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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 pipeline.param.base_param import BaseParam
- class StochasticQuasiNewtonParam(BaseParam):
- """
- Parameters used for stochastic quasi-newton method.
- Parameters
- ----------
- update_interval_L : int, default: 3
- Set how many iteration to update hess matrix
- memory_M : int, default: 5
- Stack size of curvature information, i.e. y_k and s_k in the paper.
- sample_size : int, default: 5000
- Sample size of data that used to update Hess matrix
- """
- def __init__(self, update_interval_L=3, memory_M=5, sample_size=5000, random_seed=None):
- super().__init__()
- self.update_interval_L = update_interval_L
- self.memory_M = memory_M
- self.sample_size = sample_size
- self.random_seed = random_seed
- def check(self):
- descr = "hetero sqn param's"
- self.check_positive_integer(self.update_interval_L, descr)
- self.check_positive_integer(self.memory_M, descr)
- self.check_positive_integer(self.sample_size, descr)
- if self.random_seed is not None:
- self.check_positive_integer(self.random_seed, descr)
- return True
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