import numpy as np
import pandas as pd
import lightgbm as lgb
from pandas import DataFrame
from sklearn.model_selection import train_test_split
from io import StringIO
import gc
import sys
import os
hive_cmd = "hive -f ./sql/sql.sql"
output = os.popen(hive_cmd)
data_cart_prop = pd.read_csv(StringIO(unicode(output.read(),'utf-8')), sep="\t",header=0)

hive_cmd1 = "hive -f ./user.sql"
output1 = os.popen(hive_cmd1)
test_user = pd.read_csv(StringIO(unicode(output1.read(),'utf-8')), sep="\t",header=0)
 
hive_cmd2 = "hive -f ./action.sql"
output2 = os.popen(hive_cmd2)
test_action = pd.read_csv(StringIO(unicode(output2.read(),'utf-8')), sep="\t",header=0)
 
hive_cmd3 = "hive -f ./click.sql"
output3 = os.popen(hive_cmd3)
test_click = pd.read_csv(StringIO(unicode(output3.read(),'utf-8')), sep="\t",header=0)

hive_cmd = 'hive -e "set hive.cli.print.header=true;SELECT * FROM dev.temp_dev_jypt_decor_user_label_phase_one_view_feature WHERE(dt = "2018-09-17");"'
output = os.popen(hive_cmd)
data_cart_prop = pd.read_csv(StringIO(unicode(output.read(),'utf-8')), sep="\t",header=0) 

import tensorflow as tf
tf.enable_eager_execution()
x = tf.get_variable('x', shape=[1], initializer=tf.constant_initializer(3.))
with tf.GradientTape() as tape:   
  y = tf.square(x)
  y_grad = tape.gradient(y, x)    
print([y.numpy(), y_grad.numpy()])