boosting_param¶
boosting_param
¶
hetero_deprecated_param_list
¶
homo_deprecated_param_list
¶
Classes¶
ObjectiveParam (BaseParam)
¶
Define objective parameters that used in federated ml.
Parameters¶
objective : {None, 'cross_entropy', 'lse', 'lae', 'log_cosh', 'tweedie', 'fair', 'huber'} None in host's config, should be str in guest'config. when task_type is classification, only support 'cross_entropy', other 6 types support in regression task
params : None or list should be non empty list when objective is 'tweedie','fair','huber', first element of list shoulf be a float-number large than 0.0 when objective is 'fair', 'huber', first element of list should be a float-number in [1.0, 2.0) when objective is 'tweedie'
Source code in federatedml/param/boosting_param.py
class ObjectiveParam(BaseParam):
"""
Define objective parameters that used in federated ml.
Parameters
----------
objective : {None, 'cross_entropy', 'lse', 'lae', 'log_cosh', 'tweedie', 'fair', 'huber'}
None in host's config, should be str in guest'config.
when task_type is classification, only support 'cross_entropy',
other 6 types support in regression task
params : None or list
should be non empty list when objective is 'tweedie','fair','huber',
first element of list shoulf be a float-number large than 0.0 when objective is 'fair', 'huber',
first element of list should be a float-number in [1.0, 2.0) when objective is 'tweedie'
"""
def __init__(self, objective='cross_entropy', params=None):
self.objective = objective
self.params = params
def check(self, task_type=None):
if self.objective is None:
return True
descr = "objective param's"
LOGGER.debug('check objective {}'.format(self.objective))
if task_type not in [consts.CLASSIFICATION, consts.REGRESSION]:
self.objective = self.check_and_change_lower(self.objective,
["cross_entropy", "lse", "lae", "huber", "fair",
"log_cosh", "tweedie"],
descr)
if task_type == consts.CLASSIFICATION:
if self.objective != "cross_entropy":
raise ValueError("objective param's objective {} not supported".format(self.objective))
elif task_type == consts.REGRESSION:
self.objective = self.check_and_change_lower(self.objective,
["lse", "lae", "huber", "fair", "log_cosh", "tweedie"],
descr)
params = self.params
if self.objective in ["huber", "fair", "tweedie"]:
if type(params).__name__ != 'list' or len(params) < 1:
raise ValueError(
"objective param's params {} not supported, should be non-empty list".format(params))
if type(params[0]).__name__ not in ["float", "int", "long"]:
raise ValueError("objective param's params[0] {} not supported".format(self.params[0]))
if self.objective == 'tweedie':
if params[0] < 1 or params[0] >= 2:
raise ValueError("in tweedie regression, objective params[0] should betweend [1, 2)")
if self.objective == 'fair' or 'huber':
if params[0] <= 0.0:
raise ValueError("in {} regression, objective params[0] should greater than 0.0".format(
self.objective))
return True
__init__(self, objective='cross_entropy', params=None)
special
¶
Source code in federatedml/param/boosting_param.py
def __init__(self, objective='cross_entropy', params=None):
self.objective = objective
self.params = params
check(self, task_type=None)
¶
Source code in federatedml/param/boosting_param.py
def check(self, task_type=None):
if self.objective is None:
return True
descr = "objective param's"
LOGGER.debug('check objective {}'.format(self.objective))
if task_type not in [consts.CLASSIFICATION, consts.REGRESSION]:
self.objective = self.check_and_change_lower(self.objective,
["cross_entropy", "lse", "lae", "huber", "fair",
"log_cosh", "tweedie"],
descr)
if task_type == consts.CLASSIFICATION:
if self.objective != "cross_entropy":
raise ValueError("objective param's objective {} not supported".format(self.objective))
elif task_type == consts.REGRESSION:
self.objective = self.check_and_change_lower(self.objective,
["lse", "lae", "huber", "fair", "log_cosh", "tweedie"],
descr)
params = self.params
if self.objective in ["huber", "fair", "tweedie"]:
if type(params).__name__ != 'list' or len(params) < 1:
raise ValueError(
"objective param's params {} not supported, should be non-empty list".format(params))
if type(params[0]).__name__ not in ["float", "int", "long"]:
raise ValueError("objective param's params[0] {} not supported".format(self.params[0]))
if self.objective == 'tweedie':
if params[0] < 1 or params[0] >= 2:
raise ValueError("in tweedie regression, objective params[0] should betweend [1, 2)")
if self.objective == 'fair' or 'huber':
if params[0] <= 0.0:
raise ValueError("in {} regression, objective params[0] should greater than 0.0".format(
self.objective))
return True
DecisionTreeParam (BaseParam)
¶
Define decision tree parameters that used in federated ml.
Parameters¶
criterion_method : {"xgboost"}, default: "xgboost" the criterion function to use
list or dict
should be non empty and elements are float-numbers, if a list is offered, the first one is l2 regularization value, and the second one is l1 regularization value. if a dict is offered, make sure it contains key 'l1', and 'l2'. l1, l2 regularization values are non-negative floats. default: [0.1, 0] or {'l1':0, 'l2':0,1}
positive integer
the max depth of a decision tree, default: 3
int
least quantity of nodes to split, default: 2
float
least gain of a single split need to reach, default: 1e-3
float
sum of hessian needed in child nodes. default is 0
int
when samples no more than min_leaf_node, it becomes a leave, default: 1
positive integer
we will use no more than max_split_nodes to parallel finding their splits in a batch, for memory consideration. default is 65536
{'split', 'gain'}
if is 'split', feature_importances calculate by feature split times, if is 'gain', feature_importances calculate by feature split gain. default: 'split'
Due to the safety concern, we adjust training strategy of Hetero-SBT in FATE-1.8,
When running Hetero-SBT, this parameter is now abandoned.
In Hetero-SBT of FATE-1.8, guest side will compute split, gain of local features,
and receive anonymous feature importance results from hosts. Hosts will compute split
importance of local features.
bool, accepted True, False only, default: False
use missing value in training process or not.
bool
regard 0 as missing value or not, will be use only if use_missing=True, default: False
bool
ensure stability when computing histogram. Set this to true to ensure stable result when using same data and same parameter. But it may slow down computation.
Source code in federatedml/param/boosting_param.py
class DecisionTreeParam(BaseParam):
"""
Define decision tree parameters that used in federated ml.
Parameters
----------
criterion_method : {"xgboost"}, default: "xgboost"
the criterion function to use
criterion_params: list or dict
should be non empty and elements are float-numbers,
if a list is offered, the first one is l2 regularization value, and the second one is
l1 regularization value.
if a dict is offered, make sure it contains key 'l1', and 'l2'.
l1, l2 regularization values are non-negative floats.
default: [0.1, 0] or {'l1':0, 'l2':0,1}
max_depth: positive integer
the max depth of a decision tree, default: 3
min_sample_split: int
least quantity of nodes to split, default: 2
min_impurity_split: float
least gain of a single split need to reach, default: 1e-3
min_child_weight: float
sum of hessian needed in child nodes. default is 0
min_leaf_node: int
when samples no more than min_leaf_node, it becomes a leave, default: 1
max_split_nodes: positive integer
we will use no more than max_split_nodes to
parallel finding their splits in a batch, for memory consideration. default is 65536
feature_importance_type: {'split', 'gain'}
if is 'split', feature_importances calculate by feature split times,
if is 'gain', feature_importances calculate by feature split gain.
default: 'split'
Due to the safety concern, we adjust training strategy of Hetero-SBT in FATE-1.8,
When running Hetero-SBT, this parameter is now abandoned.
In Hetero-SBT of FATE-1.8, guest side will compute split, gain of local features,
and receive anonymous feature importance results from hosts. Hosts will compute split
importance of local features.
use_missing: bool, accepted True, False only, default: False
use missing value in training process or not.
zero_as_missing: bool
regard 0 as missing value or not,
will be use only if use_missing=True, default: False
deterministic: bool
ensure stability when computing histogram. Set this to true to ensure stable result when using
same data and same parameter. But it may slow down computation.
"""
def __init__(self, criterion_method="xgboost", criterion_params=[0.1, 0], max_depth=3,
min_sample_split=2, min_impurity_split=1e-3, min_leaf_node=1,
max_split_nodes=consts.MAX_SPLIT_NODES, feature_importance_type='split',
n_iter_no_change=True, tol=0.001, min_child_weight=0,
use_missing=False, zero_as_missing=False, deterministic=False):
super(DecisionTreeParam, self).__init__()
self.criterion_method = criterion_method
self.criterion_params = criterion_params
self.max_depth = max_depth
self.min_sample_split = min_sample_split
self.min_impurity_split = min_impurity_split
self.min_leaf_node = min_leaf_node
self.min_child_weight = min_child_weight
self.max_split_nodes = max_split_nodes
self.feature_importance_type = feature_importance_type
self.n_iter_no_change = n_iter_no_change
self.tol = tol
self.use_missing = use_missing
self.zero_as_missing = zero_as_missing
self.deterministic = deterministic
def check(self):
descr = "decision tree param"
self.criterion_method = self.check_and_change_lower(self.criterion_method,
["xgboost"],
descr)
if len(self.criterion_params) == 0:
raise ValueError("decisition tree param's criterio_params should be non empty")
if isinstance(self.criterion_params, list):
assert len(self.criterion_params) == 2, 'length of criterion_param should be 2: l1, l2 regularization ' \
'values are needed'
self.check_nonnegative_number(self.criterion_params[0], 'l2 reg value')
self.check_nonnegative_number(self.criterion_params[1], 'l1 reg value')
elif isinstance(self.criterion_params, dict):
assert 'l1' in self.criterion_params and 'l2' in self.criterion_params, 'l1 and l2 keys are needed in ' \
'criterion_params dict'
self.criterion_params = [self.criterion_params['l2'], self.criterion_params['l1']]
else:
raise ValueError('criterion_params should be a dict or a list contains l1, l2 reg value')
if type(self.max_depth).__name__ not in ["int", "long"]:
raise ValueError("decision tree param's max_depth {} not supported, should be integer".format(
self.max_depth))
if self.max_depth < 1:
raise ValueError("decision tree param's max_depth should be positive integer, no less than 1")
if type(self.min_sample_split).__name__ not in ["int", "long"]:
raise ValueError("decision tree param's min_sample_split {} not supported, should be integer".format(
self.min_sample_split))
if type(self.min_impurity_split).__name__ not in ["int", "long", "float"]:
raise ValueError("decision tree param's min_impurity_split {} not supported, should be numeric".format(
self.min_impurity_split))
if type(self.min_leaf_node).__name__ not in ["int", "long"]:
raise ValueError("decision tree param's min_leaf_node {} not supported, should be integer".format(
self.min_leaf_node))
if type(self.max_split_nodes).__name__ not in ["int", "long"] or self.max_split_nodes < 1:
raise ValueError("decision tree param's max_split_nodes {} not supported, " +
"should be positive integer between 1 and {}".format(self.max_split_nodes,
consts.MAX_SPLIT_NODES))
if type(self.n_iter_no_change).__name__ != "bool":
raise ValueError("decision tree param's n_iter_no_change {} not supported, should be bool type".format(
self.n_iter_no_change))
if type(self.tol).__name__ not in ["float", "int", "long"]:
raise ValueError("decision tree param's tol {} not supported, should be numeric".format(self.tol))
self.feature_importance_type = self.check_and_change_lower(self.feature_importance_type,
["split", "gain"],
descr)
self.check_nonnegative_number(self.min_child_weight, 'min_child_weight')
self.check_boolean(self.deterministic, 'deterministic')
return True
__init__(self, criterion_method='xgboost', criterion_params=[0.1, 0], max_depth=3, min_sample_split=2, min_impurity_split=0.001, min_leaf_node=1, max_split_nodes=65536, feature_importance_type='split', n_iter_no_change=True, tol=0.001, min_child_weight=0, use_missing=False, zero_as_missing=False, deterministic=False)
special
¶
Source code in federatedml/param/boosting_param.py
def __init__(self, criterion_method="xgboost", criterion_params=[0.1, 0], max_depth=3,
min_sample_split=2, min_impurity_split=1e-3, min_leaf_node=1,
max_split_nodes=consts.MAX_SPLIT_NODES, feature_importance_type='split',
n_iter_no_change=True, tol=0.001, min_child_weight=0,
use_missing=False, zero_as_missing=False, deterministic=False):
super(DecisionTreeParam, self).__init__()
self.criterion_method = criterion_method
self.criterion_params = criterion_params
self.max_depth = max_depth
self.min_sample_split = min_sample_split
self.min_impurity_split = min_impurity_split
self.min_leaf_node = min_leaf_node
self.min_child_weight = min_child_weight
self.max_split_nodes = max_split_nodes
self.feature_importance_type = feature_importance_type
self.n_iter_no_change = n_iter_no_change
self.tol = tol
self.use_missing = use_missing
self.zero_as_missing = zero_as_missing
self.deterministic = deterministic
check(self)
¶
Source code in federatedml/param/boosting_param.py
def check(self):
descr = "decision tree param"
self.criterion_method = self.check_and_change_lower(self.criterion_method,
["xgboost"],
descr)
if len(self.criterion_params) == 0:
raise ValueError("decisition tree param's criterio_params should be non empty")
if isinstance(self.criterion_params, list):
assert len(self.criterion_params) == 2, 'length of criterion_param should be 2: l1, l2 regularization ' \
'values are needed'
self.check_nonnegative_number(self.criterion_params[0], 'l2 reg value')
self.check_nonnegative_number(self.criterion_params[1], 'l1 reg value')
elif isinstance(self.criterion_params, dict):
assert 'l1' in self.criterion_params and 'l2' in self.criterion_params, 'l1 and l2 keys are needed in ' \
'criterion_params dict'
self.criterion_params = [self.criterion_params['l2'], self.criterion_params['l1']]
else:
raise ValueError('criterion_params should be a dict or a list contains l1, l2 reg value')
if type(self.max_depth).__name__ not in ["int", "long"]:
raise ValueError("decision tree param's max_depth {} not supported, should be integer".format(
self.max_depth))
if self.max_depth < 1:
raise ValueError("decision tree param's max_depth should be positive integer, no less than 1")
if type(self.min_sample_split).__name__ not in ["int", "long"]:
raise ValueError("decision tree param's min_sample_split {} not supported, should be integer".format(
self.min_sample_split))
if type(self.min_impurity_split).__name__ not in ["int", "long", "float"]:
raise ValueError("decision tree param's min_impurity_split {} not supported, should be numeric".format(
self.min_impurity_split))
if type(self.min_leaf_node).__name__ not in ["int", "long"]:
raise ValueError("decision tree param's min_leaf_node {} not supported, should be integer".format(
self.min_leaf_node))
if type(self.max_split_nodes).__name__ not in ["int", "long"] or self.max_split_nodes < 1:
raise ValueError("decision tree param's max_split_nodes {} not supported, " +
"should be positive integer between 1 and {}".format(self.max_split_nodes,
consts.MAX_SPLIT_NODES))
if type(self.n_iter_no_change).__name__ != "bool":
raise ValueError("decision tree param's n_iter_no_change {} not supported, should be bool type".format(
self.n_iter_no_change))
if type(self.tol).__name__ not in ["float", "int", "long"]:
raise ValueError("decision tree param's tol {} not supported, should be numeric".format(self.tol))
self.feature_importance_type = self.check_and_change_lower(self.feature_importance_type,
["split", "gain"],
descr)
self.check_nonnegative_number(self.min_child_weight, 'min_child_weight')
self.check_boolean(self.deterministic, 'deterministic')
return True
BoostingParam (BaseParam)
¶
Basic parameter for Boosting Algorithms
Parameters¶
task_type : {'classification', 'regression'}, default: 'classification' task type
objective_param : ObjectiveParam Object, default: ObjectiveParam() objective param
learning_rate : float, int or long the learning rate of secure boost. default: 0.3
num_trees : int or float the max number of boosting round. default: 5
subsample_feature_rate : float a float-number in [0, 1], default: 1.0
n_iter_no_change : bool, when True and residual error less than tol, tree building process will stop. default: True
positive integer greater than 1
bin number use in quantile. default: 32
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
Source code in federatedml/param/boosting_param.py
class BoostingParam(BaseParam):
"""
Basic parameter for Boosting Algorithms
Parameters
----------
task_type : {'classification', 'regression'}, default: 'classification'
task type
objective_param : ObjectiveParam Object, default: ObjectiveParam()
objective param
learning_rate : float, int or long
the learning rate of secure boost. default: 0.3
num_trees : int or float
the max number of boosting round. default: 5
subsample_feature_rate : float
a float-number in [0, 1], default: 1.0
n_iter_no_change : bool,
when True and residual error less than tol, tree building process will stop. default: True
bin_num: positive integer greater than 1
bin number use in quantile. default: 32
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
"""
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 check(self):
descr = "boosting tree param's"
if self.task_type not in [consts.CLASSIFICATION, consts.REGRESSION]:
raise ValueError("boosting_core tree param's task_type {} not supported, should be {} or {}".format(
self.task_type, consts.CLASSIFICATION, consts.REGRESSION))
self.objective_param.check(self.task_type)
if type(self.learning_rate).__name__ not in ["float", "int", "long"]:
raise ValueError("boosting_core tree param's learning_rate {} not supported, should be numeric".format(
self.learning_rate))
if type(self.subsample_feature_rate).__name__ not in ["float", "int", "long"] or \
self.subsample_feature_rate < 0 or self.subsample_feature_rate > 1:
raise ValueError(
"boosting_core tree param's subsample_feature_rate should be a numeric number between 0 and 1")
if type(self.n_iter_no_change).__name__ != "bool":
raise ValueError("boosting_core tree param's n_iter_no_change {} not supported, should be bool type".format(
self.n_iter_no_change))
if type(self.tol).__name__ not in ["float", "int", "long"]:
raise ValueError("boosting_core tree param's tol {} not supported, should be numeric".format(self.tol))
if type(self.bin_num).__name__ not in ["int", "long"] or self.bin_num < 2:
raise ValueError(
"boosting_core tree param's bin_num {} not supported, should be positive integer greater than 1".format(
self.bin_num))
if self.validation_freqs is None:
pass
elif isinstance(self.validation_freqs, int):
if self.validation_freqs < 1:
raise ValueError("validation_freqs should be larger than 0 when it's integer")
elif not isinstance(self.validation_freqs, collections.Container):
raise ValueError("validation_freqs should be None or positive integer or container")
if self.metrics is not None and not isinstance(self.metrics, list):
raise ValueError("metrics should be a list")
if self.random_seed is not None:
assert isinstance(self.random_seed, int) and self.random_seed >= 0, 'random seed must be an integer >= 0'
self.check_decimal_float(self.binning_error, descr)
return True
__init__(self, task_type='classification', objective_param=<federatedml.param.boosting_param.ObjectiveParam object at 0x7f3a40c33410>, learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True, tol=0.0001, bin_num=32, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f3a40c335d0>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f3a40c336d0>, validation_freqs=None, metrics=None, random_seed=100, binning_error=0.0001)
special
¶
Source code in federatedml/param/boosting_param.py
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
check(self)
¶
Source code in federatedml/param/boosting_param.py
def check(self):
descr = "boosting tree param's"
if self.task_type not in [consts.CLASSIFICATION, consts.REGRESSION]:
raise ValueError("boosting_core tree param's task_type {} not supported, should be {} or {}".format(
self.task_type, consts.CLASSIFICATION, consts.REGRESSION))
self.objective_param.check(self.task_type)
if type(self.learning_rate).__name__ not in ["float", "int", "long"]:
raise ValueError("boosting_core tree param's learning_rate {} not supported, should be numeric".format(
self.learning_rate))
if type(self.subsample_feature_rate).__name__ not in ["float", "int", "long"] or \
self.subsample_feature_rate < 0 or self.subsample_feature_rate > 1:
raise ValueError(
"boosting_core tree param's subsample_feature_rate should be a numeric number between 0 and 1")
if type(self.n_iter_no_change).__name__ != "bool":
raise ValueError("boosting_core tree param's n_iter_no_change {} not supported, should be bool type".format(
self.n_iter_no_change))
if type(self.tol).__name__ not in ["float", "int", "long"]:
raise ValueError("boosting_core tree param's tol {} not supported, should be numeric".format(self.tol))
if type(self.bin_num).__name__ not in ["int", "long"] or self.bin_num < 2:
raise ValueError(
"boosting_core tree param's bin_num {} not supported, should be positive integer greater than 1".format(
self.bin_num))
if self.validation_freqs is None:
pass
elif isinstance(self.validation_freqs, int):
if self.validation_freqs < 1:
raise ValueError("validation_freqs should be larger than 0 when it's integer")
elif not isinstance(self.validation_freqs, collections.Container):
raise ValueError("validation_freqs should be None or positive integer or container")
if self.metrics is not None and not isinstance(self.metrics, list):
raise ValueError("metrics should be a list")
if self.random_seed is not None:
assert isinstance(self.random_seed, int) and self.random_seed >= 0, 'random seed must be an integer >= 0'
self.check_decimal_float(self.binning_error, descr)
return True
HeteroBoostingParam (BoostingParam)
¶
Parameters¶
encrypt_param : EncodeParam Object encrypt method use in secure boost, default: EncryptParam()
EncryptedModeCalculatorParam object
the calculation mode use in secureboost, default: EncryptedModeCalculatorParam()
Source code in federatedml/param/boosting_param.py
class HeteroBoostingParam(BoostingParam):
"""
Parameters
----------
encrypt_param : EncodeParam Object
encrypt method use in secure boost, default: EncryptParam()
encrypted_mode_calculator_param: EncryptedModeCalculatorParam object
the calculation mode use in secureboost,
default: EncryptedModeCalculatorParam()
"""
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,
random_seed=100, 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,
random_seed=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 check(self):
super(HeteroBoostingParam, self).check()
self.encrypted_mode_calculator_param.check()
self.encrypt_param.check()
if self.early_stopping_rounds is None:
pass
elif isinstance(self.early_stopping_rounds, int):
if self.early_stopping_rounds < 1:
raise ValueError("early stopping rounds should be larger than 0 when it's integer")
if self.validation_freqs is None:
raise ValueError("validation freqs must be set when early stopping is enabled")
if not isinstance(self.use_first_metric_only, bool):
raise ValueError("use_first_metric_only should be a boolean")
return True
__init__(self, task_type='classification', objective_param=<federatedml.param.boosting_param.ObjectiveParam object at 0x7f3a40c33690>, learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True, tol=0.0001, encrypt_param=<federatedml.param.encrypt_param.EncryptParam object at 0x7f3a40c33790>, bin_num=32, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f3a40c33850>, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f3a40c33650>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f3a40c337d0>, validation_freqs=None, early_stopping_rounds=None, metrics=None, use_first_metric_only=False, random_seed=100, binning_error=0.0001)
special
¶
Source code in federatedml/param/boosting_param.py
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,
random_seed=100, 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,
random_seed=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
check(self)
¶
Source code in federatedml/param/boosting_param.py
def check(self):
super(HeteroBoostingParam, self).check()
self.encrypted_mode_calculator_param.check()
self.encrypt_param.check()
if self.early_stopping_rounds is None:
pass
elif isinstance(self.early_stopping_rounds, int):
if self.early_stopping_rounds < 1:
raise ValueError("early stopping rounds should be larger than 0 when it's integer")
if self.validation_freqs is None:
raise ValueError("validation freqs must be set when early stopping is enabled")
if not isinstance(self.use_first_metric_only, bool):
raise ValueError("use_first_metric_only should be a boolean")
return True
HeteroSecureBoostParam (HeteroBoostingParam)
¶
Define boosting tree parameters that used in federated ml.
Parameters¶
task_type : {'classification', 'regression'}, default: 'classification' task type
tree_param : DecisionTreeParam Object, default: DecisionTreeParam() tree param
objective_param : ObjectiveParam Object, default: ObjectiveParam() objective param
learning_rate : float, int or long the learning rate of secure boost. default: 0.3
num_trees : int or float the max number of trees to build. default: 5
subsample_feature_rate : float a float-number in [0, 1], default: 1.0
int
seed that controls all random functions
n_iter_no_change : bool, when True and residual error less than tol, tree building process will stop. default: True
encrypt_param : EncodeParam Object encrypt method use in secure boost, default: EncryptParam(), this parameter is only for hetero-secureboost
positive integer greater than 1
bin number use in quantile. default: 32
EncryptedModeCalculatorParam object
the calculation mode use in secureboost, default: EncryptedModeCalculatorParam(), only for hetero-secureboost
bool
use missing value in training process or not. default: False
bool
regard 0 as missing value or not, will be use only if use_missing=True, default: False
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 "num_trees" is recommended, otherwise, you will miss the validation scores of last training iteration.
integer larger than 0
will stop training if one metric of one validation data doesn’t improve in last early_stopping_round rounds， need to set validation freqs and will check early_stopping every at every validation epoch,
list, default: []
Specify which metrics to be used when performing evaluation during training process. If set as empty, default metrics will be used. For regression tasks, default metrics are ['root_mean_squared_error', 'mean_absolute_error']， For binary-classificatiin tasks, default metrics are ['auc', 'ks']. For multi-classification tasks, default metrics are ['accuracy', 'precision', 'recall']
bool
use only the first metric for early stopping
bool
if use complete_secure, when use complete secure, build first tree using only guest features
Sparse_optimization
this parameter is abandoned in FATE-1.7.1
bool
activate Gradient-based One-Side Sampling, which selects large gradient and small gradient samples using top_rate and other_rate.
top_rate: float, the retain ratio of large gradient data, used when run_goss is True
other_rate: float, the retain ratio of small gradient data, used when run_goss is True
cipher_compress_error： This param is now abandoned
cipher_compress: bool, default is True, use cipher compressing to reduce computation cost and transfer cost
boosting_strategy：str
std: standard sbt setting
!!! 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
str
This parameter has the same function as boosting_strategy, but is deprecated
int, every party will alternate build 'tree_num_per_party' trees until reach max tree num, this
param is valid when boosting_strategy is mix
int, guest will build last guest_depth of a decision tree using guest features, is valid when boosting_strategy
is layered
int, host will build first host_depth of a decision tree using host features, is valid when work boosting_strategy
layered
multi_mode: str, decide which mode to use when running multi-classification task:
single_output standard gbdt multi-classification strategy
multi_output every leaf give a multi-dimension predict, using multi_mode can save time
by learning a model with less trees.
bool
default is False, this option changes the inference algorithm used in predict tasks. a secure prediction method that hides decision path to enhance security in the inference step. This method is insprired by EINI inference algorithm.
bool
default is False multiply predict result by a random float number to confuse original predict result. This operation further enhances the security of naive EINI algorithm.
bool
default is False check the complexity of tree models when running EINI algorithms. Complexity models are easy to hide their decision path, while simple tree models are not, therefore if a tree model is too simple, it is not allowed to run EINI predict algorithms.
Source code in federatedml/param/boosting_param.py
class HeteroSecureBoostParam(HeteroBoostingParam):
"""
Define boosting tree parameters that used in federated ml.
Parameters
----------
task_type : {'classification', 'regression'}, default: 'classification'
task type
tree_param : DecisionTreeParam Object, default: DecisionTreeParam()
tree param
objective_param : ObjectiveParam Object, default: ObjectiveParam()
objective param
learning_rate : float, int or long
the learning rate of secure boost. default: 0.3
num_trees : int or float
the max number of trees to build. default: 5
subsample_feature_rate : float
a float-number in [0, 1], default: 1.0
random_seed: int
seed that controls all random functions
n_iter_no_change : bool,
when True and residual error less than tol, tree building process will stop. default: True
encrypt_param : EncodeParam Object
encrypt method use in secure boost, default: EncryptParam(), this parameter
is only for hetero-secureboost
bin_num: positive integer greater than 1
bin number use in quantile. default: 32
encrypted_mode_calculator_param: EncryptedModeCalculatorParam object
the calculation mode use in secureboost, default: EncryptedModeCalculatorParam(), only for hetero-secureboost
use_missing: bool
use missing value in training process or not. default: False
zero_as_missing: bool
regard 0 as missing value or not, will be use only if use_missing=True, default: False
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 "num_trees" is recommended, otherwise, you will miss the validation scores
of last training iteration.
early_stopping_rounds: integer larger than 0
will stop training if one metric of one validation data
doesn’t improve in last early_stopping_round rounds，
need to set validation freqs and will check early_stopping every at every validation epoch,
metrics: list, default: []
Specify which metrics to be used when performing evaluation during training process.
If set as empty, default metrics will be used. For regression tasks, default metrics are
['root_mean_squared_error', 'mean_absolute_error']， For binary-classificatiin tasks, default metrics
are ['auc', 'ks']. For multi-classification tasks, default metrics are ['accuracy', 'precision', 'recall']
use_first_metric_only: bool
use only the first metric for early stopping
complete_secure: bool
if use complete_secure, when use complete secure, build first tree using only guest features
sparse_optimization:
this parameter is abandoned in FATE-1.7.1
run_goss: bool
activate Gradient-based One-Side Sampling, which selects large gradient and small
gradient samples using top_rate and other_rate.
top_rate: float, the retain ratio of large gradient data, used when run_goss is True
other_rate: float, the retain ratio of small gradient data, used when run_goss is True
cipher_compress_error： This param is now abandoned
cipher_compress: bool, default is True, use cipher compressing to reduce computation cost and transfer cost
boosting_strategy：str
std: standard sbt setting
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
work_mode: str
This parameter has the same function as boosting_strategy, but is deprecated
tree_num_per_party: int, every party will alternate build 'tree_num_per_party' trees until reach max tree num, this
param is valid when boosting_strategy is mix
guest_depth: int, guest will build last guest_depth of a decision tree using guest features, is valid when boosting_strategy
is layered
host_depth: int, host will build first host_depth of a decision tree using host features, is valid when work boosting_strategy
layered
multi_mode: str, decide which mode to use when running multi-classification task:
single_output standard gbdt multi-classification strategy
multi_output every leaf give a multi-dimension predict, using multi_mode can save time
by learning a model with less trees.
EINI_inference: bool
default is False, this option changes the inference algorithm used in predict tasks.
a secure prediction method that hides decision path to enhance security in the inference
step. This method is insprired by EINI inference algorithm.
EINI_random_mask: bool
default is False
multiply predict result by a random float number to confuse original predict result. This operation further
enhances the security of naive EINI algorithm.
EINI_complexity_check: bool
default is False
check the complexity of tree models when running EINI algorithms. Complexity models are easy to hide their
decision path, while simple tree models are not, therefore if a tree model is too simple, it is not allowed
to run EINI predict algorithms.
"""
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
def check(self):
super(HeteroSecureBoostParam, self).check()
self.tree_param.check()
if not isinstance(self.use_missing, bool):
raise ValueError('use missing should be bool type')
if not isinstance(self.zero_as_missing, bool):
raise ValueError('zero as missing should be bool type')
self.check_boolean(self.complete_secure, 'complete_secure')
self.check_boolean(self.run_goss, 'run goss')
self.check_decimal_float(self.top_rate, 'top rate')
self.check_decimal_float(self.other_rate, 'other rate')
self.check_positive_number(self.other_rate, 'other_rate')
self.check_positive_number(self.top_rate, 'top_rate')
self.check_boolean(self.new_ver, 'code version switcher')
self.check_boolean(self.cipher_compress, 'cipher compress')
self.check_boolean(self.EINI_inference, 'eini inference')
self.check_boolean(self.EINI_random_mask, 'eini random mask')
self.check_boolean(self.EINI_complexity_check, 'eini complexity check')
if self.EINI_inference and self.EINI_random_mask:
LOGGER.warning('To protect the inference decision path, notice that current setting will multiply'
' predict result by a random number, hence SecureBoost will return confused predict scores'
' that is not the same as the original predict scores')
if self.work_mode == consts.MIX_TREE and self.EINI_inference:
LOGGER.warning('Mix tree mode does not support EINI, use default predict setting')
if self.work_mode is not None:
self.boosting_strategy = self.work_mode
if self.multi_mode not in [consts.SINGLE_OUTPUT, consts.MULTI_OUTPUT]:
raise ValueError('unsupported multi-classification mode')
if self.multi_mode == consts.MULTI_OUTPUT:
if self.boosting_strategy != consts.STD_TREE:
raise ValueError('MO trees only works when boosting strategy is std tree')
if not self.cipher_compress:
raise ValueError('Mo trees only works when cipher compress is enabled')
if self.boosting_strategy not in [consts.STD_TREE, consts.LAYERED_TREE, consts.MIX_TREE]:
raise ValueError('unknown sbt boosting strategy{}'.format(self.boosting_strategy))
for p in ["early_stopping_rounds", "validation_freqs", "metrics",
"use_first_metric_only"]:
# if self._warn_to_deprecate_param(p, "", ""):
if self._deprecated_params_set.get(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
descr = "boosting_param's"
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.top_rate + self.other_rate >= 1:
raise ValueError('sum of top rate and other rate should be smaller than 1')
return True
__init__(self, tree_param=<federatedml.param.boosting_param.DecisionTreeParam object at 0x7f3a40c33910>, task_type='classification', objective_param=<federatedml.param.boosting_param.ObjectiveParam object at 0x7f3a40c33a90>, learning_rate=0.3, num_trees=5, subsample_feature_rate=1.0, n_iter_no_change=True, tol=0.0001, encrypt_param=<federatedml.param.encrypt_param.EncryptParam object at 0x7f3a40c33b10>, bin_num=32, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f3a40c33b50>, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f3a40c33ad0>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f3a40c33990>, 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=0.0001, 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='std', work_mode=None, tree_num_per_party=1, guest_depth=2, host_depth=3, callback_param=<federatedml.param.callback_param.CallbackParam object at 0x7f3a40c33c50>, multi_mode='single_output', EINI_inference=False, EINI_random_mask=False, EINI_complexity_check=False)
special
¶
Source code in federatedml/param/boosting_param.py
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
check(self)
¶
Source code in federatedml/param/boosting_param.py
def check(self):
super(HeteroSecureBoostParam, self).check()
self.tree_param.check()
if not isinstance(self.use_missing, bool):
raise ValueError('use missing should be bool type')
if not isinstance(self.zero_as_missing, bool):
raise ValueError('zero as missing should be bool type')
self.check_boolean(self.complete_secure, 'complete_secure')
self.check_boolean(self.run_goss, 'run goss')
self.check_decimal_float(self.top_rate, 'top rate')
self.check_decimal_float(self.other_rate, 'other rate')
self.check_positive_number(self.other_rate, 'other_rate')
self.check_positive_number(self.top_rate, 'top_rate')
self.check_boolean(self.new_ver, 'code version switcher')
self.check_boolean(self.cipher_compress, 'cipher compress')
self.check_boolean(self.EINI_inference, 'eini inference')
self.check_boolean(self.EINI_random_mask, 'eini random mask')
self.check_boolean(self.EINI_complexity_check, 'eini complexity check')
if self.EINI_inference and self.EINI_random_mask:
LOGGER.warning('To protect the inference decision path, notice that current setting will multiply'
' predict result by a random number, hence SecureBoost will return confused predict scores'
' that is not the same as the original predict scores')
if self.work_mode == consts.MIX_TREE and self.EINI_inference:
LOGGER.warning('Mix tree mode does not support EINI, use default predict setting')
if self.work_mode is not None:
self.boosting_strategy = self.work_mode
if self.multi_mode not in [consts.SINGLE_OUTPUT, consts.MULTI_OUTPUT]:
raise ValueError('unsupported multi-classification mode')
if self.multi_mode == consts.MULTI_OUTPUT:
if self.boosting_strategy != consts.STD_TREE:
raise ValueError('MO trees only works when boosting strategy is std tree')
if not self.cipher_compress:
raise ValueError('Mo trees only works when cipher compress is enabled')
if self.boosting_strategy not in [consts.STD_TREE, consts.LAYERED_TREE, consts.MIX_TREE]:
raise ValueError('unknown sbt boosting strategy{}'.format(self.boosting_strategy))
for p in ["early_stopping_rounds", "validation_freqs", "metrics",
"use_first_metric_only"]:
# if self._warn_to_deprecate_param(p, "", ""):
if self._deprecated_params_set.get(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
descr = "boosting_param's"
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.top_rate + self.other_rate >= 1:
raise ValueError('sum of top rate and other rate should be smaller than 1')
return True
HomoSecureBoostParam (BoostingParam)
¶
Parameters¶
{'distributed', 'memory'}
decides which backend to use when computing histograms for homo-sbt
Source code in federatedml/param/boosting_param.py
class HomoSecureBoostParam(BoostingParam):
"""
Parameters
----------
backend: {'distributed', 'memory'}
decides which backend to use when computing histograms for homo-sbt
"""
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 check(self):
super(HomoSecureBoostParam, self).check()
self.tree_param.check()
if not isinstance(self.use_missing, bool):
raise ValueError('use missing should be bool type')
if not isinstance(self.zero_as_missing, bool):
raise ValueError('zero as missing should be bool type')
if self.backend not in [consts.MEMORY_BACKEND, consts.DISTRIBUTED_BACKEND]:
raise ValueError('unsupported backend')
if self.multi_mode not in [consts.SINGLE_OUTPUT, consts.MULTI_OUTPUT]:
raise ValueError('unsupported multi-classification mode')
for p in ["validation_freqs", "metrics"]:
# if self._warn_to_deprecate_param(p, "", ""):
if self._deprecated_params_set.get(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
descr = "boosting_param's"
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("metrics", descr, "callback_param's 'metrics'"):
self.callback_param.metrics = self.metrics
if self.multi_mode not in [consts.SINGLE_OUTPUT, consts.MULTI_OUTPUT]:
raise ValueError('unsupported multi-classification mode')
if self.multi_mode == consts.MULTI_OUTPUT:
if self.task_type == consts.REGRESSION:
raise ValueError('regression tasks not support multi-output trees')
return True
__init__(self, tree_param=<federatedml.param.boosting_param.DecisionTreeParam object at 0x7f3a40c33c90>, task_type='classification', objective_param=<federatedml.param.boosting_param.ObjectiveParam object at 0x7f3a40c33d50>, learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True, tol=0.0001, bin_num=32, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f3a40c33dd0>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f3a40c33e10>, validation_freqs=None, use_missing=False, zero_as_missing=False, random_seed=100, binning_error=0.0001, backend='distributed', callback_param=<federatedml.param.callback_param.CallbackParam object at 0x7f3a40c33e90>, multi_mode='single_output')
special
¶
Source code in federatedml/param/boosting_param.py
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
check(self)
¶
Source code in federatedml/param/boosting_param.py
def check(self):
super(HomoSecureBoostParam, self).check()
self.tree_param.check()
if not isinstance(self.use_missing, bool):
raise ValueError('use missing should be bool type')
if not isinstance(self.zero_as_missing, bool):
raise ValueError('zero as missing should be bool type')
if self.backend not in [consts.MEMORY_BACKEND, consts.DISTRIBUTED_BACKEND]:
raise ValueError('unsupported backend')
if self.multi_mode not in [consts.SINGLE_OUTPUT, consts.MULTI_OUTPUT]:
raise ValueError('unsupported multi-classification mode')
for p in ["validation_freqs", "metrics"]:
# if self._warn_to_deprecate_param(p, "", ""):
if self._deprecated_params_set.get(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
descr = "boosting_param's"
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("metrics", descr, "callback_param's 'metrics'"):
self.callback_param.metrics = self.metrics
if self.multi_mode not in [consts.SINGLE_OUTPUT, consts.MULTI_OUTPUT]:
raise ValueError('unsupported multi-classification mode')
if self.multi_mode == consts.MULTI_OUTPUT:
if self.task_type == consts.REGRESSION:
raise ValueError('regression tasks not support multi-output trees')
return True
Last update:
2022-07-07