# import pandas as pd # # old_date_train = f"/Users/zhao/Desktop/Code/Python/model_monitor/XGB/data/all/20241012_predict.csv" # new_date_train = f"/Users/zhao/Desktop/Code/Python/model_monitor/XGB/20241012_predict_1.csv" # # # 读取两个 CSV 文件 # old_df = pd.read_csv(old_date_train) # new_df = pd.read_csv(new_date_train) # # if old_df.shape[0] != new_df.shape[0]: # print(f"新老训练数据集长度不一样 新数据集: {new_df.shape[0]}, 老数据集: {old_df.shape[0]}") # # old_df_col = old_df.columns # new_df_col = new_df.columns # if len(old_df_col) != len(new_df_col): # print(f"两个文件列数不一样 新文件: {new_df_col}, 老文件: {old_df_col}") # # for col in old_df_col: # if col not in new_df_col: # print(f"列 {col} 在老文件存在,新文件不存在") # # for col in new_df_col: # if col not in old_df_col: # print(f"列 {col} 在新文件存在,老文件不存在") # # old_df.set_index("vid", inplace=True) # new_df.set_index("vid", inplace=True) # # old_dict = old_df.to_dict(orient="index") # new_dict = new_df.to_dict(orient="index") # # for e in new_dict: # if e not in old_dict: # print(f"vid {e} 在新文件中存在,在老文件中不存在") # new_row = new_dict[e] # old_row = old_dict[e] # for col in new_df_col: # if col in ['vid', '曝光占比', '分子', '分母', 'label']: # continue # if col not in old_row: # print(f"vid {e} 的列 {col} 在老文件中不存在") # continue # # if col in new_row: # # print(f"vid {e} 的列 {col} 在新文件中不存在") # # continue # if old_row[col] != new_row[col]: # print(f"vid {e} 列 {col} 的值在新老文件不一样, 新文件的值: {new_row[col]}, 老文件的值: {old_row[col]}") # # # z_vid = set() # # with open("/Users/zhao/Desktop/Code/Python/rov-offline/write_redis/filtered_vid", "r") as f: # # for line in f: # # z_vid.add(line.replace("\n", "")) # # # # p_vid = set() # # with open("./filtered_vid.txt", "r") as f: # # for line in f: # # p_vid.add(line.replace("\n", "")) # # # # for e in z_vid: # # if e not in p_vid: # # print(f"VID: {e} 离线预测有,在线预测没有") # # # # for e in p_vid: # # if e not in z_vid: # # print(f"VID: {e} 在线预测有,离线预测没有")