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Add xgb_model parameter to sklearn fit (#2623)

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Adding xgb_model paramter allows the continuation of model training.
Model has to be saved by calling `model.get_booster().save_model(path)`
This commit is contained in:
Julian Niedermeier 2017-10-01 14:47:17 +02:00 committed by Yuan (Terry) Tang
parent 6e378452f2
commit 9a81c74a7b
1 changed files with 10 additions and 4 deletions
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14
python-package/xgboost/sklearn.py
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@ -216,7 +216,7 @@ class XGBModel(XGBModelBase):
return xgb_params return xgb_params
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, verbose=True): early_stopping_rounds=None, verbose=True, xgb_model=None):
# pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init # pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init
""" """
Fit the gradient boosting model Fit the gradient boosting model
@ -253,6 +253,9 @@ class XGBModel(XGBModelBase):
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.
xgb_model : str
file name of stored xgb model or 'Booster' instance Xgb model to be
loaded before training (allows training continuation).
""" """
if sample_weight is not None: if sample_weight is not None:
trainDmatrix = DMatrix(X, label=y, weight=sample_weight, trainDmatrix = DMatrix(X, label=y, weight=sample_weight,
@ -288,7 +291,7 @@ class XGBModel(XGBModelBase):
self.n_estimators, evals=evals, self.n_estimators, evals=evals,
early_stopping_rounds=early_stopping_rounds, early_stopping_rounds=early_stopping_rounds,
evals_result=evals_result, obj=obj, feval=feval, evals_result=evals_result, obj=obj, feval=feval,
verbose_eval=verbose) verbose_eval=verbose, xgb_model=xgb_model)
if evals_result: if evals_result:
for val in evals_result.items(): for val in evals_result.items():
@ -406,7 +409,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
random_state, seed, missing, **kwargs) random_state, seed, missing, **kwargs)
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, verbose=True): early_stopping_rounds=None, verbose=True, xgb_model=None):
# pylint: disable = attribute-defined-outside-init,arguments-differ # pylint: disable = attribute-defined-outside-init,arguments-differ
""" """
Fit gradient boosting classifier Fit gradient boosting classifier
@ -443,6 +446,9 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
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.
xgb_model : str
file name of stored xgb model or 'Booster' instance Xgb model to be
loaded before training (allows training continuation).
""" """
evals_result = {} evals_result = {}
self.classes_ = np.unique(y) self.classes_ = np.unique(y)
@ -498,7 +504,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
evals=evals, evals=evals,
early_stopping_rounds=early_stopping_rounds, early_stopping_rounds=early_stopping_rounds,
evals_result=evals_result, obj=obj, feval=feval, evals_result=evals_result, obj=obj, feval=feval,
verbose_eval=verbose) verbose_eval=verbose, xgb_model=None)
self.objective = xgb_options["objective"] self.objective = xgb_options["objective"]
if evals_result: if evals_result: