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REF: Combine eval_metric and feval to one parameter

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Skipper Seabold 2015-06-30 11:42:14 -05:00
parent 113285e1dc
commit b0f7ddaa2e
1 changed files with 29 additions and 19 deletions
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48
wrapper/xgboost.py
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@ -1093,7 +1093,7 @@ class XGBModel(XGBModelBase):
return xgb_params
def fit(self, X, y, eval_set=None, eval_metric=None,
early_stopping_rounds=None, feval=None, verbose=True):
early_stopping_rounds=None, verbose=True):
# pylint: disable=missing-docstring,invalid-name
"""
Fit the gradient boosting model
@ -1107,8 +1107,14 @@ class XGBModel(XGBModelBase):
eval_set : list, optional
A list of (X, y) tuple pairs to use as a validation set for
early-stopping
eval_metric : str, optional
Built-in evaluation metric to use. See doc/parameter.md.
eval_metric : str, callable, optional
If a str, should be a built-in evaluation metric to use. See
doc/parameter.md. If callable, a custom evaluation metric. The call
signature is func(y_predicted, y_true) where y_true will be a
DMatrix object such that you may need to call the get_label
method. It must return a str, value pair where the str is a name
for the evaluation and value is the value of the evaluation
function. This objective is always minimized.
early_stopping_rounds : int
Activates early stopping. Validation error needs to decrease at
least every <early_stopping_rounds> round(s) to continue training.
@ -1116,11 +1122,6 @@ class XGBModel(XGBModelBase):
will use the last. Returns the model from the last iteration
(not the best one). If early stopping occurs, the model will
have two additional fields: bst.best_score and bst.best_iteration.
feval : function, optional
Custom evaluation metric to use. The call signature is
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.
@ -1137,13 +1138,17 @@ class XGBModel(XGBModelBase):
params = self.get_xgb_params()
feval = eval_metric if callable(eval_metric) else None
if eval_metric is not None:
params.update({'eval_metric': eval_metric})
if callable(eval_metric):
eval_metric = None
else:
params.update({'eval_metric': eval_metric})
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=feval,
verbose_eval=verbose)
if eval_results:
eval_results = {k: np.array(v, dtype=float)
@ -1180,7 +1185,7 @@ 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, versbose=True):
early_stopping_rounds=None, verbose=True):
# pylint: disable = attribute-defined-outside-init,arguments-differ
"""
Fit gradient boosting classifier
@ -1196,8 +1201,14 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
eval_set : list, optional
A list of (X, y) pairs to use as a validation set for
early-stopping
eval_metric : str
Built-in evaluation metric to use. See doc/parameter.md.
eval_metric : str, callable, optional
If a str, should be a built-in evaluation metric to use. See
doc/parameter.md. If callable, a custom evaluation metric. The call
signature is func(y_predicted, y_true) where y_true will be a
DMatrix object such that you may need to call the get_label
method. It must return a str, value pair where the str is a name
for the evaluation and value is the value of the evaluation
function. This objective is always minimized.
early_stopping_rounds : int, optional
Activates early stopping. Validation error needs to decrease at
least every <early_stopping_rounds> round(s) to continue training.
@ -1205,11 +1216,6 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
will use the last. Returns the model from the last iteration
(not the best one). If early stopping occurs, the model will
have two additional fields: bst.best_score and bst.best_iteration.
feval : function, optional
Custom evaluation metric to use. The call signature is
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.
@ -1225,8 +1231,12 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
else:
xgb_options = self.get_xgb_params()
feval = eval_metric if callable(eval_metric) else None
if eval_metric is not None:
xgb_options.update({"eval_metric": eval_metric})
if callable(eval_metric):
eval_metric = None
else:
xgb_options.update({"eval_metric": eval_metric})
if eval_set is not None:
# TODO: use sample_weight if given?