Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

add more params

Browse Source
This commit is contained in:
HangLi 2015-04-24 08:50:22 -07:00
parent fcb833373b
commit 0058ebac9a
1 changed files with 18 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

25
wrapper/xgboost.py
View File

@ -764,20 +764,27 @@ class XGBModel(BaseEstimator):
nthread : int nthread : int
Number of parallel threads used to run xgboost. 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 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 : float
Subsample ratio of the training instance. Subsample ratio of the training instance.
colsample_bytree : float colsample_bytree : float
Subsample ratio of columns when constructing each tree. Subsample ratio of columns when constructing each tree.
base_score:
The initial prediction score of all instances, global bias.
eval_metric : string eval_metric : string
Evaluation metrics for validation data. Evaluation metrics for validation data.
seed : int seed : int
Random number seed. Random number seed.
""" """
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="reg:linear", 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, nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1,
eval_metric='error', seed=0): base_score=0.5, eval_metric='error', seed=0):
if not SKLEARN_INSTALLED: if not SKLEARN_INSTALLED:
raise Exception('sklearn needs to be installed in order to use this module') raise Exception('sklearn needs to be installed in order to use this module')
self.max_depth = max_depth self.max_depth = max_depth
@ -787,9 +794,13 @@ class XGBModel(BaseEstimator):
self.objective = objective self.objective = objective
self.nthread = nthread self.nthread = nthread
self.gamma = gamma
self.min_child_weight = min_child_weight self.min_child_weight = min_child_weight
self.max_delta_step = max_delta_step
self.subsample = subsample self.subsample = subsample
self.colsample_bytree = colsample_bytree self.colsample_bytree = colsample_bytree
self.base_score = base_score
self.eval_metric = eval_metric self.eval_metric = eval_metric
self.seed = seed self.seed = seed
@ -816,11 +827,11 @@ class XGBModel(BaseEstimator):
class XGBClassifier(XGBModel, ClassifierMixin): class XGBClassifier(XGBModel, ClassifierMixin):
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="binary:logistic", 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, nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1,
eval_metric='error', seed=0): base_score=0.5, eval_metric='error', seed=0):
super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective, super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective,
nthread, min_child_weight, subsample, colsample_bytree, nthread, gamma, min_child_weight, max_delta_step, subsample, colsample_bytree,
eval_metric, seed) base_score, eval_metric, seed)
def fit(self, X, y, sample_weight=None): def fit(self, X, y, sample_weight=None):
y_values = list(np.unique(y)) y_values = list(np.unique(y))