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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 0x7f27551eb850>
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 0x7f27551eb850>, 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 0x7f27551eb950>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f27551eba50>, 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 0x7f27551ebb10>
encrypted_mode_calculator_param EncryptedModeCalculatorParam object

the calculation mode use in secureboost, default: EncryptedModeCalculatorParam()

<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f27551ebbd0>
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 0x7f27551eba10>, 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 0x7f27551ebb10>, bin_num=32, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f27551ebbd0>, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f27551eb9d0>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f27551ebb50>, 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 0x7f27551ebc90>
objective_param ObjectiveParam Object, default: ObjectiveParam()

objective param

<federatedml.param.boosting_param.ObjectiveParam object at 0x7f27551ebe10>
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 0x7f27551ebe90>
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 0x7f27551ebed0>
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 0x7f27551ebc90>, task_type='classification', objective_param=<federatedml.param.boosting_param.ObjectiveParam object at 0x7f27551ebe10>, 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 0x7f27551ebe90>, bin_num=32, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f27551ebed0>, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f27551ebe50>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f27551ebd10>, 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 0x7f27551ebfd0>) 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 0x7f27551ebf50>, task_type='classification', objective_param=<federatedml.param.boosting_param.ObjectiveParam object at 0x7f27551ebd90>, 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 0x7f27551ebf10>, bin_num=32, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f27552bb0d0>, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f27552bb110>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f27552bb090>, 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 0x7f27552bb250>) 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 0x7f27552bb190>, task_type='classification', objective_param=<federatedml.param.boosting_param.ObjectiveParam object at 0x7f27552bb3d0>, 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 0x7f27552bb450>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f27552bb490>, 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 0x7f27552bb510>) 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: 2021-12-03
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