poisson_regression_param¶
poisson_regression_param
¶
Classes¶
PoissonParam (BaseParam)
¶
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 0x7ff45d91b2d0> |
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 |
predict_param |
PredictParam object, default: default PredictParam object |
predict param |
<federatedml.param.predict_param.PredictParam object at 0x7ff45d91b390> |
encrypt_param |
EncryptParam object, default: default EncryptParam object |
encrypt param |
<federatedml.param.encrypt_param.EncryptParam object at 0x7ff45d91b3d0> |
encrypted_mode_calculator_param |
EncryptedModeCalculatorParam object, default: default EncryptedModeCalculatorParam object |
encrypted mode calculator param |
<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7ff45d91b310> |
cv_param |
CrossValidationParam object, default: default CrossValidationParam object |
cv param |
<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7ff45d91b350> |
stepwise_param |
StepwiseParam object, default: default StepwiseParam object |
stepwise param |
<federatedml.param.stepwise_param.StepwiseParam object at 0x7ff45d91b510> |
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 |
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 0x7ff45d91b4d0> |
Source code in federatedml/param/poisson_regression_param.py
class PoissonParam(BaseParam):
"""
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.
predict_param: PredictParam object, default: default PredictParam object
predict param
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, predict_param=PredictParam(),
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__()
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.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param)
self.cv_param = copy.deepcopy(cv_param)
self.predict_param = copy.deepcopy(predict_param)
self.decay = decay
self.decay_sqrt = decay_sqrt
self.exposure_colname = exposure_colname
self.validation_freqs = validation_freqs
self.stepwise_param = 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 = "poisson_regression_param's "
if self.penalty is None:
self.penalty = 'NONE'
elif type(self.penalty).__name__ != "str":
raise ValueError(
descr + "penalty {} not supported, should be str type".format(self.penalty))
self.penalty = self.penalty.upper()
if self.penalty not in ['L1', 'L2', 'NONE']:
raise ValueError(
"penalty {} not supported, penalty should be 'L1', 'L2' or 'none'".format(self.penalty))
if type(self.tol).__name__ not in ["int", "float"]:
raise ValueError(
descr + "tol {} not supported, should be float type".format(self.tol))
if type(self.alpha).__name__ not in ["int", "float"]:
raise ValueError(
descr + "alpha {} not supported, should be float type".format(self.alpha))
if type(self.optimizer).__name__ != "str":
raise ValueError(
descr + "optimizer {} not supported, should be str type".format(self.optimizer))
else:
self.optimizer = self.optimizer.lower()
if self.optimizer not in ['sgd', 'rmsprop', 'adam', 'adagrad', 'nesterov_momentum_sgd']:
raise ValueError(
descr + "optimizer not supported, optimizer should be"
" 'sgd', 'rmsprop', 'adam', 'adagrad' or 'nesterov_momentum_sgd'")
if type(self.batch_size).__name__ not in ["int", "long"]:
raise ValueError(
descr + "batch_size {} not supported, should be int type".format(self.batch_size))
if self.batch_size != -1:
if type(self.batch_size).__name__ not in ["int", "long"] \
or self.batch_size < consts.MIN_BATCH_SIZE:
raise ValueError(descr + " {} not supported, should be larger than {} or "
"-1 represent for all data".format(self.batch_size, consts.MIN_BATCH_SIZE))
if type(self.learning_rate).__name__ not in ["int", "float"]:
raise ValueError(
descr + "learning_rate {} not supported, should be float type".format(
self.learning_rate))
self.init_param.check()
if self.encrypt_param.method != consts.PAILLIER:
raise ValueError(
descr + "encrypt method supports 'Paillier' only")
if type(self.max_iter).__name__ != "int":
raise ValueError(
descr + "max_iter {} not supported, should be int type".format(self.max_iter))
elif self.max_iter <= 0:
raise ValueError(
descr + "max_iter must be greater or equal to 1")
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))
if type(self.early_stop).__name__ != "str":
raise ValueError(
descr + "early_stop {} not supported, should be str type".format(
self.early_stop))
else:
self.early_stop = self.early_stop.lower()
if self.early_stop not in ['diff', 'abs', 'weight_diff']:
raise ValueError(
descr + "early_stop not supported, early_stop should be"
" 'diff' or 'abs'")
self.encrypt_param.check()
if self.encrypt_param.method != consts.PAILLIER:
raise ValueError(
descr + "encrypt method supports 'Paillier' or None only"
)
self.encrypted_mode_calculator_param.check()
if type(self.decay).__name__ not in ["int", "float"]:
raise ValueError(
descr + "decay {} not supported, should be 'int' or 'float'".format(self.decay)
)
if type(self.decay_sqrt).__name__ not in ["bool"]:
raise ValueError(
descr + "decay_sqrt {} not supported, should be 'bool'".format(self.decay)
)
self.stepwise_param.check()
for p in ["early_stopping_rounds", "validation_freqs", "metrics",
"use_first_metric_only"]:
if self._warn_to_deprecate_param(p, "", ""):
if "callback_param" in self.get_user_feeded():
raise ValueError(f"{p} and callback param should not be set simultaneously，"
f"{self._deprecated_params_set}, {self.get_user_feeded()}")
else:
self.callback_param.callbacks = ["PerformanceEvaluate"]
break
if self._warn_to_deprecate_param("validation_freqs", descr, "callback_param's 'validation_freqs'"):
self.callback_param.validation_freqs = self.validation_freqs
if self._warn_to_deprecate_param("early_stopping_rounds", descr, "callback_param's 'early_stopping_rounds'"):
self.callback_param.early_stopping_rounds = self.early_stopping_rounds
if self._warn_to_deprecate_param("metrics", descr, "callback_param's 'metrics'"):
self.callback_param.metrics = self.metrics
if self._warn_to_deprecate_param("use_first_metric_only", descr, "callback_param's 'use_first_metric_only'"):
self.callback_param.use_first_metric_only = self.use_first_metric_only
if self.floating_point_precision is not None and \
(not isinstance(self.floating_point_precision, int) or
self.floating_point_precision < 0 or self.floating_point_precision > 64):
raise ValueError("floating point precision should be null or a integer between 0 and 64")
self.callback_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 0x7ff45d91b2d0>, max_iter=20, early_stop='diff', exposure_colname=None, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7ff45d91b390>, encrypt_param=<federatedml.param.encrypt_param.EncryptParam object at 0x7ff45d91b3d0>, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7ff45d91b310>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7ff45d91b350>, stepwise_param=<federatedml.param.stepwise_param.StepwiseParam object at 0x7ff45d91b510>, 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 0x7ff45d91b4d0>)
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, predict_param=PredictParam(),
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__()
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.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param)
self.cv_param = copy.deepcopy(cv_param)
self.predict_param = copy.deepcopy(predict_param)
self.decay = decay
self.decay_sqrt = decay_sqrt
self.exposure_colname = exposure_colname
self.validation_freqs = validation_freqs
self.stepwise_param = 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/poisson_regression_param.py
def check(self):
descr = "poisson_regression_param's "
if self.penalty is None:
self.penalty = 'NONE'
elif type(self.penalty).__name__ != "str":
raise ValueError(
descr + "penalty {} not supported, should be str type".format(self.penalty))
self.penalty = self.penalty.upper()
if self.penalty not in ['L1', 'L2', 'NONE']:
raise ValueError(
"penalty {} not supported, penalty should be 'L1', 'L2' or 'none'".format(self.penalty))
if type(self.tol).__name__ not in ["int", "float"]:
raise ValueError(
descr + "tol {} not supported, should be float type".format(self.tol))
if type(self.alpha).__name__ not in ["int", "float"]:
raise ValueError(
descr + "alpha {} not supported, should be float type".format(self.alpha))
if type(self.optimizer).__name__ != "str":
raise ValueError(
descr + "optimizer {} not supported, should be str type".format(self.optimizer))
else:
self.optimizer = self.optimizer.lower()
if self.optimizer not in ['sgd', 'rmsprop', 'adam', 'adagrad', 'nesterov_momentum_sgd']:
raise ValueError(
descr + "optimizer not supported, optimizer should be"
" 'sgd', 'rmsprop', 'adam', 'adagrad' or 'nesterov_momentum_sgd'")
if type(self.batch_size).__name__ not in ["int", "long"]:
raise ValueError(
descr + "batch_size {} not supported, should be int type".format(self.batch_size))
if self.batch_size != -1:
if type(self.batch_size).__name__ not in ["int", "long"] \
or self.batch_size < consts.MIN_BATCH_SIZE:
raise ValueError(descr + " {} not supported, should be larger than {} or "
"-1 represent for all data".format(self.batch_size, consts.MIN_BATCH_SIZE))
if type(self.learning_rate).__name__ not in ["int", "float"]:
raise ValueError(
descr + "learning_rate {} not supported, should be float type".format(
self.learning_rate))
self.init_param.check()
if self.encrypt_param.method != consts.PAILLIER:
raise ValueError(
descr + "encrypt method supports 'Paillier' only")
if type(self.max_iter).__name__ != "int":
raise ValueError(
descr + "max_iter {} not supported, should be int type".format(self.max_iter))
elif self.max_iter <= 0:
raise ValueError(
descr + "max_iter must be greater or equal to 1")
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))
if type(self.early_stop).__name__ != "str":
raise ValueError(
descr + "early_stop {} not supported, should be str type".format(
self.early_stop))
else:
self.early_stop = self.early_stop.lower()
if self.early_stop not in ['diff', 'abs', 'weight_diff']:
raise ValueError(
descr + "early_stop not supported, early_stop should be"
" 'diff' or 'abs'")
self.encrypt_param.check()
if self.encrypt_param.method != consts.PAILLIER:
raise ValueError(
descr + "encrypt method supports 'Paillier' or None only"
)
self.encrypted_mode_calculator_param.check()
if type(self.decay).__name__ not in ["int", "float"]:
raise ValueError(
descr + "decay {} not supported, should be 'int' or 'float'".format(self.decay)
)
if type(self.decay_sqrt).__name__ not in ["bool"]:
raise ValueError(
descr + "decay_sqrt {} not supported, should be 'bool'".format(self.decay)
)
self.stepwise_param.check()
for p in ["early_stopping_rounds", "validation_freqs", "metrics",
"use_first_metric_only"]:
if self._warn_to_deprecate_param(p, "", ""):
if "callback_param" in self.get_user_feeded():
raise ValueError(f"{p} and callback param should not be set simultaneously，"
f"{self._deprecated_params_set}, {self.get_user_feeded()}")
else:
self.callback_param.callbacks = ["PerformanceEvaluate"]
break
if self._warn_to_deprecate_param("validation_freqs", descr, "callback_param's 'validation_freqs'"):
self.callback_param.validation_freqs = self.validation_freqs
if self._warn_to_deprecate_param("early_stopping_rounds", descr, "callback_param's 'early_stopping_rounds'"):
self.callback_param.early_stopping_rounds = self.early_stopping_rounds
if self._warn_to_deprecate_param("metrics", descr, "callback_param's 'metrics'"):
self.callback_param.metrics = self.metrics
if self._warn_to_deprecate_param("use_first_metric_only", descr, "callback_param's 'use_first_metric_only'"):
self.callback_param.use_first_metric_only = self.use_first_metric_only
if self.floating_point_precision is not None and \
(not isinstance(self.floating_point_precision, int) or
self.floating_point_precision < 0 or self.floating_point_precision > 64):
raise ValueError("floating point precision should be null or a integer between 0 and 64")
self.callback_param.check()
return True
Last update: 2022-01-27