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add more params

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HangLi 2015-04-24 08:50:22 -07:00
parent fcb833373b
commit 0058ebac9a
1 changed files with 18 additions and 7 deletions
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25
wrapper/xgboost.py
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@ -764,20 +764,27 @@ class XGBModel(BaseEstimator):
nthread : int
Number of parallel threads used to run xgboost.
gamma : float
Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight : int
minimum sum of instance weight(hessian) needed in a child.
Minimum sum of instance weight(hessian) needed in a child.
max_delta_step : int
Maximum delta step we allow each tree's weight estimation to be.
subsample : float
Subsample ratio of the training instance.
colsample_bytree : float
Subsample ratio of columns when constructing each tree.
base_score:
The initial prediction score of all instances, global bias.
eval_metric : string
Evaluation metrics for validation data.
seed : int
Random number seed.
"""
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="reg:linear",
nthread=-1, min_child_weight=1, subsample=1, colsample_bytree=1,
eval_metric='error', seed=0):
nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1,
base_score=0.5, eval_metric='error', seed=0):
if not SKLEARN_INSTALLED:
raise Exception('sklearn needs to be installed in order to use this module')
self.max_depth = max_depth
@ -787,9 +794,13 @@ class XGBModel(BaseEstimator):
self.objective = objective
self.nthread = nthread
self.gamma = gamma
self.min_child_weight = min_child_weight
self.max_delta_step = max_delta_step
self.subsample = subsample
self.colsample_bytree = colsample_bytree
self.base_score = base_score
self.eval_metric = eval_metric
self.seed = seed
@ -816,11 +827,11 @@ class XGBModel(BaseEstimator):
class XGBClassifier(XGBModel, ClassifierMixin):
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="binary:logistic",
nthread=-1, min_child_weight=1, subsample=1, colsample_bytree=1,
eval_metric='error', seed=0):
nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1,
base_score=0.5, eval_metric='error', seed=0):
super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective,
nthread, min_child_weight, subsample, colsample_bytree,
eval_metric, seed)
nthread, gamma, min_child_weight, max_delta_step, subsample, colsample_bytree,
base_score, eval_metric, seed)
def fit(self, X, y, sample_weight=None):
y_values = list(np.unique(y))