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hetero_sshe_linr_param.py
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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.
#
import copy
from federatedml.param.glm_param import LinearModelParam
from federatedml.param.callback_param import CallbackParam
from federatedml.param.encrypt_param import EncryptParam
from federatedml.param.encrypted_mode_calculation_param import EncryptedModeCalculatorParam
from federatedml.param.cross_validation_param import CrossValidationParam
from federatedml.param.init_model_param import InitParam
from federatedml.util import consts
class HeteroSSHELinRParam(LinearModelParam):
"""
Parameters used for Hetero SSHE Linear Regression.
Parameters
----------
penalty : {'L2' or 'L1'}
Penalty method used in LinR. Please note that, when using encrypted version in HeteroLinR,
'L1' is not supported.
tol : float, default: 1e-4
The tolerance of convergence
alpha : float, default: 1.0
Regularization strength coefficient.
optimizer : {'sgd', 'rmsprop', 'adam', 'adagrad'}
Optimize method
batch_size : int, default: -1
Batch size when updating model. -1 means use all data in a batch. i.e. Not to use mini-batch strategy.
learning_rate : float, default: 0.01
Learning rate
max_iter : int, default: 20
The maximum iteration for training.
init_param: InitParam object, default: default InitParam object
Init param method object.
early_stop : {'diff', 'abs', 'weight_dff'}
Method used to judge convergence.
a) diff： Use difference of loss between two iterations to judge whether converge.
b) abs: Use the absolute value of loss to judge whether converge. i.e. if loss < tol, it is converged.
c) weight_diff: Use difference between weights of two consecutive iterations
encrypt_param: EncryptParam object, default: default EncryptParam object
encrypt param
encrypted_mode_calculator_param: EncryptedModeCalculatorParam object, default: default EncryptedModeCalculatorParam object
encrypted mode calculator param
cv_param: CrossValidationParam object, default: default CrossValidationParam object
cv param
decay: int or float, default: 1
Decay rate for learning rate. learning rate will follow the following decay schedule.
lr = lr0/(1+decay*t) if decay_sqrt is False. If decay_sqrt is True, lr = lr0 / sqrt(1+decay*t)
where t is the iter number.
decay_sqrt: Bool, default: True
lr = lr0/(1+decay*t) if decay_sqrt is False, otherwise, lr = lr0 / sqrt(1+decay*t)
callback_param: CallbackParam object
callback param
reveal_strategy: str, "respectively", "encrypted_reveal_in_host", default: "respectively"
"respectively": Means guest and host can reveal their own part of weights only.
"encrypted_reveal_in_host": Means host can be revealed his weights in encrypted mode, and guest can be revealed in normal mode.
reveal_every_iter: bool, default: False
Whether reconstruct model weights every iteration. If so, Regularization is available.
The performance will be better as well since the algorithm process is simplified.
"""
def __init__(self, penalty='L2',
tol=1e-4, alpha=1.0, optimizer='sgd',
batch_size=-1, learning_rate=0.01, init_param=InitParam(),
max_iter=20, early_stop='diff',
encrypt_param=EncryptParam(),
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
cv_param=CrossValidationParam(), decay=1, decay_sqrt=True,
callback_param=CallbackParam(),
use_mix_rand=True,
reveal_strategy="respectively",
reveal_every_iter=False
):
super(HeteroSSHELinRParam, self).__init__(penalty=penalty, tol=tol, alpha=alpha, optimizer=optimizer,
batch_size=batch_size, learning_rate=learning_rate,
init_param=init_param, max_iter=max_iter, early_stop=early_stop,
encrypt_param=encrypt_param, cv_param=cv_param, decay=decay,
decay_sqrt=decay_sqrt,
callback_param=callback_param)
self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param)
self.use_mix_rand = use_mix_rand
self.reveal_strategy = reveal_strategy
self.reveal_every_iter = reveal_every_iter
def check(self):
descr = "sshe linear_regression_param's "
super(HeteroSSHELinRParam, self).check()
if self.encrypt_param.method != consts.PAILLIER:
raise ValueError(
descr + "encrypt method supports 'Paillier' only")
self.check_boolean(self.reveal_every_iter, descr)
if self.penalty is None:
pass
elif type(self.penalty).__name__ != "str":
raise ValueError(
f"{descr} penalty {self.penalty} not supported, should be str type")
else:
self.penalty = self.penalty.upper()
"""
if self.penalty not in [consts.L1_PENALTY, consts.L2_PENALTY]:
raise ValueError(
"logistic_param's penalty not supported, penalty should be 'L1', 'L2' or 'none'")
"""
if not self.reveal_every_iter:
if self.penalty not in [consts.L2_PENALTY, consts.NONE.upper()]:
raise ValueError(
f"penalty should be 'L2' or 'none', when reveal_every_iter is False"
)
if type(self.optimizer).__name__ != "str":
raise ValueError(
f"{descr} optimizer {self.optimizer} not supported, should be str type")
else:
self.optimizer = self.optimizer.lower()
if self.reveal_every_iter:
if self.optimizer not in ['sgd', 'rmsprop', 'adam', 'adagrad']:
raise ValueError(
"When reveal_every_iter is True, "
f"{descr} optimizer not supported, optimizer should be"
" 'sgd', 'rmsprop', 'adam', or 'adagrad'")
else:
if self.optimizer not in ['sgd']:
raise ValueError("When reveal_every_iter is False, "
f"{descr} optimizer not supported, optimizer should be"
" 'sgd'")
if self.callback_param.validation_freqs is not None:
if self.reveal_every_iter is False:
raise ValueError(f"When reveal_every_iter is False, validation every iter"
f" is not supported.")
self.reveal_strategy = self.check_and_change_lower(self.reveal_strategy,
["respectively", "encrypted_reveal_in_host"],
f"{descr} reveal_strategy")
if self.reveal_strategy == "encrypted_reveal_in_host" and self.reveal_every_iter:
raise PermissionError("reveal strategy: encrypted_reveal_in_host mode is not allow to reveal every iter.")
return True