def get_model_param(self):
model_param = BoostingTreeModelParam()
model_param.tree_num = len(list(self.trees_))
model_param.tree_dim = self.tree_dim
model_param.trees_.extend(self.trees_)
model_param.init_score.extend(self.init_score)
model_param.losses.extend(self.history_loss)
model_param.classes_.extend(map(str, self.classes_))
model_param.num_classes = self.num_classes
feature_importances = list(self.get_feature_importance().items())
feature_importances = sorted(feature_importances,
key=itemgetter(1),
reverse=True)
feature_importance_param = []
for (sitename, fid), _importance in feature_importances:
feature_importance_param.append(
FeatureImportanceInfo(sitename=sitename,
fid=fid,
importance=_importance))
model_param.feature_importances.extend(feature_importance_param)
model_param.feature_name_fid_mapping.update(
self.feature_name_fid_mapping)
param_name = "HeteroSecureBoostingTreeGuestParam"
return param_name, model_param
def get_model_param(self):
model_param = BoostingTreeModelParam()
model_param.tree_num = len(list(self.learnt_tree_param))
model_param.tree_dim = self.tree_dim
model_param.trees_.extend(self.learnt_tree_param)
model_param.init_score.extend(self.init_score)
model_param.losses.extend(self.local_loss_history)
model_param.classes_.extend(map(str, self.classes_))
model_param.num_classes = self.num_classes
model_param.best_iteration = -1
feature_importance = list(self.get_feature_importance().items())
feature_importance = sorted(feature_importance,
key=itemgetter(1),
reverse=True)
feature_importance_param = []
for fid, _importance in feature_importance:
feature_importance_param.append(
FeatureImportanceInfo(sitename=self.role,
fid=fid,
importance=_importance))
model_param.feature_importances.extend(feature_importance_param)
model_param.feature_name_fid_mapping.update(
self.feature_name_fid_mapping)
param_name = "HomoSecureBoostingTreeGuestParam"
return param_name, model_param
def get_model_param(self):
model_param = BoostingTreeModelParam()
model_param.tree_num = len(list(self.boosting_model_list))
model_param.tree_dim = self.booster_dim
model_param.trees_.extend(self.boosting_model_list)
model_param.init_score.extend(self.init_score)
model_param.classes_.extend(map(str, self.classes_))
model_param.num_classes = self.num_classes
model_param.best_iteration = -1
model_param.model_name = consts.HOMO_SBT
feature_importance = list(self.feature_importance.items())
feature_importance = sorted(feature_importance,
key=itemgetter(1),
reverse=True)
feature_importance_param = []
for fid, importance in feature_importance:
feature_importance_param.append(
FeatureImportanceInfo(
fid=fid,
fullname=self.feature_name_fid_mapping[fid],
sitename=self.role,
importance=importance.importance,
importance2=importance.importance_2,
main=importance.main_type))
model_param.feature_importances.extend(feature_importance_param)
model_param.feature_name_fid_mapping.update(
self.feature_name_fid_mapping)
param_name = "HomoSecureBoostingTreeGuestParam"
return param_name, model_param
def get_model_param(self):
_, model_param = super(HeteroFastSecureBoostingTreeHost,
self).get_model_param()
param_name = consts.HETERO_FAST_SBT_HOST_MODEL + "Param"
model_param.tree_plan.extend(plan.encode_plan(self.tree_plan))
model_param.model_name = consts.HETERO_FAST_SBT_MIX if self.work_mode == consts.MIX_TREE else \
consts.HETERO_FAST_SBT_LAYERED
# in mix mode, host can output feature importance
feature_importances = list(self.feature_importances_.items())
feature_importances = sorted(feature_importances,
key=itemgetter(1),
reverse=True)
feature_importance_param = []
LOGGER.debug('host feat importance is {}'.format(feature_importances))
for fid, importance in feature_importances:
feature_importance_param.append(
FeatureImportanceInfo(
sitename=self.role,
fid=fid,
importance=importance.importance,
fullname=self.feature_name_fid_mapping[fid]))
model_param.feature_importances.extend(feature_importance_param)
return param_name, model_param
def get_model_param(self):
model_param = BoostingTreeModelParam()
model_param.tree_num = len(self.boosting_model_list)
model_param.tree_dim = self.booster_dim
model_param.trees_.extend(self.boosting_model_list)
model_param.init_score.extend(self.init_score)
model_param.losses.extend(self.history_loss)
model_param.classes_.extend(map(str, self.classes_))
model_param.num_classes = self.num_classes
if self.boosting_strategy == consts.STD_TREE:
model_param.model_name = consts.HETERO_SBT
elif self.boosting_strategy == consts.LAYERED_TREE:
model_param.model_name = consts.HETERO_FAST_SBT_LAYERED
elif self.boosting_strategy == consts.MIX_TREE:
model_param.model_name = consts.HETERO_FAST_SBT_MIX
model_param.best_iteration = self.callback_variables.best_iteration
feature_importances = list(self.feature_importances_.items())
feature_importances = sorted(feature_importances,
key=itemgetter(1),
reverse=True)
feature_importance_param = []
for (sitename, fid), importance in feature_importances:
if consts.GUEST in sitename:
fullname = self.feature_name_fid_mapping[fid]
else:
role_name, party_id = sitename.split(':')
fullname = generate_anonymous(fid=fid,
party_id=party_id,
role=role_name)
feature_importance_param.append(
FeatureImportanceInfo(
sitename=sitename, # sitename to distinguish sites
fid=fid,
importance=importance.importance,
fullname=fullname,
importance2=importance.importance_2,
main=importance.main_type))
model_param.feature_importances.extend(feature_importance_param)
model_param.feature_name_fid_mapping.update(
self.feature_name_fid_mapping)
model_param.tree_plan.extend(plan.encode_plan(self.tree_plan))
param_name = consts.HETERO_SBT_GUEST_MODEL + "Param"
return param_name, model_param
def get_model_param(self):
model_param = BoostingTreeModelParam()
model_param.tree_num = len(self.boosting_model_list)
model_param.tree_dim = self.booster_dim
model_param.trees_.extend(self.boosting_model_list)
anonymous_name_mapping = {}
party_id = self.component_properties.local_partyid
for fid, name in self.feature_name_fid_mapping.items():
anonymous_name_mapping[generate_anonymous(
fid,
role=consts.HOST,
party_id=party_id,
)] = name
model_param.anonymous_name_mapping.update(anonymous_name_mapping)
model_param.feature_name_fid_mapping.update(
self.feature_name_fid_mapping)
if self.boosting_strategy == consts.STD_TREE:
model_param.model_name = consts.HETERO_SBT
elif self.boosting_strategy == consts.LAYERED_TREE:
model_param.model_name = consts.HETERO_FAST_SBT_LAYERED
elif self.boosting_strategy == consts.MIX_TREE:
model_param.model_name = consts.HETERO_FAST_SBT_MIX
model_param.best_iteration = self.callback_variables.best_iteration
model_param.tree_plan.extend(plan.encode_plan(self.tree_plan))
feature_importances = list(self.feature_importances_.items())
feature_importances = sorted(feature_importances,
key=itemgetter(1),
reverse=True)
feature_importance_param = []
LOGGER.debug('host feat importance is {}'.format(feature_importances))
for fid, importance in feature_importances:
feature_importance_param.append(
FeatureImportanceInfo(
sitename=consts.HOST_LOCAL, # host local feat
fid=fid,
importance=importance.importance,
fullname=self.feature_name_fid_mapping[fid],
main=importance.main_type))
model_param.feature_importances.extend(feature_importance_param)
param_name = "HeteroSecureBoostingTreeHostParam"
return param_name, model_param
def get_model_param(self):
model_param = BoostingTreeModelParam()
model_param.tree_num = len(self.boosting_model_list)
model_param.tree_dim = self.booster_dim
model_param.trees_.extend(self.boosting_model_list)
model_param.init_score.extend(self.init_score)
model_param.losses.extend(self.history_loss)
model_param.classes_.extend(map(str, self.classes_))
model_param.num_classes = self.num_classes
model_param.model_name = consts.HETERO_SBT
model_param.best_iteration = -1 if self.validation_strategy is None else self.validation_strategy.best_iteration
feature_importances = list(self.feature_importances_.items())
feature_importances = sorted(feature_importances,
key=itemgetter(1),
reverse=True)
feature_importance_param = []
for (sitename, fid), _importance in feature_importances:
if consts.GUEST in sitename:
fullname = self.feature_name_fid_mapping[fid]
else:
role_name, party_id = sitename.split(':')
fullname = generate_anonymous(fid=fid,
party_id=party_id,
role=role_name)
feature_importance_param.append(
FeatureImportanceInfo(sitename=sitename,
fid=fid,
importance=_importance,
fullname=fullname))
model_param.feature_importances.extend(feature_importance_param)
model_param.feature_name_fid_mapping.update(
self.feature_name_fid_mapping)
param_name = "HeteroSecureBoostingTreeGuestParam"
return param_name, model_param
from federatedml.protobuf.generated.boosting_tree_model_param_pb2 import BoostingTreeModelParam, NodeParam, \
DecisionTreeModelParam, FeatureImportanceInfo
from federatedml.protobuf.generated.boosting_tree_model_meta_pb2 import BoostingTreeModelMeta
from federatedml.protobuf.model_migrate.converter.tree_model_converter import HeteroSBTConverter
from federatedml.protobuf.model_migrate.model_migrate import model_migration
import copy
host_old = [10000, 9999]
host_new = [114, 514, ]
guest_old = [10000]
guest_new = [1919]
param = BoostingTreeModelParam()
fp0 = FeatureImportanceInfo()
fp0.fullname = 'host_10000_0'
fp0.sitename = 'host:10000'
fp1 = FeatureImportanceInfo()
fp1.sitename = 'host:9999'
fp1.fullname = 'host_9999_1'
fp2 = FeatureImportanceInfo(fullname='x0')
fp2.sitename = 'guest:10000'
feature_importance = [fp0, fp1, fp2]
param.feature_importances.extend(feature_importance)
tree_0 = DecisionTreeModelParam(tree_=[NodeParam(sitename='guest:10000'), NodeParam(sitename='guest:10000'),
NodeParam(sitename='guest:10000')])