f47c7d7196
添加完整的客户流失预测系统，包括数据处理、模型训练、预测和行动建议功能。主要包含以下模块： 1. 数据预处理流水线（Polars + Pandera） 2. 机器学习模型训练（LightGBM + Logistic Regression） 3. AI Agent预测和建议工具 4. Streamlit交互式Web界面 5. 完整的课程设计报告文档
189 lines
6.5 KiB
Python
189 lines
6.5 KiB
Python
from sklearn.model_selection import train_test_split, GridSearchCV
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import (f1_score, roc_auc_score, accuracy_score,
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precision_score, recall_score, classification_report)
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import lightgbm as lgb
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import numpy as np
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import joblib
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import os
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from data_processing import data_processing_pipeline, preprocess_data
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# 模型训练和评估类
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class ModelTrainer:
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def __init__(self):
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self.models = {}
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self.metrics = {}
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# 创建models目录（如果不存在）
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os.makedirs("models", exist_ok=True)
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# 训练Logistic Regression模型
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def train_logreg(self, X, y):
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print("训练Logistic Regression模型...")
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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(
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X, y, test_size=0.2, random_state=42, stratify=y
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)
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# 模型参数网格
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param_grid = {
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'C': [0.01, 0.1, 1.0, 10.0, 100.0],
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'max_iter': [1000],
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'solver': ['lbfgs']
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}
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# 使用GridSearchCV进行参数调优
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logreg = LogisticRegression(random_state=42)
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grid_search = GridSearchCV(estimator=logreg, param_grid=param_grid,
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cv=5, scoring='f1', n_jobs=-1)
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grid_search.fit(X_train, y_train)
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best_logreg = grid_search.best_estimator_
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# 评估模型
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y_pred = best_logreg.predict(X_test)
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y_pred_proba = best_logreg.predict_proba(X_test)[:, 1]
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metrics = self.calculate_metrics(y_test, y_pred, y_pred_proba)
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# 保存模型
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joblib.dump(best_logreg, "models/logreg_model.pkl")
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self.models["logreg"] = best_logreg
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self.metrics["logreg"] = metrics
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print("Logistic Regression模型训练完成！")
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print(f"最佳参数: {grid_search.best_params_}")
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print(f"F1分数: {metrics['f1']:.4f}")
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print(f"ROC-AUC: {metrics['roc_auc']:.4f}")
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return best_logreg, metrics
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# 训练LightGBM模型
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def train_lightgbm(self, X, y):
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print("\n训练LightGBM模型...")
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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(
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X, y, test_size=0.2, random_state=42, stratify=y
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)
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# 使用sklearn接口的LightGBM分类器
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from lightgbm import LGBMClassifier
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# 模型参数
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params = {
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'objective': 'binary',
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'metric': 'binary_logloss',
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'boosting_type': 'gbdt',
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'random_state': 42,
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'n_jobs': -1,
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'verbose': -1
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}
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# 训练模型
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lgbm = LGBMClassifier(**params)
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# 简化训练，不使用GridSearchCV
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best_lgbm = lgbm.fit(X_train, y_train)
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# 评估模型
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y_pred_proba = best_lgbm.predict_proba(X_test)[:, 1]
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y_pred = best_lgbm.predict(X_test)
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metrics = self.calculate_metrics(y_test, y_pred, y_pred_proba)
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# 保存模型
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joblib.dump(lgbm, "models/lightgbm_model.pkl")
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self.models["lightgbm"] = lgbm
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self.metrics["lightgbm"] = metrics
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print("LightGBM模型训练完成！")
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print(f"F1分数: {metrics['f1']:.4f}")
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print(f"ROC-AUC: {metrics['roc_auc']:.4f}")
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return lgbm, metrics
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# 计算模型评估指标
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def calculate_metrics(self, y_true, y_pred, y_pred_proba):
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return {
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'accuracy': accuracy_score(y_true, y_pred),
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'precision': precision_score(y_true, y_pred),
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'recall': recall_score(y_true, y_pred),
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'f1': f1_score(y_true, y_pred),
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'roc_auc': roc_auc_score(y_true, y_pred_proba)
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}
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# 对比模型性能
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def compare_models(self):
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print("\n" + "="*50)
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print("模型性能对比")
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print("="*50)
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for model_name, metrics in self.metrics.items():
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print(f"\n{model_name.upper()} 性能:")
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print(f" Accuracy: {metrics['accuracy']:.4f}")
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print(f" Precision: {metrics['precision']:.4f}")
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print(f" Recall: {metrics['recall']:.4f}")
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print(f" F1 Score: {metrics['f1']:.4f}")
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print(f" ROC-AUC: {metrics['roc_auc']:.4f}")
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# 找出最佳模型
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best_model = max(self.metrics.keys(), key=lambda x: self.metrics[x]['f1'])
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print(f"\n最佳模型: {best_model.upper()}")
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print(f"最佳F1分数: {self.metrics[best_model]['f1']:.4f}")
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return best_model
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# 加载模型进行预测
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def predict(self, model_name, X):
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if model_name not in self.models:
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# 尝试从文件加载模型
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try:
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model = joblib.load(f"models/{model_name}_model.pkl")
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self.models[model_name] = model
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except FileNotFoundError:
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raise ValueError(f"Model {model_name} not found. Please train the model first.")
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model = self.models[model_name]
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y_pred_proba = model.predict_proba(X)[:, 1]
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y_pred = (y_pred_proba >= 0.5).astype(int)
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return y_pred, y_pred_proba
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# 主函数
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if __name__ == "__main__":
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# 1. 数据处理
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print("正在处理数据...")
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X, y, df = data_processing_pipeline("data/Telco-Customer-Churn.csv")
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X_np, y_np = preprocess_data(X, y)
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# 2. 模型训练和评估
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trainer = ModelTrainer()
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# 训练Logistic Regression
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logreg_model, logreg_metrics = trainer.train_logreg(X_np, y_np)
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# 训练LightGBM
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lgbm_model, lgbm_metrics = trainer.train_lightgbm(X_np, y_np)
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# 对比模型
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best_model = trainer.compare_models()
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# 3. 检查是否达到要求
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print("\n" + "="*50)
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print("模型性能要求检查")
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print("="*50)
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best_f1 = trainer.metrics[best_model]['f1']
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best_roc_auc = trainer.metrics[best_model]['roc_auc']
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if best_f1 >= 0.70 or best_roc_auc >= 0.75:
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print(f"✓ 模型性能达标！最佳F1: {best_f1:.4f}, 最佳ROC-AUC: {best_roc_auc:.4f}")
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print("✓ 满足F1 ≥ 0.70 或 ROC-AUC ≥ 0.75 的要求")
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else:
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print(f"✗ 模型性能未达标！最佳F1: {best_f1:.4f}, 最佳ROC-AUC: {best_roc_auc:.4f}")
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print("✗ 未满足F1 ≥ 0.70 或 ROC-AUC ≥ 0.75 的要求")
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