删除 train_model.py

train_model.py

@@ -1,90 +0,0 @@
-import pandas as pd
-import numpy as np
-import xgboost as xgb
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import LabelEncoder, StandardScaler
-from sklearn.metrics import accuracy_score, classification_report
-import joblib
-import json
-
-# 1. 加载数据
-print("正在加载数据...")
-df = pd.read_csv('bank.csv')
-
-# 2. 数据预处理
-print("正在进行数据预处理...")
-
-# 移除 duration 列 (避免数据泄露)
-if 'duration' in df.columns:
-    df = df.drop('duration', axis=1)
-
-# 分离特征和目标
-X = df.drop('deposit', axis=1)
-y = df['deposit']
-
-# 处理目标变量 (yes -> 1, no -> 0)
-le_target = LabelEncoder()
-y = le_target.fit_transform(y)
-
-# 识别分类特征和数值特征
-categorical_cols = X.select_dtypes(include=['object']).columns.tolist()
-numeric_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()
-
-# 保存列名信息，供 Agent 使用
-feature_meta = {
-    'numeric_cols': numeric_cols,
-    'categorical_cols': categorical_cols,
-    'all_cols': list(X.columns)
-}
-
-# 对分类特征进行 Label Encoding
-# 注意：XGBoost 可以处理类别特征，但通常需要转换为数值。
-# 为了简化 Agent 的推理流程，我们需要保存这些 Encoder。
-encoders = {}
-for col in categorical_cols:
-    le = LabelEncoder()
-    X[col] = le.fit_transform(X[col])
-    encoders[col] = le
-
-# 3. 划分数据集
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# 4. 训练模型
-print("正在训练 XGBoost 模型...")
-model = xgb.XGBClassifier(
-    n_estimators=100,
-    learning_rate=0.1,
-    max_depth=5,
-    use_label_encoder=False,
-    eval_metric='logloss'
-)
-model.fit(X_train, y_train)
-
-# 5. 评估模型
-y_pred = model.predict(X_test)
-y_pred_proba = model.predict_proba(X_test)[:, 1]
-
-print("\n模型评估结果:")
-print(f"Accuracy: {accuracy_score(y_test, y_pred):.4f}")
-print("\nClassification Report:")
-print(classification_report(y_test, y_pred))
-
-# 6. 保存资产
-print("\n正在保存模型和预处理工具...")
-artifacts = {
-    'model': model,
-    'encoders': encoders,
-    'target_encoder': le_target,
-    'feature_meta': feature_meta
-}
-joblib.dump(artifacts, 'model_artifacts.pkl')
-
-# 另外保存一份特征重要性，供参考
-importances = model.feature_importances_
-feature_names = X.columns
-feat_imp_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})
-feat_imp_df = feat_imp_df.sort_values(by='Importance', ascending=False)
-print("\n特征重要性 Top 5:")
-print(feat_imp_df.head())
-
-print("\n完成！模型已保存为 'model_artifacts.pkl'")