#!/usr/local/bin/python
# this is the example script to use xgboost to train
import sys
import numpy as np
# add path of xgboost python module
sys.path.append('../../python/')
import xgboost as xgb
from sklearn.ensemble import GradientBoostingClassifier
import time
test_size = 550000

# path to where the data lies
dpath = 'data'

# load in training data, directly use numpy
dtrain = np.loadtxt( dpath+'/training.csv', delimiter=',', skiprows=1, converters={32: lambda x:int(x=='s') } )
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 xrange(len(label)) if label[i] == 1.0  )
sum_wneg = sum( weight[i] for i in xrange(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['loss_type'] = 1
# scale weight of positive examples
param['scale_pos_weight'] = sum_wneg/sum_wpos
param['bst:eta'] = 0.1
param['bst:max_depth'] = 6
param['eval_metric'] = 'auc'
param['silent'] = 1
param['nthread'] = 4

# you can directly throw param in, though we want to watch multiple metrics here
plst = param.items()+[('eval_metric', 'ams@0.15')]

watchlist = [ (xgmat,'train') ]
# boost 135 tres
num_round = 135
print 'loading data end, start to boost trees'
print "training GBM from sklearn"
tmp = time.time()
gbm = GradientBoostingClassifier(n_estimators=135, max_depth=6, verbose=2)
gbm.fit(data, label)
print "GBM costs: %s seconds" % str(time.time() - tmp)
#raw_input()
print "training xgboost"
tmp = time.time()
bst = xgb.train( plst, xgmat, num_round, watchlist );
print "XGBoost costs: %s seconds" % str(time.time() - tmp)

print 'finish training'