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import pandas as pd
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import numpy as np
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import joblib
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import shap
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import matplotlib
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matplotlib.use('Agg') # 使用非交互式后端
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import matplotlib.pyplot as plt
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import os
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# 添加项目根目录到Python路径
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import sys
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from data.data_generator import preprocess_data
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def explain_with_shap():
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"""
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使用SHAP解释LightGBM模型
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"""
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# 读取数据
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print("读取信贷数据...")
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df = pd.read_csv('data/credit_data.csv')
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# 数据预处理
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print("数据预处理...")
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X, y, scaler, le_education, le_home, le_purpose = preprocess_data(df)
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# 加载训练好的模型
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print("加载模型...")
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try:
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model = joblib.load('models/lightgbm_model.pkl')
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model_name = "LightGBM"
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except:
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# 如果LightGBM模型不存在，回退到XGBoost模型
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model = joblib.load('models/xgboost_model.pkl')
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model_name = "XGBoost"
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print(f"使用{model_name}模型")
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# 选择一小部分数据进行解释（避免计算时间过长）
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X_sample = X.iloc[:100] # 增加样本数量以获得更准确的解释
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# 创建SHAP解释器
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print("创建SHAP解释器...")
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explainer = shap.TreeExplainer(model)
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shap_values = explainer.shap_values(X_sample)
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# 绘制特征重要性条形图
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print("绘制SHAP特征重要性图...")
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plt.figure(figsize=(10, 6))
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shap.summary_plot(shap_values, X_sample, plot_type="bar", show=False)
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plt.title(f'{model_name}模型SHAP特征重要性')
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plt.tight_layout()
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plt.savefig('visualization/shap_feature_importance.png', dpi=300, bbox_inches='tight')
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plt.close()
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# 绘制SHAP摘要图
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print("绘制SHAP摘要图...")
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plt.figure(figsize=(10, 8))
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shap.summary_plot(shap_values, X_sample, show=False)
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plt.title(f'{model_name}模型SHAP摘要图')
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plt.tight_layout()
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plt.savefig('visualization/shap_summary.png', dpi=300, bbox_inches='tight')
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plt.close()
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# 保存SHAP解释器以便在API中使用
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joblib.dump(explainer, 'models/shap_explainer.pkl')
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print("SHAP解释完成，图表已保存到 visualization 目录")
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return shap_values, X_sample
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if __name__ == "__main__":
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shap_values, X_sample = explain_with_shap()
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print("SHAP解释模块集成完成!") |
