rov-offline/ad_feature_process.py

import pandas as pd
from utils import get_data_from_odps

features = [
    'apptype',
    'subsessionid',
    'mid',
    'videoid',
    'ad_mid',
    'share_videoid'
    'mid_preview_count_30day',
    'mid_view_count_30day',
    'mid_view_count_pv_30day',
    'mid_play_count_30day',
    'mid_play_count_pv_30day',
    'mid_share_count_30day',
    'mid_share_count_pv_30day',
    'mid_return_count_30day',
    'mid_share_rate_30day',
    'mid_return_rate_30day',
    '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',
]

train_feature = [
    'mid_preview_count_30day',
    'mid_view_count_30day',
    'mid_view_count_pv_30day',
    'mid_play_count_30day',
    'mid_play_count_pv_30day',
    'mid_share_count_30day',
    'mid_share_count_pv_30day',
    'mid_return_count_30day',
    'mid_share_rate_30day',
    'mid_return_rate_30day',
    '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',
    'ad_status',
    'share_status'
]


def get_feature_data(project, table, features, dt):
    """获取特征数据"""
    records = get_data_from_odps(date=dt, project=project, table=table)
    feature_data = []
    for record in records:
        item = {}
        for feature_name in features:
            item[feature_name] = record[feature_name]
        feature_data.append(item)
    feature_df = pd.DataFrame(feature_data)
    return feature_df


def daily_data_process(project, table, features, dt, app_type):
    """每日特征处理"""
    feature_initial_df = get_feature_data(project=project, table=table, features=features, dt=dt)
    feature_initial_df['apptype'] = feature_initial_df['apptype'].astype(int)
    feature_df = feature_initial_df[feature_initial_df['apptype'] == app_type]
    # 增加此次是否有广告字段 'ad_status' 1: 有广告, 0: 无广告
    feature_df['ad_status'] = feature_df.apply(func=lambda x: 1 if x['ad_mid'] == x['mid'] else 0)
    feature_df['share_videoid'].fillna(0, inplace=True)
    feature_df['share_videoid'] = feature_df['share_videoid'].astype(int)
    feature_df['videoid'] = feature_df['videoid'].astype(int)
    # 增加此次是否分享了该视频 'share_status' 1: 分享, 0: 为分享
    feature_df['share_status'] = feature_df.apply(func=lambda x: 1 if x['share_videoid'] == x['videoid'] else 0)
    # 缺失值填充
    feature_df.fillna(0, inplace=True)
    # 数据类型校正
    type_int_columns = [
        'mid_preview_count_30day',
        'mid_view_count_30day',
        'mid_view_count_pv_30day',
        'mid_play_count_30day',
        'mid_play_count_pv_30day',
        'mid_share_count_30day',
        'mid_share_count_pv_30day',
        'mid_return_count_30day',
        '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',
    ]
    for column_name in type_int_columns:
        feature_df[column_name].astype(int)
    type_float_columns = [
        'mid_share_rate_30day',
        'mid_return_rate_30day',
        'video_ctr_uv_30day',
        'video_ctr_pv_30day',
        'video_share_rate_uv_30day',
        'video_share_rate_pv_30day',
        'video_return_rate_30day',
    ]
    for column_name in type_float_columns:
        feature_df[column_name].astype(float)
    # 获取所需的字段
    train_df = feature_df[train_feature]
    return train_df