Brought the silent parameter for the SKLearn-like API back, marked it deprecated. (#4255)
* Brought the silent parameter for the SKLearn-like API back, marked it deprecated. - added deprecation notice and warning - removed silent from the tests for the SKLearn-like API
This commit is contained in:
Andy Adinets
committed by
Rory Mitchell
Rory Mitchell
parent
b833b642ec
commit
4352fcdb15
@@ -64,6 +64,8 @@ class XGBModel(XGBModelBase):
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Number of trees to fit.
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verbosity : int
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The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
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silent : boolean
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Whether to print messages while running boosting. Deprecated. Use verbosity instead.
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objective : string or callable
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Specify the learning task and the corresponding learning objective or
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a custom objective function to be used (see note below).
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@@ -134,7 +136,7 @@ class XGBModel(XGBModelBase):
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"""
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def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100,
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verbosity=1, objective="reg:linear", booster='gbtree',
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verbosity=1, silent=None, objective="reg:linear", booster='gbtree',
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n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
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max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1,
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colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
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@@ -146,6 +148,7 @@ class XGBModel(XGBModelBase):
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self.learning_rate = learning_rate
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self.n_estimators = n_estimators
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self.verbosity = verbosity
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self.silent = silent
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self.objective = objective
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self.booster = booster
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self.gamma = gamma
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@@ -240,6 +243,16 @@ class XGBModel(XGBModelBase):
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else:
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xgb_params['nthread'] = n_jobs
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if 'silent' in xgb_params and xgb_params['silent'] is not None:
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warnings.warn('The silent parameter is deprecated.'
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'Please use verbosity instead.'
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'silent is depreated', DeprecationWarning)
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# TODO(canonizer): set verbosity explicitly if silent is removed from xgboost,
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# but remains in this API
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else:
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# silent=None shouldn't be passed to xgboost
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xgb_params.pop('silent', None)
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if xgb_params['nthread'] <= 0:
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xgb_params.pop('nthread', None)
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return xgb_params
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@@ -588,7 +601,8 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
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__doc__ = "Implementation of the scikit-learn API for XGBoost classification.\n\n" \
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+ '\n'.join(XGBModel.__doc__.split('\n')[2:])
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def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1,
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def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100,
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verbosity=1, silent=None,
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objective="binary:logistic", booster='gbtree',
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n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0,
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subsample=1, colsample_bytree=1, colsample_bylevel=1,
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@@ -596,7 +610,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
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base_score=0.5, random_state=0, seed=None, missing=None, **kwargs):
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super(XGBClassifier, self).__init__(
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max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators,
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verbosity=verbosity, objective=objective, booster=booster,
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verbosity=verbosity, silent=silent, objective=objective, booster=booster,
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n_jobs=n_jobs, nthread=nthread, gamma=gamma,
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min_child_weight=min_child_weight, max_delta_step=max_delta_step,
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subsample=subsample, colsample_bytree=colsample_bytree,
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@@ -874,7 +888,8 @@ class XGBRFClassifier(XGBClassifier):
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+ "for XGBoost random forest classification.\n\n"\
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+ '\n'.join(XGBModel.__doc__.split('\n')[2:])
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def __init__(self, max_depth=3, learning_rate=1, n_estimators=100, verbosity=1,
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def __init__(self, max_depth=3, learning_rate=1, n_estimators=100,
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verbosity=1, silent=None,
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objective="binary:logistic", n_jobs=1, nthread=None, gamma=0,
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min_child_weight=1, max_delta_step=0, subsample=0.8, colsample_bytree=1,
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colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1,
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@@ -882,7 +897,7 @@ class XGBRFClassifier(XGBClassifier):
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missing=None, **kwargs):
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super(XGBRFClassifier, self).__init__(
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max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators,
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verbosity=verbosity, objective=objective, booster='gbtree',
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verbosity=verbosity, silent=silent, objective=objective, booster='gbtree',
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n_jobs=n_jobs, nthread=nthread, gamma=gamma,
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min_child_weight=min_child_weight, max_delta_step=max_delta_step,
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subsample=subsample, colsample_bytree=colsample_bytree,
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@@ -912,7 +927,8 @@ class XGBRFRegressor(XGBRegressor):
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+ "for XGBoost random forest regression.\n\n"\
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+ '\n'.join(XGBModel.__doc__.split('\n')[2:])
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def __init__(self, max_depth=3, learning_rate=1, n_estimators=100, verbosity=1,
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def __init__(self, max_depth=3, learning_rate=1, n_estimators=100,
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verbosity=1, silent=None,
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objective="reg:linear", n_jobs=1, nthread=None, gamma=0,
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min_child_weight=1, max_delta_step=0, subsample=0.8, colsample_bytree=1,
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colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1,
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@@ -920,7 +936,7 @@ class XGBRFRegressor(XGBRegressor):
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missing=None, **kwargs):
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super(XGBRFRegressor, self).__init__(
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max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators,
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verbosity=verbosity, objective=objective, booster='gbtree',
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verbosity=verbosity, silent=silent, objective=objective, booster='gbtree',
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n_jobs=n_jobs, nthread=nthread, gamma=gamma,
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min_child_weight=min_child_weight, max_delta_step=max_delta_step,
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subsample=subsample, colsample_bytree=colsample_bytree,
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@@ -952,6 +968,8 @@ class XGBRanker(XGBModel):
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Number of boosted trees to fit.
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verbosity : int
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The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
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silent : boolean
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Whether to print messages while running boosting. Deprecated. Use verbosity instead.
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objective : string
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Specify the learning task and the corresponding learning objective.
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The objective name must start with "rank:".
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@@ -1037,7 +1055,7 @@ class XGBRanker(XGBModel):
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"""
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def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100,
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verbosity=1, objective="rank:pairwise", booster='gbtree',
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verbosity=1, silent=None, objective="rank:pairwise", booster='gbtree',
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n_jobs=-1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0,
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subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1,
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reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
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@@ -1045,7 +1063,7 @@ class XGBRanker(XGBModel):
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super(XGBRanker, self).__init__(
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max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators,
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verbosity=verbosity, objective=objective, booster=booster,
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verbosity=verbosity, silent=silent, objective=objective, booster=booster,
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n_jobs=n_jobs, nthread=nthread, gamma=gamma,
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min_child_weight=min_child_weight, max_delta_step=max_delta_step,
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subsample=subsample, colsample_bytree=colsample_bytree,
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