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- #!/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 federatedml.param.base_param import BaseParam
- # from federatedml.param.evaluation_param import EvaluateParam
- from federatedml.util import consts
- class CrossValidationParam(BaseParam):
- """
- Define cross validation params
- Parameters
- ----------
- n_splits: int, default: 5
- Specify how many splits used in KFold
- mode: str, default: 'Hetero'
- Indicate what mode is current task
- role: {'Guest', 'Host', 'Arbiter'}, default: 'Guest'
- Indicate what role is current party
- shuffle: bool, default: True
- Define whether do shuffle before KFold or not.
- random_seed: int, default: 1
- Specify the random seed for numpy shuffle
- need_cv: bool, default False
- Indicate if this module needed to be run
- output_fold_history: bool, default True
- Indicate whether to output table of ids used by each fold, else return original input data
- returned ids are formatted as: {original_id}#fold{fold_num}#{train/validate}
- history_value_type: {'score', 'instance'}, default score
- Indicate whether to include original instance or predict score in the output fold history,
- only effective when output_fold_history set to True
- """
- def __init__(self, n_splits=5, mode=consts.HETERO, role=consts.GUEST, shuffle=True, random_seed=1,
- need_cv=False, output_fold_history=True, history_value_type="score"):
- super(CrossValidationParam, self).__init__()
- self.n_splits = n_splits
- self.mode = mode
- self.role = role
- self.shuffle = shuffle
- self.random_seed = random_seed
- # self.evaluate_param = copy.deepcopy(evaluate_param)
- self.need_cv = need_cv
- self.output_fold_history = output_fold_history
- self.history_value_type = history_value_type
- def check(self):
- model_param_descr = "cross validation param's "
- self.check_positive_integer(self.n_splits, model_param_descr)
- self.check_valid_value(self.mode, model_param_descr, valid_values=[consts.HOMO, consts.HETERO])
- self.check_valid_value(self.role, model_param_descr, valid_values=[consts.HOST, consts.GUEST, consts.ARBITER])
- self.check_boolean(self.shuffle, model_param_descr)
- self.check_boolean(self.output_fold_history, model_param_descr)
- self.history_value_type = self.check_and_change_lower(
- self.history_value_type, ["instance", "score"], model_param_descr)
- if self.random_seed is not None:
- self.check_positive_integer(self.random_seed, model_param_descr)
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