import argparse import gzip import os.path from collections import OrderedDict import pandas as pd from hdfs import InsecureClient client = InsecureClient("http://master-1-1.c-7f31a3eea195cb73.cn-hangzhou.emr.aliyuncs.com:9870", user="spark") SEGMENT_BASE_PATH = os.environ.get("SEGMENT_BASE_PATH", "/dw/recommend/model/36_score_calibration_file") PREDICT_CACHE_PATH = os.environ.get("PREDICT_CACHE_PATH", "/root/zhaohp/XGB/predict_cache") def read_predict_from_local_txt(txt_file) -> list: result = [] with open(txt_file, "r") as f: for line in f.readlines(): sp = line.replace("\n", "").split("\t") if len(sp) == 4: label = int(sp[0]) cid = sp[3].split("_")[0] score = float(sp[2].replace("[", "").replace("]", "").split(",")[1]) result.append({ "label": label, "cid": cid, "score": score }) return result def read_predict_from_hdfs(hdfs_path: str) -> list: if not hdfs_path.endswith("/"): hdfs_path += "/" result = [] for file in client.list(hdfs_path): with client.read(hdfs_path + file) as reader: with gzip.GzipFile(fileobj=reader, mode="rb") as gz_file: for line in gz_file.read().decode("utf-8").split("\n"): split = line.split("\t") if len(split) == 4: cid = split[3].split("_")[0] label = int(split[0]) score = float(split[2].replace("[", "").replace("]", "").split(",")[1]) result.append({ "cid": cid, "label": label, "score": score }) return result def _segment_v1(scores, step): bins = [] for i in range(0, len(scores), int((len(scores) / step))): if i == 0: bins.append(0) else: bins.append(scores[i]) bins.append(1) return list(OrderedDict.fromkeys(bins)) def segment_calc_diff_rate_by_score(df: pd.DataFrame, segment_file_path: str, step=100) -> [pd.DataFrame, pd.DataFrame]: sored_df = df.sort_values(by=['score']) # 评估分数分段 scores = sored_df['score'].values bins = _segment_v1(scores, step) # 等分分桶 # split_indices = np.array_split(np.arange(len(scores)), step) # bins = [scores[index[0]] for index in split_indices] + [scores[split_indices[-1][-1]]] sored_df['score_segment'] = pd.cut(sored_df['score'], bins=bins) # 计算分段内分数的差异 group_df = sored_df.groupby("score_segment", observed=True).agg( segment_label_sum=('label', 'sum'), segment_label_cnt=('label', 'count'), segment_score_avg=('score', 'mean'), ).reset_index() group_df['segment_true_score'] = group_df['segment_label_sum'] / group_df['segment_label_cnt'] group_df['segment_diff_rate'] = (group_df['segment_score_avg'] / group_df['segment_true_score'] - 1).mask(group_df['segment_true_score'] == 0, 0) # 完整的分段文件保存 csv_data = group_df.to_csv(sep="\t", index=False) with client.write(segment_file_path, encoding='utf-8', overwrite=True) as writer: writer.write(csv_data) filtered_df = group_df[(abs(group_df['segment_diff_rate']) >= 0.2) & (group_df['segment_label_cnt'] >= 1000)] filtered_df = filtered_df[['score_segment', 'segment_diff_rate']] # 每条曝光数据添加对应分数的diff merged_df = pd.merge(sored_df, filtered_df, on="score_segment", how="left") merged_df['segment_diff_rate'] = merged_df['segment_diff_rate'].fillna(0) return merged_df, filtered_df def read_and_calibration_predict(predict_path: str, step=100) -> [pd.DataFrame, pd.DataFrame, pd.DataFrame]: """ 读取评估结果，并进行校准 """ # 本地调试使用 # predicts = read_predict_from_local_txt(predict_path) predicts = read_predict_from_hdfs(predict_path) df = pd.DataFrame(predicts) # 模型分分段计算与真实ctcvr的dff_rate predict_basename = os.path.basename(predict_path) if predict_basename.endswith("/"): predict_basename = predict_basename[:-1] df, segment_df = segment_calc_diff_rate_by_score(df, segment_file_path=f"{SEGMENT_BASE_PATH}/{predict_basename}.txt", step=100) # 生成校准后的分数 df['score_2'] = df['score'] / (1 + df['segment_diff_rate']) # 按CID统计真实ctcvr和校准前后的平均模型分 grouped_df = df.groupby("cid").agg( view=('cid', 'size'), conv=('label', 'sum'), score_avg=('score', lambda x: round(x.mean(), 6)), score_2_avg=('score_2', lambda x: round(x.mean(), 6)), ).reset_index() grouped_df['true_ctcvr'] = grouped_df['conv'] / grouped_df['view'] return df, grouped_df, segment_df def predict_local_save_for_auc(old_df: pd.DataFrame, new_df: pd.DataFrame): """ 本地保存一份评估结果, 计算AUC使用 """ d = {"old": old_df, "new": new_df} for key in d: df = d[key][['label', "score"]] df.to_csv(f"{PREDICT_CACHE_PATH}/{key}_1.txt", sep="\t", index=False, header=False) df = d[key][['label', "score_2"]] df.to_csv(f"{PREDICT_CACHE_PATH}/{key}_2.txt", sep="\t", index=False, header=False) def _main(old_predict_path: str, new_predict_path: str, calibration_file: str, analyse_file: str): old_df, old_group_df, old_segment_df = read_and_calibration_predict(old_predict_path) new_df, new_group_df, new_segment_df = read_and_calibration_predict(new_predict_path) predict_local_save_for_auc(old_df, new_df) # 分段文件保存, 此处保留的最后使用的分段文件，不是所有的分段 new_segment_df.to_csv(calibration_file, sep='\t', index=False, header=False) # 字段重命名，和列过滤 old_group_df.rename(columns={'score_avg': 'old_score_avg', 'score_2_avg': 'old_score_2_avg'}, inplace=True) new_group_df.rename(columns={'score_avg': 'new_score_avg', 'score_2_avg': 'new_score_2_avg'}, inplace=True) old_group_df = old_group_df[['cid', 'view', 'conv', 'true_ctcvr', 'old_score_avg', 'old_score_2_avg']] new_group_df = new_group_df[['cid', 'new_score_avg', 'new_score_2_avg']] merged = pd.merge(old_group_df, new_group_df, on='cid', how='left') # 计算与真实ctcvr的差异值 merged["(new-true)/true"] = (merged['new_score_avg'] / merged['true_ctcvr'] - 1).mask(merged['true_ctcvr'] == 0, 0) merged["(old-true)/true"] = (merged['old_score_avg'] / merged['true_ctcvr'] - 1).mask(merged['true_ctcvr'] == 0, 0) # 计算校准后的模型分与ctcvr的差异值 merged["(new2-true)/true"] = (merged['new_score_2_avg'] / merged['true_ctcvr'] - 1).mask(merged['true_ctcvr'] == 0, 0) merged["(old2-true)/true"] = (merged['old_score_2_avg'] / merged['true_ctcvr'] - 1).mask(merged['true_ctcvr'] == 0, 0) # 按照曝光排序，写入本地文件 merged = merged.sort_values(by=['view'], ascending=False) merged = merged[[ 'cid', 'view', "conv", "true_ctcvr", "old_score_avg", "new_score_avg", "(old-true)/true", "(new-true)/true", "old_score_2_avg", "new_score_2_avg", "(old2-true)/true", "(new2-true)/true", ]] # 根据文件名保存不同的格式 if analyse_file.endswith(".csv"): merged.to_csv(analyse_file, index=False) else: with open(analyse_file, "w") as writer: writer.write(merged.to_string(index=False)) print("0") if __name__ == '__main__': parser = argparse.ArgumentParser(description="model_predict_analyse.py") parser.add_argument("-op", "--old_predict_path", required=True, help="老模型评估结果") parser.add_argument("-np", "--new_predict_path", required=True, help="新模型评估结果") parser.add_argument("-af", "--analyse_file", required=True, help="最后计算结果的保存路径") parser.add_argument("-cf", "--calibration_file", required=True, help="线上使用的segment文件保存路径") args = parser.parse_args() _main( old_predict_path=args.old_predict_path, new_predict_path=args.new_predict_path, calibration_file=args.calibration_file, analyse_file=args.analyse_file )