class GradientBoostingClassifierImpl():
def __init__(self,
loss='deviance',
learning_rate=0.1,
n_estimators=100,
subsample=1.0,
criterion='friedman_mse',
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_depth=3,
min_impurity_decrease=0.0,
min_impurity_split=None,
init=None,
random_state=None,
max_features=None,
verbose=0,
max_leaf_nodes=None,
warm_start=False,
presort='auto',
validation_fraction=0.1,
n_iter_no_change=None,
tol=0.0001):
self._hyperparams = {
'loss': loss,
'learning_rate': learning_rate,
'n_estimators': n_estimators,
'subsample': subsample,
'criterion': criterion,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'min_weight_fraction_leaf': min_weight_fraction_leaf,
'max_depth': max_depth,
'min_impurity_decrease': min_impurity_decrease,
'min_impurity_split': min_impurity_split,
'init': init,
'random_state': random_state,
'max_features': max_features,
'verbose': verbose,
'max_leaf_nodes': max_leaf_nodes,
'warm_start': warm_start,
'presort': presort,
'validation_fraction': validation_fraction,
'n_iter_no_change': n_iter_no_change,
'tol': tol
}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
def predict_proba(self, X):
return self._wrapped_model.predict_proba(X)
def decision_function(self, X):
return self._wrapped_model.decision_function(X)
