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hetero_sshe_lr_param.py
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hetero_sshe_lr_param.py
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#
# 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 pipeline.param.logistic_regression_param import LogisticParam
from pipeline.param.cross_validation_param import CrossValidationParam
from pipeline.param.callback_param import CallbackParam
from pipeline.param.encrypt_param import EncryptParam
from pipeline.param.encrypted_mode_calculation_param import EncryptedModeCalculatorParam
from pipeline.param.init_model_param import InitParam
from pipeline.param.predict_param import PredictParam
from pipeline.param import consts
class HeteroSSHELRParam(LogisticParam):
"""
Parameters used for Hetero SSHE Logistic Regression
Parameters
----------
penalty : str, 'L1', 'L2' or None. default: 'L2'
Penalty method used in LR. If it is not None, weights are required to be reconstruct every iter.
tol : float, default: 1e-4
The tolerance of convergence
alpha : float, default: 1.0
Regularization strength coefficient.
optimizer : str, 'sgd', 'rmsprop', 'adam', 'nesterov_momentum_sgd', or 'adagrad', default: 'sgd'
Optimizer
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: 100
The maximum iteration for training.
early_stop : str, 'diff', 'weight_diff' or 'abs', default: 'diff'
Method used to judge converge or not.
a) diff： Use difference of loss between two iterations to judge whether converge.
b) weight_diff: Use difference between weights of two consecutive iterations
c) abs: Use the absolute value of loss to judge whether converge. i.e. if loss < eps, it is converged.
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)
encrypt_param: EncryptParam object, default: default EncryptParam object
encrypt param
predict_param: PredictParam object, default: default PredictParam object
predict param
cv_param: CrossValidationParam object, default: default CrossValidationParam object
cv param
multi_class: str, 'ovr', default: 'ovr'
If it is a multi_class task, indicate what strategy to use. Currently, support 'ovr' short for one_vs_rest only.
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=100, early_stop='diff', encrypt_param=EncryptParam(),
predict_param=PredictParam(), cv_param=CrossValidationParam(),
decay=1, decay_sqrt=True,
multi_class='ovr', use_mix_rand=True,
reveal_strategy="respectively",
reveal_every_iter=False,
callback_param=CallbackParam(),
encrypted_mode_calculator_param=EncryptedModeCalculatorParam()
):
super(HeteroSSHELRParam, 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,
predict_param=predict_param, cv_param=cv_param,
decay=decay,
decay_sqrt=decay_sqrt, multi_class=multi_class,
encrypt_param=encrypt_param, callback_param=callback_param)
self.use_mix_rand = use_mix_rand
self.reveal_strategy = reveal_strategy
self.reveal_every_iter = reveal_every_iter
self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param)
def check(self):
descr = "logistic_param's"
super(HeteroSSHELRParam, self).check()
self.check_boolean(self.reveal_every_iter, descr)
if self.penalty is None:
pass
elif type(self.penalty).__name__ != "str":
raise ValueError(
"logistic_param's penalty {} not supported, should be str type".format(self.penalty))
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(
"logistic_param's optimizer {} not supported, should be str type".format(self.optimizer))
else:
self.optimizer = self.optimizer.lower()
if self.reveal_every_iter:
if self.optimizer not in ['sgd', 'rmsprop', 'adam', 'adagrad', 'nesterov_momentum_sgd']:
raise ValueError(
"When reveal_every_iter is True, "
"sshe logistic_param's optimizer not supported, optimizer should be"
" 'sgd', 'rmsprop', 'adam', 'nesterov_momentum_sgd', or 'adagrad'")
else:
if self.optimizer not in ['sgd', 'nesterov_momentum_sgd']:
raise ValueError("When reveal_every_iter is False, "
"sshe logistic_param's optimizer not supported, optimizer should be"
" 'sgd', 'nesterov_momentum_sgd'")
if self.encrypt_param.method not in [consts.PAILLIER, None]:
raise ValueError(
"logistic_param's encrypted method support 'Paillier' or None only")
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.")
self.encrypted_mode_calculator_param.check()
return True