def predict(self, tree_input):
# get tree_feature and input to LR, output LR score.
# input format:
# [(idx, value), ..]
tf = map(lambda x:(x + self.__feature_offset, 1), PyFly.tree_features(self.__gbdt, tree_input))
total_feature = tree_input + tf
return PyFly.predict(self.__lr, total_feature)
def predict(self, tree_input):
# get tree_feature and input to LR, output LR score.
# input format:
# [(idx, value), ..]
tf = map(lambda x: (x + self.__feature_offset, 1),
PyFly.tree_features(self.__gbdt, tree_input))
total_feature = tree_input + tf
return PyFly.predict(self.__lr, total_feature)
def __init__(self, tree_model_file, lr_model_file, tree_feature_offset):
self.__gbdt = PyFly.load_gbdt(tree_model_file)
self.__lr = PyFly.load_lr(lr_model_file)
self.__feature_offset = tree_feature_offset
def __init__(self, tree_model_file, lr_model_file, tree_feature_offset):
self.__gbdt = PyFly.load_gbdt(tree_model_file)
self.__lr = PyFly.load_lr(lr_model_file)
self.__feature_offset = tree_feature_offset
