ad-server/ad_predict.py

import json
import random
import time
import math

import gevent
from db_helper import RedisHelper
from config import set_config
from log import Log
from gevent import monkey, pool
monkey.patch_all()

config_ = set_config()
log_ = Log()
redis_helper = RedisHelper()


def thompson_process(creative_id):
    # 获取creative_id对应的Thompson参数
    thompson_param_initial = redis_helper.get_data_from_redis(key_name=f"{config_.THOMPSON_PARAM_KEY_PREFIX}{creative_id}")
    if thompson_param_initial is None or thompson_param_initial == '':
        # 参数不存在，获取默认参数
        thompson_param = redis_helper.get_data_from_redis(key_name=f"{config_.THOMPSON_PARAM_KEY_PREFIX}-1")
        param_alpha, param_beta = json.loads(thompson_param.strip())
        param_alpha, param_beta = int(param_alpha), int(param_beta)
        random_flag = 'initial_random'
    else:
        # 参数存在
        param_alpha, param_beta = json.loads(thompson_param_initial.strip())
        param_alpha, param_beta = int(param_alpha), int(param_beta)
        if param_alpha + param_beta >= 100:
            # ad_idea_id 曝光数 >= 100，生成参数为(param_alpha+1, param_beta+1)的beta分布随机数
            thompson_param = thompson_param_initial
            random_flag = 'beta'
        else:
            # ad_idea_id 曝光数 < 100，获取默认参数
            thompson_param = redis_helper.get_data_from_redis(key_name=f"{config_.THOMPSON_PARAM_KEY_PREFIX}-1")
            param_alpha, param_beta = json.loads(thompson_param.strip())
            param_alpha, param_beta = int(param_alpha), int(param_beta)
            random_flag = 'under_view_initial_random'

    # 生成参数为(param_alpha+1, param_beta+1)的beta分布随机数
    alpha = math.log(param_alpha + 1) + 1
    beta = math.log(param_beta + 1) + 1
    score = random.betavariate(alpha=alpha, beta=beta)
    betavariate_param = [alpha, beta]
    thompson_res = [creative_id, score, thompson_param_initial, thompson_param, betavariate_param, random_flag]
    return thompson_res


def get_creative_id_with_thompson(mid, creative_id_list, gevent_pool, sort_strategy):
    """利用Thompson采样获取此次要展示的广告创意ID"""
    # 限制协程最大并发数：20
    tasks = [gevent_pool.spawn(thompson_process, creative_id) for creative_id in creative_id_list]
    gevent.joinall(tasks)
    thompson_res_list = [t.get() for t in tasks]
    # 按照score排序
    thompson_res_rank = sorted(thompson_res_list, key=lambda x: x[1], reverse=True)
    rank_res = {
        'mid': mid,
        'creative_id': thompson_res_rank[0][0],
        'score': thompson_res_rank[0][1],
        'thompson_param_initial': thompson_res_rank[0][2],
        'thompson_param': thompson_res_rank[0][3],
        'betavariate_param': thompson_res_rank[0][4],
        'random_flag': thompson_res_rank[0][5],
        'sort_strategy': sort_strategy,
        'thompson_res_rank': thompson_res_rank
    }
    return rank_res


def get_creative_id_with_thompson_weight(mid, creative_id_list, gevent_pool, sort_strategy):
    """利用Thompson采样+cvr加权 获取此次要展示的广告创意ID"""
    tasks = [gevent_pool.spawn(thompson_process, creative_id) for creative_id in creative_id_list]
    gevent.joinall(tasks)
    thompson_res_list = [t.get() for t in tasks]
    # 获取creative_id对应cvr, 给定对应权重
    cvr_mapping = {}
    creative_weight = {}
    for creative_id in creative_id_list:
        creative_weight[creative_id] = config_.CREATIVE_WEIGHT_INITIAL
        cvr = redis_helper.get_data_from_redis(
            key_name=f"{config_.CREATIVE_CVR_KEY_PREFIX}{creative_id}")
        if cvr is None:
            continue
        cvr_mapping[creative_id] = cvr
    cvr_sorted = sorted(cvr_mapping.items(), key=lambda x: x[1], reverse=False)

    for i, item in enumerate(cvr_sorted):
        creative_id = item[0]
        creative_weight[creative_id] += (i * config_.WEIGHT_GRADIENT)

    # thompson_score * weight
    thompson_weight_res_list = []
    weight_sum = sum([weight for _, weight in creative_weight.items()])
    for thompson_res in thompson_res_list:
        creative_id, score = thompson_res[0], thompson_res[1]
        weight_score = score * (1 + creative_weight[creative_id] / weight_sum)
        thompson_weight_res_list.append(thompson_res.add([creative_weight[creative_id], weight_score]))
    # 重新排序
    thompson_res_rank = sorted(thompson_weight_res_list, key=lambda x: x[6], reverse=True)
    rank_res = {
        'mid': mid,
        'creative_id': thompson_res_rank[0][0],
        'score': thompson_res_rank[0][1],
        'thompson_param_initial': thompson_res_rank[0][2],
        'thompson_param': thompson_res_rank[0][3],
        'betavariate_param': thompson_res_rank[0][4],
        'random_flag': thompson_res_rank[0][5],
        'creative_weight': thompson_res_rank[0][6],
        'weight_score': thompson_res_rank[0][7],
        'sort_strategy': sort_strategy,
        'thompson_res_rank': thompson_res_rank
    }
    return rank_res