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logistic_regression_param

logistic_regression_param

Classes

LogisticParam (LinearModelParam)

Parameters used for Logistic Regression both for Homo mode or Hetero mode.

Parameters:

Name Type Description Default
penalty {'L2', 'L1' or None}

Penalty method used in LR. Please note that, when using encrypted version in HomoLR, 'L1' is not supported.

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

The tolerance of convergence

0.0001
alpha float, default: 1.0

Regularization strength coefficient.

1.0
optimizer {'rmsprop', 'sgd', 'adam', 'nesterov_momentum_sgd', 'adagrad'}, default: 'rmsprop'

Optimize method.

'rmsprop'
batch_strategy str, {'full', 'random'}, default: "full"

Strategy to generate batch data. a) full: use full data to generate batch_data, batch_nums every iteration is ceil(data_size / batch_size) b) random: select data randomly from full data, batch_num will be 1 every iteration.

'full'
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
shuffle bool, default: True

Work only in hetero logistic regression, batch data will be shuffle in every iteration.

True
masked_rate int, float: default: 5

Use masked data to enhance security of hetero logistic regression

5
learning_rate float, default: 0.01

Learning rate

0.01
max_iter int, default: 100

The maximum iteration for training.

100
early_stop {'diff', 'weight_diff', '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.

Please note that for hetero-lr multi-host situation, this parameter support "weight_diff" only.
'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 0x7f4aeb6f1fd0>
predict_param PredictParam object, default: default PredictParam object

predict param

<federatedml.param.predict_param.PredictParam object at 0x7f4aeb6940d0>
callback_param CallbackParam object

callback param

<federatedml.param.callback_param.CallbackParam object at 0x7f4aeb694290>
cv_param CrossValidationParam object, default: default CrossValidationParam object

cv param

<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f4aeb694210>
multi_class {'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'
validation_freqs int or list or tuple or set, or None, default None

validation frequency during training.

None
early_stopping_rounds int, default: None

Will stop training if one metric doesn’t improve in last early_stopping_round rounds

None
metrics list or None, default: None

Indicate when executing evaluation during train process, which metrics will be used. If set as empty, default metrics for specific task type will be used. As for binary classification, default metrics are ['auc', 'ks']

None
use_first_metric_only bool, default: False

Indicate whether use the first metric only for early stopping judgement.

False
floating_point_precision None or integer

if not None, use floating_point_precision-bit to speed up calculation, e.g.: convert an x to round(x * 2**floating_point_precision) during Paillier operation, divide the result by 2**floating_point_precision in the end.

23
Source code in federatedml/param/logistic_regression_param.py
class LogisticParam(LinearModelParam):
    """
    Parameters used for Logistic Regression both for Homo mode or Hetero mode.

    Parameters
    ----------
    penalty : {'L2', 'L1' or None}
        Penalty method used in LR. Please note that, when using encrypted version in HomoLR,
        'L1' is not supported.

    tol : float, default: 1e-4
        The tolerance of convergence

    alpha : float, default: 1.0
        Regularization strength coefficient.

    optimizer : {'rmsprop', 'sgd', 'adam', 'nesterov_momentum_sgd', 'adagrad'}, default: 'rmsprop'
        Optimize method.

    batch_strategy : str, {'full', 'random'}, default: "full"
        Strategy to generate batch data.
            a) full: use full data to generate batch_data, batch_nums every iteration is ceil(data_size /  batch_size)
            b) random: select data randomly from full data, batch_num will be 1 every iteration.

    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.

    shuffle : bool, default: True
        Work only in hetero logistic regression, batch data will be shuffle in every iteration.

    masked_rate: int, float: default: 5
        Use masked data to enhance security of hetero logistic regression

    learning_rate : float, default: 0.01
        Learning rate

    max_iter : int, default: 100
        The maximum iteration for training.

    early_stop : {'diff', 'weight_diff', '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.

            Please note that for hetero-lr multi-host situation, this parameter support "weight_diff" only.

    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

    callback_param: CallbackParam object
        callback param

    cv_param: CrossValidationParam object, default: default CrossValidationParam object
        cv param

    multi_class: {'ovr'}, default: 'ovr'
        If it is a multi_class task, indicate what strategy to use. Currently, support 'ovr' short for one_vs_rest only.

    validation_freqs: int or list or tuple or set, or None, default None
        validation frequency during training.

    early_stopping_rounds: int, default: None
        Will stop training if one metric doesn’t improve in last early_stopping_round rounds

    metrics: list or None, default: None
        Indicate when executing evaluation during train process, which metrics will be used. If set as empty,
        default metrics for specific task type will be used. As for binary classification, default metrics are
        ['auc', 'ks']

    use_first_metric_only: bool, default: False
        Indicate whether use the first metric only for early stopping judgement.

    floating_point_precision: None or integer
        if not None, use floating_point_precision-bit to speed up calculation,
        e.g.: convert an x to round(x * 2**floating_point_precision) during Paillier operation, divide
                the result by 2**floating_point_precision in the end.

    """

    def __init__(self, penalty='L2',
                 tol=1e-4, alpha=1.0, optimizer='rmsprop',
                 batch_size=-1, shuffle=True, batch_strategy="full", masked_rate=5,
                 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', validation_freqs=None, early_stopping_rounds=None,
                 stepwise_param=StepwiseParam(), floating_point_precision=23,
                 metrics=None,
                 use_first_metric_only=False,
                 callback_param=CallbackParam()
                 ):
        super(LogisticParam, self).__init__()
        self.penalty = penalty
        self.tol = tol
        self.alpha = alpha
        self.optimizer = optimizer
        self.batch_size = batch_size
        self.learning_rate = learning_rate
        self.init_param = copy.deepcopy(init_param)
        self.max_iter = max_iter
        self.early_stop = early_stop
        self.encrypt_param = encrypt_param
        self.shuffle = shuffle
        self.batch_strategy = batch_strategy
        self.masked_rate = masked_rate
        self.predict_param = copy.deepcopy(predict_param)
        self.cv_param = copy.deepcopy(cv_param)
        self.decay = decay
        self.decay_sqrt = decay_sqrt
        self.multi_class = multi_class
        self.validation_freqs = validation_freqs
        self.stepwise_param = copy.deepcopy(stepwise_param)
        self.early_stopping_rounds = early_stopping_rounds
        self.metrics = metrics or []
        self.use_first_metric_only = use_first_metric_only
        self.floating_point_precision = floating_point_precision
        self.callback_param = copy.deepcopy(callback_param)

    def check(self):
        descr = "logistic_param's"
        super(LogisticParam, self).check()
        self.predict_param.check()
        if self.encrypt_param.method not in [consts.PAILLIER, None]:
            raise ValueError(
                "logistic_param's encrypted method support 'Paillier' or None only")
        self.multi_class = self.check_and_change_lower(self.multi_class, ["ovr"], f"{descr}")
        if not isinstance(self.masked_rate, (float, int)) or self.masked_rate < 0:
            raise ValueError("masked rate should be non-negative numeric number")
        if not isinstance(self.batch_strategy, str) or self.batch_strategy.lower() not in ["full", "random"]:
            raise ValueError("batch strategy should be full or random")
        self.batch_strategy = self.batch_strategy.lower()
        if not isinstance(self.shuffle, bool):
            raise ValueError("shuffle should be boolean type")
        return True
__init__(self, penalty='L2', tol=0.0001, alpha=1.0, optimizer='rmsprop', batch_size=-1, shuffle=True, batch_strategy='full', masked_rate=5, learning_rate=0.01, init_param=<federatedml.param.init_model_param.InitParam object at 0x7f4aeb6f1b50>, max_iter=100, early_stop='diff', encrypt_param=<federatedml.param.encrypt_param.EncryptParam object at 0x7f4aeb6f1fd0>, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f4aeb6940d0>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f4aeb694210>, decay=1, decay_sqrt=True, multi_class='ovr', validation_freqs=None, early_stopping_rounds=None, stepwise_param=<federatedml.param.stepwise_param.StepwiseParam object at 0x7f4aeb6942d0>, floating_point_precision=23, metrics=None, use_first_metric_only=False, callback_param=<federatedml.param.callback_param.CallbackParam object at 0x7f4aeb694290>) special
Source code in federatedml/param/logistic_regression_param.py
def __init__(self, penalty='L2',
             tol=1e-4, alpha=1.0, optimizer='rmsprop',
             batch_size=-1, shuffle=True, batch_strategy="full", masked_rate=5,
             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', validation_freqs=None, early_stopping_rounds=None,
             stepwise_param=StepwiseParam(), floating_point_precision=23,
             metrics=None,
             use_first_metric_only=False,
             callback_param=CallbackParam()
             ):
    super(LogisticParam, self).__init__()
    self.penalty = penalty
    self.tol = tol
    self.alpha = alpha
    self.optimizer = optimizer
    self.batch_size = batch_size
    self.learning_rate = learning_rate
    self.init_param = copy.deepcopy(init_param)
    self.max_iter = max_iter
    self.early_stop = early_stop
    self.encrypt_param = encrypt_param
    self.shuffle = shuffle
    self.batch_strategy = batch_strategy
    self.masked_rate = masked_rate
    self.predict_param = copy.deepcopy(predict_param)
    self.cv_param = copy.deepcopy(cv_param)
    self.decay = decay
    self.decay_sqrt = decay_sqrt
    self.multi_class = multi_class
    self.validation_freqs = validation_freqs
    self.stepwise_param = copy.deepcopy(stepwise_param)
    self.early_stopping_rounds = early_stopping_rounds
    self.metrics = metrics or []
    self.use_first_metric_only = use_first_metric_only
    self.floating_point_precision = floating_point_precision
    self.callback_param = copy.deepcopy(callback_param)
check(self)
Source code in federatedml/param/logistic_regression_param.py
def check(self):
    descr = "logistic_param's"
    super(LogisticParam, self).check()
    self.predict_param.check()
    if self.encrypt_param.method not in [consts.PAILLIER, None]:
        raise ValueError(
            "logistic_param's encrypted method support 'Paillier' or None only")
    self.multi_class = self.check_and_change_lower(self.multi_class, ["ovr"], f"{descr}")
    if not isinstance(self.masked_rate, (float, int)) or self.masked_rate < 0:
        raise ValueError("masked rate should be non-negative numeric number")
    if not isinstance(self.batch_strategy, str) or self.batch_strategy.lower() not in ["full", "random"]:
        raise ValueError("batch strategy should be full or random")
    self.batch_strategy = self.batch_strategy.lower()
    if not isinstance(self.shuffle, bool):
        raise ValueError("shuffle should be boolean type")
    return True

HomoLogisticParam (LogisticParam)

Parameters:

Name Type Description Default
re_encrypt_batches int, default: 2

Required when using encrypted version HomoLR. Since multiple batch updating coefficient may cause overflow error. The model need to be re-encrypt for every several batches. Please be careful when setting this parameter. Too large batches may cause training failure.

2
aggregate_iters int, default: 1

Indicate how many iterations are aggregated once.

1
use_proximal bool, default: False

Whether to turn on additional proximial term. For more details of FedProx, Please refer to https://arxiv.org/abs/1812.06127

False
mu float, default 0.1

To scale the proximal term

0.1
Source code in federatedml/param/logistic_regression_param.py
class HomoLogisticParam(LogisticParam):
    """
    Parameters
    ----------
    re_encrypt_batches : int, default: 2
        Required when using encrypted version HomoLR. Since multiple batch updating coefficient may cause
        overflow error. The model need to be re-encrypt for every several batches. Please be careful when setting
        this parameter. Too large batches may cause training failure.

    aggregate_iters : int, default: 1
        Indicate how many iterations are aggregated once.

    use_proximal: bool, default: False
        Whether to turn on additional proximial term. For more details of FedProx, Please refer to
        https://arxiv.org/abs/1812.06127

    mu: float, default 0.1
        To scale the proximal term

    """

    def __init__(self, penalty='L2',
                 tol=1e-4, alpha=1.0, optimizer='rmsprop',
                 batch_size=-1, learning_rate=0.01, init_param=InitParam(),
                 max_iter=100, early_stop='diff',
                 encrypt_param=EncryptParam(method=None), re_encrypt_batches=2,
                 predict_param=PredictParam(), cv_param=CrossValidationParam(),
                 decay=1, decay_sqrt=True,
                 aggregate_iters=1, multi_class='ovr', validation_freqs=None,
                 early_stopping_rounds=None,
                 metrics=['auc', 'ks'],
                 use_first_metric_only=False,
                 use_proximal=False,
                 mu=0.1, callback_param=CallbackParam()
                 ):
        super(HomoLogisticParam, 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, predict_param=predict_param,
                                                cv_param=cv_param, multi_class=multi_class,
                                                validation_freqs=validation_freqs,
                                                decay=decay, decay_sqrt=decay_sqrt,
                                                early_stopping_rounds=early_stopping_rounds,
                                                metrics=metrics, use_first_metric_only=use_first_metric_only,
                                                callback_param=callback_param)
        self.re_encrypt_batches = re_encrypt_batches
        self.aggregate_iters = aggregate_iters
        self.use_proximal = use_proximal
        self.mu = mu

    def check(self):
        super().check()
        if type(self.re_encrypt_batches).__name__ != "int":
            raise ValueError(
                "logistic_param's re_encrypt_batches {} not supported, should be int type".format(
                    self.re_encrypt_batches))
        elif self.re_encrypt_batches < 0:
            raise ValueError(
                "logistic_param's re_encrypt_batches must be greater or equal to 0")

        if not isinstance(self.aggregate_iters, int):
            raise ValueError(
                "logistic_param's aggregate_iters {} not supported, should be int type".format(
                    self.aggregate_iters))

        if self.encrypt_param.method == consts.PAILLIER:
            if self.optimizer != 'sgd':
                raise ValueError("Paillier encryption mode supports 'sgd' optimizer method only.")

            if self.penalty == consts.L1_PENALTY:
                raise ValueError("Paillier encryption mode supports 'L2' penalty or None only.")

        return True
__init__(self, penalty='L2', tol=0.0001, alpha=1.0, optimizer='rmsprop', batch_size=-1, learning_rate=0.01, init_param=<federatedml.param.init_model_param.InitParam object at 0x7f4aeb694390>, max_iter=100, early_stop='diff', encrypt_param=<federatedml.param.encrypt_param.EncryptParam object at 0x7f4aeb6943d0>, re_encrypt_batches=2, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f4aeb694250>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f4aeb6941d0>, decay=1, decay_sqrt=True, aggregate_iters=1, multi_class='ovr', validation_freqs=None, early_stopping_rounds=None, metrics=['auc', 'ks'], use_first_metric_only=False, use_proximal=False, mu=0.1, callback_param=<federatedml.param.callback_param.CallbackParam object at 0x7f4aeb694450>) special
Source code in federatedml/param/logistic_regression_param.py
def __init__(self, penalty='L2',
             tol=1e-4, alpha=1.0, optimizer='rmsprop',
             batch_size=-1, learning_rate=0.01, init_param=InitParam(),
             max_iter=100, early_stop='diff',
             encrypt_param=EncryptParam(method=None), re_encrypt_batches=2,
             predict_param=PredictParam(), cv_param=CrossValidationParam(),
             decay=1, decay_sqrt=True,
             aggregate_iters=1, multi_class='ovr', validation_freqs=None,
             early_stopping_rounds=None,
             metrics=['auc', 'ks'],
             use_first_metric_only=False,
             use_proximal=False,
             mu=0.1, callback_param=CallbackParam()
             ):
    super(HomoLogisticParam, 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, predict_param=predict_param,
                                            cv_param=cv_param, multi_class=multi_class,
                                            validation_freqs=validation_freqs,
                                            decay=decay, decay_sqrt=decay_sqrt,
                                            early_stopping_rounds=early_stopping_rounds,
                                            metrics=metrics, use_first_metric_only=use_first_metric_only,
                                            callback_param=callback_param)
    self.re_encrypt_batches = re_encrypt_batches
    self.aggregate_iters = aggregate_iters
    self.use_proximal = use_proximal
    self.mu = mu
check(self)
Source code in federatedml/param/logistic_regression_param.py
def check(self):
    super().check()
    if type(self.re_encrypt_batches).__name__ != "int":
        raise ValueError(
            "logistic_param's re_encrypt_batches {} not supported, should be int type".format(
                self.re_encrypt_batches))
    elif self.re_encrypt_batches < 0:
        raise ValueError(
            "logistic_param's re_encrypt_batches must be greater or equal to 0")

    if not isinstance(self.aggregate_iters, int):
        raise ValueError(
            "logistic_param's aggregate_iters {} not supported, should be int type".format(
                self.aggregate_iters))

    if self.encrypt_param.method == consts.PAILLIER:
        if self.optimizer != 'sgd':
            raise ValueError("Paillier encryption mode supports 'sgd' optimizer method only.")

        if self.penalty == consts.L1_PENALTY:
            raise ValueError("Paillier encryption mode supports 'L2' penalty or None only.")

    return True

HeteroLogisticParam (LogisticParam)

Source code in federatedml/param/logistic_regression_param.py
class HeteroLogisticParam(LogisticParam):
    def __init__(self, penalty='L2',
                 tol=1e-4, alpha=1.0, optimizer='rmsprop',
                 batch_size=-1, shuffle=True, batch_strategy="full", masked_rate=5,
                 learning_rate=0.01, init_param=InitParam(),
                 max_iter=100, early_stop='diff',
                 encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
                 predict_param=PredictParam(), cv_param=CrossValidationParam(),
                 decay=1, decay_sqrt=True, sqn_param=StochasticQuasiNewtonParam(),
                 multi_class='ovr', validation_freqs=None, early_stopping_rounds=None,
                 metrics=['auc', 'ks'], floating_point_precision=23,
                 encrypt_param=EncryptParam(),
                 use_first_metric_only=False, stepwise_param=StepwiseParam(),
                 callback_param=CallbackParam()
                 ):
        super(HeteroLogisticParam, self).__init__(penalty=penalty, tol=tol, alpha=alpha, optimizer=optimizer,
                                                  batch_size=batch_size, shuffle=shuffle, batch_strategy=batch_strategy,
                                                  masked_rate=masked_rate,
                                                  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,
                                                  validation_freqs=validation_freqs,
                                                  early_stopping_rounds=early_stopping_rounds,
                                                  metrics=metrics, floating_point_precision=floating_point_precision,
                                                  encrypt_param=encrypt_param,
                                                  use_first_metric_only=use_first_metric_only,
                                                  stepwise_param=stepwise_param,
                                                  callback_param=callback_param)
        self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param)
        self.sqn_param = copy.deepcopy(sqn_param)

    def check(self):
        super().check()
        self.encrypted_mode_calculator_param.check()
        self.sqn_param.check()
        return True
__init__(self, penalty='L2', tol=0.0001, alpha=1.0, optimizer='rmsprop', batch_size=-1, shuffle=True, batch_strategy='full', masked_rate=5, learning_rate=0.01, init_param=<federatedml.param.init_model_param.InitParam object at 0x7f4aeb694510>, max_iter=100, early_stop='diff', encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f4aeb6944d0>, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f4aeb6945d0>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f4aeb694550>, decay=1, decay_sqrt=True, sqn_param=<federatedml.param.sqn_param.StochasticQuasiNewtonParam object at 0x7f4aeb694bd0>, multi_class='ovr', validation_freqs=None, early_stopping_rounds=None, metrics=['auc', 'ks'], floating_point_precision=23, encrypt_param=<federatedml.param.encrypt_param.EncryptParam object at 0x7f4aeb694b90>, use_first_metric_only=False, stepwise_param=<federatedml.param.stepwise_param.StepwiseParam object at 0x7f4aeb694c10>, callback_param=<federatedml.param.callback_param.CallbackParam object at 0x7f4aeb694c90>) special
Source code in federatedml/param/logistic_regression_param.py
def __init__(self, penalty='L2',
             tol=1e-4, alpha=1.0, optimizer='rmsprop',
             batch_size=-1, shuffle=True, batch_strategy="full", masked_rate=5,
             learning_rate=0.01, init_param=InitParam(),
             max_iter=100, early_stop='diff',
             encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
             predict_param=PredictParam(), cv_param=CrossValidationParam(),
             decay=1, decay_sqrt=True, sqn_param=StochasticQuasiNewtonParam(),
             multi_class='ovr', validation_freqs=None, early_stopping_rounds=None,
             metrics=['auc', 'ks'], floating_point_precision=23,
             encrypt_param=EncryptParam(),
             use_first_metric_only=False, stepwise_param=StepwiseParam(),
             callback_param=CallbackParam()
             ):
    super(HeteroLogisticParam, self).__init__(penalty=penalty, tol=tol, alpha=alpha, optimizer=optimizer,
                                              batch_size=batch_size, shuffle=shuffle, batch_strategy=batch_strategy,
                                              masked_rate=masked_rate,
                                              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,
                                              validation_freqs=validation_freqs,
                                              early_stopping_rounds=early_stopping_rounds,
                                              metrics=metrics, floating_point_precision=floating_point_precision,
                                              encrypt_param=encrypt_param,
                                              use_first_metric_only=use_first_metric_only,
                                              stepwise_param=stepwise_param,
                                              callback_param=callback_param)
    self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param)
    self.sqn_param = copy.deepcopy(sqn_param)
check(self)
Source code in federatedml/param/logistic_regression_param.py
def check(self):
    super().check()
    self.encrypted_mode_calculator_param.check()
    self.sqn_param.check()
    return True

Last update: 2022-04-15
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