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Remove remaining reg:linear. (#4544)

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Jiaming Yuan 2019-06-11 16:04:09 +08:00 committed by GitHub
parent 4e9965cb9d
commit 4591039eba
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3 changed files with 10 additions and 9 deletions
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2
include/xgboost/objective.h
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@ -108,7 +108,7 @@ struct ObjFunctionReg
* *
* \code * \code
* // example of registering a objective * // example of registering a objective
* XGBOOST_REGISTER_OBJECTIVE(LinearRegression, "reg:linear") * XGBOOST_REGISTER_OBJECTIVE(LinearRegression, "reg:squarederror")
* .describe("Linear regression objective") * .describe("Linear regression objective")
* .set_body([]() { * .set_body([]() {
* return new RegLossObj(LossType::kLinearSquare); * return new RegLossObj(LossType::kLinearSquare);

15
python-package/xgboost/sklearn.py
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@ -136,11 +136,12 @@ class XGBModel(XGBModelBase):
""" """
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100,
verbosity=1, silent=None, objective="reg:linear", booster='gbtree', verbosity=1, silent=None, objective="reg:squarederror",
n_jobs=1, nthread=None, gamma=0, min_child_weight=1, booster='gbtree', n_jobs=1, nthread=None, gamma=0,
max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, min_child_weight=1, max_delta_step=0, subsample=1,
colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1,
base_score=0.5, random_state=0, seed=None, missing=None, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5,
random_state=0, seed=None, missing=None,
importance_type="gain", **kwargs): importance_type="gain", **kwargs):
if not SKLEARN_INSTALLED: if not SKLEARN_INSTALLED:
raise XGBoostError('sklearn needs to be installed in order to use this module') raise XGBoostError('sklearn needs to be installed in order to use this module')
@ -377,7 +378,7 @@ class XGBModel(XGBModelBase):
if callable(self.objective): if callable(self.objective):
obj = _objective_decorator(self.objective) obj = _objective_decorator(self.objective)
params["objective"] = "reg:linear" params["objective"] = "reg:squarederror"
else: else:
obj = None obj = None
@ -928,7 +929,7 @@ class XGBRFRegressor(XGBRegressor):
def __init__(self, max_depth=3, learning_rate=1, n_estimators=100, def __init__(self, max_depth=3, learning_rate=1, n_estimators=100,
verbosity=1, silent=None, verbosity=1, silent=None,
objective="reg:linear", n_jobs=1, nthread=None, gamma=0, objective="reg:squarederror", n_jobs=1, nthread=None, gamma=0,
min_child_weight=1, max_delta_step=0, subsample=0.8, colsample_bytree=1, min_child_weight=1, max_delta_step=0, subsample=0.8, colsample_bytree=1,
colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1, colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1,
scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None,

2
tests/cli/machine.conf.in
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@ -1,6 +1,6 @@
# Originally an example in demo/regression/ # Originally an example in demo/regression/
booster = gbtree booster = gbtree
objective = reg:linear objective = reg:squarederror
eta = 1.0 eta = 1.0
gamma = 1.0 gamma = 1.0
seed = 0 seed = 0