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changed the param show_progress by verbose_eval in cv and aggcv functions

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This commit is contained in:
ivallesp 2016-02-21 01:28:55 +01:00
parent 532615a32a
commit c17d0ef560
1 changed files with 21 additions and 20 deletions
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41
python-package/xgboost/training.py
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@ -295,14 +295,14 @@ def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None, stratified=False,
ret.append(CVPack(dtrain, dtest, plst))
return ret
def aggcv(rlist, show_stdv=True, show_progress=None, as_pandas=True, trial=0):
def aggcv(rlist, show_stdv=True, verbose_eval=None, as_pandas=True, trial=0):
# pylint: disable=invalid-name
"""
Aggregate cross-validation results.
If show_progress is true, progress is displayed in every call. If
show_progress is an integer, progress will only be displayed every
`show_progress` trees, tracked via trial.
If verbose_eval is true, progress is displayed in every call. If
verbose_eval is an integer, progress will only be displayed every
`verbose_eval` trees, tracked via trial.
"""
cvmap = {}
idx = rlist[0].split()[0]
@ -341,16 +341,16 @@ def aggcv(rlist, show_stdv=True, show_progress=None, as_pandas=True, trial=0):
import pandas as pd
results = pd.Series(results, index=index)
except ImportError:
if show_progress is None:
show_progress = True
if verbose_eval is None:
verbose_eval = True
else:
# if show_progress is default (None),
# if verbose_eval is default (None),
# result will be np.ndarray as it can't hold column name
if show_progress is None:
show_progress = True
if verbose_eval is None:
verbose_eval = True
if (isinstance(show_progress, int) and show_progress > 0 and trial % show_progress == 0) or \
(isinstance(show_progress, bool) and show_progress):
if (isinstance(verbose_eval, int) and verbose_eval > 0 and trial % verbose_eval == 0) or \
(isinstance(verbose_eval, bool) and verbose_eval):
sys.stderr.write(msg + '\n')
sys.stderr.flush()
@ -359,7 +359,7 @@ def aggcv(rlist, show_stdv=True, show_progress=None, as_pandas=True, trial=0):
def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None,
metrics=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None,
fpreproc=None, as_pandas=True, show_progress=None, show_stdv=True, seed=0):
fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True, seed=0):
# pylint: disable = invalid-name
"""Cross-validation with given paramaters.
@ -395,11 +395,11 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
as_pandas : bool, default True
Return pd.DataFrame when pandas is installed.
If False or pandas is not installed, return np.ndarray
show_progress : bool, int, or None, default None
verbose_eval : bool, int, or None, default None
Whether to display the progress. If None, progress will be displayed
when np.ndarray is returned. If True, progress will be displayed at
boosting stage. If an integer is given, progress will be displayed
at every given `show_progress` boosting stage.
at every given `verbose_eval` boosting stage.
show_stdv : bool, default True
Whether to display the standard deviation in progress.
Results are not affected, and always contains std.
@ -436,9 +436,9 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
if len(metrics) > 1:
raise ValueError('Check your params. '\
'Early stopping works with single eval metric only.')
sys.stderr.write("Will train until cv error hasn't decreased in {} rounds.\n".format(\
early_stopping_rounds))
if verbose_eval:
sys.stderr.write("Will train until cv error hasn't decreased in {} rounds.\n".format(\
early_stopping_rounds))
maximize_score = False
if len(metrics) == 1:
@ -460,7 +460,7 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
for fold in cvfolds:
fold.update(i, obj)
res = aggcv([f.eval(i, feval) for f in cvfolds],
show_stdv=show_stdv, show_progress=show_progress,
show_stdv=show_stdv, verbose_eval=verbose_eval,
as_pandas=as_pandas, trial=i)
results.append(res)
@ -472,8 +472,9 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
best_score_i = i
elif i - best_score_i >= early_stopping_rounds:
results = results[:best_score_i+1]
sys.stderr.write("Stopping. Best iteration:\n[{}] cv-mean:{}\tcv-std:{}\n".
format(best_score_i, results[-1][0], results[-1][1]))
if verbose_eval:
sys.stderr.write("Stopping. Best iteration:\n[{}] cv-mean:{}\tcv-std:{}\n".
format(best_score_i, results[-1][0], results[-1][1]))
break
if as_pandas:
try: