recommend-emr-dataprocess/ad/model_predict_analyse.py

import argparse
import gzip
import os.path

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_model_attachment/score_calibration_file")
PREDICT_CACHE_PATH = os.environ.get("PREDICT_CACHE_PATH", "/root/zhaohp/XGB/predict_cache")


def parse_predict_line(line: str) -> [bool, dict]:
    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])
        return True, {
            "label": label,
            "cid": cid,
            "score": score
        }
    return False, {}


def read_predict_file(file_path: str) -> pd.DataFrame:
    result = []
    if file_path.startswith("/dw"):
        if not file_path.endswith("/"):
            file_path += "/"
        for file in client.list(file_path):
            with client.read(file_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"):
                        b, d = parse_predict_line(line)
                        if b: result.append(d)
    else:
        with open(file_path, "r") as f:
            for line in f.readlines():
                b, d = parse_predict_line(line)
                if b: result.append(d)
    return pd.DataFrame(result)


def calibration_file_save(df: pd.DataFrame, file_path: str):
    if file_path.startswith("/dw"):
        # 完整的分段文件保存
        with client.write(file_path, encoding='utf-8', overwrite=True) as writer:
            writer.write(df.to_csv(sep="\t", index=False))
    else:
        df.tocsv(file_path, sep="\t", index=False)


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]
        if 'score' in df.columns:
            score_df = df[['label', "score"]]
            score_df.to_csv(f"{PREDICT_CACHE_PATH}/{key}_1.txt", sep="\t", index=False, header=False)
        if 'score_2' in df.columns:
            score_2_df = d[key][['label', "score_2"]]
            score_2_df.to_csv(f"{PREDICT_CACHE_PATH}/{key}_2.txt", sep="\t", index=False, header=False)


def save_full_calibration_file(df: pd.DataFrame, segment_file_path: str):
    if segment_file_path.startswith("/dw"):
        # 完整的分段文件保存
        with client.write(segment_file_path, encoding='utf-8', overwrite=True) as writer:
            writer.write(df.to_csv(sep="\t", index=False))
    else:
        df.to_csv(segment_file_path, sep="\t", index=False)


def get_predict_calibration_file(df: pd.DataFrame, predict_basename: str) -> [pd.DataFrame]:
    """
    计算模型分的diff_rate
    """
    agg_df = predict_df_agg(df)
    agg_df['diff_rate'] = (agg_df['score_avg'] / agg_df['true_ctcvr'] - 1).mask(agg_df['true_ctcvr'] == 0, 0).round(6)
    condition = 'view > 1000 and diff_rate >= 0.2'
    save_full_calibration_file(agg_df, f"{SEGMENT_BASE_PATH}/{predict_basename}.txt")
    calibration = agg_df[(agg_df['view'] > 1000) & ((agg_df['diff_rate'] >= 0.2) | (agg_df['diff_rate'] <= 0.2)) & agg_df['diff_rate'] != 0]
    return calibration


def get_predict_basename(predict_path) -> [str]:
    """
    获取文件路径的最后一部分，作为与模型关联的文件名
    """
    predict_basename = os.path.basename(predict_path)
    if predict_basename.endswith("/"):
        predict_basename = predict_basename[:-1]

    return predict_basename


def calc_calibration_score2(df: pd.DataFrame, calibration_df: pd.DataFrame) -> [pd.DataFrame]:
    calibration_df = calibration_df[['cid', 'diff_rate']]
    df = pd.merge(df, calibration_df, on='cid', how='left').fillna(0)
    df['score_2'] = df['score'] / (1 + df['diff_rate'])
    return df


def predict_df_agg(df: pd.DataFrame) -> [pd.DataFrame]:
    # 基础聚合操作
    agg_operations = {
        'view': ('cid', 'size'),
        'conv': ('label', 'sum'),
        'score_avg': ('score', lambda x: round(x.mean(), 6)),
    }

    # 如果存在 score_2 列，则增加相关聚合
    if "score_2" in df.columns:
        agg_operations['score_2_avg'] = ('score_2', lambda x: round(x.mean(), 6))

    grouped_df = df.groupby("cid").agg(**agg_operations).reset_index()
    grouped_df['true_ctcvr'] = grouped_df['conv'] / grouped_df['view']

    return grouped_df


def _main(old_predict_path: str, new_predict_path: str, calibration_file: str, analyse_file: str):
    old_df = read_predict_file(old_predict_path)
    new_df = read_predict_file(new_predict_path)

    old_calibration_df = get_predict_calibration_file(old_df, get_predict_basename(old_predict_path))
    old_df = calc_calibration_score2(old_df, old_calibration_df)

    new_calibration_df = get_predict_calibration_file(new_df, get_predict_basename(new_predict_path))
    new_df = calc_calibration_score2(new_df, new_calibration_df)

    # 本地保存label、score以及校准后的score，用于计算AUC等信息
    predict_local_save_for_auc(old_df, new_df)

    # 新模型校准文件保存本地，用于同步OSS
    new_calibration_df[['cid', 'diff_rate']].to_csv(calibration_file, sep="\t", index=False, header=False)

    old_agg_df = predict_df_agg(old_df)
    new_agg_df = predict_df_agg(new_df)

    # 字段重命名，和列过滤
    old_agg_df.rename(columns={'score_avg': 'old_score_avg', 'score_2_avg': 'old_score_2_avg'}, inplace=True)
    new_agg_df.rename(columns={'score_avg': 'new_score_avg', 'score_2_avg': 'new_score_2_avg'}, inplace=True)
    old_group_df = old_agg_df[['cid', 'view', 'conv', 'true_ctcvr', 'old_score_avg', 'old_score_2_avg']]
    new_group_df = new_agg_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_20241101.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
    )