def __init__(self, tree_param: DecisionTreeParam = DecisionTreeParam(), task_type=consts.CLASSIFICATION,
objective_param=ObjectiveParam(),
learning_rate=0.3, num_trees=5, subsample_feature_rate=1.0, n_iter_no_change=True,
tol=0.0001, encrypt_param=EncryptParam(),
bin_num=32,
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
predict_param=PredictParam(), cv_param=CrossValidationParam(),
validation_freqs=None, early_stopping_rounds=None, use_missing=False, zero_as_missing=False,
complete_secure=False, metrics=None, use_first_metric_only=False, subsample_random_seed=None,
binning_error=consts.DEFAULT_RELATIVE_ERROR,
sparse_optimization=False):
super(HeteroSecureBoostParam, self).__init__(task_type, objective_param, learning_rate, num_trees,
subsample_feature_rate, n_iter_no_change, tol, encrypt_param,
bin_num, encrypted_mode_calculator_param, predict_param, cv_param,
validation_freqs, early_stopping_rounds, metrics=metrics,
use_first_metric_only=use_first_metric_only,
subsample_random_seed=subsample_random_seed,
binning_error=binning_error)
self.tree_param = tree_param
self.zero_as_missing = zero_as_missing
self.use_missing = use_missing
self.complete_secure = complete_secure
self.sparse_optimization = sparse_optimization
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=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-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='L2',
tol=1e-5,
alpha=1.0,
optimizer='sgd',
batch_size=-1,
learning_rate=0.01,
init_param=MtxFInitParam(),
max_iter=100,
early_stop='diff',
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
decay=1,
decay_sqrt=True,
aggregate_iters=1,
validation_freqs=None):
super(HeteroMatrixParam,
self).__init__(optimizer=optimizer,
batch_size=batch_size,
init_param=init_param,
max_iter=max_iter,
early_stop=early_stop,
predict_param=predict_param,
cv_param=cv_param,
validation_freqs=validation_freqs)
self.aggregate_iters = aggregate_iters
def __init__(
self,
secure_aggregate: bool = True,
aggregate_every_n_epoch: int = 1,
early_stop: typing.Union[str, dict,
SimpleNamespace] = {"early_stop": "diff"},
optimizer: typing.Union[str, dict, SimpleNamespace] = {
"optimizer": "SGD",
"learning_rate": 0.01
},
batch_size=-1,
init_param=SVDInitParam(),
max_iter=100,
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
validation_freqs=None,
metrics: typing.Union[str, list] = None,
loss: str = 'mse',
):
super(SVDParam, self).__init__()
self.secure_aggregate = secure_aggregate
self.aggregate_every_n_epoch = aggregate_every_n_epoch
self.early_stop = early_stop
self.metrics = metrics
self.loss = loss
self.optimizer = optimizer
self.batch_size = batch_size
self.init_param = copy.deepcopy(init_param)
self.max_iter = max_iter
self.predict_param = copy.deepcopy(predict_param)
self.cv_param = copy.deepcopy(cv_param)
self.validation_freqs = validation_freqs
def __init__(self,
secure_aggregate: bool = True,
aggregate_every_n_epoch: int = 1,
config_type: str = "nn",
nn_define: dict = None,
optimizer: typing.Union[str, dict, SimpleNamespace] = 'SGD',
loss: str = None,
metrics: typing.Union[str, list] = None,
max_iter: int = 100,
batch_size: int = -1,
early_stop: typing.Union[str, dict, SimpleNamespace] = "diff",
predict_param=PredictParam(),
cv_param=CrossValidationParam()):
super(HomoNNParam, self).__init__()
self.secure_aggregate = secure_aggregate
self.aggregate_every_n_epoch = aggregate_every_n_epoch
self.config_type = config_type
self.nn_define = nn_define or []
self.batch_size = batch_size
self.max_iter = max_iter
self.early_stop = early_stop
self.metrics = metrics
self.optimizer = optimizer
self.loss = loss
self.predict_param = copy.deepcopy(predict_param)
self.cv_param = copy.deepcopy(cv_param)
def __init__(
self,
batch_size=-1,
init_param=SVDppInitParam(),
max_iter=100,
early_stop: typing.Union[str, dict,
SimpleNamespace] = {"early_stop": "diff"},
optimizer: typing.Union[str, dict, SimpleNamespace] = {
"optimizer": "SGD",
"learning_rate": 0.01
},
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
aggregate_iters=1,
validation_freqs=None):
super(HeteroSVDppParam,
self).__init__(optimizer=optimizer,
batch_size=batch_size,
init_param=init_param,
max_iter=max_iter,
early_stop=early_stop,
predict_param=predict_param,
cv_param=cv_param,
validation_freqs=validation_freqs)
self.aggregate_iters = aggregate_iters
def __init__(self,
task_type=consts.CLASSIFICATION,
objective_param=ObjectiveParam(),
learning_rate=0.3,
num_trees=5,
subsample_feature_rate=1,
n_iter_no_change=True,
tol=0.0001,
bin_num=32,
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
validation_freqs=None,
metrics=None,
random_seed=100,
binning_error=consts.DEFAULT_RELATIVE_ERROR):
super(BoostingParam, self).__init__()
self.task_type = task_type
self.objective_param = copy.deepcopy(objective_param)
self.learning_rate = learning_rate
self.num_trees = num_trees
self.subsample_feature_rate = subsample_feature_rate
self.n_iter_no_change = n_iter_no_change
self.tol = tol
self.bin_num = bin_num
self.predict_param = copy.deepcopy(predict_param)
self.cv_param = copy.deepcopy(cv_param)
self.validation_freqs = validation_freqs
self.metrics = metrics
self.random_seed = random_seed
self.binning_error = binning_error
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-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 __init__(
self,
tree_param=DecisionTreeParam(),
task_type=consts.CLASSIFICATION,
objective_param=ObjectiveParam(),
learning_rate=0.3,
num_trees=5,
subsample_feature_rate=0.8,
n_iter_no_change=True,
tol=0.0001,
encrypt_param=EncryptParam(),
bin_num=32,
use_missing=False,
zero_as_missing=False,
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
validation_freqs=None):
self.tree_param = copy.deepcopy(tree_param)
self.task_type = task_type
self.objective_param = copy.deepcopy(objective_param)
self.learning_rate = learning_rate
self.num_trees = num_trees
self.subsample_feature_rate = subsample_feature_rate
self.n_iter_no_change = n_iter_no_change
self.tol = tol
self.encrypt_param = copy.deepcopy(encrypt_param)
self.bin_num = bin_num
self.use_missing = use_missing
self.zero_as_missing = zero_as_missing
self.encrypted_mode_calculator_param = copy.deepcopy(
encrypted_mode_calculator_param)
self.predict_param = copy.deepcopy(predict_param)
self.cv_param = copy.deepcopy(cv_param)
self.validation_freqs = validation_freqs
def __init__(self,
tree_param: DecisionTreeParam = DecisionTreeParam(),
task_type=consts.CLASSIFICATION,
objective_param=ObjectiveParam(),
learning_rate=0.3,
num_trees=5,
subsample_feature_rate=1,
n_iter_no_change=True,
tol=0.0001,
bin_num=32,
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
validation_freqs=None,
use_missing=False,
zero_as_missing=False,
subsample_random_seed=None,
binning_error=consts.DEFAULT_RELATIVE_ERROR):
super(HomoSecureBoostParam,
self).__init__(task_type=task_type,
objective_param=objective_param,
learning_rate=learning_rate,
num_trees=num_trees,
subsample_feature_rate=subsample_feature_rate,
n_iter_no_change=n_iter_no_change,
tol=tol,
bin_num=bin_num,
predict_param=predict_param,
cv_param=cv_param,
validation_freqs=validation_freqs,
subsample_random_seed=subsample_random_seed,
binning_error=binning_error)
self.use_missing = use_missing
self.zero_as_missing = zero_as_missing
self.tree_param = tree_param
def __init__(self,
task_type='classification',
config_type="keras",
bottom_nn_define=None,
top_nn_define=None,
interactive_layer_define=None,
interactive_layer_lr=0.9,
optimizer='SGD',
loss=None,
epochs=100,
batch_size=-1,
early_stop="diff",
tol=1e-5,
encrypt_param=EncryptParam(),
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(
mode="confusion_opt"),
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
validation_freqs=None,
early_stopping_rounds=None,
metrics=None,
use_first_metric_only=True,
selector_param=SelectorParam(),
floating_point_precision=23,
drop_out_keep_rate=1.0,
callback_param=CallbackParam()):
super(HeteroNNParam, self).__init__()
self.task_type = task_type
self.config_type = config_type
self.bottom_nn_define = bottom_nn_define
self.interactive_layer_define = interactive_layer_define
self.interactive_layer_lr = interactive_layer_lr
self.top_nn_define = top_nn_define
self.batch_size = batch_size
self.epochs = epochs
self.early_stop = early_stop
self.tol = tol
self.optimizer = optimizer
self.loss = loss
self.validation_freqs = validation_freqs
self.early_stopping_rounds = early_stopping_rounds
self.metrics = metrics or []
self.use_first_metric_only = use_first_metric_only
self.encrypt_param = copy.deepcopy(encrypt_param)
self.encrypted_model_calculator_param = encrypted_mode_calculator_param
self.predict_param = copy.deepcopy(predict_param)
self.cv_param = copy.deepcopy(cv_param)
self.selector_param = selector_param
self.floating_point_precision = floating_point_precision
self.drop_out_keep_rate = drop_out_keep_rate
self.callback_param = copy.deepcopy(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 __init__(self, penalty='L2',
tol=1e-5, alpha=1.0, optimizer='sgd',
batch_size=-1, learning_rate=0.01, init_param=FMInitParam(),
max_iter=100, early_stop='diff', encrypt_param=EncryptParam(),
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
predict_param=PredictParam(), cv_param=CrossValidationParam(),
decay=1, decay_sqrt=True,
multi_class='ovr', validation_freqs=None,clip_gradient=None
):
super(HeteroFactorizationParam, self).__init__(penalty=penalty, tol=tol, alpha=alpha, optimizer=optimizer,
batch_size=batch_size,
learning_rate=learning_rate, encrypt_param=encrypt_param,
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,
clip_gradient=clip_gradient)
self.encrypted_mode_calculator_param = 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-5, alpha=1.0, optimizer='sgd',
batch_size=-1, learning_rate=0.01, init_param=FMInitParam(),
max_iter=100, early_stop='diff',
predict_param=PredictParam(), cv_param=CrossValidationParam(),
decay=1, decay_sqrt=True,
aggregate_iters=1, multi_class='ovr', validation_freqs=None,
clip_gradient=None
):
super(HomoFactorizationParam, 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, multi_class=multi_class,
validation_freqs=validation_freqs,
decay=decay, decay_sqrt=decay_sqrt,
clip_gradient=clip_gradient)
self.aggregate_iters = aggregate_iters
def __init__(
self,
task_type=consts.CLASSIFICATION,
objective_param=ObjectiveParam(),
learning_rate=0.3,
num_trees=5,
subsample_feature_rate=1,
n_iter_no_change=True,
tol=0.0001,
encrypt_param=EncryptParam(),
bin_num=32,
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
validation_freqs=None,
early_stopping_rounds=None,
metrics=None,
use_first_metric_only=False,
subsample_random_seed=None,
binning_error=consts.DEFAULT_RELATIVE_ERROR):
super(HeteroBoostingParam,
self).__init__(task_type,
objective_param,
learning_rate,
num_trees,
subsample_feature_rate,
n_iter_no_change,
tol,
bin_num,
predict_param,
cv_param,
validation_freqs,
metrics=metrics,
subsample_random_seed=subsample_random_seed,
binning_error=binning_error)
self.encrypt_param = copy.deepcopy(encrypt_param)
self.encrypted_mode_calculator_param = copy.deepcopy(
encrypted_mode_calculator_param)
self.early_stopping_rounds = early_stopping_rounds
self.use_first_metric_only = use_first_metric_only
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,
optimizer={
"optimizer": "SGD",
"learning_rate": 0.01
},
batch_size=-1,
init_param=GMFInitParam(),
max_iter=100,
early_stop={"early_stop": "diff"},
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
neg_count: int = 4):
super(HeteroGMFParam, self).__init__(optimizer=optimizer,
batch_size=batch_size,
init_param=init_param,
max_iter=max_iter,
early_stop=early_stop,
predict_param=predict_param,
cv_param=cv_param,
neg_count=neg_count)
def __init__(self,
tree_param: DecisionTreeParam = DecisionTreeParam(),
task_type=consts.CLASSIFICATION,
objective_param=ObjectiveParam(),
learning_rate=0.3,
num_trees=5,
subsample_feature_rate=1,
n_iter_no_change=True,
tol=0.0001,
bin_num=32,
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
validation_freqs=None,
use_missing=False,
zero_as_missing=False,
random_seed=100,
binning_error=consts.DEFAULT_RELATIVE_ERROR,
backend=consts.DISTRIBUTED_BACKEND,
callback_param=CallbackParam(),
multi_mode=consts.SINGLE_OUTPUT):
super(HomoSecureBoostParam,
self).__init__(task_type=task_type,
objective_param=objective_param,
learning_rate=learning_rate,
num_trees=num_trees,
subsample_feature_rate=subsample_feature_rate,
n_iter_no_change=n_iter_no_change,
tol=tol,
bin_num=bin_num,
predict_param=predict_param,
cv_param=cv_param,
validation_freqs=validation_freqs,
random_seed=random_seed,
binning_error=binning_error)
self.use_missing = use_missing
self.zero_as_missing = zero_as_missing
self.tree_param = copy.deepcopy(tree_param)
self.backend = backend
self.callback_param = copy.deepcopy(callback_param)
self.multi_mode = multi_mode
def __init__(self,
task_type='classification',
config_type="keras",
bottom_nn_define=None,
top_nn_define=None,
interactive_layer_define=None,
interactive_layer_lr=0.9,
optimizer='SGD',
loss=None,
metrics=None,
epochs=100,
batch_size=-1,
early_stop="diff",
tol=1e-5,
encrypt_param=EncryptParam(),
encrypted_mode_calculator_param = EncryptedModeCalculatorParam(mode="confusion_opt"),
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
validation_freqs=None):
super(HeteroNNParam, self).__init__()
self.task_type = task_type
self.config_type = config_type
self.bottom_nn_define = bottom_nn_define
self.interactive_layer_define = interactive_layer_define
self.interactive_layer_lr = interactive_layer_lr
self.top_nn_define = top_nn_define
self.batch_size = batch_size
self.epochs = epochs
self.early_stop = early_stop
self.tol = tol
self.metrics = metrics
self.optimizer = optimizer
self.loss = loss
self.validation_freqs = validation_freqs
self.encrypt_param = copy.deepcopy(encrypt_param)
self.encrypted_model_calculator_param = encrypted_mode_calculator_param
self.predict_param = copy.deepcopy(predict_param)
self.cv_param = copy.deepcopy(cv_param)
def __init__(self, tree_param: DecisionTreeParam = DecisionTreeParam(), task_type=consts.CLASSIFICATION,
objective_param=ObjectiveParam(),
learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True,
tol=0.0001, encrypt_param=EncryptParam(),
bin_num=32,
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
predict_param=PredictParam(), cv_param=CrossValidationParam(),
validation_freqs=None, early_stopping=None, use_missing=False, zero_as_missing=False,
complete_secure=False, tree_num_per_party=1, guest_depth=1, host_depth=1, work_mode='mix', metrics=None,
sparse_optmization=False, subsample_random_seed=None, binning_error=consts.DEFAULT_RELATIVE_ERROR):
"""
work_mode：
mix: alternate using guest/host features to build trees. For example, the first 'tree_num_per_party' trees use guest features,
the second k trees use host features, and so on
layered: only support 2 party, when running layered mode, first 'host_depth' layer will use host features,
and then next 'guest_depth' will only use guest features
tree_num_per_party: every party will alternate build 'tree_num_per_party' trees until reach max tree num, this param is valid when work_mode is
mix
guest_depth: guest will build last guest_depth of a decision tree using guest features, is valid when work mode
is layered
host depth: host will build first host_depth of a decision tree using host features, is valid when work mode is
layered
other params are the same as HeteroSecureBoost
"""
super(HeteroFastSecureBoostParam, self).__init__(tree_param, task_type, objective_param, learning_rate,
num_trees, subsample_feature_rate, n_iter_no_change, tol,
encrypt_param, bin_num, encrypted_mode_calculator_param,
predict_param, cv_param, validation_freqs, early_stopping,
use_missing, zero_as_missing, complete_secure, metrics=metrics,
subsample_random_seed=subsample_random_seed,
sparse_optmization=sparse_optmization,
binning_error=binning_error)
self.tree_num_per_party = tree_num_per_party
self.guest_depth = guest_depth
self.host_depth = host_depth
self.work_mode = work_mode
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',
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)
def __init__(
self,
penalty='L2',
eps=1e-5,
alpha=1.0,
optimizer='sgd',
party_weight=1,
batch_size=-1,
learning_rate=0.01,
init_param=InitParam(),
max_iter=100,
converge_func='diff',
encrypt_param=EncryptParam(),
re_encrypt_batches=2,
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
need_run=True,
predict_param=PredictParam(),
cv_param=CrossValidationParam(),
one_vs_rest_param=OneVsRestParam()):
super(LogisticParam, self).__init__()
self.penalty = penalty
self.eps = eps
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.converge_func = converge_func
self.encrypt_param = copy.deepcopy(encrypt_param)
self.re_encrypt_batches = re_encrypt_batches
self.party_weight = party_weight
self.encrypted_mode_calculator_param = copy.deepcopy(
encrypted_mode_calculator_param)
self.need_run = need_run
self.predict_param = copy.deepcopy(predict_param)
self.cv_param = copy.deepcopy(cv_param)
self.one_vs_rest_param = copy.deepcopy(one_vs_rest_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(),
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
