled[feature_names.index(feature)] +
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rf_scores_scaled[feature_names.index(feature)] +
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f_scores_scaled[feature_names.index(feature)]) / 3
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# получаем среднюю оценку признаков
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sorted_features = sorted(average_scores.items(), key=lambda x: x[1], reverse=True)
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# получаем самых важных признака
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top_4_features = sorted_features[:4]
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# отображаем 4 самые важные
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print("\n4 самых важных признака в среднем:")
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for feature, score in top_4_features:
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print(f"Признак: {feature}, Оценка: {round(score, 4)}")
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# отображаем самых важных признака для каждого метода/модели
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top_lr_indices = np.argsort(lr_scores)[-4:][::-1]
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top_rf_indices = np.argsort(rf_scores)[-4:][::-1]
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top_f_indices = np.argsort(f_scores)[-4:][::-1]
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top_lr_features = [feature_names[i] for i in top_lr_indices]
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top_rf_features = [feature_names[i] for i in top_rf_indices]
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top_f_features = [feature_names[i] for i in top_f_indices]
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top_lr_features_score = [lr_scores[i] for i in top_lr_indices]
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top_rf_features_score = [rf_scores[i] for i in top_rf_indices]
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top_f_features_score = [f_scores[i] for i in top_f_indices]
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print("\n4 самых важных для lr_scores:")
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print(top_lr_features)
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for i in top_lr_features_score:
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print(round(i, 4))
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print("\n4 самых важных для rf_scores:")
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print(top_rf_features)
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for i in top_rf_features_score:
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print(round(i, 4))
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print("\n4 самых важных для f_scores:")
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print(top_f_features)
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for i in top_f_features_score:
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print(round(i, 4)) |