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*Fix Sklearn.grid_search error

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white1033 2015-04-07 23:57:01 +08:00
parent e626b62daa
commit b4545df0e3
1 changed files with 15 additions and 11 deletions
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26
wrapper/xgboost.py
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@ -763,26 +763,30 @@ class XGBModel(BaseEstimator):
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
self.eta = learning_rate self.learning_rate = learning_rate
self.silent = 1 if silent else 0 self.silent = silent
self.n_rounds = n_estimators self.n_estimators = n_estimators
self.objective = objective self.objective = objective
self._Booster = Booster() self._Booster = Booster()
def get_params(self, deep=True): def get_params(self, deep=True):
return {'max_depth': self.max_depth, return {'max_depth': self.max_depth,
'learning_rate': self.eta, 'learning_rate': self.learning_rate,
'n_estimators': self.n_rounds, 'n_estimators': self.n_estimators,
'silent': True if self.silent == 1 else False, 'silent': self.silent,
'objective': self.objective 'objective': self.objective
} }
def get_xgb_params(self): def get_xgb_params(self):
return {'eta': self.eta, 'max_depth': self.max_depth, 'silent': self.silent, 'objective': self.objective} return {'eta': self.learning_rate,
'max_depth': self.max_depth,
'silent': 1 if self.silent else 0,
'objective': self.objective
}
def fit(self, X, y): def fit(self, X, y):
trainDmatrix = DMatrix(X, label=y) trainDmatrix = DMatrix(X, label=y)
self._Booster = train(self.get_xgb_params(), trainDmatrix, self.n_rounds) self._Booster = train(self.get_xgb_params(), trainDmatrix, self.n_estimators)
return self return self
def predict(self, X): def predict(self, X):
@ -791,8 +795,8 @@ 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): def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="binary:logistic"):
super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective="binary:logistic") super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective)
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))
@ -812,7 +816,7 @@ class XGBClassifier(XGBModel, ClassifierMixin):
else: else:
trainDmatrix = DMatrix(X, label=training_labels) trainDmatrix = DMatrix(X, label=training_labels)
self._Booster = train(xgb_options, trainDmatrix, self.n_rounds) self._Booster = train(xgb_options, trainDmatrix, self.n_estimators)
return self return self