腾讯2018广告算法大赛思路解析 100行代码带你上0.73
用不了多少内存，稀疏存储的40w特征，占10多g内存，大概3个小时左右能跑完
首先处理4个G的用户特征
拼接用户特征，广告特征
将单取值的离散特征使用稀疏方式one-hot
将多取值的离散特征使用稀疏方式向量化
线下测试
线上提交
# coding=utf-8
# @author:bryan
import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import OneHotEncoder,LabelEncoder
from scipy import sparse
import os
ad_feature=pd.read_csv('../data/adFeature.csv')
if os.path.exists('../data/userFeature.csv'):
user_feature=pd.read_csv('../data/userFeature.csv')
else:
userFeature_data = []
with open('../data/userFeature.data', 'r') as f:
for i, line in enumerate(f):
line = line.strip().split('|')
userFeature_dict = {}
for each in line:
each_list = each.split(' ')
userFeature_dict[each_list[0]] = ' '.join(each_list[1:])
userFeature_data.append(userFeature_dict)
if i % 100000 == 0:
print(i)
user_feature = pd.DataFrame(userFeature_data)
user_feature.to_csv('../data/userFeature.csv', index=False)
train=pd.read_csv('../data/train.csv')
predict=pd.read_csv('../data/test1.csv')
train.loc[train['label']==-1,'label']=0
predict['label']=-1
data=pd.concat([train,predict])
data=pd.merge(data,ad_feature,on='aid',how='left')
data=pd.merge(data,user_feature,on='uid',how='left')
data=data.fillna('-1')
one_hot_feature=['LBS','age','carrier','consumptionAbility','education','gender','house','os','ct','marriageStatus','advertiserId','campaignId', 'creativeId',
'adCategoryId', 'productId', 'productType']
vector_feature=['appIdAction','appIdInstall','interest1','interest2','interest3','interest4','interest5','kw1','kw2','kw3','topic1','topic2','topic3']
for feature in one_hot_feature:
try:
data[feature] = LabelEncoder().fit_transform(data[feature].apply(int))
except:
data[feature] = LabelEncoder().fit_transform(data[feature])
train=data[data.label!=-1]
train_y=train.pop('label')
# train, test, train_y, test_y = train_test_split(train,train_y,test_size=0.2, random_state=2018)
test=data[data.label==-1]
res=test[['aid','uid']]
test=test.drop('label',axis=1)
enc = OneHotEncoder()
train_x=train[['creativeSize']]
test_x=test[['creativeSize']]
for feature in one_hot_feature:
enc.fit(data[feature].values.reshape(-1, 1))
train_a=enc.transform(train[feature].values.reshape(-1, 1))
test_a = enc.transform(test[feature].values.reshape(-1, 1))
train_x= sparse.hstack((train_x, train_a))
test_x = sparse.hstack((test_x, test_a))
print('one-hot prepared !')
cv=CountVectorizer()
for feature in vector_feature:
cv.fit(data[feature])
train_a = cv.transform(train[feature])
test_a = cv.transform(test[feature])
train_x = sparse.hstack((train_x, train_a))
test_x = sparse.hstack((test_x, test_a))
print('cv prepared !')
def LGB_test(train_x,train_y,test_x,test_y):
from multiprocessing import cpu_count
print("LGB test")
clf = lgb.LGBMClassifier(
boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,
max_depth=-1, n_estimators=1000, objective='binary',
subsample=0.7, colsample_bytree=0.7, subsample_freq=1,
learning_rate=0.05, min_child_weight=50,random_state=2018,n_jobs=cpu_count()-1
)
clf.fit(train_x, train_y,eval_set=[(train_x, train_y),(test_x,test_y)],eval_metric='auc',early_stopping_rounds=100)
# print(clf.feature_importances_)
return clf,clf.best_score_[ 'valid_1']['auc']
def LGB_predict(train_x,train_y,test_x,res):
print("LGB test")
clf = lgb.LGBMClassifier(
boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,
max_depth=-1, n_estimators=1500, objective='binary',
subsample=0.7, colsample_bytree=0.7, subsample_freq=1,
learning_rate=0.05, min_child_weight=50, random_state=2018, n_jobs=100
)
clf.fit(train_x, train_y, eval_set=[(train_x, train_y)], eval_metric='auc',early_stopping_rounds=100)
res['score'] = clf.predict_proba(test_x)[:,1]
res['score'] = res['score'].apply(lambda x: float('%.6f' % x))
res.to_csv('../data/submission.csv', index=False)
os.system('zip baseline.zip ../data/submission.csv')
return clf
model=LGB_predict(train_x,train_y,test_x,res)
RP调参，可上0.75
https://github.com/jiaqiangbandongg/RPTune
