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import os
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import time
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import datetime
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import pandas as pd
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from typing import List
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from common import evaluate_model
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import logging
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import numpy as np
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import yaml
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from feature_process import create_feature_df, apply_feature_weights, Features, process_features_list
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from data_process import preprocess_data, convert_to_list
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if __name__ == "__main__":
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# 读取原始数据表
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df_src = pd.read_excel("data/data_src.xlsx")
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df_leave = pd.read_excel("data_processed/Leave_Record_RES.xlsx")
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df_dropout_warning = pd.read_excel("data_processed/Dropout_Warning_RES.xlsx")
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# 数据预处理
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df = preprocess_data(df_src, df_leave, df_dropout_warning)
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# 转换成数据列表
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features_data_list = convert_to_list(df)
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processed_features_list: List[Features] = process_features_list(features_data_list)
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# 特征预处理
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all_features = create_feature_df(processed_features_list)
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# 读取 YAML 配置文件
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config_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "train_local.yaml"))
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with open(config_path, 'r') as f:
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config = yaml.load(f, Loader=yaml.FullLoader)
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feature_names = config['feature_names']
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feature_weights = config['feature_weights']
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# 应用特征权重
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feature_label_weighted = apply_feature_weights(all_features, feature_names, feature_weights)
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start_time = time.time() # 记录开始时间
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# 创建静态文件存放文件夹
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static_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "evaluate_local")) # 设置模型文件和配置文件的存放目录，和本py同级
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os.makedirs(static_dir, exist_ok=True)
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# 训练前设置
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now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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data_path = os.path.abspath(os.path.join(os.path.dirname(__file__), static_dir, f"all_features_label_{now}.xlsx"))
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config['data_path'] = data_path
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feature_label_weighted.to_excel(data_path, index=False)
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# 配置日志
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log_path = os.path.abspath(os.path.join(os.path.dirname(__file__), static_dir, f"evaluate_{now}.log"))
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logging.basicConfig(filename=log_path, level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
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# 开始验证
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list_avg_f1 = []
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list_wrong_percentage = []
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list_precision = []
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list_recall = []
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list_f1 = []
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# 特征和标签
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X = feature_label_weighted[config['feature_names']].values
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y = feature_label_weighted[config['label_name']].values
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print(config)
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avg_f1, wrong_percentage, precision, recall, f1 = evaluate_model(config["model_path"], X, y, config)
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list_avg_f1.append(avg_f1)
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list_wrong_percentage.append(wrong_percentage)
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list_precision.append(precision)
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list_recall.append(recall)
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list_f1.append(f1)
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logging.info(f"Result: Avg F1: {sum(list_avg_f1) / len(list_avg_f1):.4f} Avg Wrong Percentage: {sum(list_wrong_percentage) / len(list_wrong_percentage):.2f}%")
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logging.info(f"Result: Avg Precision: {[sum(p[i] for p in list_precision) / len(list_precision) for i in range(len(list_precision[0]))]} | {np.mean(list_precision)}")
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logging.info(f"Result: Avg Recall: {[sum(r[i] for r in list_recall) / len(list_recall) for i in range(len(list_recall[0]))]} | {np.mean(list_recall)}")
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logging.info(f"Result: Avg F1: {[sum(f1[i] for f1 in list_f1) / len(list_f1) for i in range(len(list_f1[0]))]} | {np.mean(list_f1)}")
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print(f"Result: Avg F1: {sum(list_avg_f1) / len(list_avg_f1):.4f} Avg Wrong Percentage: {sum(list_wrong_percentage) / len(list_wrong_percentage):.2f}%")
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print(f"Result: Avg Precision: {[sum(p[i] for p in list_precision) / len(list_precision) for i in range(len(list_precision[0]))]} | {np.mean(list_precision)}")
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print(f"Result: Avg Recall: {[sum(r[i] for r in list_recall) / len(list_recall) for i in range(len(list_recall[0]))]} | {np.mean(list_recall)}")
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print(f"Result: Avg F1: {[sum(f1[i] for f1 in list_f1) / len(list_f1) for i in range(len(list_f1[0]))]} | {np.mean(list_f1)}")
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end_time = time.time() # 记录结束时间
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# 训练结束
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print("预测耗时:", end_time - start_time, "秒") # 打印执行时间 |