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poisson_regression_param

poisson_regression_param

Classes

PoissonParam (LinearModelParam)

Parameters used for Poisson Regression.

Parameters:

Name Type Description Default
penalty {'L2', 'L1'}, default: 'L2'

Penalty method used in Poisson. Please note that, when using encrypted version in HeteroPoisson, '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', 'adagrad'}, default: 'rmsprop'

Optimize method

'rmsprop'
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: 20

The maximum iteration for training.

20
init_param InitParam object, default: default InitParam object

Init param method object.

<federatedml.param.init_model_param.InitParam object at 0x7f4aeb6890d0>
early_stop str, 'weight_diff', 'diff' or 'abs', default: 'diff'

Method used to judge convergence. 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'
exposure_colname str or None, default: None

Name of optional exposure variable in dTable.

None
encrypt_param EncryptParam object, default: default EncryptParam object

encrypt param

<federatedml.param.encrypt_param.EncryptParam object at 0x7f4aeb689210>
encrypted_mode_calculator_param EncryptedModeCalculatorParam object, default: default EncryptedModeCalculatorParam object

encrypted mode calculator param

<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f4aeb689250>
cv_param CrossValidationParam object, default: default CrossValidationParam object

cv param

<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f4aeb689190>
stepwise_param StepwiseParam object, default: default StepwiseParam object

stepwise param

<federatedml.param.stepwise_param.StepwiseParam object at 0x7f4aeb6896d0>
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
validation_freqs int, list, tuple, set, or None

validation frequency during training, required when using early stopping. The default value is None, 1 is suggested. You can set it to a number larger than 1 in order to speed up training by skipping validation rounds. When it is larger than 1, a number which is divisible by "max_iter" is recommended, otherwise, you will miss the validation scores of the last training iteration.

None
early_stopping_rounds int, default: None

If positive number specified, at every specified training rounds, program checks for early stopping criteria. Validation_freqs must also be set when using early stopping.

None
metrics list or None, default: None

Specify which metrics to be used when performing evaluation during training process. If metrics have not improved at early_stopping rounds, trianing stops before convergence. If set as empty, default metrics will be used. For regression tasks, default metrics are ['root_mean_squared_error', 'mean_absolute_error']

None
use_first_metric_only bool, default: False

Indicate whether to use the first metric in metrics as the only criterion 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
callback_param CallbackParam object

callback param

<federatedml.param.callback_param.CallbackParam object at 0x7f4aeb689690>
Source code in federatedml/param/poisson_regression_param.py
class PoissonParam(LinearModelParam):
    """
    Parameters used for Poisson Regression.

    Parameters
    ----------
    penalty : {'L2', 'L1'}, default: 'L2'
        Penalty method used in Poisson. Please note that, when using encrypted version in HeteroPoisson,
        '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', 'adagrad'}, default: 'rmsprop'
        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 : str, 'weight_diff', 'diff' or 'abs', default: 'diff'
        Method used to judge convergence.
            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.

    exposure_colname: str or None, default: None
        Name of optional exposure variable in dTable.

    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

    stepwise_param: StepwiseParam object, default: default StepwiseParam object
        stepwise 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)

    validation_freqs: int, list, tuple, set, or None
        validation frequency during training, required when using early stopping.
        The default value is None, 1 is suggested. You can set it to a number larger than 1 in order to speed up training by skipping validation rounds.
        When it is larger than 1, a number which is divisible by "max_iter" is recommended, otherwise, you will miss the validation scores of the last training iteration.

    early_stopping_rounds: int, default: None
        If positive number specified, at every specified training rounds, program checks for early stopping criteria.
        Validation_freqs must also be set when using early stopping.

    metrics: list or None, default: None
        Specify which metrics to be used when performing evaluation during training process. If metrics have not improved at early_stopping rounds, trianing stops before convergence.
        If set as empty, default metrics will be used. For regression tasks, default metrics are ['root_mean_squared_error', 'mean_absolute_error']

    use_first_metric_only: bool, default: False
        Indicate whether to use the first metric in `metrics` as the only criterion 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.

    callback_param: CallbackParam object
        callback param

    """

    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=20, early_stop='diff',
                 exposure_colname=None,
                 encrypt_param=EncryptParam(),
                 encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
                 cv_param=CrossValidationParam(), stepwise_param=StepwiseParam(),
                 decay=1, decay_sqrt=True,
                 validation_freqs=None, early_stopping_rounds=None, metrics=None, use_first_metric_only=False,
                 floating_point_precision=23, callback_param=CallbackParam()):
        super(PoissonParam, 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, cv_param=cv_param, decay=decay,
                                           decay_sqrt=decay_sqrt, 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.exposure_colname = exposure_colname

    def check(self):
        descr = "poisson_regression_param's "
        super(PoissonParam, self).check()
        if self.encrypt_param.method != consts.PAILLIER:
            raise ValueError(
                descr + "encrypt method supports 'Paillier' only")
        if self.optimizer not in ['sgd', 'rmsprop', 'adam', 'adagrad']:
            raise ValueError(
                descr + "optimizer not supported, optimizer should be"
                        " 'sgd', 'rmsprop', 'adam', or 'adagrad'")
        if self.exposure_colname is not None:
            if type(self.exposure_colname).__name__ != "str":
                raise ValueError(
                    descr + "exposure_colname {} not supported, should be string type".format(self.exposure_colname))
        self.encrypted_mode_calculator_param.check()
        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 0x7f4aeb6890d0>, max_iter=20, early_stop='diff', exposure_colname=None, encrypt_param=<federatedml.param.encrypt_param.EncryptParam object at 0x7f4aeb689210>, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f4aeb689250>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f4aeb689190>, stepwise_param=<federatedml.param.stepwise_param.StepwiseParam object at 0x7f4aeb6896d0>, decay=1, decay_sqrt=True, validation_freqs=None, early_stopping_rounds=None, metrics=None, use_first_metric_only=False, floating_point_precision=23, callback_param=<federatedml.param.callback_param.CallbackParam object at 0x7f4aeb689690>) special
Source code in federatedml/param/poisson_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=20, early_stop='diff',
             exposure_colname=None,
             encrypt_param=EncryptParam(),
             encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
             cv_param=CrossValidationParam(), stepwise_param=StepwiseParam(),
             decay=1, decay_sqrt=True,
             validation_freqs=None, early_stopping_rounds=None, metrics=None, use_first_metric_only=False,
             floating_point_precision=23, callback_param=CallbackParam()):
    super(PoissonParam, 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, cv_param=cv_param, decay=decay,
                                       decay_sqrt=decay_sqrt, 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.exposure_colname = exposure_colname
check(self)
Source code in federatedml/param/poisson_regression_param.py
def check(self):
    descr = "poisson_regression_param's "
    super(PoissonParam, self).check()
    if self.encrypt_param.method != consts.PAILLIER:
        raise ValueError(
            descr + "encrypt method supports 'Paillier' only")
    if self.optimizer not in ['sgd', 'rmsprop', 'adam', 'adagrad']:
        raise ValueError(
            descr + "optimizer not supported, optimizer should be"
                    " 'sgd', 'rmsprop', 'adam', or 'adagrad'")
    if self.exposure_colname is not None:
        if type(self.exposure_colname).__name__ != "str":
            raise ValueError(
                descr + "exposure_colname {} not supported, should be string type".format(self.exposure_colname))
    self.encrypted_mode_calculator_param.check()
    return True

Last update: 2022-04-15
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