def predict(self, data_inst, predict_param=None):
LOGGER.info("start predict")
cache_dataset_key = self.predict_data_cache.get_data_key(data_inst)
if cache_dataset_key in self.data_alignment_map:
data_inst = self.data_alignment_map[cache_dataset_key]
else:
data_inst = self.data_alignment(data_inst)
header = [None] * len(self.feature_name_fid_mapping)
for idx, col in self.feature_name_fid_mapping.items():
header[idx] = col
data_inst = data_overview.header_alignment(data_inst, header)
self.data_alignment_map[cache_dataset_key] = data_inst
rounds = len(self.trees_) // self.tree_dim
predict_start_round = self.sync_predict_start_round()
for i in range(predict_start_round, rounds):
# n_tree = self.trees_[i]
for tidx in range(self.tree_dim):
LOGGER.info(
"start to predict, boost round: {}, tree index: {}".format(
i, tidx))
tree_inst = HeteroDecisionTreeHost(self.tree_param)
tree_inst.load_model(self.tree_meta,
self.trees_[i * self.tree_dim + tidx])
# tree_inst.set_tree_model(self.trees_[i * self.tree_dim + tidx])
tree_inst.set_flowid(self.generate_flowid(i, tidx))
tree_inst.set_runtime_idx(
self.component_properties.local_partyid)
tree_inst.predict(data_inst)
LOGGER.info("end predict")
def predict(self, data_inst, predict_param=None):
LOGGER.info("start predict")
for i in range(len(self.trees_)):
n_tree = self.trees_[i]
for tidx in range(len(n_tree)):
tree_inst = HeteroDecisionTreeHost(self.tree_param)
tree_inst.set_tree_model(n_tree[tidx])
tree_inst.set_flowid(self.generate_flowid(i, tidx))
tree_inst.predict(data_inst)
LOGGER.info("end predict")
def predict_f_value(self, data_inst, predict_start_round, rounds):
for i in range(predict_start_round, rounds):
# n_tree = self.trees_[i]
for tidx in range(self.tree_dim):
tree_inst = HeteroDecisionTreeHost(self.tree_param)
tree_inst.load_model(self.tree_meta,
self.trees_[i * self.tree_dim + tidx])
# tree_inst.set_tree_model(self.trees_[i * self.tree_dim + tidx])
tree_inst.set_flowid(self.generate_flowid(i, tidx))
tree_inst.set_runtime_idx(
self.component_properties.local_partyid)
tree_inst.predict(data_inst)
def predict(self, data_inst, predict_param=None):
LOGGER.info("start predict")
data_inst = self.data_alignment(data_inst)
rounds = len(self.trees_) // self.tree_dim
for i in range(rounds):
for tidx in range(self.tree_dim):
tree_inst = HeteroDecisionTreeHost(self.tree_param)
tree_inst.load_model(self.tree_meta, self.trees_[i * self.tree_dim + tidx])
tree_inst.set_flowid(self.generate_flowid(i, tidx))
tree_inst.set_runtime_idx(self.runtime_idx)
tree_inst.predict(data_inst)
LOGGER.info("end predict")
def predict(self, data_inst, predict_param=None):
LOGGER.info("start predict")
data_inst = self.data_alignment(data_inst)
rounds = len(self.trees_) // self.tree_dim
predict_start_round = self.sync_predict_start_round()
for i in range(predict_start_round, rounds):
# n_tree = self.trees_[i]
for tidx in range(self.tree_dim):
tree_inst = HeteroDecisionTreeHost(self.tree_param)
tree_inst.load_model(self.tree_meta,
self.trees_[i * self.tree_dim + tidx])
# tree_inst.set_tree_model(self.trees_[i * self.tree_dim + tidx])
tree_inst.set_flowid(self.generate_flowid(i, tidx))
tree_inst.set_runtime_idx(
self.component_properties.local_partyid)
tree_inst.predict(data_inst)
LOGGER.info("end predict")