55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
import pandas as pd
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import numpy as np
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
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from sklearn.model_selection import train_test_split
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if __name__ == '__main__':
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data = pd.read_csv('tr_user_tj.csv', header=0)
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data1 = data[['star_num', 'sign_num', 'coll_num', 'dna_num', 'task_num', 'word_num', 'balance_amt', 'earn_amt',
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'season_point', 'point', 'star_score', 'term_amt', 'match_num']]
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# 分离特征和目标变量
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X = data1.drop('match_num', axis=1)
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y = data1['match_num']
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# 方法1: 增强重要特征的权重 - 通过对重要特征进行放大
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X_weighted = X.copy()
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# 对重要特征进行加权（增加倍数）
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X_weighted['dna_num'] = X_weighted['dna_num'] * 2
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X_weighted['task_num'] = X_weighted['task_num'] * 1.5
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X_weighted['season_point'] = X_weighted['season_point'] * 1.5
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# 划分训练集和测试集
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X_train, X_test, y_train, y_test = train_test_split(X_weighted, y, test_size=0.2, random_state=42)
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# 创建随机森林回归器
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rf_regressor = RandomForestRegressor(n_estimators=100, random_state=42)
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# 训练模型
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rf_regressor.fit(X_train, y_train)
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# 预测
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y_pred = rf_regressor.predict(X_test)
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# 计算评估指标
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mse = mean_squared_error(y_test, y_pred)
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rmse = np.sqrt(mse)
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mae = mean_absolute_error(y_test, y_pred)
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r2 = r2_score(y_test, y_pred)
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print(f"Weighted Features Results:")
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print(f"Mean Squared Error: {mse}")
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print(f"Root Mean Squared Error: {rmse}")
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print(f"Mean Absolute Error: {mae}")
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print(f"R² Score: {r2}")
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# 特征重要性
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feature_importance = pd.DataFrame({
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'feature': X_weighted.columns,
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'importance': rf_regressor.feature_importances_
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}).sort_values('importance', ascending=False)
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print("\nFeature Importance:")
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print(feature_importance)
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