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lgb_whole_after.py
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# -*- coding: utf-8 -*-
"""
@author: Gene Baratheon
@Email : [email protected]
@Main : LightGBM单模型：全局统计
"""
import pandas as pd
import numpy as np
import time
from sklearn.metrics import log_loss
from collections import Counter
import extract_feature_whole as ext_feat_wh
import lightgbm as lgb
import warnings
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
from scipy import sparse
from sklearn.linear_model import LogisticRegression
import xgboost as xgb
warnings.filterwarnings("ignore")
#线下xgboost
def xgbCV(X_train,X_test):
Y_train=X_train.pop('is_trade')
Y_test=X_test.pop('is_trade')
index_train=X_train.pop('instance_id')
index_test =X_test.pop('instance_id')
print('train DMatrix')
train = xgb.DMatrix(X_train,label=Y_train)
del X_train ; gc.collect()
print('test DMatrix')
test = xgb.DMatrix(X_test,label=Y_test)
del X_test
print('xgb model start......')
params={'booster':'gbtree',
'objective':'binary:logistic',
'eval_metric':'logloss',
#'gamma':0.1,
'min_child_weight':1.1,
'max_depth':5,
'lambda':10,
'subsample':0.75,
'colsample_bytree':0.7,
'colsample_bylevel':0.7,
'eta': 0.03,
'tree_method':'hist',
'seed':0,
'nthread':10
}
watchlist = [(train,'train'),(test,'val')]
model = xgb.train(params,train,num_boost_round=5000,evals=watchlist,early_stopping_rounds=500)
#predict test set
Pre_test=model.predict(test)
feat_imp=model.get_fscore()
loss = log_loss(Y_test,Pre_test)
print('xgb logloss is: ',loss)
best_int=model.best_iteration
# #保存线下结果
# sub = pd.DataFrame()
# sub['instance_id'] = list(index_test)
# sub['instance_id'] = list(index_test)
# sub['predicted_score'] = list(Pre_test)
# sub.to_csv('../result/20180423_gene_xgb_offline.txt',sep=" ",index=False)
return feat_imp,Pre_test,loss,best_int
#线上xgboost
def sub(X_train,X_test,best_int):
Y_train = X_train.pop('is_trade')
Y_test = X_test.pop('is_trade')
index_train = X_train.pop('instance_id')
index_test = X_test.pop('instance_id')
print('train DMatrix')
train = lgb.Dataset(X_train,label=Y_train)
del X_train ; gc.collect()
print('test DMatrix')
print('lgb model start......')
lgb_params={'boosting':'gbdt',
'application': 'binary',
'metric':'binary_logloss',
# 'min_split_gain':0.1,
'min_child_weight':1.1,
'max_depth':5,
'num_leaves':15,
'lambda_l2':10,
'bagging_fraction':0.7,
'feature_fraction':0.7,
'bagging_freq':5,
'learning_rate': 0.03,
'min_data_in_leaf':10,
# 'tree_method':'exact',
'bagging_seed':5,
'feature_fraction_seed':0,
'verbose':0,
'num_threads':10
}
watchlist = [train]
model = lgb.train(lgb_params, train, num_boost_round =best_int ,valid_sets = watchlist)
print('predict test set......')
Y_test=model.predict(X_test)
sub = pd.DataFrame()
sub['instance_id'] = list(index_test)
sub['instance_id'] = list(index_test)
sub['predicted_score'] = list(Y_test)
sub.to_csv('./20180511_gene_lgb_after.txt',sep=" ",index=False)
return model
#保存feature_importance
def save_feature_importance(feat_imp):
feat_imp=sorted(feat_imp.items(),key=lambda x:x[1],reverse=True)
features = [i[0] for i in feat_imp ]
importances = [i[1] for i in feat_imp ]
feat = pd.DataFrame()
feat['feature'] = features
feat['importance'] = importances
feat.to_csv('../feature/gene/feature_importance_gene_0507.csv',sep=",",index=False)
#交叉验证
from sklearn.cross_validation import KFold
def CV_train(cv_data,n_folds=5):
logloss_sum=[]
cv_data=cv_data.reset_index()
kf=KFold(len(cv_data),n_folds=n_folds,shuffle=False)
for traincv,testcv in kf:
print('train part: ',traincv,' test part: ',testcv)
train=cv_data.loc[traincv,:];test=cv_data.loc[testcv,:]
_,Pre_test,loss=xgbCV(train,test)
logloss_sum.append(loss)
print('CV all logloss are: ',logloss_sum)
logloss_avg=np.mean(logloss_sum)
print('CV average logloss is : ',logloss_avg)
return logloss_avg
import gc
def change_dtypes(data):
all_data_int = data.select_dtypes(include = ['int64'])
all_data_convert_int = all_data_int.apply(pd.to_numeric, downcast = 'unsigned')
data[all_data_convert_int.columns] = all_data_convert_int
del all_data_int ; del all_data_convert_int
gc.collect()
return data
#保存添加属性
def save_add_feature(data,cols,path):
print('save add feature: ',cols)
t0 = data[cols]
print('save add shape: ',t0.shape)
t0.to_csv(path,index=False)
del t0 ; gc.collect()
if __name__ == "__main__":
start_time=time.time()
print('----------------------------读取基本特征-----------------------------------------------------------')
#1. 读取特征
data = pd.read_csv('../feature/gene/basic_all.csv')
print('data shape: ',data.shape) # 12161692
print('----------------------------数值特征贝叶斯平滑------------------------------------------------------')
#2. 暂无
print('----------------------------增加一些特征------------------------------------------------------------')
#3.增加5月6日特征
feature_add1 = pd.read_csv('../feature/gene/add_0506.csv')
data = pd.merge(data,feature_add1,on='instance_id',how='left')
del feature_add1
gc.collect()
#3.增加5月7日特征
feature_add2 = pd.read_csv('../feature/gene/add_0507.csv')
data = pd.merge(data,feature_add2,on='instance_id',how='left')
del feature_add2
gc.collect()
#4.增加5月8日特征
feature_add3 = pd.read_csv('../feature/gene/add_0508.csv')
data = pd.merge(data,feature_add3,on='instance_id',how='left')
del feature_add3
gc.collect()
# #4.增加5月9日特征
# feature_add4 = pd.read_csv('../feature/gene/add_0509.csv')
# data = pd.merge(data,feature_add4,on='instance_id',how='left')
# del feature_add4
# gc.collect()
data = data.fillna(0)
print('data shape after add: ',data.shape)
print('----------------------------删除一些特征------------------------------------------------------------')
#5.删除特征
drop_list=['context_timestamp','user_id','shop_id','item_id','property_0','property_1','item_category_list','item_brand_id'] #item_brand_id , item_category_list
data=data.drop(drop_list,axis=1) ; gc.collect()
print('data shape after drop: ',data.shape)
print('----------------------------one_hot编码------------------------------------------------------------')
lr=LabelEncoder()
#1.ecoder的特征id
feat_set = ['item_city_id','user_occupation_id','user_gender_id',
'context_page_id','time_interval','week']
#4.进行encoder
for col in feat_set:
print('encoder feature is: ',col)
data[col]=lr.fit_transform(data[col])
#6.type transform
data = change_dtypes(data) ; gc.collect()
print('all data shape: ',data.shape)
print('----------------------------------线上-------------------------------------------------------------')
#1. 预测集
test_a = pd.read_csv('../training_data/round2_ijcai_18_test_a_20180425.txt',sep=" ") #519888
test_b = pd.read_csv('../training_data/round2_ijcai_18_test_b_20180510.txt',sep=" ") #1209768
testa_ins = list(test_a.instance_id)
testb_ins = list(test_b.instance_id)
del test_a ; del test_b
gc.collect()
test = data[data['instance_id'].isin(testb_ins)] #1209768
#2. 训练集
train_ins = list(data.instance_id)
train_ins = list(set(train_ins).difference(set(testa_ins)))
train_ins = list(set(train_ins).difference(set(testb_ins)))
train = data[data['instance_id'].isin(train_ins)] #得到训练集 10432036
train = train[train.day!=6] #去掉6号
del data ; gc.collect()
print('train shape: ',train.shape,' test shape: ',test.shape)
#3. 训练
best_int = 3900
model = sub(train, test,best_int)
#计算算法时间
end_time=time.time()
print('all time is: %.1f'%(end_time-start_time),' 秒')
"""
print('----------------------------------线下-------------------------------------------------------------')
#xgb CV 使用7号最后一个小时进行验证
#1. 得到测试集
test = data[(data['day'] == 7) & (data['hour'] == 11)]
test_online = pd.read_csv('../training_data/round2_ijcai_18_test_a_20180425.txt',sep=' ')
train_ins = list(data.instance_id) ; test_ins=list(test.instance_id)+list(test_online.instance_id)
train_ins = list(set(train_ins).difference(set(test_ins)))
#2. 得到训练集
train = data[data['instance_id'].isin(train_ins)]
del test_online ; del data ; gc.collect()
print('train shape: ',train.shape,' test shape: ',test.shape)
#3. 线下训练
feat_imp,Pre_test,loss,best_int=xgbCV(train,test)
#4. 保存feature_importance
print('best interation is: ',best_int)
save_feature_importance(feat_imp) ; gc.collect()
#计算算法时间
end_time=time.time()
print('all time is: %.1f'%(end_time-start_time),' 秒')
"""