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-5, alpha=1.0, optimizer='sgd',
batch_size=-1, learning_rate=0.01, init_param=InitParam(),
max_iter=100, 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-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, 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,
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(),
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-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(),
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 __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=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 = cv_param
self.encrypted_mode_calculator_param = copy.deepcopy(
encrypted_mode_calculator_param)
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=20,
early_stop='diff',
encrypt_param=EncryptParam(),
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
cv_param=CrossValidationParam(),
decay=1,
decay_sqrt=True,
callback_param=CallbackParam(),
use_mix_rand=True,
reveal_strategy="respectively",
reveal_every_iter=False):
super(HeteroSSHELinRParam,
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,
callback_param=callback_param)
self.encrypted_mode_calculator_param = copy.deepcopy(
encrypted_mode_calculator_param)
self.use_mix_rand = use_mix_rand
self.reveal_strategy = reveal_strategy
self.reveal_every_iter = reveal_every_iter
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=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,
subsample_random_seed=None, binning_error=consts.DEFAULT_RELATIVE_ERROR, sparse_optimization=False):
"""
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,
binning_error=binning_error, sparse_optimization=sparse_optimization)
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,
alpha=1,
tol=0.000001,
n_iter_no_change=False,
validation_freqs=None,
optimizer={
'optimizer': 'Adam',
'learning_rate': 0.01
},
nn_define={},
epochs=1,
intersect_param=IntersectParam(consts.RSA),
config_type='keras',
batch_size=-1,
encrypte_param=EncryptParam(),
encrypted_mode_calculator_param=EncryptedModeCalculatorParam(
mode="confusion_opt"),
predict_param=PredictParam(),
mode='plain',
communication_efficient=False,
local_round=5,
callback_param=CallbackParam()):
"""
Args:
alpha: float, a loss coefficient defined in paper, it defines the importance of alignment loss
tol: float, loss tolerance
n_iter_no_change: bool, check loss convergence or not
validation_freqs: None or positive integer or container object in python. Do validation in training process or Not.
if equals None, will not do validation in train process;
if equals positive integer, will validate data every validation_freqs epochs passes;
if container object in python, will validate data if epochs belong to this container.
e.g. validation_freqs = [10, 15], will validate data when epoch equals to 10 and 15.
Default: None
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 "epochs" is recommended, otherwise, you will miss the validation scores
of last training epoch.
optimizer: optimizer method, accept following types:
1. a string, one of "Adadelta", "Adagrad", "Adam", "Adamax", "Nadam", "RMSprop", "SGD"
2. a dict, with a required key-value pair keyed by "optimizer",
with optional key-value pairs such as learning rate.
defaults to "SGD"
nn_define: dict, a dict represents the structure of neural network, it can be output by tf-keras
epochs: int, epochs num
intersect_param: define the intersect method
config_type: now only 'tf-keras' is supported
batch_size: batch size when computing transformed feature embedding, -1 use full data.
encrypte_param: encrypted param
encrypted_mode_calculator_param:
predict_param: predict param
mode:
plain: will not use any encrypt algorithms, data exchanged in plaintext
encrypted: use paillier to encrypt gradients
communication_efficient:
bool, will use communication efficient or not. when communication efficient is enabled, FTL model will
update gradients by several local rounds using intermediate data
local_round: local update round when using communication efficient
"""
super(FTLParam, self).__init__()
self.alpha = alpha
self.tol = tol
self.n_iter_no_change = n_iter_no_change
self.validation_freqs = validation_freqs
self.optimizer = optimizer
self.nn_define = nn_define
self.epochs = epochs
self.intersect_param = copy.deepcopy(intersect_param)
self.config_type = config_type
self.batch_size = batch_size
self.encrypted_mode_calculator_param = copy.deepcopy(
encrypted_mode_calculator_param)
self.encrypt_param = copy.deepcopy(encrypte_param)
self.predict_param = copy.deepcopy(predict_param)
self.mode = mode
self.communication_efficient = communication_efficient
self.local_round = local_round
self.callback_param = copy.deepcopy(callback_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=1.0,
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,
random_seed=100,
binning_error=consts.DEFAULT_RELATIVE_ERROR,
sparse_optimization=False,
run_goss=False,
top_rate=0.2,
other_rate=0.1,
cipher_compress_error=None,
cipher_compress=True,
new_ver=True,
boosting_strategy=consts.STD_TREE,
work_mode=None,
tree_num_per_party=1,
guest_depth=2,
host_depth=3,
callback_param=CallbackParam(),
multi_mode=consts.SINGLE_OUTPUT,
EINI_inference=False,
EINI_random_mask=False,
EINI_complexity_check=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,
random_seed=random_seed,
binning_error=binning_error)
self.tree_param = copy.deepcopy(tree_param)
self.zero_as_missing = zero_as_missing
self.use_missing = use_missing
self.complete_secure = complete_secure
self.sparse_optimization = sparse_optimization
self.run_goss = run_goss
self.top_rate = top_rate
self.other_rate = other_rate
self.cipher_compress_error = cipher_compress_error
self.cipher_compress = cipher_compress
self.new_ver = new_ver
self.EINI_inference = EINI_inference
self.EINI_random_mask = EINI_random_mask
self.EINI_complexity_check = EINI_complexity_check
self.boosting_strategy = boosting_strategy
self.work_mode = work_mode
self.tree_num_per_party = tree_num_per_party
self.guest_depth = guest_depth
self.host_depth = host_depth
self.callback_param = copy.deepcopy(callback_param)
self.multi_mode = multi_mode
