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remove eval_metric

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HangLi 2015-04-24 10:37:20 -07:00
parent 0058ebac9a
commit c6d2e16b61
1 changed files with 3 additions and 6 deletions
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9
wrapper/xgboost.py
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@ -777,14 +777,12 @@ class XGBModel(BaseEstimator):
base_score: base_score:
The initial prediction score of all instances, global bias. The initial prediction score of all instances, global bias.
eval_metric : string
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, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, 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): base_score=0.5, 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
@ -801,7 +799,6 @@ class XGBModel(BaseEstimator):
self.colsample_bytree = colsample_bytree self.colsample_bytree = colsample_bytree
self.base_score = base_score self.base_score = base_score
self.eval_metric = eval_metric
self.seed = seed self.seed = seed
self._Booster = Booster() self._Booster = Booster()
@ -828,10 +825,10 @@ 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, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, 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): base_score=0.5, 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, gamma, min_child_weight, max_delta_step, subsample, colsample_bytree, nthread, gamma, min_child_weight, max_delta_step, subsample, colsample_bytree,
base_score, eval_metric, seed) base_score, 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))