def __init__(self, tree_param: DecisionTreeModelParam, valid_feature: dict,
epoch_idx: int, tree_idx: int, flow_id: int):
super(HomoDecisionTreeArbiter, self).__init__(tree_param)
self.splitter = Splitter(self.criterion_method, self.criterion_params,
self.min_impurity_split,
self.min_sample_split, self.min_leaf_node,
self.min_child_weight)
self.transfer_inst = HomoDecisionTreeTransferVariable()
"""
initializing here
"""
self.valid_features = valid_feature
self.tree_node = [] # start from root node
self.tree_node_num = 0
self.cur_layer_node = []
self.runtime_idx = 0
self.sitename = consts.ARBITER
self.epoch_idx = epoch_idx
self.tree_idx = tree_idx
# secure aggregator
self.set_flowid(flow_id)
self.aggregator = DecisionTreeArbiterAggregator(verbose=False)
# stored histogram for faster computation {node_id:histogram_bag}
self.stored_histograms = {}
class HomoDecisionTreeArbiter(DecisionTree):
def __init__(self, tree_param: DecisionTreeModelParam, valid_feature: dict,
epoch_idx: int, tree_idx: int, flow_id: int):
super(HomoDecisionTreeArbiter, self).__init__(tree_param)
self.splitter = Splitter(self.criterion_method, self.criterion_params,
self.min_impurity_split,
self.min_sample_split, self.min_leaf_node,
self.min_child_weight)
self.transfer_inst = HomoDecisionTreeTransferVariable()
"""
initializing here
"""
self.valid_features = valid_feature
self.tree_node = [] # start from root node
self.tree_node_num = 0
self.cur_layer_node = []
self.runtime_idx = 0
self.sitename = consts.ARBITER
self.epoch_idx = epoch_idx
self.tree_idx = tree_idx
# secure aggregator
self.set_flowid(flow_id)
self.aggregator = DecisionTreeArbiterAggregator(verbose=False)
# stored histogram for faster computation {node_id:histogram_bag}
self.stored_histograms = {}
def set_flowid(self, flowid=0):
LOGGER.info("set flowid, flowid is {}".format(flowid))
self.transfer_inst.set_flowid(flowid)
"""
Federation Functions
"""
def sync_node_sample_numbers(self, suffix):
cur_layer_node_num = self.transfer_inst.cur_layer_node_num.get(
-1, suffix=suffix)
for num in cur_layer_node_num[1:]:
assert num == cur_layer_node_num[0]
return cur_layer_node_num[0]
def sync_best_splits(self, split_info, suffix):
LOGGER.debug('sending best split points')
self.transfer_inst.best_split_points.remote(split_info,
idx=-1,
suffix=suffix)
def sync_local_histogram(self, suffix) -> List[HistogramBag]:
node_local_histogram = self.aggregator.aggregate_histogram(
suffix=suffix)
LOGGER.debug('num of histograms {}'.format(len(node_local_histogram)))
return node_local_histogram
"""
Split finding
"""
def federated_find_best_split(self,
node_histograms,
parallel_partitions=10) -> List[SplitInfo]:
LOGGER.debug('aggregating histograms')
acc_histogram = node_histograms
best_splits = self.splitter.find_split(acc_histogram,
self.valid_features,
parallel_partitions,
self.sitename, self.use_missing,
self.zero_as_missing)
return best_splits
@staticmethod
def histogram_subtraction(left_node_histogram, stored_histograms):
# histogram subtraction
all_histograms = []
for left_hist in left_node_histogram:
all_histograms.append(left_hist)
# LOGGER.debug('hist id is {}, pid is {}'.format(left_hist.hid, left_hist.p_hid))
# root node hist
if left_hist.hid == 0:
continue
right_hist = stored_histograms[left_hist.p_hid] - left_hist
right_hist.hid, right_hist.p_hid = left_hist.hid + 1, right_hist.p_hid
all_histograms.append(right_hist)
return all_histograms
"""
Fit
"""
def fit(self):
LOGGER.info(
'begin to fit h**o decision tree, epoch {}, tree idx {}'.format(
self.epoch_idx, self.tree_idx))
g_sum, h_sum = self.aggregator.aggregate_root_node_info(
suffix=('root_node_sync1', self.epoch_idx))
self.aggregator.broadcast_root_info(g_sum,
h_sum,
suffix=('root_node_sync2',
self.epoch_idx))
if self.max_split_nodes != 0 and self.max_split_nodes % 2 == 1:
self.max_split_nodes += 1
LOGGER.warning(
'an even max_split_nodes value is suggested when using histogram-subtraction, '
'max_split_nodes reset to {}'.format(self.max_split_nodes))
tree_height = self.max_depth + 1 # non-leaf node height + 1 layer leaf
for dep in range(tree_height):
if dep + 1 == tree_height:
break
LOGGER.debug('current dep is {}'.format(dep))
split_info = []
# get cur layer node num:
cur_layer_node_num = self.sync_node_sample_numbers(
suffix=(dep, self.epoch_idx, self.tree_idx))
layer_stored_hist = {}
for batch_id, i in enumerate(
range(0, cur_layer_node_num, self.max_split_nodes)):
left_node_histogram = self.sync_local_histogram(
suffix=(batch_id, dep, self.epoch_idx, self.tree_idx))
all_histograms = self.histogram_subtraction(
left_node_histogram, self.stored_histograms)
# store histogram
for hist in all_histograms:
layer_stored_hist[hist.hid] = hist
# FIXME stable parallel_partitions
best_splits = self.federated_find_best_split(
all_histograms, parallel_partitions=10)
split_info += best_splits
self.stored_histograms = layer_stored_hist
self.sync_best_splits(split_info, suffix=(dep, self.epoch_idx))
LOGGER.debug('best_splits_sent')
def predict(self, data_inst=None):
"""
Do nothing
"""
LOGGER.debug('start predicting')
"""
These functions are not needed in h**o-decision-tree
"""
def initialize_root_node(self, *args):
pass
def compute_best_splits(self, *args):
pass
def assign_an_instance(self, *args):
pass
def assign_instances_to_new_node(self, *args):
pass
def update_tree(self, *args):
pass
def convert_bin_to_real(self, *args):
pass
def get_model_meta(self):
pass
def get_model_param(self):
pass
def set_model_param(self, model_param):
pass
def set_model_meta(self, model_meta):
pass
def traverse_tree(self, *args):
pass
def update_instances_node_positions(self, *args):
pass