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added missing params

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This commit is contained in:
Faron 2015-10-25 10:58:07 +01:00
parent 224f574420
commit 422febd18e
1 changed files with 17 additions and 4 deletions
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21
python-package/xgboost/sklearn.py
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@ -54,6 +54,14 @@ class XGBModel(XGBModelBase):
Subsample ratio of the training instance.
colsample_bytree : float
Subsample ratio of columns when constructing each tree.
colsample_bylevel : float
Subsample ratio of columns for each split, in each level.
reg_alpha : float (xgb's alpha)
L2 regularization term on weights
reg_lambda : float (xgb's lambda)
L1 regularization term on weights
scale_pos_weight : float
Balancing of positive and negative weights.
base_score:
The initial prediction score of all instances, global bias.
@ -66,7 +74,7 @@ class XGBModel(XGBModelBase):
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100,
silent=True, objective="reg:linear",
nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0,
subsample=1, colsample_bytree=1,
subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=1, reg_lambda=0, scale_pos_weight=1,
base_score=0.5, seed=0, missing=None):
if not SKLEARN_INSTALLED:
raise XGBoostError('sklearn needs to be installed in order to use this module')
@ -82,6 +90,10 @@ class XGBModel(XGBModelBase):
self.max_delta_step = max_delta_step
self.subsample = subsample
self.colsample_bytree = colsample_bytree
self.colsample_bylevel = colsample_bylevel
self.reg_alpha = reg_alpha
self.reg_lambda = reg_lambda
self.scale_pos_weight = scale_pos_weight
self.base_score = base_score
self.seed = seed
@ -251,14 +263,15 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
n_estimators=100, silent=True,
objective="binary:logistic",
nthread=-1, gamma=0, min_child_weight=1,
max_delta_step=0, subsample=1, colsample_bytree=1,
max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1,
reg_alpha=1, reg_lambda=0, scale_pos_weight=1,
base_score=0.5, seed=0, missing=None):
super(XGBClassifier, self).__init__(max_depth, learning_rate,
n_estimators, silent, objective,
nthread, gamma, min_child_weight,
max_delta_step, subsample,
colsample_bytree,
base_score, seed, missing)
colsample_bytree, colsample_bylevel, reg_alpha, reg_lambda,
scale_pos_weight, base_score, seed, missing)
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
early_stopping_rounds=None, verbose=True):