import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
import time
from xgb_make import xgboost_make
from lgb_make import lightgbm_make
from feature_extract import make_data_feat
if __name__ == '__main__':
t1 = time.time()
train_feat, test_feat = make_data_feat()
lightgbm_make(train_feat, test_feat)
xgboost_make(train_feat, test_feat)
sub0 = pd.read_csv('../sub/sub_lgb0.csv')
sub0.columns = ['pred0']
sub1 = pd.read_csv('../sub/sub_xgb0.csv')
sub1.columns = ['pred1']
result = pd.concat([sub0, sub1], axis=1)
rate = 0.8
print('模型融合： {}'.format(rate * lightgbm_make(train_feat, test_feat) +
(1 - rate) * xgboost_make(train_feat, test_feat)))
result['pred'] = result['pred0'] * rate + result['pred1'] * (1 - rate)
import pandas as pd
from sklearn.metrics import mean_squared_error
import time
from xgb_make import xgboost_make
from lgb_make import lightgbm_make
from rf_make import randomforest_make
from feature_extract import make_data_feat
if __name__ == '__main__':
t1 = time.time()
train_feat, test_feat = make_data_feat()
lgb_label, lgb_pred = lightgbm_make(train_feat, test_feat)
xgb_label, xgb_pred = xgboost_make(train_feat, test_feat)
rf_label, rf_pred = randomforest_make(train_feat, test_feat)
print('线下得分： {}'.format(mean_squared_error(lgb_label, lgb_pred) * 0.5))
print('线下得分： {}'.format(mean_squared_error(xgb_label, xgb_pred) * 0.5))
print('线下得分： {}'.format(
mean_squared_error(xgb_label, (
(lgb_pred * 0.8 + xgb_pred * 0.2) * 1 + rf_pred * 0)) * 0.5))
#
# df = lgb_label.to_frame()
# df['pred'] = (lgb_pred * 0.8 + xgb_pred * 0.2)*0.9 + rf_pred*0.1
# df = df.sort_values('pred', ascending=True)
sub0 = pd.read_csv('../sub/sub_lgb0.csv')
sub0.columns = ['pred0']
import pandas as pd
from sklearn.metrics import mean_squared_error
import time
from xgb_make import xgboost_make
from lgb_make import lightgbm_make
from feature_extract import make_data_feat
import datetime
if __name__ == '__main__':
t1 = time.time()
train_feat, test_feat = make_data_feat()
lightgbm_make(train_feat, test_feat)
xgboost_make(train_feat, test_feat)
sub0 = pd.read_csv('../sub/sub_lgb0.csv')
sub0.columns = ['pred0']
sub1 = pd.read_csv('../sub/sub_xgb0.csv')
sub1.columns = ['pred1']
result = pd.concat([sub0, sub1], axis=1)
result['pred'] = result['pred0'] * 0.8 + result['pred1'] * 0.2
result = result[['pred']]
# result.to_csv('../sub/sub_final0.csv', index=False)
result.to_csv(r'../sub/sub{}.csv'.format(
datetime.datetime.now().strftime('%Y%m%d_%H%M%S')),
header=None,