#!/usr/bin/pytho#!/usr/bin/python
#pylint: skip-file
# this is the example script to use xgboost to train
import numpy as np
import xgboost as xgb
import time

# path to where the data lies
dpath = '../../demo/data'

# load in training data, directly use numpy
dtrain = np.loadtxt( dpath+'/training.csv', delimiter=',', skiprows=1, converters={32: lambda x:int(x=='s') } )
dtrain = np.concatenate((dtrain, np.copy(dtrain)))
dtrain = np.concatenate((dtrain, np.copy(dtrain)))
dtrain = np.concatenate((dtrain, np.copy(dtrain)))
test_size = len(dtrain)

print(len(dtrain))
print ('finish loading from csv ')

label  = dtrain[:,32]
data   = dtrain[:,1:31]
# rescale weight to make it same as test set
weight = dtrain[:,31] * float(test_size) / len(label)

sum_wpos = sum( weight[i] for i in range(len(label)) if label[i] == 1.0  )
sum_wneg = sum( weight[i] for i in range(len(label)) if label[i] == 0.0  )

# print weight statistics
print ('weight statistics: wpos=%g, wneg=%g, ratio=%g' % ( sum_wpos, sum_wneg, sum_wneg/sum_wpos ))

# construct xgboost.DMatrix from numpy array, treat -999.0 as missing value
xgmat = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )

# setup parameters for xgboost
param = {}
# use logistic regression loss
param['objective'] = 'binary:logitraw'
# scale weight of positive examples
param['scale_pos_weight'] = sum_wneg/sum_wpos
param['bst:eta'] = 0.1
param['max_depth'] = 15
param['eval_metric'] = 'auc'
param['nthread'] = 16

plst = param.items()+[('eval_metric', 'ams@0.15')]

watchlist = [ (xgmat,'train') ]
num_round = 10
print ("training xgboost")
threads = [16]
for i in threads:
    param['nthread'] = i
    tmp = time.time()
    plst = param.items()+[('eval_metric', 'ams@0.15')]
    bst = xgb.train( plst, xgmat, num_round, watchlist );
    print ("XGBoost with %d thread costs: %s seconds" % (i, str(time.time() - tmp)))

print ("training xgboost - gpu tree construction")
param['updater'] = 'grow_gpu'
tmp = time.time()
plst = param.items()+[('eval_metric', 'ams@0.15')]
bst = xgb.train( plst, xgmat, num_round, watchlist );
print ("XGBoost GPU: %s seconds" % (str(time.time() - tmp)))
print ('finish training')