linear_regression_param¶
linear_regression_param
¶
Classes¶
LinearParam (LinearModelParam)
¶
Parameters used for 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', 'sqn', '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.
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
EncryptParam object, default: default EncryptParam object
encrypt param
EncryptedModeCalculatorParam object, default: default EncryptedModeCalculatorParam object
encrypted mode calculator param
CrossValidationParam object, default: default CrossValidationParam object
cv param
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.
Bool, default: True
lr = lr0/(1+decay*t) if decay_sqrt is False, otherwise, lr = lr0 / sqrt(1+decay*t)
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.
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.
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']
bool, default: False
Indicate whether to use the first metric in metrics as the only criterion for early stopping judgement.
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.
CallbackParam object
callback param
Source code in federatedml/param/linear_regression_param.py
class LinearParam(LinearModelParam):
"""
Parameters used for 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', 'sqn', '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)
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='sgd',
batch_size=-1, learning_rate=0.01, init_param=InitParam(),
max_iter=20, early_stop='diff',
encrypt_param=EncryptParam(), sqn_param=StochasticQuasiNewtonParam(),
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
cv_param=CrossValidationParam(), decay=1, decay_sqrt=True, validation_freqs=None,
early_stopping_rounds=None, stepwise_param=StepwiseParam(), metrics=None, use_first_metric_only=False,
floating_point_precision=23, callback_param=CallbackParam()):
super(LinearParam, 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, validation_freqs=validation_freqs,
early_stopping_rounds=early_stopping_rounds,
stepwise_param=stepwise_param, metrics=metrics,
use_first_metric_only=use_first_metric_only,
floating_point_precision=floating_point_precision,
callback_param=callback_param)
self.sqn_param = copy.deepcopy(sqn_param)
self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param)
def check(self):
descr = "linear_regression_param's "
super(LinearParam, self).check()
if self.optimizer not in ['sgd', 'rmsprop', 'adam', 'adagrad', 'sqn']:
raise ValueError(
descr + "optimizer not supported, optimizer should be"
" 'sgd', 'rmsprop', 'adam', 'sqn' or 'adagrad'")
self.sqn_param.check()
if self.encrypt_param.method != consts.PAILLIER:
raise ValueError(
descr + "encrypt method supports 'Paillier' only")
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 0x7f3a40c768d0>, max_iter=20, early_stop='diff', encrypt_param=<federatedml.param.encrypt_param.EncryptParam object at 0x7f3a40c5cc90>, sqn_param=<federatedml.param.sqn_param.StochasticQuasiNewtonParam object at 0x7f3a40c5cb50>, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f3a40c5ce10>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f3a40c5cd90>, decay=1, decay_sqrt=True, validation_freqs=None, early_stopping_rounds=None, stepwise_param=<federatedml.param.stepwise_param.StepwiseParam object at 0x7f3a40c5ce90>, metrics=None, use_first_metric_only=False, floating_point_precision=23, callback_param=<federatedml.param.callback_param.CallbackParam object at 0x7f3a40c5ce50>)
special
¶
Source code in federatedml/param/linear_regression_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=20, early_stop='diff',
encrypt_param=EncryptParam(), sqn_param=StochasticQuasiNewtonParam(),
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
cv_param=CrossValidationParam(), decay=1, decay_sqrt=True, validation_freqs=None,
early_stopping_rounds=None, stepwise_param=StepwiseParam(), metrics=None, use_first_metric_only=False,
floating_point_precision=23, callback_param=CallbackParam()):
super(LinearParam, 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, validation_freqs=validation_freqs,
early_stopping_rounds=early_stopping_rounds,
stepwise_param=stepwise_param, metrics=metrics,
use_first_metric_only=use_first_metric_only,
floating_point_precision=floating_point_precision,
callback_param=callback_param)
self.sqn_param = copy.deepcopy(sqn_param)
self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param)
check(self)
¶
Source code in federatedml/param/linear_regression_param.py
def check(self):
descr = "linear_regression_param's "
super(LinearParam, self).check()
if self.optimizer not in ['sgd', 'rmsprop', 'adam', 'adagrad', 'sqn']:
raise ValueError(
descr + "optimizer not supported, optimizer should be"
" 'sgd', 'rmsprop', 'adam', 'sqn' or 'adagrad'")
self.sqn_param.check()
if self.encrypt_param.method != consts.PAILLIER:
raise ValueError(
descr + "encrypt method supports 'Paillier' only")
return True