import os
import pandas as pd

predict_data_dir = './data/predict_data'
user_filename = 'user_feature.csv'
video_filename = 'video_feature.csv'


def read_csv_data(filepath):
    if os.path.exists(filepath):
        data = pd.read_csv(filepath, sep=',', engine='python', iterator=True)
        chunk_size = 1000000
        chunks = []
        loop = True
        while loop:
            try:
                chunk_data = data.get_chunk(chunk_size)
                chunks.append(chunk_data)
            except StopIteration:
                loop = False
        df = pd.concat(chunks, ignore_index=True)
        return df
    else:
        print("Don't have this file!")
        return None


if __name__ == '__main__':
    # 1. 获取用户特征数据
    user_filepath = f"{predict_data_dir}/{user_filename}"
    user_df = read_csv_data(filepath=user_filepath)
    # 2. 获取视频特征数据
    video_filepath = f"{predict_data_dir}/{video_filename}"
    video_df = read_csv_data(filepath=video_filepath)
    # 3. 用户特征和视频特征进行拼接
    video_features = [
        'videoid',
        'video_preview_count_uv_30day',
        'video_preview_count_pv_30day',
        'video_view_count_uv_30day',
        'video_view_count_pv_30day',
        'video_play_count_uv_30day',
        'video_play_count_pv_30day',
        'video_share_count_uv_30day',
        'video_share_count_pv_30day',
        'video_return_count_30day',
        'video_ctr_uv_30day',
        'video_ctr_pv_30day',
        'video_share_rate_uv_30day',
        'video_share_rate_pv_30day',
        'video_return_rate_30day',
    ]
    merge_df_list = []
    for ind, row in video_df.iterrows():
        merge_df_temp = user_df.copy()
        for feature in video_features:
            merge_df_temp[feature] = row[feature]
        merge_df_list.append(merge_df_temp)
    merge_df = pd.concat(merge_df_list, ignore_index=True)
    # 4. 拼接广告特征ad_status
    for ad_status in [0, 1]:
        res_df = merge_df.copy()
        res_df['ad_status'] = ad_status
        # 写入csv
        predict_data_dir = './data/predict_data'
        if not os.path.exists(predict_data_dir):
            os.makedirs(predict_data_dir)
        res_df.to_csv(f"{predict_data_dir}/predict_data_{ad_status}.csv", index=False)