import time
import datetime
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from threading import Timer
from utils import RedisHelper, data_check
from config import set_config
from ad_feature_process import get_feature_data
redis_helper = RedisHelper()
config_, _ = set_config()
xgb_config = config_.AD_MODEL_ABTEST_CONFIG['xgb']

features = [
    'apptype',
    'subsessionid',
    'mid',
    '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',
]

# 模型加载
model = XGBClassifier()
booster = xgb.Booster()
booster.load_model('./data/ad_xgb.model')
model._Booster = booster


def threshold_update(project, table, dt, app_type):
    # 使用前一天的数据进行预测，给定阈值
    # 1. 获取特征
    feature_initial_df = get_feature_data(project=project, table=table, features=features, dt=dt, app_type=app_type)
    # 缺失值填充
    feature_initial_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_initial_df[column_name] = feature_initial_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_initial_df[column_name] = feature_initial_df[column_name].astype(float)
    print(f"feature_initial_df shape: {feature_initial_df.shape}")
    # 获取所需的字段
    predict_df = feature_initial_df[features[4:]]
    print(f"predict_df shape: {predict_df.shape}")

    # 2. 不出广告情况下的预测
    predict_df_0 = predict_df.copy()
    predict_df_0['ad_status'] = 0
    y_pred_proba_0 = model.predict_proba(predict_df_0)

    # 3. 出广告情况下的预测
    predict_df_1 = predict_df.copy()
    predict_df_1['ad_status'] = 1
    y_pred_proba_1 = model.predict_proba(predict_df_1)

    predict_df['y_0'] = [x[1] for x in y_pred_proba_0]
    print(f"predict_df shape: {predict_df.shape}")
    predict_df['y_1'] = [x[1] for x in y_pred_proba_1]
    print(f"predict_df shape: {predict_df.shape}")

    # 4. 做差值
    predict_df['res_predict'] = predict_df['y_0'] - predict_df['y_1']
    print(f"predict_df shape: {predict_df.shape}")

    # 5. 计算阈值
    # 获取对应实验id
    abtest_id_mapping = xgb_config['abtest_id_mapping']
    abtest_id = abtest_id_mapping[app_type]
    # 获取阈值参数记录
    threshold_record = redis_helper.get_data_from_redis(key_name=xgb_config['threshold_record'])
    threshold_record = eval(threshold_record)
    record = threshold_record[abtest_id]
    # 分实验组进行阈值计算
    predict_mean = predict_df['res_predict'].mean()
    for ab_code, param in record.items():
        threshold = predict_mean * param
        print(f"{abtest_id}-{ab_code}: {threshold}")
        # 写入redis
        threshold_key = f"{xgb_config['threshold_key_prefix']}{abtest_id}:{ab_code}"
        redis_helper.set_data_to_redis(key_name=threshold_key, value=threshold, expire_time=48 * 3600)
    redis_helper.set_data_to_redis(key_name=xgb_config['threshold_record'],
                                   value=str(threshold_record),
                                   expire_time=2 * 24 * 3600)
    print("update threshold finished!")


def timer_check():
    project = 'loghubods'
    table = 'admodel_data_train'
    now_date = datetime.datetime.today()
    dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d')
    # 查看当前更新的数据是否已准备好
    data_count = data_check(project=project, table=table, dt=dt)
    if data_count > 0:
        print(f"data count = {data_count}")
        # 数据准备好，进行更新
        for app_type, _ in xgb_config['abtest_id_mapping'].items():
            print(f"app_type: {app_type} threshold update start...")
            threshold_update(project=project, table=table, dt=dt, app_type=app_type)
            print(f"app_type: {app_type} threshold update finished!")
        print(f"threshold update end!")
    else:
        # 数据没准备好，1分钟后重新检查
        Timer(60, timer_check).start()


if __name__ == '__main__':
    st_time = time.time()
    timer_check()
    print(f"execute time: {time.time() - st_time}s")