#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright 2019 The FATE Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pipeline.param.base_param import BaseParam from pipeline.param.encrypt_param import EncryptParam from pipeline.param.encrypted_mode_calculation_param import EncryptedModeCalculatorParam from pipeline.param.cross_validation_param import CrossValidationParam from pipeline.param.predict_param import PredictParam from pipeline.param import consts from pipeline.param.callback_param import CallbackParam import copy import collections class ObjectiveParam(BaseParam): """ Define objective parameters that used in federated ml. Parameters ---------- objective : {None, 'cross_entropy', 'lse', 'lae', 'log_cosh', 'tweedie', 'fair', 'huber'} None in host's config, should be str in guest'config. when task_type is classification, only support 'cross_entropy', other 6 types support in regression task params : None or list should be non empty list when objective is 'tweedie','fair','huber', first element of list shoulf be a float-number large than 0.0 when objective is 'fair', 'huber', first element of list should be a float-number in [1.0, 2.0) when objective is 'tweedie' """ def __init__(self, objective='cross_entropy', params=None): self.objective = objective self.params = params def check(self, task_type=None): if self.objective is None: return True descr = "objective param's" if task_type not in [consts.CLASSIFICATION, consts.REGRESSION]: self.objective = self.check_and_change_lower(self.objective, ["cross_entropy", "lse", "lae", "huber", "fair", "log_cosh", "tweedie"], descr) if task_type == consts.CLASSIFICATION: if self.objective != "cross_entropy": raise ValueError("objective param's objective {} not supported".format(self.objective)) elif task_type == consts.REGRESSION: self.objective = self.check_and_change_lower(self.objective, ["lse", "lae", "huber", "fair", "log_cosh", "tweedie"], descr) params = self.params if self.objective in ["huber", "fair", "tweedie"]: if type(params).__name__ != 'list' or len(params) < 1: raise ValueError( "objective param's params {} not supported, should be non-empty list".format(params)) if type(params[0]).__name__ not in ["float", "int", "long"]: raise ValueError("objective param's params[0] {} not supported".format(self.params[0])) if self.objective == 'tweedie': if params[0] < 1 or params[0] >= 2: raise ValueError("in tweedie regression, objective params[0] should betweend [1, 2)") if self.objective == 'fair' or 'huber': if params[0] <= 0.0: raise ValueError("in {} regression, objective params[0] should greater than 0.0".format( self.objective)) return True class DecisionTreeParam(BaseParam): """ Define decision tree parameters that used in federated ml. Parameters ---------- criterion_method : {"xgboost"}, default: "xgboost" the criterion function to use criterion_params: list or dict should be non empty and elements are float-numbers, if a list is offered, the first one is l2 regularization value, and the second one is l1 regularization value. if a dict is offered, make sure it contains key 'l1', and 'l2'. l1, l2 regularization values are non-negative floats. default: [0.1, 0] or {'l1':0, 'l2':0,1} max_depth: positive integer the max depth of a decision tree, default: 3 min_sample_split: int least quantity of nodes to split, default: 2 min_impurity_split: float least gain of a single split need to reach, default: 1e-3 min_child_weight: float sum of hessian needed in child nodes. default is 0 min_leaf_node: int when samples no more than min_leaf_node, it becomes a leave, default: 1 max_split_nodes: positive integer we will use no more than max_split_nodes to parallel finding their splits in a batch, for memory consideration. default is 65536 feature_importance_type: {'split', 'gain'} if is 'split', feature_importances calculate by feature split times, if is 'gain', feature_importances calculate by feature split gain. default: 'split' Due to the safety concern, we adjust training strategy of Hetero-SBT in FATE-1.8, When running Hetero-SBT, this parameter is now abandoned. In Hetero-SBT of FATE-1.8, guest side will compute split, gain of local features, and receive anonymous feature importance results from hosts. Hosts will compute split importance of local features. use_missing: bool, accepted True, False only, use missing value in training process or not. default: False zero_as_missing: bool regard 0 as missing value or not, will be use only if use_missing=True, default: False deterministic: bool ensure stability when computing histogram. Set this to true to ensure stable result when using same data and same parameter. But it may slow down computation. """ def __init__(self, criterion_method="xgboost", criterion_params=[0.1, 0], max_depth=3, min_sample_split=2, min_impurity_split=1e-3, min_leaf_node=1, max_split_nodes=consts.MAX_SPLIT_NODES, feature_importance_type='split', n_iter_no_change=True, tol=0.001, min_child_weight=0, use_missing=False, zero_as_missing=False, deterministic=False): super(DecisionTreeParam, self).__init__() self.criterion_method = criterion_method self.criterion_params = criterion_params self.max_depth = max_depth self.min_sample_split = min_sample_split self.min_impurity_split = min_impurity_split self.min_leaf_node = min_leaf_node self.min_child_weight = min_child_weight self.max_split_nodes = max_split_nodes self.feature_importance_type = feature_importance_type self.n_iter_no_change = n_iter_no_change self.tol = tol self.use_missing = use_missing self.zero_as_missing = zero_as_missing self.deterministic = deterministic def check(self): descr = "decision tree param" self.criterion_method = self.check_and_change_lower(self.criterion_method, ["xgboost"], descr) if len(self.criterion_params) == 0: raise ValueError("decisition tree param's criterio_params should be non empty") if isinstance(self.criterion_params, list): assert len(self.criterion_params) == 2, 'length of criterion_param should be 2: l1, l2 regularization ' \ 'values are needed' self.check_nonnegative_number(self.criterion_params[0], 'l2 reg value') self.check_nonnegative_number(self.criterion_params[1], 'l1 reg value') elif isinstance(self.criterion_params, dict): assert 'l1' in self.criterion_params and 'l2' in self.criterion_params, 'l1 and l2 keys are needed in ' \ 'criterion_params dict' self.criterion_params = [self.criterion_params['l2'], self.criterion_params['l1']] else: raise ValueError('criterion_params should be a dict or a list contains l1, l2 reg value') if type(self.max_depth).__name__ not in ["int", "long"]: raise ValueError("decision tree param's max_depth {} not supported, should be integer".format( self.max_depth)) if self.max_depth < 1: raise ValueError("decision tree param's max_depth should be positive integer, no less than 1") if type(self.min_sample_split).__name__ not in ["int", "long"]: raise ValueError("decision tree param's min_sample_split {} not supported, should be integer".format( self.min_sample_split)) if type(self.min_impurity_split).__name__ not in ["int", "long", "float"]: raise ValueError("decision tree param's min_impurity_split {} not supported, should be numeric".format( self.min_impurity_split)) if type(self.min_leaf_node).__name__ not in ["int", "long"]: raise ValueError("decision tree param's min_leaf_node {} not supported, should be integer".format( self.min_leaf_node)) if type(self.max_split_nodes).__name__ not in ["int", "long"] or self.max_split_nodes < 1: raise ValueError("decision tree param's max_split_nodes {} not supported, " + "should be positive integer between 1 and {}".format(self.max_split_nodes, consts.MAX_SPLIT_NODES)) if type(self.n_iter_no_change).__name__ != "bool": raise ValueError("decision tree param's n_iter_no_change {} not supported, should be bool type".format( self.n_iter_no_change)) if type(self.tol).__name__ not in ["float", "int", "long"]: raise ValueError("decision tree param's tol {} not supported, should be numeric".format(self.tol)) self.feature_importance_type = self.check_and_change_lower(self.feature_importance_type, ["split", "gain"], descr) self.check_nonnegative_number(self.min_child_weight, 'min_child_weight') self.check_boolean(self.deterministic, 'deterministic') return True class BoostingParam(BaseParam): """ Basic parameter for Boosting Algorithms Parameters ---------- task_type : {'classification', 'regression'}, default: 'classification' task type objective_param : ObjectiveParam Object, default: ObjectiveParam() objective param learning_rate : float, int or long the learning rate of secure boost. default: 0.3 num_trees : int or float the max number of boosting round. default: 5 subsample_feature_rate : float a float-number in [0, 1], default: 1.0 n_iter_no_change : bool, when True and residual error less than tol, tree building process will stop. default: True bin_num: positive integer greater than 1 bin number use in quantile. default: 32 validation_freqs: None or positive integer or container object in python Do validation in training process or Not. if equals None, will not do validation in train process; if equals positive integer, will validate data every validation_freqs epochs passes; if container object in python, will validate data if epochs belong to this container. e.g. validation_freqs = [10, 15], will validate data when epoch equals to 10 and 15. Default: None """ def __init__(self, task_type=consts.CLASSIFICATION, objective_param=ObjectiveParam(), learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True, tol=0.0001, bin_num=32, predict_param=PredictParam(), cv_param=CrossValidationParam(), validation_freqs=None, metrics=None, random_seed=100, binning_error=consts.DEFAULT_RELATIVE_ERROR): super(BoostingParam, self).__init__() self.task_type = task_type self.objective_param = copy.deepcopy(objective_param) self.learning_rate = learning_rate self.num_trees = num_trees self.subsample_feature_rate = subsample_feature_rate self.n_iter_no_change = n_iter_no_change self.tol = tol self.bin_num = bin_num self.predict_param = copy.deepcopy(predict_param) self.cv_param = copy.deepcopy(cv_param) self.validation_freqs = validation_freqs self.metrics = metrics self.random_seed = random_seed self.binning_error = binning_error def check(self): descr = "boosting tree param's" if self.task_type not in [consts.CLASSIFICATION, consts.REGRESSION]: raise ValueError("boosting_core tree param's task_type {} not supported, should be {} or {}".format( self.task_type, consts.CLASSIFICATION, consts.REGRESSION)) self.objective_param.check(self.task_type) if type(self.learning_rate).__name__ not in ["float", "int", "long"]: raise ValueError("boosting_core tree param's learning_rate {} not supported, should be numeric".format( self.learning_rate)) if type(self.subsample_feature_rate).__name__ not in ["float", "int", "long"] or \ self.subsample_feature_rate < 0 or self.subsample_feature_rate > 1: raise ValueError( "boosting_core tree param's subsample_feature_rate should be a numeric number between 0 and 1") if type(self.n_iter_no_change).__name__ != "bool": raise ValueError("boosting_core tree param's n_iter_no_change {} not supported, should be bool type".format( self.n_iter_no_change)) if type(self.tol).__name__ not in ["float", "int", "long"]: raise ValueError("boosting_core tree param's tol {} not supported, should be numeric".format(self.tol)) if type(self.bin_num).__name__ not in ["int", "long"] or self.bin_num < 2: raise ValueError( "boosting_core tree param's bin_num {} not supported, should be positive integer greater than 1".format( self.bin_num)) if self.validation_freqs is None: pass elif isinstance(self.validation_freqs, int): if self.validation_freqs < 1: raise ValueError("validation_freqs should be larger than 0 when it's integer") elif not isinstance(self.validation_freqs, collections.Container): raise ValueError("validation_freqs should be None or positive integer or container") if self.metrics is not None and not isinstance(self.metrics, list): raise ValueError("metrics should be a list") if self.random_seed is not None: assert isinstance(self.random_seed, int) and self.random_seed >= 0, 'random seed must be an integer >= 0' self.check_decimal_float(self.binning_error, descr) return True class HeteroBoostingParam(BoostingParam): """ Parameters ---------- encrypt_param : EncodeParam Object encrypt method use in secure boost, default: EncryptParam() encrypted_mode_calculator_param: EncryptedModeCalculatorParam object the calculation mode use in secureboost, default: EncryptedModeCalculatorParam() """ def __init__(self, task_type=consts.CLASSIFICATION, objective_param=ObjectiveParam(), learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True, tol=0.0001, encrypt_param=EncryptParam(), bin_num=32, encrypted_mode_calculator_param=EncryptedModeCalculatorParam(), predict_param=PredictParam(), cv_param=CrossValidationParam(), validation_freqs=None, early_stopping_rounds=None, metrics=None, use_first_metric_only=False, random_seed=100, binning_error=consts.DEFAULT_RELATIVE_ERROR): super(HeteroBoostingParam, self).__init__(task_type, objective_param, learning_rate, num_trees, subsample_feature_rate, n_iter_no_change, tol, bin_num, predict_param, cv_param, validation_freqs, metrics=metrics, random_seed=random_seed, binning_error=binning_error) self.encrypt_param = copy.deepcopy(encrypt_param) self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param) self.early_stopping_rounds = early_stopping_rounds self.use_first_metric_only = use_first_metric_only def check(self): super(HeteroBoostingParam, self).check() self.encrypted_mode_calculator_param.check() self.encrypt_param.check() if self.early_stopping_rounds is None: pass elif isinstance(self.early_stopping_rounds, int): if self.early_stopping_rounds < 1: raise ValueError("early stopping rounds should be larger than 0 when it's integer") if self.validation_freqs is None: raise ValueError("validation freqs must be set when early stopping is enabled") if not isinstance(self.use_first_metric_only, bool): raise ValueError("use_first_metric_only should be a boolean") return True class HeteroSecureBoostParam(HeteroBoostingParam): """ Define boosting tree parameters that used in federated ml. Parameters ---------- task_type : {'classification', 'regression'}, default: 'classification' task type tree_param : DecisionTreeParam Object, default: DecisionTreeParam() tree param objective_param : ObjectiveParam Object, default: ObjectiveParam() objective param learning_rate : float, int or long the learning rate of secure boost. default: 0.3 num_trees : int or float the max number of trees to build. default: 5 subsample_feature_rate : float a float-number in [0, 1], default: 1.0 random_seed: int seed that controls all random functions n_iter_no_change : bool, when True and residual error less than tol, tree building process will stop. default: True encrypt_param : EncodeParam Object encrypt method use in secure boost, default: EncryptParam(), this parameter is only for hetero-secureboost bin_num: positive integer greater than 1 bin number use in quantile. default: 32 encrypted_mode_calculator_param: EncryptedModeCalculatorParam object the calculation mode use in secureboost, default: EncryptedModeCalculatorParam(), only for hetero-secureboost use_missing: bool use missing value in training process or not. default: False zero_as_missing: bool regard 0 as missing value or not, will be use only if use_missing=True, default: False validation_freqs: None or positive integer or container object in python Do validation in training process or Not. if equals None, will not do validation in train process; if equals positive integer, will validate data every validation_freqs epochs passes; if container object in python, will validate data if epochs belong to this container. e.g. validation_freqs = [10, 15], will validate data when epoch equals to 10 and 15. Default: None The default value is None, 1 is suggested. You can set it to a number larger than 1 in order to speed up training by skipping validation rounds. When it is larger than 1, a number which is divisible by "num_trees" is recommended, otherwise, you will miss the validation scores of last training iteration. early_stopping_rounds: integer larger than 0 will stop training if one metric of one validation data doesn’t improve in last early_stopping_round rounds， need to set validation freqs and will check early_stopping every at every validation epoch, metrics: list, default: [] Specify which metrics to be used when performing evaluation during training process. If set as empty, default metrics will be used. For regression tasks, default metrics are ['root_mean_squared_error', 'mean_absolute_error']， For binary-classificatiin tasks, default metrics are ['auc', 'ks']. For multi-classification tasks, default metrics are ['accuracy', 'precision', 'recall'] use_first_metric_only: bool use only the first metric for early stopping complete_secure: bool if use complete_secure, when use complete secure, build first tree using only guest features sparse_optimization: this parameter is abandoned in FATE-1.7.1 run_goss: bool activate Gradient-based One-Side Sampling, which selects large gradient and small gradient samples using top_rate and other_rate. top_rate: float, the retain ratio of large gradient data, used when run_goss is True other_rate: float, the retain ratio of small gradient data, used when run_goss is True cipher_compress_error： This param is now abandoned cipher_compress: bool, default is True, use cipher compressing to reduce computation cost and transfer cost boosting_strategy：str std: standard sbt setting mix: alternate using guest/host features to build trees. For example, the first 'tree_num_per_party' trees use guest features, the second k trees use host features, and so on layered: only support 2 party, when running layered mode, first 'host_depth' layer will use host features, and then next 'guest_depth' will only use guest features work_mode: str This parameter has the same function as boosting_strategy, but is deprecated tree_num_per_party: int, every party will alternate build 'tree_num_per_party' trees until reach max tree num, this param is valid when boosting_strategy is mix guest_depth: int, guest will build last guest_depth of a decision tree using guest features, is valid when boosting_strategy is layered host_depth: int, host will build first host_depth of a decision tree using host features, is valid when work boosting_strategy layered multi_mode: str, decide which mode to use when running multi-classification task: single_output standard gbdt multi-classification strategy multi_output every leaf give a multi-dimension predict, using multi_mode can save time by learning a model with less trees. EINI_inference: bool default is False, this option changes the inference algorithm used in predict tasks. a secure prediction method that hides decision path to enhance security in the inference step. This method is insprired by EINI inference algorithm. EINI_random_mask: bool default is False multiply predict result by a random float number to confuse original predict result. This operation further enhances the security of naive EINI algorithm. EINI_complexity_check: bool default is False check the complexity of tree models when running EINI algorithms. Complexity models are easy to hide their decision path, while simple tree models are not, therefore if a tree model is too simple, it is not allowed to run EINI predict algorithms. """ def __init__(self, tree_param: DecisionTreeParam = DecisionTreeParam(), task_type=consts.CLASSIFICATION, objective_param=ObjectiveParam(), learning_rate=0.3, num_trees=5, subsample_feature_rate=1.0, n_iter_no_change=True, tol=0.0001, encrypt_param=EncryptParam(), bin_num=32, encrypted_mode_calculator_param=EncryptedModeCalculatorParam(), predict_param=PredictParam(), cv_param=CrossValidationParam(), validation_freqs=None, early_stopping_rounds=None, use_missing=False, zero_as_missing=False, complete_secure=False, metrics=None, use_first_metric_only=False, random_seed=100, binning_error=consts.DEFAULT_RELATIVE_ERROR, sparse_optimization=False, run_goss=False, top_rate=0.2, other_rate=0.1, cipher_compress_error=None, cipher_compress=True, new_ver=True, boosting_strategy=consts.STD_TREE, work_mode=None, tree_num_per_party=1, guest_depth=2, host_depth=3, callback_param=CallbackParam(), multi_mode=consts.SINGLE_OUTPUT, EINI_inference=False, EINI_random_mask=False, EINI_complexity_check=False): super(HeteroSecureBoostParam, self).__init__(task_type, objective_param, learning_rate, num_trees, subsample_feature_rate, n_iter_no_change, tol, encrypt_param, bin_num, encrypted_mode_calculator_param, predict_param, cv_param, validation_freqs, early_stopping_rounds, metrics=metrics, use_first_metric_only=use_first_metric_only, random_seed=random_seed, binning_error=binning_error) self.tree_param = copy.deepcopy(tree_param) self.zero_as_missing = zero_as_missing self.use_missing = use_missing self.complete_secure = complete_secure self.sparse_optimization = sparse_optimization self.run_goss = run_goss self.top_rate = top_rate self.other_rate = other_rate self.cipher_compress_error = cipher_compress_error self.cipher_compress = cipher_compress self.new_ver = new_ver self.EINI_inference = EINI_inference self.EINI_random_mask = EINI_random_mask self.EINI_complexity_check = EINI_complexity_check self.boosting_strategy = boosting_strategy self.work_mode = work_mode self.tree_num_per_party = tree_num_per_party self.guest_depth = guest_depth self.host_depth = host_depth self.callback_param = copy.deepcopy(callback_param) self.multi_mode = multi_mode def check(self): super(HeteroSecureBoostParam, self).check() self.tree_param.check() if not isinstance(self.use_missing, bool): raise ValueError('use missing should be bool type') if not isinstance(self.zero_as_missing, bool): raise ValueError('zero as missing should be bool type') self.check_boolean(self.complete_secure, 'complete_secure') self.check_boolean(self.run_goss, 'run goss') self.check_decimal_float(self.top_rate, 'top rate') self.check_decimal_float(self.other_rate, 'other rate') self.check_positive_number(self.other_rate, 'other_rate') self.check_positive_number(self.top_rate, 'top_rate') self.check_boolean(self.new_ver, 'code version switcher') self.check_boolean(self.cipher_compress, 'cipher compress') self.check_boolean(self.EINI_inference, 'eini inference') self.check_boolean(self.EINI_random_mask, 'eini random mask') self.check_boolean(self.EINI_complexity_check, 'eini complexity check') if self.work_mode is not None: self.boosting_strategy = self.work_mode if self.multi_mode not in [consts.SINGLE_OUTPUT, consts.MULTI_OUTPUT]: raise ValueError('unsupported multi-classification mode') if self.multi_mode == consts.MULTI_OUTPUT: if self.boosting_strategy != consts.STD_TREE: raise ValueError('MO trees only works when boosting strategy is std tree') if not self.cipher_compress: raise ValueError('Mo trees only works when cipher compress is enabled') if self.boosting_strategy not in [consts.STD_TREE, consts.LAYERED_TREE, consts.MIX_TREE]: raise ValueError('unknown sbt boosting strategy{}'.format(self.boosting_strategy)) for p in ["early_stopping_rounds", "validation_freqs", "metrics", "use_first_metric_only"]: # if self._warn_to_deprecate_param(p, "", ""): if self._deprecated_params_set.get(p): if "callback_param" in self.get_user_feeded(): raise ValueError(f"{p} and callback param should not be set simultaneously，" f"{self._deprecated_params_set}, {self.get_user_feeded()}") else: self.callback_param.callbacks = ["PerformanceEvaluate"] break descr = "boosting_param's" if self._warn_to_deprecate_param("validation_freqs", descr, "callback_param's 'validation_freqs'"): self.callback_param.validation_freqs = self.validation_freqs if self._warn_to_deprecate_param("early_stopping_rounds", descr, "callback_param's 'early_stopping_rounds'"): self.callback_param.early_stopping_rounds = self.early_stopping_rounds if self._warn_to_deprecate_param("metrics", descr, "callback_param's 'metrics'"): self.callback_param.metrics = self.metrics if self._warn_to_deprecate_param("use_first_metric_only", descr, "callback_param's 'use_first_metric_only'"): self.callback_param.use_first_metric_only = self.use_first_metric_only if self.top_rate + self.other_rate >= 1: raise ValueError('sum of top rate and other rate should be smaller than 1') return True class HomoSecureBoostParam(BoostingParam): """ Parameters ---------- backend: {'distributed', 'memory'} decides which backend to use when computing histograms for homo-sbt """ def __init__(self, tree_param: DecisionTreeParam = DecisionTreeParam(), task_type=consts.CLASSIFICATION, objective_param=ObjectiveParam(), learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True, tol=0.0001, bin_num=32, predict_param=PredictParam(), cv_param=CrossValidationParam(), validation_freqs=None, use_missing=False, zero_as_missing=False, random_seed=100, binning_error=consts.DEFAULT_RELATIVE_ERROR, backend=consts.DISTRIBUTED_BACKEND, callback_param=CallbackParam(), multi_mode=consts.SINGLE_OUTPUT): super(HomoSecureBoostParam, self).__init__(task_type=task_type, objective_param=objective_param, learning_rate=learning_rate, num_trees=num_trees, subsample_feature_rate=subsample_feature_rate, n_iter_no_change=n_iter_no_change, tol=tol, bin_num=bin_num, predict_param=predict_param, cv_param=cv_param, validation_freqs=validation_freqs, random_seed=random_seed, binning_error=binning_error ) self.use_missing = use_missing self.zero_as_missing = zero_as_missing self.tree_param = copy.deepcopy(tree_param) self.backend = backend self.callback_param = copy.deepcopy(callback_param) self.multi_mode = multi_mode def check(self): super(HomoSecureBoostParam, self).check() self.tree_param.check() if not isinstance(self.use_missing, bool): raise ValueError('use missing should be bool type') if not isinstance(self.zero_as_missing, bool): raise ValueError('zero as missing should be bool type') if self.backend not in [consts.MEMORY_BACKEND, consts.DISTRIBUTED_BACKEND]: raise ValueError('unsupported backend') if self.multi_mode not in [consts.SINGLE_OUTPUT, consts.MULTI_OUTPUT]: raise ValueError('unsupported multi-classification mode') for p in ["validation_freqs", "metrics"]: # if self._warn_to_deprecate_param(p, "", ""): if self._deprecated_params_set.get(p): if "callback_param" in self.get_user_feeded(): raise ValueError(f"{p} and callback param should not be set simultaneously，" f"{self._deprecated_params_set}, {self.get_user_feeded()}") else: self.callback_param.callbacks = ["PerformanceEvaluate"] break descr = "boosting_param's" if self._warn_to_deprecate_param("validation_freqs", descr, "callback_param's 'validation_freqs'"): self.callback_param.validation_freqs = self.validation_freqs if self._warn_to_deprecate_param("metrics", descr, "callback_param's 'metrics'"): self.callback_param.metrics = self.metrics if self.multi_mode not in [consts.SINGLE_OUTPUT, consts.MULTI_OUTPUT]: raise ValueError('unsupported multi-classification mode') if self.multi_mode == consts.MULTI_OUTPUT: if self.task_type == consts.REGRESSION: raise ValueError('regression tasks not support multi-output trees') return True