title_with_video/applications/functions/calculate.py

"""
@author: luojunhui
"""
import json
import os

from applications.log import logging
from applications.functions.date import generate_daily_strings, five_days_before


def read_single_file(filename):
    """
    :param filename:
    """
    with open(filename, encoding="utf-8") as f:
        data = json.loads(f.read())
    if data:
        return data
    else:
        return {}


def compute_similarity(file_1, file_2):
    """
    计算
    :param file_1:
    :param file_2:
    :return:
    """
    data_1 = read_single_file(file_1)
    data_2 = read_single_file(file_2)

    def calculate_v1(d1, d2):
        """
        通过交并集来判断
        :param d1:
        :param d2:
        :return:
        """
        f1_keys = set(d1["key_words"])
        f2_keys = set(d2["key_words"])
        keys_union = f1_keys | f2_keys
        keys_intersection = f1_keys & f2_keys
        f1_search_keys = set(d1["search_keys"])
        f2_search_keys = set(d2["search_keys"])
        search_keys_union = f1_search_keys | f2_search_keys
        search_keys_intersection = f1_search_keys & f2_search_keys
        f1_extra_keys = set(d1["extra_keys"])
        f2_extra_keys = set(d2["extra_keys"])
        extra_keys_union = f1_extra_keys | f2_extra_keys
        extra_keys_intersection = f1_extra_keys & f2_extra_keys
        score_1 = len(keys_intersection) / len(keys_union)
        score_2 = len(search_keys_intersection) / len(search_keys_union)
        score_3 = len(extra_keys_intersection) / len(extra_keys_union)
        return score_1 * 0.4 + score_2 * 0.4 + score_3 * 0.2

    def calculate_v2(d1, d2):
        """
        计算方法 v2
        :param d1:
        :param d2:
        :return:
        """
        score = 0
        tone_1 = d1["tone"]
        tone_2 = d2["tone"]
        if tone_1 == tone_2:
            score += 0.1
        target_audience_1 = d1["target_audience"]
        target_audience_2 = d2["target_audience"]
        if target_audience_1 == target_audience_2:
            score += 0.2
        target_age_1 = d1["target_age"]
        target_age_2 = d2["target_age"]
        if target_age_1 == target_age_2:
            score += 0.2
        address_1 = d1["address"]
        address_2 = d2["address"]
        if address_1 == address_2:
            score += 0.2
        gender_1 = d1["theme"]
        gender_2 = d2["theme"]
        if gender_1 == gender_2:
            score += 0.5
        return score

    if data_1 and data_2:
        try:
            score_1 = calculate_v1(data_1, data_2)
            return score_1
            # score_2 = calculate_v2(data_1, data_2)
            # return score_1, score_2
        except Exception as e:
            return 0
    else:
        return 0


def title_mix(title_p, dt, trace_id):
    """
    执行代码
    :param trace_id: 请求唯一 id
    :param title_p:
    :param dt: dt
    """
    five_days_ago = five_days_before(ori_dt=dt)
    days_list = generate_daily_strings(five_days_ago, dt)
    L = []
    for day_str in days_list:
        json_path = os.path.join(os.getcwd(), 'applications', 'static', day_str)
        # 处理标题信息
        files = os.listdir(json_path)
        for file in files:
            if file.endswith(".json"):
                L.append(os.path.join(json_path, file))
    print("召回的视频量", len(L))
    score_list_1 = []
    # score_list_2 = []
    for file in L:
        file_name = file.split('/')[-1].replace(".json", "")
        v_id = file_name.split('_')[1]
        uid = file_name.split('_')[0]
        # score1, score2 = compute_similarity(title_p, file)
        score1 = compute_similarity(title_p, file)
        score_list_1.append([score1, v_id, uid])
        # score_list_2.append([score2, v_id, uid])

    s1_list = sorted(score_list_1, key=lambda x: x[0], reverse=True)
    # s2_list = sorted(score_list_2, key=lambda x: x[0], reverse=True)
    title = title_p.split("/")[-1].replace(".json", "")
    obj = {
        "title": title,
        "s1_vid": s1_list[0][1],
        "s1_score": s1_list[0][0],
        "s1_uid": s1_list[0][2],
        # "s2_vid": s2_list[0][1],
        # "s2_score": s2_list[0][0],
        # "s2_uid": s2_list[0][2]
    }
    logging(
        code="1003",
        info="计算结果得分",
        data=obj,
        function="title_mix",
        trace_id=trace_id
    )
    return obj