"""
process the data to satisfy the lightgbm
"""
import sys
import os
import json
from tqdm import tqdm

sys.path.append(os.getcwd())

from functions import generate_label_date


def generate_train_label(item, y_ori_data, cate):
    """
    生成训练数据，用 np.array矩阵的方式返回，
    :return: x_train, 训练数据， y_train, 训练 label
    """
    video_id = item['video_id']
    dt = item['dt']
    userful_features = [
        "uid",
        "type",
        "channel",
        "fans",
        "view_count_user_30days",
        "share_count_user_30days",
        "return_count_user_30days",
        "rov_user",
        "str_user",
        "out_user_id",
        "mode",
        "out_play_cnt",
        "out_like_cnt",
        "out_share_cnt",
        "out_collection_cnt"
    ]
    item_features = [item[i] for i in userful_features]
    label_dt = generate_label_date(dt)
    label_obj = y_ori_data.get(label_dt, {}).get(video_id)
    if label_obj:
        label = int(label_obj[cate]) if label_obj[cate] else 0
    else:
        label = 0
    return label, item_features


if __name__ == '__main__':
    x_path = 'data/hour_train.json'
    y_path = 'data/daily-label-20240101-20240320.json'

    with open(x_path) as f:
        x_data = json.loads(f.read())

    with open(y_path) as f:
        y_data = json.loads(f.read())
    cate_list = ['total_return', '3day_up_level', 'total_view', 'total_share']
    for c in cate_list:
        x_list = []
        y_list = []
        for video_obj in tqdm(x_data):
            print(video_obj)
            our_label, features = generate_train_label(video_obj, y_data, c)
            x_list.append(features)
            y_list.append(our_label)
        # print(len(y_list))
        with open("whole_data/x_data_{}.json".format(c), "w") as f1:
            f1.write(json.dumps(x_list, ensure_ascii=False))

        with open("whole_data/y_data_{}.json".format(c), "w") as f2:
            f2.write(json.dumps(y_list, ensure_ascii=False))