def __init__(self):
super(HeteroSecureBoostingTreeHost, self).__init__()
self.use_missing = False
self.zero_as_missing = False
self.grad_and_hess = None
self.tree_param = DecisionTreeParam() # decision tree param
self.model_param = HeteroSecureBoostParam()
self.complete_secure = False
self.model_name = 'HeteroSecureBoost'
self.enable_goss = False
self.cipher_compressing = False
self.max_sample_weight = None
self.round_decimal = None
self.new_ver = True
self.boosting_strategy = consts.STD_TREE
# fast sbt param
self.tree_num_per_party = 1
self.guest_depth = 0
self.host_depth = 0
self.init_tree_plan = False
self.tree_plan = []
self.feature_importances_ = {}
# EINI predict param
self.EINI_inference = False
self.EINI_random_mask = False
self.EINI_complexity_check = False
self.multi_mode = consts.SINGLE_OUTPUT
self.hetero_sbt_transfer_variable = HeteroSecureBoostTransferVariable()
def test_param_embedding(self):
boosting_tree_param = HeteroSecureBoostParam()
extractor = ParamExtract()
boosting_tree_param = extractor.parse_param_from_config(
boosting_tree_param, self.config_dict)
print("boosting_tree_param.tree_param.criterion_method {}".format(
boosting_tree_param.tree_param.criterion_method))
self.assertTrue(boosting_tree_param.tree_param.criterion_method ==
"test_decisiontree")
def setUp(self):
self.init_param = InitParam()
self.boosting_tree_param = HeteroSecureBoostParam()
self.config_dict = \
{"HeteroSecureBoostParam": {
"init_param": {"init_method": "test_init", "fit_intercept": False},
"tree_param": {"criterion_method": "test_decisiontree"},
"task_type": "test_boostingtree",
"test_variable": "test"}
}
def __init__(self):
super(HeteroSecureBoostingTreeGuest, self).__init__()
self.tree_param = None # decision tree param
self.use_missing = False
self.zero_as_missing = False
self.cur_epoch_idx = -1
self.grad_and_hess = None
self.feature_importances_ = {}
self.model_param = HeteroSecureBoostParam()
self.complete_secure = False
self.data_alignment_map = {}
self.predict_transfer_inst = HeteroSecureBoostTransferVariable()
self.model_name = 'HeteroSecureBoost'
def __init__(self):
super(HeteroSecureBoostingTreeHost, self).__init__()
self.tree_param = None # decision tree param
self.use_missing = False
self.zero_as_missing = False
self.cur_epoch_idx = -1
self.grad_and_hess = None
self.model_param = HeteroSecureBoostParam()
self.complete_secure = False
self.model_name = 'HeteroSecureBoost'
# for fast hist
self.sparse_opt_para = False
self.run_sparse_opt = False
self.has_transformed_data = False
self.data_bin_dense = None
self.predict_transfer_inst = HeteroSecureBoostTransferVariable()
def __init__(self):
super(HeteroSecureBoostingTreeGuest, self).__init__()
self.tree_param = DecisionTreeParam() # decision tree param
self.use_missing = False
self.zero_as_missing = False
self.cur_epoch_idx = -1
self.grad_and_hess = None
self.feature_importances_ = {}
self.model_param = HeteroSecureBoostParam()
self.complete_secure = False
self.data_alignment_map = {}
self.hetero_sbt_transfer_variable = HeteroSecureBoostTransferVariable()
self.model_name = 'HeteroSecureBoost'
self.max_sample_weight = 1
self.max_sample_weight_computed = False
self.re_compute_goss_sample_weight = False
self.cipher_compressing = False
self.enable_goss = False # GOSS
self.top_rate = None
self.other_rate = None
self.new_ver = True
self.boosting_strategy = consts.STD_TREE # default work mode is std
# fast sbt param
self.tree_num_per_party = 1
self.guest_depth = 0
self.host_depth = 0
self.init_tree_plan = False
self.tree_plan = []
# multi-classification mode
self.multi_mode = consts.SINGLE_OUTPUT
# EINI predict param
self.EINI_inference = False
self.EINI_random_mask = False
def __init__(self):
super(HeteroSecureBoostingTreeHost, self).__init__()
self.use_missing = False
self.zero_as_missing = False
self.cur_epoch_idx = -1
self.grad_and_hess = None
self.tree_param = DecisionTreeParam() # decision tree param
self.model_param = HeteroSecureBoostParam()
self.complete_secure = False
self.model_name = 'HeteroSecureBoost'
self.enable_goss = False
self.cipher_compressing = False
self.max_sample_weight = None
self.round_decimal = None
self.new_ver = True
# for fast hist
self.sparse_opt_para = False
self.run_sparse_opt = False
self.has_transformed_data = False
self.data_bin_dense = None
self.predict_transfer_inst = HeteroSecureBoostTransferVariable()
def __init__(self):
super(HeteroSecureBoostingTreeGuest, self).__init__()
self.tree_param = DecisionTreeParam() # decision tree param
self.use_missing = False
self.zero_as_missing = False
self.cur_epoch_idx = -1
self.grad_and_hess = None
self.feature_importances_ = {}
self.model_param = HeteroSecureBoostParam()
self.complete_secure = False
self.data_alignment_map = {}
self.predict_transfer_inst = HeteroSecureBoostTransferVariable()
self.model_name = 'HeteroSecureBoost'
self.max_sample_weight = 1
self.max_sample_weight_computed = False
self.cipher_compressing = False
self.enable_goss = False # GOSS
self.top_rate = None
self.other_rate = None
self.new_ver = True
def test_undefine_variable_extract(self):
boosting_tree_param = HeteroSecureBoostParam()
extractor = ParamExtract()
boosting_tree_param = extractor.parse_param_from_config(
boosting_tree_param, self.config_dict)
self.assertTrue(not hasattr(boosting_tree_param, "test_variable"))
def test_directly_extract(self):
boosting_tree_param = HeteroSecureBoostParam()
extractor = ParamExtract()
boosting_tree_param = extractor.parse_param_from_config(
boosting_tree_param, self.config_dict)
self.assertTrue(boosting_tree_param.task_type == "test_boostingtree")