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hetero_sshe_lr_param

hetero_sshe_lr_param

Classes

HeteroSSHELRParam (LogisticParam)

Parameters used for Hetero SSHE Logistic Regression

Parameters:

Name Type Description Default
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.

'L2'
tol float, default: 1e-4

The tolerance of convergence

0.0001
alpha float, default: 1.0

Regularization strength coefficient.

1.0
optimizer str, 'sgd', 'rmsprop', 'adam', 'nesterov_momentum_sgd', or 'adagrad', default: 'sgd'

Optimizer

'sgd'
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.

-1
learning_rate float, default: 0.01

Learning rate

0.01
max_iter int, default: 100

The maximum iteration for training.

100
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.

'diff'
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.

1
decay_sqrt Bool, default: True

lr = lr0/(1+decay*t) if decay_sqrt is False, otherwise, lr = lr0 / sqrt(1+decay*t)

True
encrypt_param EncryptParam object, default: default EncryptParam object

encrypt param

<federatedml.param.encrypt_param.EncryptParam object at 0x7f4aeb5a6650>
predict_param PredictParam object, default: default PredictParam object

predict param

<federatedml.param.predict_param.PredictParam object at 0x7f4aeb5a69d0>
cv_param CrossValidationParam object, default: default CrossValidationParam object

cv param

<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f4aeb5a6590>
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.

'ovr'
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.

'respectively'
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.

False
Source code in federatedml/param/hetero_sshe_lr_param.py
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, consts.NONE.upper()]:
                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
__init__(self, penalty='L2', tol=0.0001, alpha=1.0, optimizer='sgd', batch_size=-1, learning_rate=0.01, init_param=<federatedml.param.init_model_param.InitParam object at 0x7f4aeb5a64d0>, max_iter=100, early_stop='diff', encrypt_param=<federatedml.param.encrypt_param.EncryptParam object at 0x7f4aeb5a6650>, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f4aeb5a69d0>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f4aeb5a6590>, decay=1, decay_sqrt=True, multi_class='ovr', use_mix_rand=True, reveal_strategy='respectively', reveal_every_iter=False, callback_param=<federatedml.param.callback_param.CallbackParam object at 0x7f4aeb5a6ad0>, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f4aeb5a6a50>) special
Source code in federatedml/param/hetero_sshe_lr_param.py
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)
check(self)
Source code in federatedml/param/hetero_sshe_lr_param.py
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, consts.NONE.upper()]:
            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

Last update: 2022-04-15
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