91 lines
2.6 KiB
Python
91 lines
2.6 KiB
Python
import pandas as pd
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import numpy as np
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import xgboost as xgb
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import LabelEncoder, StandardScaler
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from sklearn.metrics import accuracy_score, classification_report
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import joblib
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import json
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# 1. 加载数据
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print("正在加载数据...")
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df = pd.read_csv('bank.csv')
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# 2. 数据预处理
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print("正在进行数据预处理...")
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# 移除 duration 列 (避免数据泄露)
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if 'duration' in df.columns:
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df = df.drop('duration', axis=1)
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# 分离特征和目标
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X = df.drop('deposit', axis=1)
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y = df['deposit']
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# 处理目标变量 (yes -> 1, no -> 0)
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le_target = LabelEncoder()
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y = le_target.fit_transform(y)
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# 识别分类特征和数值特征
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categorical_cols = X.select_dtypes(include=['object']).columns.tolist()
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numeric_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()
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# 保存列名信息，供 Agent 使用
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feature_meta = {
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'numeric_cols': numeric_cols,
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'categorical_cols': categorical_cols,
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'all_cols': list(X.columns)
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}
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# 对分类特征进行 Label Encoding
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# 注意：XGBoost 可以处理类别特征，但通常需要转换为数值。
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# 为了简化 Agent 的推理流程，我们需要保存这些 Encoder。
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encoders = {}
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for col in categorical_cols:
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le = LabelEncoder()
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X[col] = le.fit_transform(X[col])
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encoders[col] = le
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# 3. 划分数据集
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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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# 4. 训练模型
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print("正在训练 XGBoost 模型...")
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model = xgb.XGBClassifier(
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n_estimators=100,
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learning_rate=0.1,
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max_depth=5,
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use_label_encoder=False,
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eval_metric='logloss'
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)
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model.fit(X_train, y_train)
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# 5. 评估模型
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y_pred = model.predict(X_test)
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y_pred_proba = model.predict_proba(X_test)[:, 1]
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print("\n模型评估结果:")
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print(f"Accuracy: {accuracy_score(y_test, y_pred):.4f}")
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print("\nClassification Report:")
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print(classification_report(y_test, y_pred))
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# 6. 保存资产
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print("\n正在保存模型和预处理工具...")
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artifacts = {
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'model': model,
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'encoders': encoders,
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'target_encoder': le_target,
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'feature_meta': feature_meta
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}
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joblib.dump(artifacts, 'model_artifacts.pkl')
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# 另外保存一份特征重要性，供参考
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importances = model.feature_importances_
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feature_names = X.columns
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feat_imp_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})
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feat_imp_df = feat_imp_df.sort_values(by='Importance', ascending=False)
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print("\n特征重要性 Top 5:")
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print(feat_imp_df.head())
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print("\n完成！模型已保存为 'model_artifacts.pkl'")
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