Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Minor edits to coding style (#2835)

Browse Source 
* Some minor changes to the code style

Some minor changes to the code style in file basic_walkthrough.py

* coding style changes

* coding style changes arrcording PEP8

* Update basic_walkthrough.py
This commit is contained in:
LevineHuang
2017-10-27 11:12:54 +08:00
committed by Yuan (Terry) Tang
parent d9d5293cdb
commit 91af8f7106
10 changed files with 51 additions and 52 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
demo/guide-python/cross_validation.py
View File

@@ -7,30 +7,30 @@ dtrain = xgb.DMatrix('../data/agaricus.txt.train')
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
num_round = 2
print ('running cross validation')
print('running cross validation')
# do cross validation, this will print result out as
# [iteration] metric_name:mean_value+std_value
# std_value is standard deviation of the metric
xgb.cv(param, dtrain, num_round, nfold=5,
metrics={'error'}, seed = 0,
metrics={'error'}, seed=0,
callbacks=[xgb.callback.print_evaluation(show_stdv=True)])
print ('running cross validation, disable standard deviation display')
print('running cross validation, disable standard deviation display')
# do cross validation, this will print result out as
# [iteration] metric_name:mean_value
res = xgb.cv(param, dtrain, num_boost_round=10, nfold=5,
metrics={'error'}, seed = 0,
metrics={'error'}, seed=0,
callbacks=[xgb.callback.print_evaluation(show_stdv=False),
xgb.callback.early_stop(3)])
print (res)
print ('running cross validation, with preprocessing function')
print(res)
print('running cross validation, with preprocessing function')
# define the preprocessing function
# used to return the preprocessed training, test data, and parameter
# we can use this to do weight rescale, etc.
# as a example, we try to set scale_pos_weight
def fpreproc(dtrain, dtest, param):
label = dtrain.get_label()
ratio = float(np.sum(label == 0)) / np.sum(label==1)
ratio = float(np.sum(label == 0)) / np.sum(label == 1)
param['scale_pos_weight'] = ratio
return (dtrain, dtest, param)
@@ -39,18 +39,18 @@ def fpreproc(dtrain, dtest, param):
# then the return value of fpreproc will be used to generate
# results of that fold
xgb.cv(param, dtrain, num_round, nfold=5,
metrics={'auc'}, seed = 0, fpreproc = fpreproc)
metrics={'auc'}, seed=0, fpreproc=fpreproc)
###
# you can also do cross validation with cutomized loss function
# See custom_objective.py
##
print ('running cross validation, with cutomsized loss function')
print('running cross validation, with cutomsized loss function')
def logregobj(preds, dtrain):
labels = dtrain.get_label()
preds = 1.0 / (1.0 + np.exp(-preds))
grad = preds - labels
hess = preds * (1.0-preds)
hess = preds * (1.0 - preds)
return grad, hess
def evalerror(preds, dtrain):
labels = dtrain.get_label()
@@ -58,5 +58,5 @@ def evalerror(preds, dtrain):
param = {'max_depth':2, 'eta':1, 'silent':1}
# train with customized objective
xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
obj = logregobj, feval=evalerror)
xgb.cv(param, dtrain, num_round, nfold=5, seed=0,
obj=logregobj, feval=evalerror)