相似人群画像算法

一、数据源

1、相似人群数据存在TDW库中,数据字典说明:

CREATE TABLE sim_people_tdw_tbl(
    uid STRING COMMENT 'reader id',
    sim_uids STRING COMMENT 'sim_uids',
    sim_num BIGINT COMMENT 'sim_num',
    update_date STRING COMMENT 'update_date'
)

字段

类型

含义

uid

string

用户标识

sim_uids

string

与uid喜好相似的人群,格式为用户编号:相同阅读量,相似用户之间以逗号分隔

sim_num

BIGINT

相似人群的人数

update_date

string

数据日期

2、基础用户画像存在MongoDB中

基础用户画像

字段

含义

_id

用户id

profile(离线)positive(实时)

用户正画像(喜欢),每个维度以分号间隔,每个子维度以逗号间隔,值格式为key_id:weight,维度含义依次为一级分类、二级分类、关键字、topic、阅读来源

negative

负画像(不喜欢),其他字段的含义与正画像一样

update_time

更新时间

cityCode或city

城市编码

3、相似人群画像也存在MongoDB中

二、整体思路

由于TESLA集群无法直接操作MongoDB,需要将TDW里面的用户画像数据,通过洛子系统导出至HDFS,再与MongoDB中原有群画像进行合并。

整体流程

三、算法流程

算法流程图

四、核心代码

#! /usr/bin/python2.7
# -*- coding: utf8 -*-
import decimal
import time
import math
import sys
import os
import param_map
from pymongo.collection import Collection
from decimal import Decimal
import datetime

reload(sys)
sys.setdefaultencoding("utf-8")
sys.path.append("../")
from utils import mongoUtils, confUtils

decimal.getcontext().prec = 6
BATCH_NUM = 100000

now_time = datetime.datetime.now()
delta = datetime.timedelta(days=30)
delta30 = now_time - delta
time_limit = int(time.mktime(delta30.timetuple()))
print(time_limit)


def split_uid_similarity(uid_num_str):
    """
    拆分uid和相似度,并分别返回
    :param uid_num_str:
    :return:uid,相似度
    """
    uid_num = uid_num_str.split(":")
    return uid_num[0], float(uid_num[1])


def split_uid_sim_user(user_hd):
    """
    拆分uid和相似人群,并分别返回
    :param user_hd:
    :return: uid,相似人群
    """
    uid_sim_user = user_hd.strip().split("\t")
    return uid_sim_user[0], uid_sim_user[1]


def dimension_profile_limit(dimension_profile, min_i, max_i, limit, cluster_profile_str):
    """
    :param dimension_profile:
    :param min_i:
    :param max_i:
    :param limit:
    :param cluster_profile_str:
    :return: 返回前limit个特征标签,并对特征权重进行映射
    """
    if len(dimension_profile) != 0:
        # 先排序
        dimension_profile = sorted(dimension_profile.iteritems(), key=lambda c: c[1], reverse=True)
        # 再对前limit条记录进行映射
        size = limit if len(dimension_profile) > limit else len(dimension_profile)
        for i in range(size):
            tag = dimension_profile[i]
            tag_id = tag[0]
            tag_value = tag[1]
            tag_value = max_i if tag_value > max_i else tag_value
            if tag_value >= min_i:
                cluster_profile_str = cluster_profile_str + str(tag_id) + ":" + str(tag_value) + ","
        if len(dimension_profile) != 0:
            # 假如长度不为0,将最后一个逗号删掉
            cluster_profile_str = cluster_profile_str[:-1]
    return cluster_profile_str


def cluster_profile_dic2list(cluster_profile, dimension_param_dic):
    """
    相似用户群画像阈值过滤,dic->list
    :param dimension_param_dic: 维度阈值
    :return: 相似用户群特征list
    :param cluster_profile:群体画像
    """
    cluster_profile_str = ""
    if len(cluster_profile) == 0:
        return None
    for key, dimension_profile in cluster_profile.items():
        # 取出维度的阈值
        dimention_param = dimension_param_dic.get(str(key))
        if dimention_param is not None:
            min_i = dimention_param.get("min")
            max_i = dimention_param.get("max")
            limit = dimention_param.get("limit")
            if dimension_profile is not None:
                cluster_profile_str = dimension_profile_limit(dimension_profile, min_i, max_i, limit,
                                                              cluster_profile_str)
        # values为不为None 都需要追加一个分号
        cluster_profile_str = cluster_profile_str + ";"
    cluster_profile_list = cluster_profile_str[:-1].split(";")
    return cluster_profile_list


def sim_users_dic2list(cluster_dic, sim_users_max_size):
    """
    # 相似人群数量限制,dic->list
    :param sim_users_max_size: 相似人群的最大值
    :type cluster_dic: 字典表
    :param cluster_dic:相似人群字典表
    :return: 相似度最高的相似人群
    """
    user_similarity_list = sorted(cluster_dic.iteritems(), key=lambda b: b[1], reverse=True)
    sim_users_s = ""
    i = 0
    new_cluster_dic = {}
    for i in range(len(user_similarity_list)):
        if i < sim_users_max_size:
            user_similarity = user_similarity_list[i]
            key = user_similarity[0]
            value = user_similarity[1]
            new_cluster_dic[key] = value
            sim_users_s = sim_users_s + key + ":" + str(value) + ","
        else:
            break
        i = i + 1
    sim_users_list = sim_users_s[:-1].split(",")
    return sim_users_list, new_cluster_dic


class ClusterProfileComputer(object):
    cf = confUtils.getConfig("../conf/setting.conf")

    def __init__(self, environment):
        self.xw_database, self.xw_client = mongoUtils.getMongodb("XW")
        self.pac_database, self.pac_client = mongoUtils.getMongodb("PAC")
        self.om_database, self.pac_client = mongoUtils.getMongodb("OM")
        item = "LOCAL_SIM_USERS_PATH" if environment == "local" else "SIM_USERS_PATH"
        self.sim_users_path = confUtils.getFilePath(self.cf, "SIM_USERS", item)
        self.decay_factor = param_map.SIM_USERS_PARAM.get("decay_factor")
        self.sim_users_max_size = param_map.SIM_USERS_PARAM.get("sim_users_max_size")
        self.similarity_low = param_map.SIM_USERS_PARAM.get("similarity_low")
        self.similarity_high = param_map.SIM_USERS_PARAM.get("similarity_high")

    @staticmethod
    def basic_cursor2dic(platform, mongodb_cursor):
        """
        mongodb取出的基础画像存到字典表
        :param platform: 平台
        :param mongodb_cursor:
        :return:
        """
        users_profile_map = {}
        for user_profile in mongodb_cursor:
            _uid = user_profile["name"] if platform == "PAC" else user_profile["_id"]
            users_profile_map[_uid] = user_profile
        return users_profile_map

    @staticmethod
    def get_sim_users_profile(all_users_profile, users_similarity):
        """
        :param all_users_profile:
        :param users_similarity:
        :return:相似人群的画像
        """
        rs = []
        for uid_similarity in users_similarity:
            uid, similarity = split_uid_similarity(uid_similarity)
            profile = all_users_profile.get(uid)
            if profile is not None:
                rs.append(profile)
        return rs

    def dump_basic_profile(self, all_uid, batch_num, platform, profile_collection):
        # type: (list, int) -> dict
        """
        :return: 平台基础画像
        :param platform: 平台
        :return: 基础画像字典表
        :param profile_collection: 数据库集合
        :param all_uid:用户的编号列表
        :type batch_num: int
        """
        rs = {}
        # 数据库查询所有人群用户画像,此画像中没有相似人群
        for x in xrange(0, int(math.ceil(len(all_uid) / float(batch_num)))):
            key = "name" if platform == "PAC" else "_id"
            cursor = profile_collection.find({"$and": [{key: {'$in': all_uid[x * batch_num:(x + 1) * batch_num]}},
                                                       {"update_time": {"$gt": time_limit}}]}, no_cursor_timeout=True)
            rs.update(self.basic_cursor2dic(platform, cursor))
            cursor.close()
        return rs

    def compute_single_file(self, path, xw_profile_collection, pac_profile_collection, om_profile_collection):
        users = open(path)
        all_uid_list = []
        uid_sim_map = {}
        # uid_sim_map["1_291083852"] = ["1_757155427:8"]
        for user_str in users:
            # 从hdfs中取出udi的相似人群
            uid_hf, sim_users_hd = split_uid_sim_user(user_str)
            uid_sim_map[uid_hf] = sim_users_hd.split(",")
            all_uid_list.append(uid_hf)
        print("uid_sim_map : %d" % len(uid_sim_map))
        # 数据库查询所有用户的基础画像,此画像中没有相似人群
        platform_basic_profile_dic = {}

        xw_users_basic_profile_map = self.dump_basic_profile(all_uid_list, BATCH_NUM, "XW", xw_profile_collection)
        platform_basic_profile_dic["XW"] = xw_users_basic_profile_map

        pac_users_basic_profile_map = self.dump_basic_profile(all_uid_list, BATCH_NUM, "PAC", pac_profile_collection)
        platform_basic_profile_dic["PAC"] = pac_users_basic_profile_map

        om_users_basic_profile_map = self.dump_basic_profile(all_uid_list, BATCH_NUM, "OM", om_profile_collection)
        platform_basic_profile_dic["OM"] = om_users_basic_profile_map
        # print("dump basic profile %d records" % len(pac_all_users_profile_map))
        # 数据库查询相似人群画像
        cluster_profile_collection = self.xw_database.get_collection(
            param_map.MONGODB_CLUSTER_PROFILE_MAP["Cluster"])  # type: Collection
        old_cluster_profile_map = dump_cluster_profile_history(self, all_uid_list, cluster_profile_collection,
                                                               BATCH_NUM)
        print("dump cluster profile %d records" % len(old_cluster_profile_map))
        #index = 0
        for uid, sim_users_list in uid_sim_map.items():
            print ("uid = %s" % uid)
            # 合并新老相似人群,并使用衰减因子来计算相似度
            users_similarity_dic = merge_sim_users(uid, sim_users_list, self.decay_factor, self.similarity_low,
                                                   self.similarity_high, old_cluster_profile_map)
            # 相似人群---->将字典表转化为list,存储mongodb
            sim_users_list, users_similarity_dic = sim_users_dic2list(users_similarity_dic, self.sim_users_max_size)
            print("similar people len: %d" % len(sim_users_list))
            platform_cluster_profile_list = []
            for platform_name, platform_basic_profile in platform_basic_profile_dic.items():
                # 取出用户i相似人群的画像
                sim_users_profile_list = self.get_sim_users_profile(platform_basic_profile, sim_users_list)
                cluster_profile_dic = cluster_profile_compute(platform_name, sim_users_profile_list,
                                                              users_similarity_dic)
                # 结果区间映射,相似人群画像特征----->字典表转list,便于存储mongodb
                cluster_profile_list = cluster_profile_dic2list(cluster_profile_dic, param_map.DIMENSION_PARAM)
                platform_cluster_profile_list.append(cluster_profile_list)
               
            xw_cluster_profile = platform_cluster_profile_list[0]
            pac_cluster_profile = platform_cluster_profile_list[1]
            om_cluster_profile = platform_cluster_profile_list[2]

            old_profile = cluster_profile_collection.find_one({"_id": uid})
            if old_profile is None:
                create_time = int(time.time())
            else:
                create_time = old_profile["create_time"]
            document = ({"_id": uid, "sim_users": sim_users_list, "xw_cluster_profile": xw_cluster_profile,
                         "pac_cluster_profile": pac_cluster_profile, "om_cluster_profile": om_cluster_profile,
                         "create_time": create_time,
                         "update_time": int(time.time())})
            cluster_profile_collection.save(document)
            #if index >= 100:
            #    break
            #index = index + 1
        print("end")
        users.close()

    def run(self):
        # 相似人群HDFS
        xw_profile_collection = self.xw_database.get_collection(param_map.MONGODB_PROFILE_MAP["XW"])
        pac_profile_collection = self.pac_database.get_collection(param_map.MONGODB_PROFILE_MAP["PAC"])
        om_profile_collection = self.om_database.get_collection(param_map.MONGODB_PROFILE_MAP["OM"])
        for dir_path, dir_names, file_names in os.walk(self.sim_users_path):
            print(dir_names)
            for file_name in file_names:
                if "attempt_" in file_name:
                    print(file_name)
                    path = os.path.join(dir_path, file_name)
                    self.compute_single_file(path, xw_profile_collection, pac_profile_collection, om_profile_collection)


def accumulate_dimension_profile(cluster_dimension_feature, user_dimension, ratio):
    """
    将user指定维度的特征累加到群画像
    :param cluster_dimension_feature:群画像某个维度的特征
    :param user_dimension:用户某个维度的特征
    :param ratio:user的权重,公式为相似度/(相似度+10),区间为(1/3,10/11)
    :return:指定维度的群画像
    """
    if user_dimension != "":
        user_feature_list = user_dimension.split(",")
        for feature in user_feature_list:
            atom = feature.split(":")
            if len(atom) == 2:
                k = atom[0]
                w = atom[1]
                if cluster_dimension_feature.get(k) is None:
                    cluster_dimension_feature[k] = Decimal(w) * ratio
                else:
                    cluster_dimension_feature[k] = Decimal(w) * ratio + Decimal(cluster_dimension_feature[k])
    return cluster_dimension_feature


def dump_cluster_profile_history(self, all_uid, collection, batch_num):
    rs = {}
    for x in xrange(0, int(math.ceil(len(all_uid) / float(batch_num)))):
        cursor = collection.find({'_id': {'$in': all_uid[x * batch_num:(x + 1) * batch_num]}},
                                 no_cursor_timeout=True)
        rs.update(cluster_cursor2dic(cursor))
        cursor.close()
    return rs


def cluster_cursor2dic(mongodb_cursor):
    """
    mongodb取出的人群画像存到字典表
    :param mongodb_cursor:
    :return:
    """
    users_profile_map = {}
    for user_profile in mongodb_cursor:
        _uid = user_profile["_id"]
        users_profile_map[_uid] = user_profile
    return users_profile_map


def merge_sim_users(uid_hdf, sim_users_new, decay_factor, similarity_low, similarity_high, old_cluster_profile_dic):
    """
    合并相似人群
    :param similarity_low: 相似度最低值
    :param similarity_high: 相似度最高值
    :param uid_hdf: 用户编号
    :param sim_users_new: 最新的相似用户
    :param decay_factor: 衰减因子
    :param old_cluster_profile_dic:老群体画像
    :return:最新的相似人群
    """
    cluster_union_dic = {}
    # 提取uid和相似度到字典表
    for user_similarity in sim_users_new:
        _uid, similarity = split_uid_similarity(user_similarity)
        cluster_union_dic[_uid] = similarity

    # 从mongodb中读取老画像
    old = old_cluster_profile_dic.get(uid_hdf)
    if old is not None:
        sim_users_old = old['sim_users']
        for uid_similarity_old in sim_users_old:
            uid_similarity_old_list = uid_similarity_old.split(":")
            if len(uid_similarity_old_list) == 2:
                sim_uid_old = uid_similarity_old_list[0]
                try:
                    weight_old = float(uid_similarity_old_list[1]) * float(decay_factor)
                except IndexError:
                    pass
                else:
                    if (cluster_union_dic.get(sim_uid_old) is None) and (weight_old >= similarity_low):
                        cluster_union_dic[sim_uid_old] = weight_old
                    else:
                        weight_new = weight_old + cluster_union_dic[sim_uid_old]
                        if weight_new > similarity_high:
                            weight_new = similarity_high
                        if weight_new < similarity_low:
                            del cluster_union_dic[sim_uid_old]
                        else:
                            cluster_union_dic[sim_uid_old] = weight_new
    return cluster_union_dic


def cluster_profile_compute(platform, sim_users_profile_array, sim_users_dic):
    # type: (String, list, dic) -> dic
    """
    相似人群特征计算
    :param platform:平台
    :param sim_users_profile_array: 从mongodb中查出来的相似人群的画像
    :param sim_users_dic: 相似人群的相似度字典表
    :return: 相似人群画像字典表
    """
    cluster_profile_rs = {}
    for sim_user_obj in sim_users_profile_array:
        key = "name" if platform == "PAC" else "_id"
        sim_user_id = sim_user_obj.get(key)
        # 获取两两用户的相似度
        similarity = sim_users_dic.get(sim_user_id)
        if similarity is not None:
            sim_num = Decimal(similarity)
            # 用户对应的权重
            rate = Decimal(sim_num / (10 + sim_num))
            # 取出某一个人的画像
            profile = sim_user_obj.get("profile") if sim_user_obj.get("profile") is not None else ""
            dimension_list = profile.split(";")
            i = 0
            for u_dimension in dimension_list:
                # 获取群体维度i的特征
                dimension_feature = cluster_profile_rs.get(i)
                if dimension_feature is None:
                    dimension_feature = {}
                # 更新维度i的特征
                cluster_profile_rs[i] = accumulate_dimension_profile(dimension_feature, u_dimension, rate)
                i = i + 1
    return cluster_profile_rs


if __name__ == "__main__":
    if len(sys.argv) == 2:
        env = sys.argv[1]
    else:
        env = "local"
    computer = ClusterProfileComputer(env)
    computer.run()

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