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feature_choice_xgb.py
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feature_choice_xgb.py
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import pandas as pd
test_id=pd.read_csv('meinian_round1_test_b_20180505.csv',encoding='gbk',usecols=['vid'])
train_id=pd.read_csv('train_data.csv',usecols=['vid'])
train_label=pd.read_csv('train_data.csv',usecols=[1,2,3,4,5])
train_label1=train_label['收缩压']
train_label2=train_label['舒张压']
train_label3=train_label['血清甘油三酯']
train_label4=train_label['血清高密度脂蛋白']
train_label5=train_label['血清低密度脂蛋白']
feat_history=pd.read_csv('feat_history_4_24_21_25.csv')
train_data=pd.merge(train_id,feat_history,on='vid',how='left')
test_data=pd.merge(test_id,feat_history,on='vid',how='left')
#feat_history_second=pd.read_csv('feat_second_history_4_21_19_23.csv')
#train_data=pd.merge(train_data,feat_history_second,on='vid',how='left')
#test_data=pd.merge(test_data,feat_history_second,on='vid',how='left')
feat_history_float=pd.read_csv('ata_history_float_.csv')
train_data=pd.merge(train_data,feat_history_float,on='vid',how='left')
test_data=pd.merge(test_data,feat_history_float,on='vid',how='left')
#del train_data['vid']
#del test_data['vid']
#test_data=pd.concat([test_id,test_data],axis=1)
new_feature=pd.read_csv('feat_history_5_5_20_43.csv')
train_data=pd.merge(train_data,new_feature,on='vid',how='left')
test_data=pd.merge(test_data,new_feature,on='vid',how='left')
del train_data['vid']
del test_data['vid']
from sklearn.model_selection import train_test_split
#preds_sub = gbm.predict(testt_feat)
import xgboost as xgb
import operator
import xgboost as xgb
#test_data['y3']=y3
#test_data['y4']=y4
#test_data['y5']=y5
#train_data['y3']=train_label3
#train_data['y4']=train_label4
#train_data['y5']=train_label5
x_train,x_val,y_train,y_val=train_test_split(train_data,train_label1,test_size=0.2,random_state=100)
print ('start running ....')
dtrain = xgb.DMatrix(x_train,label=y_train)
dval = xgb.DMatrix(x_val,label=y_val)
param = {'learning_rate' : 0.1,
'n_estimators': 1000,
'max_depth': 4,
'min_child_weight': 7,
'gamma': 0,
'subsample': 0.8,
'colsample_bytree': 0.8,
'eta': 0.05,
'silent': 1,
}
num_round =250
plst = list(param.items())
plst += [('eval_metric', 'rmse')]
evallist = [(dval, 'eval'), (dtrain, 'train')]
bst=xgb.train(plst,dtrain,num_round,evallist,early_stopping_rounds=50)
dtest = xgb.DMatrix(test_data)
y1 = bst.predict(dtest)
#del test_data['y3']
#del test_data['y4']
#del test_data['y5']
#del train_data['y3']
#del train_data['y4']
#del train_data['y5']
x_train,x_val,y_train,y_val=train_test_split(train_data,train_label2,test_size=0.2,random_state=100)
print ('start running ....')
dtrain = xgb.DMatrix(x_train,label=y_train)
dval = xgb.DMatrix(x_val,label=y_val)
param = {'learning_rate' : 0.1,
'n_estimators': 1000,
'max_depth': 4,
'min_child_weight': 7,
'gamma': 0,
'subsample': 0.8,
'colsample_bytree': 0.8,
'eta': 0.05,
'silent': 1,
}
num_round =250
plst = list(param.items())
plst += [('eval_metric', 'rmse')]
evallist = [(dval, 'eval'), (dtrain, 'train')]
bst=xgb.train(plst,dtrain,num_round,evallist,early_stopping_rounds=50)
dtest = xgb.DMatrix(test_data)
y2 = bst.predict(dtest)
x_train,x_val,y_train,y_val=train_test_split(train_data,train_label3,test_size=0.2,random_state=100)
print ('start running ....')
#preds_sub = gbm.predict(testt_feat)
print ('start running ....')
dtrain = xgb.DMatrix(x_train,label=y_train)
dval = xgb.DMatrix(x_val,label=y_val)
param = {'learning_rate' : 0.1,
'n_estimators': 1000,
'max_depth': 3,
'min_child_weight': 8,
'gamma': 0,
'subsample': 0.8,
'colsample_bytree': 0.8,
'eta': 0.05,
'silent': 1,
}
num_round =200
plst = list(param.items())
plst += [('eval_metric', 'rmse')]
evallist = [(dval, 'eval'), (dtrain, 'train')]
bst=xgb.train(plst,dtrain,num_round,evallist,early_stopping_rounds=50)
dtest = xgb.DMatrix(test_data)
y3 = bst.predict(dtest)
x_train,x_val,y_train,y_val=train_test_split(train_data,train_label4,test_size=0.2,random_state=100)
print ('start running ....')
dtrain = xgb.DMatrix(x_train,label=y_train)
dval = xgb.DMatrix(x_val,label=y_val)
param = {'learning_rate' : 0.1,
'n_estimators': 1000,
'max_depth': 4,
'min_child_weight': 7,
'gamma': 0,
'subsample': 0.8,
'colsample_bytree': 0.8,
'eta': 0.05,
'silent': 1,
}
num_round =250
plst = list(param.items())
plst += [('eval_metric', 'rmse')]
evallist = [(dval, 'eval'), (dtrain, 'train')]
bst=xgb.train(plst,dtrain,num_round,evallist,early_stopping_rounds=50)
dtest = xgb.DMatrix(test_data)
y4 = bst.predict(dtest)
x_train,x_val,y_train,y_val=train_test_split(train_data,train_label5,test_size=0.2,random_state=100)
print ('start running ....')
dtrain = xgb.DMatrix(x_train,label=y_train)
dval = xgb.DMatrix(x_val,label=y_val)
param = {'learning_rate' : 0.1,
'n_estimators': 1000,
'max_depth': 4,
'min_child_weight': 7,
'gamma': 0,
'subsample': 0.8,
'colsample_bytree': 0.8,
'eta': 0.05,
'silent': 1,
}
num_round =200
plst = list(param.items())
plst += [('eval_metric', 'rmse')]
evallist = [(dval, 'eval'), (dtrain, 'train')]
bst=xgb.train(plst,dtrain,num_round,evallist,early_stopping_rounds=50)
dtest = xgb.DMatrix(test_data)
y5 = bst.predict(dtest)
test_id['y1']=y1
test_id['y2']=y2
test_id['y3']=y3
test_id['y4']=y4
test_id['y5']=y5
test_id.to_csv('tijiao_new_5_6_11_b_xgb.csv',index=False,header=None)