def encrypt(self, input_data):
"""
Encrypt data according to different mode
Parameters
----------
input_data: DTable
Returns
-------
new_data: DTable, encrypted result of input_data
"""
if self.prev_data is None or self.mode == "strict" \
or (self.mode == "balance" and self.gen_random_number() <= self.re_encrypted_rate + consts.FLOAT_ZERO):
new_data = input_data.mapValues(self.encrypt_row)
else:
diff_data = input_data.join(self.prev_data, self.get_differance)
new_data = diff_data.join(self.prev_encrypted_data,
self.add_differance)
"""temporary code, clear unused table begin"""
rubbish_list = [diff_data]
rubbish_clear(rubbish_list)
"""temporary code, clear unused table end"""
# new_data = input_data.join(self.prev_data, self.get_differance).join(self.prev_encrypted_data, self.add_differance)
"""temporary code, clear unused table begin"""
rubbish_list = [self.prev_data, self.prev_encrypted_data]
rubbish_clear(rubbish_list)
"""temporary code, clear unused table end"""
self.prev_data = input_data.mapValues(lambda val: val)
self.prev_encrypted_data = new_data.mapValues(lambda val: val)
return new_data
def compute_forward(self,
data_instances,
coef_,
intercept_,
batch_index=-1):
"""
Compute W * X + b and (W * X + b)^2, where X is the input data, W is the coefficient of lr,
and b is the interception
Parameters
----------
data_instances: DTable of Instance, input data
coef_: list, coefficient of lr
intercept_: float, the interception of lr
"""
wx = self.compute_wx(data_instances, coef_, intercept_)
en_wx = self.encrypted_calculator[batch_index].encrypt(wx)
wx_square = wx.mapValues(lambda v: np.square(v))
en_wx_square = self.encrypted_calculator[batch_index].encrypt(
wx_square)
host_forward = en_wx.join(en_wx_square, lambda wx, wx_square:
(wx, wx_square))
# temporary resource recovery and will be removed in the future
rubbish_list = [wx, en_wx, wx_square, en_wx_square]
rubbish_clear(rubbish_list)
return host_forward
def compute_gradient_and_loss(self, data_instance, fore_gradient, encrypted_ee, en_ee_square):
"""
Compute gradient and loss
Parameters
----------
data_instance: DTable, input data
fore_gradient:DTable, fore_gradient = (1/2*y*e_i*e_j-1)*1/(2y)
encrypted_ee: DTable, encrypted ee
encrypted_sum_squre: DTable, encrypted ee^2
Return
------
DTable
the hetero-lr gradient and loss
"""
# compute gradient
gradients = self.compute_gradient(data_instance, fore_gradient)
# compute loss
half_yee = encrypted_ee.join(data_instance, lambda ee, d: 0.5 * ee * int(d.label))
half_yee_join_en_ee_square = half_yee.join(en_ee_square, lambda yz, ez: (yz, ez))
f = functools.partial(self.__compute_loss)
loss_partition = half_yee_join_en_ee_square.mapPartitions(f).reduce(lambda x, y: x + y)
loss = loss_partition[0] / loss_partition[1]
# temporary resource recovery and will be removed in the future
rubbish_list = [half_yee, half_yee_join_en_ee_square]
rubbish_clear(rubbish_list)
return gradients, loss
def compute_gradient(self, data_instance, fore_gradient, fit_intercept):
"""
Compute hetero-lr gradient
Parameters
----------
data_instance: DTable, input data
fore_gradient: DTable, fore_gradient = (1/2*ywx-1)*1/2y
fit_intercept: bool, if hetero-lr has interception or not
Returns
----------
DTable
the hetero-lr's gradient
"""
feat_join_grad = data_instance.join(fore_gradient, lambda d, g:
(d.features, g))
f = functools.partial(self.__compute_gradient,
fit_intercept=fit_intercept)
gradient_partition = feat_join_grad.mapPartitions(f).reduce(
lambda x, y: x + y)
gradient = gradient_partition[:-1] / gradient_partition[-1]
for i in range(len(gradient)):
if not isinstance(gradient[i], PaillierEncryptedNumber):
gradient[i] = self.encrypt_operator.encrypt(gradient[i])
# temporary resource recovery and will be removed in the future
rubbish_list = [feat_join_grad]
rubbish_clear(rubbish_list)
return gradient
def compute_gradient_and_loss(self, data_instance, fore_gradient, encrypted_wx, en_sum_wx_square, fit_intercept):
"""
Compute gradient and loss
Parameters
----------
data_instance: DTable, input data
fore_gradient: DTable, fore_gradient = (1/2*ywx-1)*1/2y
encrypted_wx: DTable, encrypted wx
en_sum_wx_square: DTable, encrypted wx^2
fit_intercept: bool, if hetero-lr has interception or not
Returns
----------
DTable
the hetero-lr gradient and loss
"""
# compute gradient
gradient = self.compute_gradient(data_instance, fore_gradient, fit_intercept)
# compute and loss
half_ywx = encrypted_wx.join(data_instance, lambda wx, d: 0.5 * wx * int(d.label))
half_ywx_join_en_sum_wx_square = half_ywx.join(en_sum_wx_square, lambda yz, ez: (yz, ez))
f = functools.partial(self.__compute_loss)
loss_partition = half_ywx_join_en_sum_wx_square.mapPartitions(f).reduce(lambda x, y: x + y)
loss = loss_partition[0] / loss_partition[1]
# temporary resource recovery and will be removed in the future
rubbish_list = [half_ywx, half_ywx_join_en_sum_wx_square]
rubbish_clear(rubbish_list)
return gradient, loss
def compute_forward(self, data_instance, embedding_, node2id, batch_index=-1):
"""
Compute e_guest, where e_guest is the node embedding which stays on guest
"""
e = self.compute_embedding(data_instance, embedding_, node2id)
en_e = self.encrypted_calculator[batch_index].encrypt(e)
forward = en_e.join(e, lambda en_e, e: (en_e, e))
rubbish_list = [e, en_e]
rubbish_clear(rubbish_list)
return forward
def fit(self,
data_instances,
node2id,
local_instances=None,
common_nodes=None):
"""
Train node embedding for role guest
Parameters
----------
data_instances: DTable of target node and label, input data
node2id: a dict which can map node name to id
"""
LOGGER.info("samples number:{}".format(data_instances.count()))
LOGGER.info("Enter network embedding procedure:")
self.n_node = len(node2id)
LOGGER.info("Bank A has {} nodes".format(self.n_node))
data_instances = data_instances.mapValues(HeteroNEGuest.load_data)
LOGGER.info("Transform input data to train instance")
public_key = federation.get(
name=self.transfer_variable.paillier_pubkey.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.paillier_pubkey),
idx=0)
LOGGER.info("Get public_key from arbiter:{}".format(public_key))
self.encrypt_operator.set_public_key(public_key)
# hetero network embedding
LOGGER.info("Generate mini-batch from input data")
mini_batch_obj = MiniBatch(data_instances, batch_size=self.batch_size)
batch_num = mini_batch_obj.batch_nums
LOGGER.info("samples number:{}".format(data_instances.count()))
if self.batch_size == -1:
LOGGER.info(
"batch size is -1, set it to the number of data in data_instances"
)
self.batch_size = data_instances.count()
##############
# horizontal federated learning
LOGGER.info("Generate mini-batch for local instances in guest")
mini_batch_obj_local = MiniBatch(local_instances,
batch_size=self.batch_size)
local_batch_num = mini_batch_obj_local.batch_nums
common_node_instances = eggroll.parallelize(
((node, node) for node in common_nodes),
include_key=True,
name='common_nodes')
##############
batch_info = {'batch_size': self.batch_size, "batch_num": batch_num}
LOGGER.info("batch_info:{}".format(batch_info))
federation.remote(batch_info,
name=self.transfer_variable.batch_info.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.batch_info),
role=consts.HOST,
idx=0)
LOGGER.info("Remote batch_info to Host")
federation.remote(batch_info,
name=self.transfer_variable.batch_info.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.batch_info),
role=consts.ARBITER,
idx=0)
LOGGER.info("Remote batch_info to Arbiter")
self.encrypted_calculator = [
EncryptModeCalculator(
self.encrypt_operator,
self.encrypted_mode_calculator_param.mode,
self.encrypted_mode_calculator_param.re_encrypted_rate)
for _ in range(batch_num)
]
LOGGER.info("Start initialize model.")
self.embedding_ = self.initializer.init_model((self.n_node, self.dim),
self.init_param_obj)
LOGGER.info("Embedding shape={}".format(self.embedding_.shape))
is_send_all_batch_index = False
self.n_iter_ = 0
index_data_inst_map = {}
while self.n_iter_ < self.max_iter:
LOGGER.info("iter:{}".format(self.n_iter_))
#################
local_batch_data_generator = mini_batch_obj_local.mini_batch_data_generator(
)
total_loss = 0
local_batch_num = 0
LOGGER.info("Enter the horizontally federated learning procedure:")
for local_batch_data in local_batch_data_generator:
n = local_batch_data.count()
#LOGGER.info("Local batch data count:{}".format(n))
E_Y = self.compute_local_embedding(local_batch_data,
self.embedding_, node2id)
local_grads_e1, local_grads_e2, local_loss = self.local_gradient_operator.compute(
E_Y, 'E_1')
local_grads_e1 = local_grads_e1.mapValues(
lambda g: self.local_optimizer.apply_gradients(g / n))
local_grads_e2 = local_grads_e2.mapValues(
lambda g: self.local_optimizer.apply_gradients(g / n))
e1id_join_grads = local_batch_data.join(
local_grads_e1, lambda v, g: (node2id[v[0]], g))
e2id_join_grads = local_batch_data.join(
local_grads_e2, lambda v, g: (node2id[v[1]], g))
self.update_model(e1id_join_grads)
self.update_model(e2id_join_grads)
local_loss = local_loss / n
local_batch_num += 1
total_loss += local_loss
#LOGGER.info("gradient count:{}".format(e1id_join_grads.count()))
guest_common_embedding = common_node_instances.mapValues(
lambda node: self.embedding_[node2id[node]])
federation.remote(
guest_common_embedding,
name=self.transfer_variable.guest_common_embedding.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.guest_common_embedding,
self.n_iter_, 0),
role=consts.ARBITER,
idx=0)
LOGGER.info("Remote the embedding of common node to arbiter!")
common_embedding = federation.get(
name=self.transfer_variable.common_embedding.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.common_embedding, self.n_iter_, 0),
idx=0)
LOGGER.info(
"Get the aggregated embedding of common node from arbiter!")
self.update_common_nodes(common_embedding, common_nodes, node2id)
total_loss /= local_batch_num
LOGGER.info(
"Iter {}, horizontally feaderated learning loss: {}".format(
self.n_iter_, total_loss))
#################
# verticallly feaderated learning
# each iter will get the same batch_data_generator
LOGGER.info("Enter the vertically federated learning:")
batch_data_generator = mini_batch_obj.mini_batch_data_generator(
result='index')
batch_index = 0
for batch_data_index in batch_data_generator:
LOGGER.info("batch:{}".format(batch_index))
# only need to send one times
if not is_send_all_batch_index:
LOGGER.info("remote mini-batch index to Host")
federation.remote(
batch_data_index,
name=self.transfer_variable.batch_data_index.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.batch_data_index,
self.n_iter_, batch_index),
role=consts.HOST,
idx=0)
if batch_index >= mini_batch_obj.batch_nums - 1:
is_send_all_batch_index = True
# in order to avoid joining in next iteration
# Get mini-batch train data
if len(index_data_inst_map) < batch_num:
batch_data_inst = data_instances.join(
batch_data_index, lambda data_inst, index: data_inst)
index_data_inst_map[batch_index] = batch_data_inst
else:
batch_data_inst = index_data_inst_map[batch_index]
# For inductive learning: transform node attributes to node embedding
# self.transform(batch_data_inst)
self.guest_forward = self.compute_forward(
batch_data_inst, self.embedding_, node2id, batch_index)
host_forward = federation.get(
name=self.transfer_variable.host_forward_dict.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_forward_dict, self.n_iter_,
batch_index),
idx=0)
LOGGER.info("Get host_forward from host")
aggregate_forward_res = self.aggregate_forward(host_forward)
en_aggregate_ee = aggregate_forward_res.mapValues(
lambda v: v[0])
en_aggregate_ee_square = aggregate_forward_res.mapValues(
lambda v: v[1])
# compute [[d]]
if self.gradient_operator is None:
self.gradient_operator = HeteroNetworkEmbeddingGradient(
self.encrypt_operator)
fore_gradient = self.gradient_operator.compute_fore_gradient(
batch_data_inst, en_aggregate_ee)
host_gradient = self.gradient_operator.compute_gradient(
self.guest_forward.mapValues(
lambda v: Instance(features=v[1])), fore_gradient)
federation.remote(
host_gradient,
name=self.transfer_variable.host_gradient.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_gradient, self.n_iter_,
batch_index),
role=consts.ARBITER,
idx=0)
LOGGER.info("Remote host_gradient to arbiter")
composed_data_inst = host_forward.join(
batch_data_inst,
lambda hf, d: Instance(features=hf[1], label=d.label))
guest_gradient, loss = self.gradient_operator.compute_gradient_and_loss(
composed_data_inst, fore_gradient, en_aggregate_ee,
en_aggregate_ee_square)
federation.remote(
guest_gradient,
name=self.transfer_variable.guest_gradient.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.guest_gradient, self.n_iter_,
batch_index),
role=consts.ARBITER,
idx=0)
LOGGER.info("Remote guest_gradient to arbiter")
optim_guest_gradient = federation.get(
name=self.transfer_variable.guest_optim_gradient.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.guest_optim_gradient,
self.n_iter_, batch_index),
idx=0)
LOGGER.info("Get optim_guest_gradient from arbiter")
# update node embedding
LOGGER.info("Update node embedding")
nodeid_join_gradient = batch_data_inst.join(
optim_guest_gradient, lambda instance, gradient:
(node2id[instance.features], gradient))
self.update_model(nodeid_join_gradient)
# update local model that transform attribute to node embedding
training_info = {
'iteration': self.n_iter_,
'batch_index': batch_index
}
self.update_local_model(fore_gradient, batch_data_inst,
self.embedding_, **training_info)
# loss need to be encrypted !!!!!!
federation.remote(
loss,
name=self.transfer_variable.loss.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.loss, self.n_iter_,
batch_index),
role=consts.ARBITER,
idx=0)
LOGGER.info("Remote loss to arbiter")
# is converge of loss in arbiter
batch_index += 1
# remove temporary resource
rubbish_list = [
host_forward, aggregate_forward_res, en_aggregate_ee,
en_aggregate_ee_square, fore_gradient, self.guest_forward
]
rubbish_clear(rubbish_list)
##########
guest_common_embedding = common_node_instances.mapValues(
lambda node: self.embedding_[node2id[node]])
federation.remote(
guest_common_embedding,
name=self.transfer_variable.guest_common_embedding.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.guest_common_embedding,
self.n_iter_, 1),
role=consts.ARBITER,
idx=0)
common_embedding = federation.get(
name=self.transfer_variable.common_embedding.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.common_embedding, self.n_iter_, 1),
idx=0)
self.update_common_nodes(common_embedding, common_nodes, node2id)
##########
is_stopped = federation.get(
name=self.transfer_variable.is_stopped.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.is_stopped, self.n_iter_),
idx=0)
LOGGER.info("Get is_stop flag from arbiter:{}".format(is_stopped))
self.n_iter_ += 1
if is_stopped:
LOGGER.info(
"Get stop signal from arbiter, model is converged, iter:{}"
.format(self.n_iter_))
break
embedding_table = eggroll.table(name='guest',
namespace='node_embedding',
partition=10)
id2node = dict(zip(node2id.values(), node2id.keys()))
for id, embedding in enumerate(self.embedding_):
embedding_table.put(id2node[id], embedding)
embedding_table.save_as(name='guest',
namespace='node_embedding',
partition=10)
LOGGER.info("Reach max iter {}, train model finish!".format(
self.max_iter))
def fit(self, data_instances):
"""
Train lr model of role guest
Parameters
----------
data_instances: DTable of Instance, input data
"""
LOGGER.info("Enter hetero_lr_guest fit")
self._abnormal_detection(data_instances)
self.header = self.get_header(data_instances)
data_instances = data_instances.mapValues(HeteroLRGuest.load_data)
# 获得密钥
public_key = federation.get(
name=self.transfer_variable.paillier_pubkey.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.paillier_pubkey),
idx=0)
LOGGER.info("Get public_key from arbiter:{}".format(public_key))
self.encrypt_operator.set_public_key(public_key)
LOGGER.info("Generate mini-batch from input data")
mini_batch_obj = MiniBatch(data_instances, batch_size=self.batch_size)
batch_num = mini_batch_obj.batch_nums
if self.batch_size == -1:
LOGGER.info(
"batch size is -1, set it to the number of data in data_instances"
)
self.batch_size = data_instances.count()
batch_info = {"batch_size": self.batch_size, "batch_num": batch_num}
LOGGER.info("batch_info:{}".format(batch_info))
federation.remote(batch_info,
name=self.transfer_variable.batch_info.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.batch_info),
role=consts.HOST,
idx=0)
LOGGER.info("Remote batch_info to Host")
federation.remote(batch_info,
name=self.transfer_variable.batch_info.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.batch_info),
role=consts.ARBITER,
idx=0)
LOGGER.info("Remote batch_info to Arbiter")
self.encrypted_calculator = [
EncryptModeCalculator(
self.encrypt_operator,
self.encrypted_mode_calculator_param.mode,
self.encrypted_mode_calculator_param.re_encrypted_rate)
for _ in range(batch_num)
]
LOGGER.info("Start initialize model.")
LOGGER.info("fit_intercept:{}".format(
self.init_param_obj.fit_intercept))
model_shape = self.get_features_shape(data_instances)
weight = self.initializer.init_model(model_shape,
init_params=self.init_param_obj)
if self.init_param_obj.fit_intercept is True:
self.coef_ = weight[:-1]
self.intercept_ = weight[-1]
else:
self.coef_ = weight
is_send_all_batch_index = False
self.n_iter_ = 0
index_data_inst_map = {}
while self.n_iter_ < self.max_iter:
LOGGER.info("iter:{}".format(self.n_iter_))
# each iter will get the same batch_data_generator
batch_data_generator = mini_batch_obj.mini_batch_data_generator(
result='index')
batch_index = 0
for batch_data_index in batch_data_generator:
LOGGER.info("batch:{}".format(batch_index))
if not is_send_all_batch_index:
LOGGER.info("remote mini-batch index to Host")
federation.remote(
batch_data_index,
name=self.transfer_variable.batch_data_index.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.batch_data_index,
self.n_iter_, batch_index),
role=consts.HOST,
idx=0)
if batch_index >= mini_batch_obj.batch_nums - 1:
is_send_all_batch_index = True
# Get mini-batch train data
if len(index_data_inst_map) < batch_num:
batch_data_inst = data_instances.join(
batch_data_index, lambda data_inst, index: data_inst)
index_data_inst_map[batch_index] = batch_data_inst
else:
batch_data_inst = index_data_inst_map[batch_index]
# transforms features of raw input 'batch_data_inst' into more representative features 'batch_feat_inst'
batch_feat_inst = self.transform(batch_data_inst)
# guest/host forward
self.compute_forward(batch_feat_inst, self.coef_,
self.intercept_, batch_index)
host_forward = federation.get(
name=self.transfer_variable.host_forward_dict.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_forward_dict, self.n_iter_,
batch_index),
idx=0)
LOGGER.info("Get host_forward from host")
aggregate_forward_res = self.aggregate_forward(host_forward)
en_aggregate_wx = aggregate_forward_res.mapValues(
lambda v: v[0])
en_aggregate_wx_square = aggregate_forward_res.mapValues(
lambda v: v[1])
# compute [[d]]
if self.gradient_operator is None:
self.gradient_operator = HeteroLogisticGradient(
self.encrypt_operator)
fore_gradient = self.gradient_operator.compute_fore_gradient(
batch_feat_inst, en_aggregate_wx)
federation.remote(
fore_gradient,
name=self.transfer_variable.fore_gradient.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.fore_gradient, self.n_iter_,
batch_index),
role=consts.HOST,
idx=0)
LOGGER.info("Remote fore_gradient to Host")
# compute guest gradient and loss
guest_gradient, loss = self.gradient_operator.compute_gradient_and_loss(
batch_feat_inst, fore_gradient, en_aggregate_wx,
en_aggregate_wx_square, self.fit_intercept)
# loss regulation if necessary
if self.updater is not None:
guest_loss_regular = self.updater.loss_norm(self.coef_)
loss += self.encrypt_operator.encrypt(guest_loss_regular)
federation.remote(
guest_gradient,
name=self.transfer_variable.guest_gradient.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.guest_gradient, self.n_iter_,
batch_index),
role=consts.ARBITER,
idx=0)
LOGGER.info("Remote guest_gradient to arbiter")
optim_guest_gradient = federation.get(
name=self.transfer_variable.guest_optim_gradient.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.guest_optim_gradient,
self.n_iter_, batch_index),
idx=0)
LOGGER.info("Get optim_guest_gradient from arbiter")
# update model
LOGGER.info("update_model")
self.update_model(optim_guest_gradient)
# update local model that transforms features of raw input 'batch_data_inst'
training_info = {
"iteration": self.n_iter_,
"batch_index": batch_index
}
self.update_local_model(fore_gradient, batch_data_inst,
self.coef_, **training_info)
# Get loss regulation from Host if regulation is set
if self.updater is not None:
en_host_loss_regular = federation.get(
name=self.transfer_variable.host_loss_regular.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_loss_regular,
self.n_iter_, batch_index),
idx=0)
LOGGER.info("Get host_loss_regular from Host")
loss += en_host_loss_regular
federation.remote(
loss,
name=self.transfer_variable.loss.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.loss, self.n_iter_,
batch_index),
role=consts.ARBITER,
idx=0)
LOGGER.info("Remote loss to arbiter")
# is converge of loss in arbiter
batch_index += 1
# temporary resource recovery and will be removed in the future
rubbish_list = [
host_forward, aggregate_forward_res, en_aggregate_wx,
en_aggregate_wx_square, fore_gradient, self.guest_forward
]
rubbish_clear(rubbish_list)
is_stopped = federation.get(
name=self.transfer_variable.is_stopped.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.is_stopped, self.n_iter_,
batch_index),
idx=0)
LOGGER.info("Get is_stop flag from arbiter:{}".format(is_stopped))
self.n_iter_ += 1
if is_stopped:
LOGGER.info(
"Get stop signal from arbiter, model is converged, iter:{}"
.format(self.n_iter_))
break
LOGGER.info("Reach max iter {}, train model finish!".format(
self.max_iter))
def fit(self, data_instances):
"""
Train lr model of role host
Parameters
----------
data_instances: DTable of Instance, input data
"""
LOGGER.info("Enter hetero_lr host")
self._abnormal_detection(data_instances)
self.header = self.get_header(data_instances)
public_key = federation.get(
name=self.transfer_variable.paillier_pubkey.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.paillier_pubkey),
idx=0)
LOGGER.info("Get public_key from arbiter:{}".format(public_key))
self.encrypt_operator.set_public_key(public_key)
batch_info = federation.get(
name=self.transfer_variable.batch_info.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.batch_info),
idx=0)
LOGGER.info("Get batch_info from guest:" + str(batch_info))
self.batch_size = batch_info["batch_size"]
self.batch_num = batch_info["batch_num"]
if self.batch_size < consts.MIN_BATCH_SIZE and self.batch_size != -1:
raise ValueError(
"Batch size get from guest should not less than 10, except -1, batch_size is {}"
.format(self.batch_size))
self.encrypted_calculator = [
EncryptModeCalculator(
self.encrypt_operator,
self.encrypted_mode_calculator_param.mode,
self.encrypted_mode_calculator_param.re_encrypted_rate)
for _ in range(self.batch_num)
]
LOGGER.info("Start initialize model.")
model_shape = self.get_features_shape(data_instances)
if self.init_param_obj.fit_intercept:
self.init_param_obj.fit_intercept = False
if self.fit_intercept:
self.fit_intercept = False
self.coef_ = self.initializer.init_model(
model_shape, init_params=self.init_param_obj)
self.n_iter_ = 0
index_data_inst_map = {}
while self.n_iter_ < self.max_iter:
LOGGER.info("iter:" + str(self.n_iter_))
batch_index = 0
while batch_index < self.batch_num:
LOGGER.info("batch:{}".format(batch_index))
# set batch_data
if len(self.batch_index_list) < self.batch_num:
batch_data_index = federation.get(
name=self.transfer_variable.batch_data_index.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.batch_data_index,
self.n_iter_, batch_index),
idx=0)
LOGGER.info("Get batch_index from Guest")
self.batch_index_list.append(batch_data_index)
else:
batch_data_index = self.batch_index_list[batch_index]
# Get mini-batch train data
if len(index_data_inst_map) < self.batch_num:
batch_data_inst = batch_data_index.join(
data_instances, lambda g, d: d)
index_data_inst_map[batch_index] = batch_data_inst
else:
batch_data_inst = index_data_inst_map[batch_index]
LOGGER.info("batch_data_inst size:{}".format(
batch_data_inst.count()))
# transforms features of raw input 'batch_data_inst' into more representative features 'batch_feat_inst'
batch_feat_inst = self.transform(batch_data_inst)
# compute forward
host_forward = self.compute_forward(batch_feat_inst,
self.coef_,
self.intercept_,
batch_index)
federation.remote(
host_forward,
name=self.transfer_variable.host_forward_dict.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_forward_dict, self.n_iter_,
batch_index),
role=consts.GUEST,
idx=0)
LOGGER.info("Remote host_forward to guest")
# compute host gradient
fore_gradient = federation.get(
name=self.transfer_variable.fore_gradient.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.fore_gradient, self.n_iter_,
batch_index),
idx=0)
LOGGER.info("Get fore_gradient from guest")
if self.gradient_operator is None:
self.gradient_operator = HeteroLogisticGradient(
self.encrypt_operator)
host_gradient = self.gradient_operator.compute_gradient(
batch_feat_inst, fore_gradient, fit_intercept=False)
# regulation if necessary
if self.updater is not None:
loss_regular = self.updater.loss_norm(self.coef_)
en_loss_regular = self.encrypt_operator.encrypt(
loss_regular)
federation.remote(
en_loss_regular,
name=self.transfer_variable.host_loss_regular.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_loss_regular,
self.n_iter_, batch_index),
role=consts.GUEST,
idx=0)
LOGGER.info("Remote host_loss_regular to guest")
federation.remote(
host_gradient,
name=self.transfer_variable.host_gradient.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_gradient, self.n_iter_,
batch_index),
role=consts.ARBITER,
idx=0)
LOGGER.info("Remote host_gradient to arbiter")
# Get optimize host gradient and update model
optim_host_gradient = federation.get(
name=self.transfer_variable.host_optim_gradient.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_optim_gradient,
self.n_iter_, batch_index),
idx=0)
LOGGER.info("Get optim_host_gradient from arbiter")
LOGGER.info("update_model")
self.update_model(optim_host_gradient)
# update local model that transforms features of raw input 'batch_data_inst'
training_info = {
"iteration": self.n_iter_,
"batch_index": batch_index
}
self.update_local_model(fore_gradient, batch_data_inst,
self.coef_, **training_info)
batch_index += 1
# temporary resource recovery and will be removed in the future
rubbish_list = [host_forward, fore_gradient]
data_overview.rubbish_clear(rubbish_list)
is_stopped = federation.get(
name=self.transfer_variable.is_stopped.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.is_stopped, self.n_iter_,
batch_index),
idx=0)
LOGGER.info("Get is_stop flag from arbiter:{}".format(is_stopped))
self.n_iter_ += 1
if is_stopped:
LOGGER.info(
"Get stop signal from arbiter, model is converged, iter:{}"
.format(self.n_iter_))
break
LOGGER.info("Reach max iter {}, train model finish!".format(
self.max_iter))
def fit(self, data_instances, node2id):
"""
Train ne model pf role host
Parameters
----------
data_instances: Dtable of anchor node, input data
"""
LOGGER.info("Enter hetero_ne host")
self.n_node = len(node2id)
LOGGER.info("Host party has {} nodes".format(self.n_node))
data_instances = data_instances.mapValues(HeteroNEHost.load_data)
LOGGER.info("Transform input data to train instance")
public_key = federation.get(
name=self.transfer_variable.paillier_pubkey.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.paillier_pubkey),
idx=0)
LOGGER.info("Get Publick key from arbiter:{}".format(public_key))
self.encrypt_operator.set_public_key(public_key)
batch_info = federation.get(
name=self.transfer_variable.batch_info.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.batch_info),
idx=0)
LOGGER.info("Get batch_info from guest: {}".format(batch_info))
self.batch_size = batch_info['batch_size']
self.batch_num = batch_info['batch_num']
if self.batch_size < consts.MIN_BATCH_SIZE and self.batch_size != -1:
raise ValueError(
"Batch size get from guest should not less than 10, except -1, batch_size is {}"
.format(self.batch_size))
self.encrypted_calculator = [
EncryptModeCalculator(
self.encrypt_operator,
self.encrypted_mode_calculator_param.mode,
self.encrypted_mode_calculator_param.re_encrypted_rate)
for _ in range(self.batch_num)
]
LOGGER.info("Start initilize model.")
self.embedding_ = self.initializer.init_model((self.n_node, self.dim),
self.init_param_obj)
self.n_iter_ = 0
index_data_inst_map = {}
while self.n_iter_ < self.max_iter:
LOGGER.info("iter: {}".format(self.n_iter_))
batch_index = 0
while batch_index < self.batch_num:
LOGGER.info("batch:{}".format(batch_index))
# set batch_data
# in order to avoid communicating in next iteration
# in next iteration, the sequence of batches is the same
if len(self.batch_index_list) < self.batch_num:
batch_data_index = federation.get(
name=self.transfer_variable.batch_data_index.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.batch_data_index,
self.n_iter_, batch_index),
idx=0)
LOGGER.info("Get batch_index from Guest")
self.batch_index_list.append(batch_index)
else:
batch_data_index = self.batch_index_list[batch_index]
# Get mini-batch train_data
# in order to avoid joining for next iteration
if len(index_data_inst_map) < self.batch_num:
batch_data_inst = batch_data_index.join(
data_instances, lambda g, d: d)
index_data_inst_map[batch_index] = batch_data_inst
else:
batch_data_inst = index_data_inst_map[batch_data_index]
LOGGER.info("batch_data_inst size:{}".format(
batch_data_inst.count()))
#self.transform(data_inst)
# compute forward
host_forward = self.compute_forward(batch_data_inst,
self.embedding_, node2id,
batch_index)
federation.remote(
host_forward,
name=self.transfer_variable.host_forward_dict.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_forward_dict, self.n_iter_,
batch_index),
role=consts.GUEST,
idx=0)
LOGGER.info("Remote host_forward to guest")
# Get optimize host gradient and update model
optim_host_gradient = federation.get(
name=self.transfer_variable.host_optim_gradient.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_optim_gradient,
self.n_iter_, batch_index),
idx=0)
LOGGER.info("Get optim_host_gradient from arbiter")
nodeid_join_gradient = batch_data_inst.join(
optim_host_gradient, lambda instance, gradient:
(node2id[instance.features], gradient))
LOGGER.info("update_model")
self.update_model(nodeid_join_gradient)
# update local model
#training_info = {"iteration": self.n_iter_, "batch_index": batch_index}
#self.update_local_model(fore_gradient, batch_data_inst, self.coef_, **training_info)
batch_index += 1
rubbish_list = [host_forward]
rubbish_clear(rubbish_list)
is_stopped = federation.get(
name=self.transfer_variable.is_stopped.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.is_stopped,
self.n_iter_,
))
LOGGER.info("Get is_stop flag from arbiter:{}".format(is_stopped))
self.n_iter_ += 1
if is_stopped:
LOGGER.info("Reach max iter {}, train mode finish!".format(
self.max_iter))
embedding_table = eggroll.table(name='host',
namespace='node_embedding',
partition=10)
id2node = dict(zip(node2id.values(), node2id.keys()))
for id, embedding in enumerate(self.embedding_):
embedding_table.put(id2node[id], embedding)
embedding_table.save_as(name='host',
namespace='node_embedding',
partition=10)
LOGGER.info("Reach max iter {}, train model finish!".format(
self.max_iter))
def fit(self,
data_instances,
node2id,
local_instances=None,
common_nodes=None):
"""
Train ne model pf role host
Parameters
----------
data_instances: Dtable of anchor node, input data
"""
LOGGER.info("Enter hetero_ne host")
self.n_node = len(node2id)
LOGGER.info("Host party has {} nodes".format(self.n_node))
data_instances = data_instances.mapValues(HeteroNEHost.load_data)
LOGGER.info("Transform input data to train instance")
public_key = federation.get(
name=self.transfer_variable.paillier_pubkey.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.paillier_pubkey),
idx=0)
LOGGER.info("Get Publick key from arbiter:{}".format(public_key))
self.encrypt_operator.set_public_key(public_key)
##############
# horizontal federated learning
LOGGER.info("Generate mini-batch for local instances in guest")
mini_batch_obj_local = MiniBatch(local_instances,
batch_size=self.batch_size)
common_node_instances = eggroll.parallelize(
((node, node) for node in common_nodes),
include_key=True,
name='common_nodes')
##############
batch_info = federation.get(
name=self.transfer_variable.batch_info.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.batch_info),
idx=0)
LOGGER.info("Get batch_info from guest: {}".format(batch_info))
self.batch_size = batch_info['batch_size']
self.batch_num = batch_info['batch_num']
if self.batch_size < consts.MIN_BATCH_SIZE and self.batch_size != -1:
raise ValueError(
"Batch size get from guest should not less than 10, except -1, batch_size is {}"
.format(self.batch_size))
self.encrypted_calculator = [
EncryptModeCalculator(
self.encrypt_operator,
self.encrypted_mode_calculator_param.mode,
self.encrypted_mode_calculator_param.re_encrypted_rate)
for _ in range(self.batch_num)
]
LOGGER.info("Start initilize model.")
self.embedding_ = self.initializer.init_model((self.n_node, self.dim),
self.init_param_obj)
self.n_iter_ = 0
index_data_inst_map = {}
while self.n_iter_ < self.max_iter:
LOGGER.info("iter: {}".format(self.n_iter_))
#################
local_batch_data_generator = mini_batch_obj_local.mini_batch_data_generator(
)
total_loss = 0
local_batch_num = 0
LOGGER.info("Horizontally learning")
for local_batch_data in local_batch_data_generator:
n = local_batch_data.count()
LOGGER.info("Local batch data count:{}".format(n))
E_Y = self.compute_local_embedding(local_batch_data,
self.embedding_, node2id)
local_grads_e1, local_grads_e2, local_loss = self.local_gradient_operator.compute(
E_Y, 'E_1')
local_grads_e1 = local_grads_e1.mapValues(
lambda g: self.local_optimizer.apply_gradients(g / n))
local_grads_e2 = local_grads_e2.mapValues(
lambda g: self.local_optimizer.apply_gradients(g / n))
e1id_join_grads = local_batch_data.join(
local_grads_e1, lambda v, g: (node2id[v[0]], g))
e2id_join_grads = local_batch_data.join(
local_grads_e2, lambda v, g: (node2id[v[1]], g))
self.update_model(e1id_join_grads)
self.update_model(e2id_join_grads)
local_loss = local_loss / n
local_batch_num += 1
total_loss += local_loss
LOGGER.info("gradient count:{}".format(
e1id_join_grads.count()))
host_common_embedding = common_node_instances.mapValues(
lambda node: self.embedding_[node2id[node]])
federation.remote(
host_common_embedding,
name=self.transfer_variable.host_common_embedding.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_common_embedding, self.n_iter_,
0),
role=consts.ARBITER,
idx=0)
common_embedding = federation.get(
name=self.transfer_variable.common_embedding.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.common_embedding, self.n_iter_, 0),
idx=0)
self.update_common_nodes(common_embedding, common_nodes, node2id)
total_loss /= local_batch_num
LOGGER.info("Iter {}, Local loss: {}".format(
self.n_iter_, total_loss))
batch_index = 0
while batch_index < self.batch_num:
LOGGER.info("batch:{}".format(batch_index))
# set batch_data
# in order to avoid communicating in next iteration
# in next iteration, the sequence of batches is the same
if len(self.batch_index_list) < self.batch_num:
batch_data_index = federation.get(
name=self.transfer_variable.batch_data_index.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.batch_data_index,
self.n_iter_, batch_index),
idx=0)
LOGGER.info("Get batch_index from Guest")
self.batch_index_list.append(batch_index)
else:
batch_data_index = self.batch_index_list[batch_index]
# Get mini-batch train_data
# in order to avoid joining for next iteration
if len(index_data_inst_map) < self.batch_num:
batch_data_inst = batch_data_index.join(
data_instances, lambda g, d: d)
index_data_inst_map[batch_index] = batch_data_inst
else:
batch_data_inst = index_data_inst_map[batch_data_index]
LOGGER.info("batch_data_inst size:{}".format(
batch_data_inst.count()))
#self.transform(data_inst)
# compute forward
host_forward = self.compute_forward(batch_data_inst,
self.embedding_, node2id,
batch_index)
federation.remote(
host_forward,
name=self.transfer_variable.host_forward_dict.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_forward_dict, self.n_iter_,
batch_index),
role=consts.GUEST,
idx=0)
LOGGER.info("Remote host_forward to guest")
# Get optimize host gradient and update model
optim_host_gradient = federation.get(
name=self.transfer_variable.host_optim_gradient.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_optim_gradient,
self.n_iter_, batch_index),
idx=0)
LOGGER.info("Get optim_host_gradient from arbiter")
nodeid_join_gradient = batch_data_inst.join(
optim_host_gradient, lambda instance, gradient:
(node2id[instance.features], gradient))
LOGGER.info("update_model")
self.update_model(nodeid_join_gradient)
# update local model
#training_info = {"iteration": self.n_iter_, "batch_index": batch_index}
#self.update_local_model(fore_gradient, batch_data_inst, self.coef_, **training_info)
batch_index += 1
rubbish_list = [host_forward]
rubbish_clear(rubbish_list)
#######
host_common_embedding = common_node_instances.mapValues(
lambda node: self.embedding_[node2id[node]])
federation.remote(
host_common_embedding,
name=self.transfer_variable.host_common_embedding.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.host_common_embedding, self.n_iter_,
1),
role=consts.ARBITER,
idx=0)
common_embedding = federation.get(
name=self.transfer_variable.common_embedding.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.common_embedding, self.n_iter_, 1),
idx=0)
self.update_common_nodes(common_embedding, common_nodes, node2id)
#######
is_stopped = federation.get(
name=self.transfer_variable.is_stopped.name,
tag=self.transfer_variable.generate_transferid(
self.transfer_variable.is_stopped,
self.n_iter_,
),
idx=0)
LOGGER.info("Get is_stop flag from arbiter:{}".format(is_stopped))
self.n_iter_ += 1
if is_stopped:
break
LOGGER.info("Reach max iter {}, train mode finish!".format(
self.max_iter))
embedding_table = eggroll.table(name='host',
namespace='node_embedding',
partition=10)
id2node = dict(zip(node2id.values(), node2id.keys()))
for id, embedding in enumerate(self.embedding_):
embedding_table.put(id2node[id], embedding)
embedding_table.save_as(name='host',
namespace='node_embedding',
partition=10)
LOGGER.info("Reach max iter {}, train model finish!".format(
self.max_iter))
