def compute_half_d(self, data_instances, w, cipher, batch_index,
current_suffix):
if self.use_sample_weight:
self.half_d = data_instances.mapValues(lambda v: (vec_dot(
v.features, w.coef_) + w.intercept_ - v.label) * v.weight)
else:
self.half_d = data_instances.mapValues(lambda v: vec_dot(
v.features, w.coef_) + w.intercept_ - v.label)
return self.half_d
def compute_forwards(self, data_instances, model_weights):
if self.use_sample_weight:
wx = data_instances.mapValues(
lambda v: (vec_dot(v.features, model_weights.coef_) +
model_weights.intercept_) * v.weight)
else:
wx = data_instances.mapValues(lambda v: vec_dot(
v.features, model_weights.coef_) + model_weights.intercept_)
return wx
def compute_half_g(self, data_instances, w, cipher, batch_index):
if self.use_sample_weight:
half_g = data_instances.mapValues(lambda v: (vec_dot(
v.features, w.coef_) + w.intercept_) * v.weight)
else:
half_g = data_instances.mapValues(
lambda v: vec_dot(v.features, w.coef_) + w.intercept_)
encrypt_half_g = cipher[batch_index].encrypt(half_g)
return half_g, encrypt_half_g
def compute_forwards(self, data_instances, model_weights):
"""
forwards = 1/4 * wx
"""
# wx = data_instances.mapValues(lambda v: vec_dot(v.features, model_weights.coef_) + model_weights.intercept_)
if self.use_sample_weight:
self.forwards = data_instances.mapValues(lambda v: 0.25 * vec_dot(
v.features, model_weights.coef_) * v.weight)
else:
self.forwards = data_instances.mapValues(
lambda v: 0.25 * vec_dot(v.features, model_weights.coef_))
return self.forwards
def compute_half_d(self, data_instances, w, cipher, batch_index,
current_suffix):
if self.use_sample_weight:
self.half_d = data_instances.mapValues(
lambda v: 0.25 * (vec_dot(v.features, w.coef_) + w.intercept_
) * v.weight - 0.5 * v.label * v.weight)
else:
self.half_d = data_instances.mapValues(lambda v: 0.25 * (vec_dot(
v.features, w.coef_) + w.intercept_) - 0.5 * v.label)
# encrypted_half_d = cipher[batch_index].encrypt(self.half_d)
# self.fore_gradient_transfer.remote(encrypted_half_d, suffix=current_suffix)
return self.half_d
def compute_loss(self,
data_instances,
w,
n_iter_,
batch_index,
loss_norm=None):
"""
Compute hetero-lr loss for:
loss = (1/N)*∑(log2 - 1/2*ywx + 1/8*(wx)^2), where y is label, w is model weight and x is features
where (wx)^2 = (Wg * Xg + Wh * Xh)^2 = (Wg*Xg)^2 + (Wh*Xh)^2 + 2 * Wg*Xg * Wh*Xh
Then loss = log2 - (1/N)*0.5*∑ywx + (1/N)*0.125*[∑(Wg*Xg)^2 + ∑(Wh*Xh)^2 + 2 * ∑(Wg*Xg * Wh*Xh)]
where Wh*Xh is a table obtain from host and ∑(Wh*Xh)^2 is a sum number get from host.
"""
current_suffix = (n_iter_, batch_index)
n = data_instances.count()
quarter_wx = self.host_forwards[0].join(self.half_d,
lambda x, y: x + y)
ywx = quarter_wx.join(data_instances, lambda wx, d: wx *
(4 * d.label) + 2).reduce(reduce_add)
# self_wx_square = self.forwards.mapValues(lambda x: np.square(x)).reduce(reduce_add)
self_wx_square = data_instances.mapValues(lambda v: np.square(
vec_dot(v.features, w.coef_) + w.intercept_)).reduce(reduce_add)
half_wx = data_instances.mapValues(
lambda v: vec_dot(v.features, w.coef_) + w.intercept_)
loss_list = []
wx_squares = self.get_host_loss_intermediate(suffix=current_suffix)
if loss_norm is not None:
host_loss_regular = self.get_host_loss_regular(
suffix=current_suffix)
else:
host_loss_regular = []
# for host_idx, host_forward in enumerate(self.host_forwards):
if len(self.host_forwards) > 1:
LOGGER.info("More than one host exist, loss is not available")
else:
host_forward = self.host_forwards[0]
wx_square = wx_squares[0]
wxg_wxh = half_wx.join(
host_forward, lambda wxg, wxh: wxg * wxh).reduce(reduce_add)
loss = np.log(2) - 0.5 * (1 / n) * ywx + 0.125 * (1 / n) * \
(self_wx_square + wx_square + 2 * wxg_wxh)
if loss_norm is not None:
loss += loss_norm
loss += host_loss_regular[0]
loss_list.append(loss)
LOGGER.debug("In compute_loss, loss list are: {}".format(loss_list))
self.sync_loss_info(loss_list, suffix=current_suffix)
def compute_mu(self, data_instances, coef_, intercept_=0, exposure=None):
if exposure is None:
mu = data_instances.mapValues(
lambda v: np.exp(vec_dot(v.features, coef_) + intercept_))
else:
offset = exposure.mapValues(
lambda v: BasePoissonRegression.safe_log(v))
mu = data_instances.join(
offset, lambda v, m: np.exp(
vec_dot(v.features, coef_) + intercept_ + m))
return mu
def predict(self, data_instances):
self._abnormal_detection(data_instances)
self.init_schema(data_instances)
data_instances = self.align_data_header(data_instances, self.header)
LOGGER.info("Start predict is a one_vs_rest task: {}".format(
self.need_one_vs_rest))
if self.need_one_vs_rest:
predict_result = self.one_vs_rest_obj.predict(data_instances)
return predict_result
# predict_wx = self.compute_wx(data_instances, self.model_weights.coef_, self.model_weights.intercept_)
pred_prob = data_instances.mapValues(lambda v: activation.sigmoid(
vec_dot(v.features, self.model_weights.coef_) + self.model_weights.
intercept_))
predict_result = self.predict_score_to_output(
data_instances,
pred_prob,
classes=[0, 1],
threshold=self.model_param.predict_param.threshold)
return predict_result
def compute_and_aggregate_forwards(self,
data_instances,
model_weights,
encrypted_calculator,
batch_index,
offset=None):
"""
gradient = (1/N)*∑(1/2*ywx-1)*1/2yx = (1/N)*∑(0.25 * wx - 0.5 * y) * x, where y = 1 or -1
Define wx as guest_forward or host_forward
Define (0.25 * wx - 0.5 * y) as fore_gradient
"""
half_wx = data_instances.mapValues(lambda v: vec_dot(
v.features, model_weights.coef_) + model_weights.intercept_)
self.forwards = half_wx
# LOGGER.debug("half_wx: {}".format(half_wx.take(20)))
self.aggregated_forwards = encrypted_calculator[batch_index].encrypt(
half_wx)
for host_forward in self.host_forwards:
self.aggregated_forwards = self.aggregated_forwards.join(
host_forward, lambda g, h: g + h)
fore_gradient = self.aggregated_forwards.join(
data_instances, lambda wx, d: 0.25 * wx - 0.5 * d.label)
return fore_gradient
def compute_forwards(self, data_instances, model_weights):
"""
forwards = wx
"""
wx = data_instances.mapValues(lambda v: vec_dot(
v.features, model_weights.coef_) + model_weights.intercept_)
return wx
def compute_and_aggregate_forwards(self,
data_instances,
model_weights,
encrypted_calculator,
batch_index,
current_suffix,
offset=None):
'''
Compute gradients:
gradient = (1/N) * \sum(exp(wx) - y) * x
Define exp(wx) as mu, named it as guest_forward or host_forward
Define (mu-y) as fore_gradient
Then, gradient = fore_gradient * x
'''
if offset is None:
raise ValueError(
"Offset should be provided when compute poisson forwards")
mu = data_instances.join(
offset, lambda d, m: np.exp(
vec_dot(d.features, model_weights.coef_) + model_weights.
intercept_ + m))
self.forwards = mu
self.host_forwards = self.get_host_forward(suffix=current_suffix)
self.aggregated_forwards = self.forwards.join(self.host_forwards[0],
lambda g, h: g * h)
fore_gradient = self.aggregated_forwards.join(
data_instances, lambda mu, d: mu - d.label)
return fore_gradient
def compute_loss(self, data_instances, model_weights, encrypted_calculator,
optimizer, n_iter_, batch_index, cipher_operator):
'''
Compute hetero poisson loss:
h_loss = sum(exp(mu_h))
Parameters:
___________
data_instances: DTable, input data
model_weights: model weight object, stores intercept_ and coef_
encrypted_calculator: ecnrypted calculator object
optimizer: optimizer object
n_iter_: int, current number of iter.
batch_index: int, use to obtain current encrypted_calculator index
cipher_operator: cipher for encrypt intermediate loss and loss_regular
'''
current_suffix = (n_iter_, batch_index)
self_wx = data_instances.mapValues(lambda v: vec_dot(
v.features, model_weights.coef_) + model_weights.intercept_)
en_wx = encrypted_calculator[batch_index].encrypt(self_wx)
self.remote_loss_intermediate(en_wx, suffix=current_suffix)
loss_regular = optimizer.loss_norm(model_weights)
if loss_regular is not None:
en_loss_regular = cipher_operator.encrypt(loss_regular)
self.remote_loss_regular(en_loss_regular, suffix=current_suffix)
def compute_loss(self,
data_instances,
model_weights,
n_iter_,
batch_index,
offset,
loss_norm=None):
'''
Compute hetero poisson loss:
loss = sum(exp(mu_g)*exp(mu_h) - y(wx_g + wx_h) + log(exposure))
Parameters:
___________
data_instances: DTable, input data
model_weights: model weight object, stores intercept_ and coef_
n_iter_: int, current number of iter.
batch_index: int, use to obtain current encrypted_calculator index
offset: log(exposure)
loss_norm: penalty term, default to None
'''
current_suffix = (n_iter_, batch_index)
n = data_instances.count()
guest_wx_y = data_instances.join(
offset, lambda v, m: (vec_dot(v.features, model_weights.coef_) +
model_weights.intercept_ + m, v.label))
loss_list = []
host_wxs = self.get_host_loss_intermediate(current_suffix)
if loss_norm is not None:
host_loss_regular = self.get_host_loss_regular(
suffix=current_suffix)
else:
host_loss_regular = []
if len(self.host_forwards) > 1:
raise ValueError(
"More than one host exists. Poisson regression does not support multi-host."
)
host_mu = self.host_forwards[0]
host_wx = host_wxs[0]
loss_wx = guest_wx_y.join(host_wx, lambda g, h: g[1] *
(g[0] + h)).reduce(reduce_add)
loss_mu = self.forwards.join(host_mu,
lambda g, h: g * h).reduce(reduce_add)
loss = (loss_mu - loss_wx) / n
if loss_norm is not None:
loss = loss + loss_norm + host_loss_regular[0]
loss_list.append(loss)
self.sync_loss_info(loss_list, suffix=current_suffix)
def compute_sqn_forwards(self, data_instances, delta_s, cipher_operator):
"""
To compute Hessian matrix, y, s are needed.
g = (1/N)*∑(0.25 * wx - 0.5 * y) * x
y = ∇2^F(w_t)s_t = g' * s = (1/N)*∑(0.25 * x * s) * x
define forward_hess = ∑(0.25 * x * s)
"""
sqn_forwards = data_instances.mapValues(
lambda v: cipher_operator.encrypt(fate_operator.vec_dot(v.features, delta_s.coef_) + delta_s.intercept_))
# forward_sum = sqn_forwards.reduce(reduce_add)
return sqn_forwards
def predict(self, data_instances):
self._abnormal_detection(data_instances)
self.init_schema(data_instances)
data_instances = self.align_data_header(data_instances, self.header)
# predict_wx = self.compute_wx(data_instances, self.model_weights.coef_, self.model_weights.intercept_)
pred_prob = data_instances.mapValues(lambda v: activation.sigmoid(vec_dot(v.features, self.model_weights.coef_)
+ self.model_weights.intercept_))
predict_result = self.predict_score_to_output(data_instances, pred_prob, classes=[0, 1],
threshold=self.model_param.predict_param.threshold)
return predict_result
def predict(self, data_instances):
LOGGER.info(f'Start predict task')
self._abnormal_detection(data_instances)
self.init_schema(data_instances)
data_instances = self.align_data_header(data_instances, self.header)
suffix = ('predict', )
if self.component_properties.has_arbiter:
pubkey = self.cipher.gen_paillier_pubkey(enable=self.use_encrypt,
suffix=suffix)
else:
if self.use_encrypt:
raise ValueError(f"In use_encrypt case, arbiter should be set")
pubkey = None
if self.use_encrypt:
self.cipher_operator.set_public_key(pubkey)
final_model = self.transfer_variable.aggregated_model.get(
idx=0, suffix=suffix)
model_weights = LogisticRegressionWeights(final_model.unboxed,
self.fit_intercept)
wx = self.compute_wx(data_instances, model_weights.coef_,
model_weights.intercept_)
self.transfer_variable.predict_wx.remote(wx,
consts.ARBITER,
0,
suffix=suffix)
predict_result = self.transfer_variable.predict_result.get(
idx=0, suffix=suffix)
# predict_result = predict_result.join(data_instances, lambda p, d: [d.label, p, None,
# {"0": None, "1": None}])
predict_result = predict_result.join(
data_instances, lambda p, d: Instance(
features=[d.label, p, None, {
"0": None,
"1": None
}],
inst_id=d.inst_id))
else:
pred_prob = data_instances.mapValues(lambda v: activation.sigmoid(
vec_dot(v.features, self.model_weights.coef_) + self.
model_weights.intercept_))
predict_result = self.predict_score_to_output(
data_instances,
pred_prob,
classes=[0, 1],
threshold=self.model_param.predict_param.threshold)
return predict_result
def compute_forward_hess(self, data_instances, delta_s, host_forwards):
"""
To compute Hessian matrix, y, s are needed.
g = (1/N)*∑(0.25 * wx - 0.5 * y) * x
y = ∇2^F(w_t)s_t = g' * s = (1/N)*∑(0.25 * x * s) * x
define forward_hess = (1/N)*∑(0.25 * x * s)
"""
forwards = data_instances.mapValues(lambda v: (vec_dot(
v.features, delta_s.coef_) + delta_s.intercept_) * 0.25)
for host_forward in host_forwards:
forwards = forwards.join(host_forward, lambda g, h: g + (h * 0.25))
# forward_hess = forwards.mapValues(lambda x: 0.25 * x / sample_size)
hess_vector = self.compute_gradient(data_instances, forwards,
delta_s.fit_intercept)
return forwards, np.array(hess_vector)
def compute_forward_hess(self, data_instances, delta_s, host_forwards):
"""
To compute Hessian matrix, y, s are needed.
g = (1/N)*∑(wx - y) * x
y = ∇2^F(w_t)s_t = g' * s = (1/N)*∑(x * s) * x
define forward_hess = (1/N)*∑(x * s)
"""
forwards = data_instances.mapValues(lambda v: (vec_dot(
v.features, delta_s.coef_) + delta_s.intercept_))
for host_forward in host_forwards:
forwards = forwards.join(host_forward, lambda g, h: g + h)
if self.use_sample_weight:
forwards = forwards.join(data_instances, lambda h, d: h * d.weight)
hess_vector = self.compute_gradient(data_instances, forwards,
delta_s.fit_intercept)
return forwards, np.array(hess_vector)
def compute_and_aggregate_forwards(self,
data_instances,
model_weights,
encrypted_calculator,
batch_index,
current_suffix,
offset=None):
"""
Compute gradients:
gradient = (1/N)*\sum(wx -y)*x
Define wx as guest_forward or host_forward
Define (wx-y) as fore_gradient
Parameters
----------
data_instances: DTable of Instance, input data
model_weights: LinearRegressionWeights
Stores coef_ and intercept_ of model
encrypted_calculator: Use for different encrypted methods
offset: Used in Poisson only.
batch_index: int, use to obtain current encrypted_calculator index:
current_suffix: tuple or string. Used in transfer_variable
"""
wx = data_instances.mapValues(lambda v: vec_dot(
v.features, model_weights.coef_) + model_weights.intercept_)
self.forwards = wx
self.aggregated_forwards = encrypted_calculator[batch_index].encrypt(
wx)
self.host_forwards = self.get_host_forward(suffix=current_suffix)
for host_forward in self.host_forwards:
self.aggregated_forwards = self.aggregated_forwards.join(
host_forward, lambda g, h: g + h)
fore_gradient = self.aggregated_forwards.join(
data_instances, lambda wx, d: wx - d.label)
return fore_gradient
def compute_wx(self, data_instances, coef_, intercept_=0):
return data_instances.mapValues(
lambda v: vec_dot(v.features, coef_) + intercept_)
def _vec_dot(v, coef, intercept):
return fate_operator.vec_dot(v.features, coef) + intercept
def compute_half_g(self, data_instances, w, cipher, batch_index):
half_g = data_instances.mapValues(
lambda v: vec_dot(v.features, w.coef_) * 0.25 + w.intercept_)
encrypt_half_g = cipher[batch_index].encrypt(half_g)
return half_g, encrypt_half_g
def compute_loss(self,
data_instances,
w,
n_iter_,
batch_index,
loss_norm=None,
batch_masked=False):
"""
Compute hetero-lr loss for:
loss = (1/N)*∑(log2 - 1/2*ywx + 1/8*(wx)^2), where y is label, w is model weight and x is features
where (wx)^2 = (Wg * Xg + Wh * Xh)^2 = (Wg*Xg)^2 + (Wh*Xh)^2 + 2 * Wg*Xg * Wh*Xh
Then loss = log2 - (1/N)*0.5*∑ywx + (1/N)*0.125*[∑(Wg*Xg)^2 + ∑(Wh*Xh)^2 + 2 * ∑(Wg*Xg * Wh*Xh)]
where Wh*Xh is a table obtain from host and ∑(Wh*Xh)^2 is a sum number get from host.
"""
current_suffix = (n_iter_, batch_index)
n = data_instances.count()
# host_wx_y = self.host_forwards[0].join(data_instances, lambda x, y: (x, y.label))
host_wx_y = data_instances.join(self.host_forwards[0], lambda y, x:
(x, y.label))
self_wx_y = self.half_d.join(data_instances, lambda x, y: (x, y.label))
def _sum_ywx(wx_y):
sum1, sum2 = 0, 0
for _, (x, y) in wx_y:
if y == 1:
sum1 += x
else:
sum2 -= x
return sum1 + sum2
ywx = host_wx_y.applyPartitions(_sum_ywx).reduce(reduce_add) + \
self_wx_y.applyPartitions(_sum_ywx).reduce(reduce_add)
ywx = ywx * 4 + 2 * n
# quarter_wx = self.host_forwards[0].join(self.half_d, lambda x, y: x + y)
# ywx = quarter_wx.join(data_instances, lambda wx, d: wx * (4 * d.label) + 2).reduce(reduce_add)
half_wx = data_instances.mapValues(
lambda v: vec_dot(v.features, w.coef_) + w.intercept_)
self_wx_square = half_wx.mapValues(lambda v: np.square(v)).reduce(
reduce_add)
# self_wx_square = data_instances.mapValues(
# lambda v: np.square(vec_dot(v.features, w.coef_) + w.intercept_)).reduce(reduce_add)
loss_list = []
wx_squares = self.get_host_loss_intermediate(suffix=current_suffix)
if batch_masked:
wx_squares_sum = []
for square_table in wx_squares:
square_sum = data_instances.join(
square_table,
lambda inst, enc_h_squares: enc_h_squares).reduce(
lambda x, y: x + y)
wx_squares_sum.append(square_sum)
wx_squares = wx_squares_sum
if loss_norm is not None:
host_loss_regular = self.get_host_loss_regular(
suffix=current_suffix)
else:
host_loss_regular = []
# for host_idx, host_forward in enumerate(self.host_forwards):
if len(self.host_forwards) > 1:
LOGGER.info("More than one host exist, loss is not available")
else:
host_forward = self.host_forwards[0]
wx_square = wx_squares[0]
wxg_wxh = half_wx.join(
host_forward, lambda wxg, wxh: wxg * wxh).reduce(reduce_add)
loss = np.log(2) - 0.5 * (1 / n) * ywx + 0.125 * (1 / n) * \
(self_wx_square + wx_square + 8 * wxg_wxh)
if loss_norm is not None:
loss += loss_norm
loss += host_loss_regular[0]
loss_list.append(loss)
LOGGER.debug("In compute_loss, loss list are: {}".format(loss_list))
self.sync_loss_info(loss_list, suffix=current_suffix)
def compute_forwards(self, data_instances, model_weights):
mu = data_instances.mapValues(lambda v: np.exp(
vec_dot(v.features, model_weights.coef_) + model_weights.intercept_
))
return mu
