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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 return xgb_params
def fit(self, X, y, eval_set=None, eval_metric=None, 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 # pylint: disable=missing-docstring,invalid-name
""" """
Fit the gradient boosting model Fit the gradient boosting model
@ -1107,8 +1107,14 @@ class XGBModel(XGBModelBase):
eval_set : list, optional eval_set : list, optional
A list of (X, y) tuple pairs to use as a validation set for A list of (X, y) tuple pairs to use as a validation set for
early-stopping early-stopping
eval_metric : str, optional eval_metric : str, callable, optional
Built-in evaluation metric to use. See doc/parameter.md. 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 early_stopping_rounds : int
Activates early stopping. Validation error needs to decrease at Activates early stopping. Validation error needs to decrease at
least every <early_stopping_rounds> round(s) to continue training. 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 will use the last. Returns the model from the last iteration
(not the best one). If early stopping occurs, the model will (not the best one). If early stopping occurs, the model will
have two additional fields: bst.best_score and bst.best_iteration. 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 verbose : bool
If `verbose` and an evaluation set is used, writes the evaluation If `verbose` and an evaluation set is used, writes the evaluation
metric measured on the validation set to stderr. metric measured on the validation set to stderr.
@ -1137,13 +1138,17 @@ class XGBModel(XGBModelBase):
params = self.get_xgb_params() params = self.get_xgb_params()
feval = eval_metric if callable(eval_metric) else None
if eval_metric is not 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._Booster = train(params, trainDmatrix,
self.n_estimators, evals=evals, self.n_estimators, evals=evals,
early_stopping_rounds=early_stopping_rounds, early_stopping_rounds=early_stopping_rounds,
evals_result=eval_results, feval=None, evals_result=eval_results, feval=feval,
verbose_eval=verbose) verbose_eval=verbose)
if eval_results: if eval_results:
eval_results = {k: np.array(v, dtype=float) eval_results = {k: np.array(v, dtype=float)
@ -1180,7 +1185,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
base_score, seed, missing) base_score, seed, missing)
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None, 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 # pylint: disable = attribute-defined-outside-init,arguments-differ
""" """
Fit gradient boosting classifier Fit gradient boosting classifier
@ -1196,8 +1201,14 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
eval_set : list, optional eval_set : list, optional
A list of (X, y) pairs to use as a validation set for A list of (X, y) pairs to use as a validation set for
early-stopping early-stopping
eval_metric : str eval_metric : str, callable, optional
Built-in evaluation metric to use. See doc/parameter.md. 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 early_stopping_rounds : int, optional
Activates early stopping. Validation error needs to decrease at Activates early stopping. Validation error needs to decrease at
least every <early_stopping_rounds> round(s) to continue training. 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 will use the last. Returns the model from the last iteration
(not the best one). If early stopping occurs, the model will (not the best one). If early stopping occurs, the model will
have two additional fields: bst.best_score and bst.best_iteration. 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 verbose : bool
If `verbose` and an evaluation set is used, writes the evaluation If `verbose` and an evaluation set is used, writes the evaluation
metric measured on the validation set to stderr. metric measured on the validation set to stderr.
@ -1225,8 +1231,12 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
else: else:
xgb_options = self.get_xgb_params() xgb_options = self.get_xgb_params()
feval = eval_metric if callable(eval_metric) else None
if eval_metric is not 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: if eval_set is not None:
# TODO: use sample_weight if given? # TODO: use sample_weight if given?