boosting_param¶
boosting_param
¶
hetero_deprecated_param_list
¶
homo_deprecated_param_list
¶
Classes¶
ObjectiveParam (BaseParam)
¶
Define objective parameters that used in federated ml.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
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 |
'cross_entropy' |
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' |
None |
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:
| Name | Type | Description | Default |
|---|---|---|---|
criterion_method |
{"xgboost"}, default: "xgboost" |
the criterion function to use |
'xgboost' |
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} |
[0.1, 0] |
max_depth |
positive integer |
the max depth of a decision tree, default: 3 |
3 |
min_sample_split |
int |
least quantity of nodes to split, default: 2 |
2 |
min_impurity_split |
float |
least gain of a single split need to reach, default: 1e-3 |
0.001 |
min_child_weight |
float |
sum of hessian needed in child nodes. default is 0 |
0 |
min_leaf_node |
int |
when samples no more than min_leaf_node, it becomes a leave, default: 1 |
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 |
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' |
'split' |
use_missing |
bool |
use missing value in training process or not. default: False |
False |
zero_as_missing |
bool |
regard 0 as missing value or not, will be use only if use_missing=True, default: False |
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. |
False |
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'
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
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 type(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 type(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 type(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 type(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:
| Name | Type | Description | Default |
|---|---|---|---|
task_type |
{'classification', 'regression'}, default: 'classification' |
task type |
'classification' |
objective_param |
ObjectiveParam Object, default: ObjectiveParam() |
objective param |
<federatedml.param.boosting_param.ObjectiveParam object at 0x7f3f8a6a3810> |
learning_rate |
float, int or long |
the learning rate of secure boost. default: 0.3 |
0.3 |
num_trees |
int or float |
the max number of boosting round. default: 5 |
5 |
subsample_feature_rate |
float |
a float-number in [0, 1], default: 1.0 |
1 |
n_iter_no_change |
bool, |
when True and residual error less than tol, tree building process will stop. default: True |
True |
bin_num |
positive integer greater than 1 |
bin number use in quantile. default: 32 |
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 |
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 type(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 0x7f3f8a6a3810>, 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 0x7f3f8a6a3910>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f3f8a6a3a10>, 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 type(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:
| Name | Type | Description | Default |
|---|---|---|---|
encrypt_param |
EncodeParam Object |
encrypt method use in secure boost, default: EncryptParam() |
<federatedml.param.encrypt_param.EncryptParam object at 0x7f3f8a6a3ad0> |
encrypted_mode_calculator_param |
EncryptedModeCalculatorParam object |
the calculation mode use in secureboost, default: EncryptedModeCalculatorParam() |
<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f3f8a6a3b90> |
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 0x7f3f8a6a39d0>, 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 0x7f3f8a6a3ad0>, bin_num=32, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f3f8a6a3b90>, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f3f8a6a3990>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f3f8a6a3b10>, 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:
| Name | Type | Description | Default |
|---|---|---|---|
task_type |
{'classification', 'regression'}, default: 'classification' |
task type |
'classification' |
tree_param |
DecisionTreeParam |
tree param |
<federatedml.param.boosting_param.DecisionTreeParam object at 0x7f3f8a6a3c50> |
objective_param |
ObjectiveParam Object, default: ObjectiveParam() |
objective param |
<federatedml.param.boosting_param.ObjectiveParam object at 0x7f3f8a6a3dd0> |
learning_rate |
float, int or long |
the learning rate of secure boost. default: 0.3 |
0.3 |
num_trees |
int or float |
the max number of trees to build. default: 5 |
5 |
subsample_feature_rate |
float |
a float-number in [0, 1], default: 1.0 |
1.0 |
random_seed |
int |
seed that controls all random functions |
100 |
n_iter_no_change |
bool, |
when True and residual error less than tol, tree building process will stop. default: True |
True |
encrypt_param |
EncodeParam Object |
encrypt method use in secure boost, default: EncryptParam(), this parameter is only for hetero-secureboost |
<federatedml.param.encrypt_param.EncryptParam object at 0x7f3f8a6a3e50> |
bin_num |
positive integer greater than 1 |
bin number use in quantile. default: 32 |
32 |
encrypted_mode_calculator_param |
EncryptedModeCalculatorParam object |
the calculation mode use in secureboost, default: EncryptedModeCalculatorParam(), only for hetero-secureboost |
<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f3f8a6a3e90> |
use_missing |
bool |
use missing value in training process or not. default: False |
False |
zero_as_missing |
bool |
regard 0 as missing value or not, will be use only if use_missing=True, default: False |
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. |
None |
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, |
None |
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'] |
None |
use_first_metric_only |
bool |
use only the first metric for early stopping |
False |
complete_secure |
bool |
if use complete_secure, when use complete secure, build first tree using only guest features |
False |
sparse_optimization |
bool |
Available when encrypted method is 'iterativeAffine' An optimized mode for high-dimension, sparse data. |
False |
run_goss |
bool |
activate Gradient-based One-Side Sampling, which selects large gradient and small gradient samples using top_rate and other_rate. |
False |
top_rate |
float |
the retain ratio of large gradient data, used when run_goss is True |
0.2 |
other_rate |
float |
the retain ratio of small gradient data, used when run_goss is True |
0.1 |
cipher_compress_error |
{None} |
This param is now abandoned |
None |
cipher_compress |
bool |
default is True, use cipher compressing to reduce computation cost and transfer cost |
True |
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: bool
Available when encrypted method is 'iterativeAffine'
An optimized mode for high-dimension, sparse data.
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: {None}
This param is now abandoned
cipher_compress: bool
default is True, use cipher compressing to reduce computation cost and transfer cost
"""
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,
callback_param=CallbackParam()):
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.callback_param = copy.deepcopy(callback_param)
def check(self):
super(HeteroSecureBoostParam, self).check()
self.tree_param.check()
if type(self.use_missing) != bool:
raise ValueError('use missing should be bool type')
if type(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.sparse_optimization, 'sparse optimization')
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')
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')
if self.sparse_optimization and self.cipher_compress:
raise ValueError('cipher compress is not supported in sparse optimization mode')
return True
__init__(self, tree_param=<federatedml.param.boosting_param.DecisionTreeParam object at 0x7f3f8a6a3c50>, task_type='classification', objective_param=<federatedml.param.boosting_param.ObjectiveParam object at 0x7f3f8a6a3dd0>, 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 0x7f3f8a6a3e50>, bin_num=32, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f3f8a6a3e90>, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f3f8a6a3e10>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f3f8a6a3cd0>, 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, callback_param=<federatedml.param.callback_param.CallbackParam object at 0x7f3f8a6a3f90>)
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,
callback_param=CallbackParam()):
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.callback_param = copy.deepcopy(callback_param)
check(self)
¶
Source code in federatedml/param/boosting_param.py
def check(self):
super(HeteroSecureBoostParam, self).check()
self.tree_param.check()
if type(self.use_missing) != bool:
raise ValueError('use missing should be bool type')
if type(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.sparse_optimization, 'sparse optimization')
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')
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')
if self.sparse_optimization and self.cipher_compress:
raise ValueError('cipher compress is not supported in sparse optimization mode')
return True
HeteroFastSecureBoostParam (HeteroSecureBoostParam)
¶
Source code in federatedml/param/boosting_param.py
class HeteroFastSecureBoostParam(HeteroSecureBoostParam):
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_rounds=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_optimization=False, random_seed=100, binning_error=consts.DEFAULT_RELATIVE_ERROR,
cipher_compress_error=None, new_ver=True, run_goss=False, top_rate=0.2, other_rate=0.1,
cipher_compress=True, callback_param=CallbackParam()):
"""
Parameters
----------
work_mode: {"mix", "layered"}
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: int
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: int
guest will build last guest_depth of a decision tree using guest features, is valid when work mode is layered
host depth: int
host will build first host_depth of a decision tree using host features, is valid when work mode is layered
"""
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_rounds,
use_missing, zero_as_missing, complete_secure, metrics=metrics,
random_seed=random_seed,
sparse_optimization=sparse_optimization,
binning_error=binning_error,
cipher_compress_error=cipher_compress_error,
new_ver=new_ver,
cipher_compress=cipher_compress,
run_goss=run_goss, top_rate=top_rate, other_rate=other_rate,
)
self.tree_num_per_party = tree_num_per_party
self.guest_depth = guest_depth
self.host_depth = host_depth
self.work_mode = work_mode
self.callback_param = copy.deepcopy(callback_param)
def check(self):
super(HeteroFastSecureBoostParam, self).check()
if type(self.guest_depth).__name__ not in ["int", "long"] or self.guest_depth <= 0:
raise ValueError("guest_depth should be larger than 0")
if type(self.host_depth).__name__ not in ["int", "long"] or self.host_depth <= 0:
raise ValueError("host_depth should be larger than 0")
if type(self.tree_num_per_party).__name__ not in ["int", "long"] or self.tree_num_per_party <= 0:
raise ValueError("tree_num_per_party should be larger than 0")
work_modes = [consts.MIX_TREE, consts.LAYERED_TREE]
if self.work_mode not in work_modes:
raise ValueError('only work_modes: {} are supported, input work mode is {}'.
format(work_modes, self.work_mode))
return True
Methods¶
__init__(self, tree_param=<federatedml.param.boosting_param.DecisionTreeParam object at 0x7f3f8a6a3f10>, task_type='classification', objective_param=<federatedml.param.boosting_param.ObjectiveParam object at 0x7f3f8a6a3d50>, 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 0x7f3f8a6a3fd0>, bin_num=32, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f3f8a75b050>, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f3f8a75b0d0>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f3f8a75b190>, validation_freqs=None, early_stopping_rounds=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_optimization=False, random_seed=100, binning_error=0.0001, cipher_compress_error=None, new_ver=True, run_goss=False, top_rate=0.2, other_rate=0.1, cipher_compress=True, callback_param=<federatedml.param.callback_param.CallbackParam object at 0x7f3f8a75b210>)
special
¶
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
work_mode |
{"mix", "layered"} |
'mix' |
|
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 work_mode is mix |
1 |
guest_depth |
int |
guest will build last guest_depth of a decision tree using guest features, is valid when work mode is layered |
1 |
host depth |
int |
host will build first host_depth of a decision tree using host features, is valid when work mode is layered |
required |
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, 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, tree_num_per_party=1, guest_depth=1, host_depth=1, work_mode='mix', metrics=None,
sparse_optimization=False, random_seed=100, binning_error=consts.DEFAULT_RELATIVE_ERROR,
cipher_compress_error=None, new_ver=True, run_goss=False, top_rate=0.2, other_rate=0.1,
cipher_compress=True, callback_param=CallbackParam()):
"""
Parameters
----------
work_mode: {"mix", "layered"}
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: int
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: int
guest will build last guest_depth of a decision tree using guest features, is valid when work mode is layered
host depth: int
host will build first host_depth of a decision tree using host features, is valid when work mode is layered
"""
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_rounds,
use_missing, zero_as_missing, complete_secure, metrics=metrics,
random_seed=random_seed,
sparse_optimization=sparse_optimization,
binning_error=binning_error,
cipher_compress_error=cipher_compress_error,
new_ver=new_ver,
cipher_compress=cipher_compress,
run_goss=run_goss, top_rate=top_rate, other_rate=other_rate,
)
self.tree_num_per_party = tree_num_per_party
self.guest_depth = guest_depth
self.host_depth = host_depth
self.work_mode = work_mode
self.callback_param = copy.deepcopy(callback_param)
check(self)
¶
Source code in federatedml/param/boosting_param.py
def check(self):
super(HeteroFastSecureBoostParam, self).check()
if type(self.guest_depth).__name__ not in ["int", "long"] or self.guest_depth <= 0:
raise ValueError("guest_depth should be larger than 0")
if type(self.host_depth).__name__ not in ["int", "long"] or self.host_depth <= 0:
raise ValueError("host_depth should be larger than 0")
if type(self.tree_num_per_party).__name__ not in ["int", "long"] or self.tree_num_per_party <= 0:
raise ValueError("tree_num_per_party should be larger than 0")
work_modes = [consts.MIX_TREE, consts.LAYERED_TREE]
if self.work_mode not in work_modes:
raise ValueError('only work_modes: {} are supported, input work mode is {}'.
format(work_modes, self.work_mode))
return True
HomoSecureBoostParam (BoostingParam)
¶
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
backend |
{'distributed', 'memory'} |
decides which backend to use when computing histograms for homo-sbt |
'distributed' |
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()):
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)
def check(self):
super(HomoSecureBoostParam, self).check()
self.tree_param.check()
if type(self.use_missing) != bool:
raise ValueError('use missing should be bool type')
if type(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')
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
return True
__init__(self, tree_param=<federatedml.param.boosting_param.DecisionTreeParam object at 0x7f3f8a75b150>, task_type='classification', objective_param=<federatedml.param.boosting_param.ObjectiveParam object at 0x7f3f8a75b390>, 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 0x7f3f8a75b410>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f3f8a75b450>, 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 0x7f3f8a75b4d0>)
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()):
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)
check(self)
¶
Source code in federatedml/param/boosting_param.py
def check(self):
super(HomoSecureBoostParam, self).check()
self.tree_param.check()
if type(self.use_missing) != bool:
raise ValueError('use missing should be bool type')
if type(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')
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
return True
Last update: 2021-12-01