腾讯广告算法大赛19_Top5方案&代码

转载自https://mp.weixin.qq.com/s/j5YICHrkHLDm7OldPFPOjw

本篇文章分享2019年腾讯广告算法大赛的方法，文末附带本次比赛简短高效的代码，代码的解析。

赛题理解

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特征工程

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模型介绍

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思考总结

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# -*- coding: utf-8 -*-

import pandas as pd

import numpy as np

import lightgbm as lgb

from sklearn.model_selection import KFold

path = './data/'



def get_base_data():

    ad_static = pd.read_csv(path + '/map_ad_static.out',

                            names=['aid', 'create_time', 'adv_id', 'product_id', 'ad_cate_id',

                                   'industry_id', 'creative_size'], sep='\t')

    ad_static['create_time_int'] = ad_static['create_time']

    ad_static['create_time'] = pd.to_datetime(ad_static['create_time'] + 8 * 3600, unit='s')

    cond_req = pd.read_csv(path + '//Btest_select_request_20190424.out',

                           names=['aid', 'req_set'], sep='\t')

    cond_req['req_set_len'] = cond_req['req_set'].apply(lambda x: len(x.split('|')))

    test = pd.read_csv(path + '/Btest_sample_bid.out',

                       names=['sample_id', 'aid', 'target_type', 'bid_type', 'bid'], sep='\t')

    test['day'] = 24

    test['aid_day_count'] = test['aid'].map(cond_req[['aid', 'req_set_len']].set_index('aid')['req_set_len'])

    return ad_static, test

ad_static, test = get_base_data()

def get_train_data():

    aid_day_label_stat = {}

    aid_day_count_stat = {}

    for day in range(10, 23):

        temp_track_log = pd.read_csv(path + '/track_log/track_log_201904' + str(day) + '.out', names=[

            'req_id', 'req_time', 'uid', 'loc_id', 'comp_info'], sep='\t')

        for comp_info in temp_track_log['comp_info']:

            for ad in comp_info.split(';'):

                ad = ad.split(',')

                aid_day = int(ad[0]) * 100 + day

                try:

                    aid_day_count_stat[aid_day] = aid_day_count_stat[aid_day] + 1

                except:

                    aid_day_count_stat[aid_day] = 1

                if ad[6] == '1':

                    try:

                        aid_day_label_stat[aid_day] = aid_day_label_stat[aid_day] + 1

                    except:

                        aid_day_label_stat[aid_day] = 1

    aid_day_label = pd.DataFrame(

        {'aid_day': list(aid_day_label_stat.keys()), 'label': list(aid_day_label_stat.values())})

    aid_day_count = pd.DataFrame(

        {'aid_day': list(aid_day_count_stat.keys()), 'aid_day_count': list(aid_day_count_stat.values())})



    train = aid_day_count.merge(aid_day_label, 'left', 'aid_day').fillna(0)

    train['aid'] = train['aid_day'] // 100

    train['day'] = train['aid_day'] % 100

    train = train[(train['aid'].isin(test.aid.unique()) | (train['aid_day_count'] > 100))]

    return train, aid_day_label

train, aid_day_label = get_train_data()



def get_test_expos():

    track_test_a = pd.read_csv(path + '/test_tracklog_20190423.last.out', names=[

        'req_id', 'req_time', 'uid', 'loc_id', 'comp_info'], sep='\t')

    aid_expos = []

    aid_not_expos = []

    for i in track_test_a['comp_info']:

        tmp_expos = False

        for ad in i.split(';'):

            ad = ad.split(',')

            if ad[-1] == '0' and tmp_expos is False:

                tmp_expos = True

                aid_expos.append(int(ad[0]))

            else:

                aid_not_expos.append(int(ad[0]))

    aid_not_expos_df = pd.Series(aid_not_expos).value_counts()

    aid_not_expos_df = aid_not_expos_df.reset_index().rename(columns={'index': 'aid', 0: 'not_count'})

    aid_expos_df = pd.Series(aid_expos).value_counts()

    aid_expos_df = aid_expos_df.reset_index().rename(columns={'index': 'aid', 0: 'label'})

    test_expos = aid_expos_df.merge(aid_not_expos_df, 'outer', 'aid')

    test_expos = test_expos.fillna(0)

    test_expos['aid_day_count'] = test_expos['label'] + test_expos['not_count']

    test_expos['day'] = 23

    test_expos = test_expos[test_expos['aid_day_count'] > 50]



    test_23 = pd.read_csv(path + '/final_select_test_request.out', names=['aid', 'req_set'], sep='\t')

    test_23['day'] = 23

    test_23['aid_day_count'] = test_23['req_set'].apply(lambda x: len(x.split('|')))

    return test_expos, test_23

test_expos, test_23 = get_test_expos()



data = pd.concat([train, test_expos, test_23, test], ignore_index=True)

data = data.merge(ad_static, 'left', 'aid')

data['olabel'] = data['label']

data['oaid_day_count'] = data['aid_day_count']

data['not_count'] = data['aid_day_count'] - data['olabel']

data['aid_day'] = data['aid'] * 100 + data['day']

data['label'] = np.log1p(data['label'])

data['rate_label'] = data['olabel'] / data['aid_day_count']

data['sample_id'] = data['sample_id'].fillna(0).astype(int)

data['week'] = (data['day'] - 1) % 7

data['create_year'] = data['create_time'].dt.year

data['day_keep'] = data['day'] - data['create_time'].dt.month * 31 - data['create_time'].dt.day



ad_static_fea = ['adv_id', 'product_id', 'ad_cate_id', 'industry_id', 'creative_size']

cnt_feat = ['cnt_static_' + i for i in ad_static_fea + ['create_time']]

for i in ad_static_fea + ['create_time']:

    data['cnt_static_' + i] = data[i].map(ad_static[i].value_counts())



data['day_unique'] = data.groupby('aid')['day'].transform('unique')

day_nunique = []

for x in data[['day', 'day_unique']].values:

    x_1 = x[0] - 1

    day_nunique.append(len([i for i in x[1] if i < x_1 and i > x_1 - 7]))

data['day_nunique'] = day_nunique

aid_day_label['day'] = aid_day_label['aid_day'] % 100

data['day_count'] = data['day'].map(aid_day_label.groupby('day')['label'].sum())

data_23 = data[data.day == 23].set_index('aid')

data['rate_label_23'] = data['aid'].map(data_23['rate_label'])

data['label_23'] = data['aid'].map(data_23['label'])

data['aid_day_count_23'] = data['aid'].map(data_23['aid_day_count'])

data['aid_day_1'] = data['aid_day'] + 1

data['aid_day_count_1'] = data['aid_day'].map(data[data.day < 24].set_index('aid_day_1')['aid_day_count'])

data['pred_23'] = data['rate_label_23'] * data['aid_day_count']

data['n_label'] = data['label'] / data['day_count']

data['n_rate_label'] = data['rate_label'] / data.groupby('day')['rate_label'].transform('mean')



shift_feat = []

data['aid_day'] = data['aid'] * 100 + data['day']

for i in range(14):

    i = i + 1

    shift_feat.append('aid_day_label_' + str(i))

    shift_feat.append('day_label_' + str(i))

    shift_feat.append('day_not_' + str(i))

    shift_feat.append('rate_label_' + str(i))

    data['aid_day_' + str(i)] = data['aid_day'] + i

    data_last = data[~data.label.isnull()].set_index('aid_day_' + str(i))

    data['day_label_' + str(i)] = data['aid_day'].map(data_last['n_label'])

    data['rate_label_' + str(i)] = data['aid_day'].map(data_last['n_rate_label'])

    data['day_not_' + str(i)] = data['aid_day'].map(data_last['not_count'])

    data['aid_day_label_' + str(i)] = data['aid_day_count'] * data['aid_day'].map(data_last['rate_label'])



data['adv_id_day'] = data['adv_id'] * 100 + data['day']

data['adv_id_day_count_sum'] = data['adv_id_day'].map(

    data.drop_duplicates(['aid_day']).groupby('adv_id_day')['aid_day_count'].sum())

data['adv_id_day_count'] = data['adv_id_day'].map(

    data.drop_duplicates(['aid_day']).groupby('adv_id_day')['aid_day_count'].mean())



adv_shift_feat = []

for i in range(1, 10):

    i = i + 1

    adv_shift_feat.append('adv_id_day_label_' + str(i))

    data['adv_id_day_' + str(i)] = data['adv_id_day'] + i

    data['adv_id_day_label_' + str(i)] = data['adv_id_day_count'] * data['adv_id_day'].map(

        data[~data.label.isnull()].groupby('adv_id_day_' + str(i))['rate_label'].mean())



data = data.sort_values(['sample_id', 'day']).reset_index(drop=True)

cate_feature = ['week', 'create_year'] + ad_static_fea

num_feature = shift_feat + adv_shift_feat + ['day_keep'] + [

    'day_nunique', 'aid_day_count', 'rate_label_23', 'pred_23', 'label_23', 'aid_day_count_1',

    'adv_id_day_count_sum', 'adv_id_day_count', 'aid_day_count_23',

] + cnt_feat

features = cate_feature + num_feature

lgb_model = lgb.LGBMRegressor(

    num_leaves=150, reg_alpha=0., reg_lambda=0.01, objective='mae', metric='mae',

    max_depth=-1, learning_rate=0.05, min_child_samples=100, n_jobs=-1,

    n_estimators=1000, subsample=0.7, colsample_bytree=0.8, subsample_freq=1, random_state=2019

)

data['rule_pred'] = data[['aid_day_label_2', 'aid_day_label_3', 'aid_day_label_4']].mean(axis=1).fillna(0)

data['loss'] = data['label'] - np.log1p(data['rule_pred'])

lgb_model = lgb_model.fit(data[(data.day < 23)][features], data[(data.day < 23)]['loss'])

sub_control = data[data.sample_id > 0][['sample_id', 'rule_pred', 'aid', 'bid']].reset_index(drop=True)

sub_control['pred'] = lgb_model.predict(data[data.sample_id > 0][features])

sub_control['pred'] = np.expm1(sub_control['pred'] + np.log1p(sub_control['rule_pred']))



def get_predict_w(model, data, label='label', feature=[], random_state=2018, n_splits=5):

    model.random_state = random_state

    predict_label = 'predict_' + label

    kfold = KFold(n_splits=n_splits, shuffle=True, random_state=random_state)

    data[predict_label] = 0

    test_index = (data[label].isnull()) | (data[label] == -1)

    train_data = data[~test_index].reset_index(drop=True)

    test_data = data[test_index]



    for train_idx, val_idx in kfold.split(train_data):

        model.random_state = model.random_state + 1

        train_x = train_data.loc[train_idx][feature]

        train_y = train_data.loc[train_idx][label].values

        test_x = train_data.loc[val_idx][feature]

        test_y = train_data.loc[val_idx][label].values

        model.fit(train_x, train_y, eval_set=[(test_x, test_y)], verbose=None)

        train_data.loc[val_idx, predict_label] = model.predict(test_x)

        if len(test_data) != 0:

            test_data[predict_label] = test_data[predict_label] + model.predict(test_data[feature])

    test_data[predict_label] = test_data[predict_label] / n_splits

    return pd.concat([train_data, test_data], ignore_index=True), predict_label



lgb_model.n_estimators = 500

data.loc[data.day == 24, 'aid_day_count'] = data[data.day == 24]['oaid_day_count'] / 1.9

data, pred_label = get_predict_w(lgb_model, data, 'label', [i for i in features if i not in shift_feat[:4]],random_state=2018, n_splits=5)



sub_norm = data[(data.day == 24)][['sample_id', 'aid', 'bid', pred_label]].reset_index(drop=True)

sub_norm['pred'] = sub_norm[pred_label]

sub_norm['pred'] = np.expm1(sub_norm['pred']) * 1.3

sub_merge = sub_norm.copy()

sub_merge['rank_num'] = sub_merge.groupby('aid')['bid'].rank()

sub_merge['pred'] = sub_norm['pred'] * 0.4 + sub_control['pred'] * 0.6

print(sub_merge['pred'].min())

sub_merge.loc[sub_merge['pred'] < 0, 'pred'] = 0

sub_merge['pred'] = sub_merge['pred'].round() + sub_merge['rank_num'] * 0.0001

sub_merge['pred'] = sub_merge['pred'].round(4)

sub_merge[['sample_id', 'pred']].to_csv('submission.csv', index=False, header=None)

PS: 关于w2v：
这个方案在初赛的时候是一个很强的操作，但是在复赛阶段由于大量0样本的泄露问题，调整采样，以及增加随机数带来的误差过大，导致并不适用。故代码未加入进来。（在PPT不足与反思处也已经注明。)

代码性能解析

计算速度部分，即算法运行时间，我的代码的前100行，分别为逐个读入每个文件，并单纯的仅仅统计label 以及队列长度。由于数据量较大，将花费约7min左右的时间载入内存、统计的过程也是大约7min。合计14min 左右。

接下来的100 – 170行为特征提取部分，由于我特征较少，且未提取原始日志的特征，故，约会在30s左右的时间运行完毕。
170行至222行结束，就是紧张刺激的模型训练预测环节，包含两个模型，模型1在10核机器上约30s。模型2为5折，约2min。
共计约20min （大家可以自行运行检验）

计算资源部分，这里略有遗憾，由于提取标签的部分未采用流式读取，故内存峰值存在于读取单日日志到内存的时候。最大内存峰值点，为单个日志文件读入的最大内存消耗。（我可没把全量日志一起读入）约4G内存即可。

代码解析：
1、 定义函数get_base_data():这里读取并解析广告静态文件，以及测试集
2、 定义函数get_train_data():这里是分日读取日志并提取每个广告当日的统计，作为标签。从而获得训练集。
3、 定义函数get_test_expos():这里是获取23号测试集的曝光数据。
4、 接下来就是连续的特征提取
5、 接下来是定义lgb模型、标签转换以及5折训练预测。