def train_ST():
RF_st1 = RF.RF_ST_train('./data/testingDataSetST1.csv',
'./data/VALIDATION_DataSetST1.csv')
RF_st2 = RF.RF_ST_train('./data/EVAL_DataSetST1.csv',
'./data/VALIDATION_DataSetST1.csv')
gdbt_st1 = GDBT_ST.GDBT_ST_train('./data/EVAL_DataSetST1.csv',
'./data/VALIDATION_DataSetST1.csv')
gdbt_st2 = GDBT_ST.GDBT_ST_train('./data/testingDataSetST1.csv',
'./data/VALIDATION_DataSetST1.csv')
gdbt_st3 = GDBT_ST.GDBT_ST_train('./data/VALIDATION_DataSetST1.csv',
'./data/VALIDATION_DataSetST1.csv')
svr_st = s.SVR_ST_train()
# # 3个GBDT，1个sSVR再做一次SVR
# store = ['1', '2', '3', '4', '5'];pred_y = []
# for st in store:
# X = [];Y = []
# for i in range(len(gdbt_st1)):
# if gdbt_st1[i][1] != st: continue
# X.append((RF_st1[i][2], gdbt_st1[i][2], gdbt_st2[i][2], svr_st[i][2]))
# Y.append(gdbt_st1[i][3])
# svr = SVR(kernel='linear', epsilon=0.5, C=1).fit(X, Y)
# svr_res = svr.predict(X)
# for x in svr_res:
# pred_y.append(x)
fw = open(filename, 'a')
for i in range(len(gdbt_st1)):
fw.write('%s,%s,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f\n' %
(gdbt_st1[i][0], gdbt_st1[i][1],
float(RF_st1[i][2]),
float(RF_st2[i][2]),
float(gdbt_st1[i][2]),
float(gdbt_st2[i][2]),
float(gdbt_st3[i][2]),
float(svr_st[i][2]),
float(svr_st[i][3]),
float(svr_st[i][4]),
(float(RF_st2[i][2])+float(gdbt_st1[i][2]) + float(gdbt_st2[i][2]) + \
float(svr_st[i][2])+float(svr_st[i][3])+float(svr_st[i][4])) / 6,
cost_dict[gdbt_st1[i][1]][gdbt_st1[i][0]][0],
cost_dict[gdbt_st1[i][1]][gdbt_st1[i][0]][1],
float(gdbt_st1[i][3])
)
)
fw.close()
