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):
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
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',
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'],
use_first_metric_only=False, stepwise_param=StepwiseParam()
):
super(HeteroLogisticParam, 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,
validation_freqs=validation_freqs,
early_stopping_rounds=early_stopping_rounds,
metrics=metrics,
use_first_metric_only=use_first_metric_only,
stepwise_param=stepwise_param)
self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param)
self.sqn_param = copy.deepcopy(sqn_param)
def __init__(self,
penalty='L2',
tol=1e-5,
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',
validation_freqs=None):
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.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
def __init__(self, penalty=None,
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=True,
callback_param=CallbackParam(),
encrypted_mode_calculator_param=EncryptedModeCalculatorParam()
):
super(LogisticRegressionParam, 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.predict_param = copy.deepcopy(predict_param)
self.decay = decay
self.decay_sqrt = decay_sqrt
self.multi_class = multi_class
self.use_mix_rand = use_mix_rand
self.reveal_strategy = reveal_strategy
self.reveal_every_iter = reveal_every_iter
self.callback_param = copy.deepcopy(callback_param)
self.cv_param = copy.deepcopy(cv_param)
self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_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', predict_param=PredictParam(),
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):
super(LinearParam, 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.validation_freqs = validation_freqs
self.sqn_param = copy.deepcopy(sqn_param)
self.early_stopping_rounds = early_stopping_rounds
self.stepwise_param = copy.deepcopy(stepwise_param)
self.metrics = metrics or []
self.use_first_metric_only = use_first_metric_only
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(), 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
):
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)
self.re_encrypt_batches = re_encrypt_batches
self.aggregate_iters = aggregate_iters
self.use_proximal = use_proximal
self.mu = mu
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(),
predict_param=PredictParam(), cv_param=CrossValidationParam(),
decay=1, decay_sqrt=True,
multi_class='ovr', validation_freqs=None, early_stopping_rounds=None,
stepwise_param=StepwiseParam(),
metrics=None,
use_first_metric_only=False
):
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.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
def load_model(self, model_dict):
LOGGER.debug("Start Loading model")
result_obj = list(model_dict.get('model').values())[0].get(self.model_param_name)
meta_obj = list(model_dict.get('model').values())[0].get(self.model_meta_name)
if self.init_param_obj is None:
self.init_param_obj = InitParam()
self.init_param_obj.fit_intercept = meta_obj.fit_intercept
self.model_param.reveal_strategy = meta_obj.reveal_strategy
LOGGER.debug(f"reveal_strategy: {self.model_param.reveal_strategy}, {self.is_respectively_reveal}")
self.header = list(result_obj.header)
return result_obj, meta_obj
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 __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 __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(),
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(LinearModelParam, 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.cv_param = copy.deepcopy(cv_param)
self.decay = decay
self.decay_sqrt = decay_sqrt
self.validation_freqs = validation_freqs
self.early_stopping_rounds = early_stopping_rounds
self.stepwise_param = copy.deepcopy(stepwise_param)
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 __init__(
self,
penalty='L2',
tol=1e-5,
alpha=1.0,
optimizer='sgd',
batch_size=-1,
learning_rate=0.01,
init_param=InitParam(),
max_iter=100,
early_stop='diff',
exposure_colname=None,
predict_param=PredictParam(),
encrypt_param=EncryptParam(),
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
cv_param=CrossValidationParam(),
decay=1,
decay_sqrt=True,
validation_freqs=None):
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
def __init__(self,
penalty='L2',
tol=1e-5,
alpha=1.0,
optimizer='sgd',
batch_size=-1,
learning_rate=0.01,
init_param=InitParam(),
max_iter=100,
early_stop='diff',
encrypt_param=EncryptParam(),
re_encrypt_batches=2,
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
decay=1,
decay_sqrt=True,
aggregate_iters=1,
multi_class='ovr',
validation_freqs=None):
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)
self.re_encrypt_batches = re_encrypt_batches
self.aggregate_iters = aggregate_iters
def __init__(
self,
penalty='L2',
tol=1e-5,
alpha=1.0,
optimizer='sgd',
batch_size=-1,
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):
super(HeteroLogisticParam,
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,
validation_freqs=validation_freqs)
self.encrypted_mode_calculator_param = copy.deepcopy(
encrypted_mode_calculator_param)
self.sqn_param = copy.deepcopy(sqn_param)
