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ENH: Allow for silent evaluation

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Skipper Seabold 2015-05-20 14:27:22 -05:00
parent 3952b525b8
commit cf89ae64e2
1 changed files with 17 additions and 7 deletions
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24
wrapper/xgboost.py
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@ -738,7 +738,7 @@ class Booster(object):
def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
early_stopping_rounds=None, evals_result=None):
early_stopping_rounds=None, evals_result=None, verbose_eval=True):
# pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init
"""Train a booster with given parameters.
@ -793,7 +793,8 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
else:
msg = bst_eval_set.decode()
sys.stderr.write(msg + '\n')
if verbose_eval:
sys.stderr.write(msg + '\n')
if evals_result is not None:
res = re.findall(":-?([0-9.]+).", msg)
for key, val in zip(evals_name, res):
@ -839,7 +840,8 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
else:
msg = bst_eval_set.decode()
sys.stderr.write(msg + '\n')
if verbose_eval:
sys.stderr.write(msg + '\n')
if evals_result is not None:
res = re.findall(":-([0-9.]+).", msg)
@ -1088,7 +1090,7 @@ class XGBModel(XGBModelBase):
return xgb_params
def fit(self, X, y, eval_set=None, eval_metric=None,
early_stopping_rounds=None, feval=None):
early_stopping_rounds=None, feval=None, verbose=True):
# pylint: disable=missing-docstring,invalid-name
"""
Fit the gradient boosting model
@ -1116,6 +1118,9 @@ class XGBModel(XGBModelBase):
feval(y_predicted, y_true) where y_true will be a DMatrix object
such that you may need to call the get_label method. This objective
if always assumed to be minimized, so use -feval when appropriate.
verbose : bool
If `verbose` and an evaluation set is used, writes the evaluation
metric measured on the validation set to stderr.
"""
trainDmatrix = DMatrix(X, label=y, missing=self.missing)
@ -1135,7 +1140,8 @@ class XGBModel(XGBModelBase):
self._Booster = train(params, trainDmatrix,
self.n_estimators, evals=evals,
early_stopping_rounds=early_stopping_rounds,
evals_result=eval_results, feval=None)
evals_result=eval_results, feval=None,
verbose_eval=verbose)
if eval_results:
eval_results = {k: np.array(v, dtype=float)
for k, v in eval_results.items()}
@ -1171,7 +1177,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
base_score, seed, missing)
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
early_stopping_rounds=None, feval=None):
early_stopping_rounds=None, feval=None, versbose=True):
# pylint: disable = attribute-defined-outside-init,arguments-differ
"""
Fit gradient boosting classifier
@ -1201,6 +1207,9 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
feval(y_predicted, y_true) where y_true will be a DMatrix object
such that you may need to call the get_label method. This objective
if always assumed to be minimized, so use -feval when appropriate.
verbose : bool
If `verbose` and an evaluation set is used, writes the evaluation
metric measured on the validation set to stderr.
"""
eval_results = {}
self.classes_ = list(np.unique(y))
@ -1238,7 +1247,8 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
self._Booster = train(xgb_options, train_dmatrix, self.n_estimators,
evals=evals,
early_stopping_rounds=early_stopping_rounds,
evals_result=eval_results, feval=feval)
evals_result=eval_results, feval=feval,
verbose_eval=verbose)
if eval_results:
eval_results = {k: np.array(v, dtype=float)