def test_partition_matrix(self):
idx = 1
M = support.projection_matrix(self.mapping, idx).toarray()
np.testing.assert_array_equal(
np.nonzero(M)[1],
np.arange(10)[self.mapping == idx])
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
striped_mapping = mapper.Striped(self.domain,
self.stripe_dim).mapping()
x_sub_list = meta.SplitByPartition(striped_mapping).transform(x)
Ms = []
ys = []
scale_factors = []
group_idx = sorted(set(striped_mapping))
for i in group_idx:
x_i = x_sub_list[group_idx.index(i)]
P_i = support.projection_matrix(striped_mapping, i)
M_bar = selection.HB(x_i.shape).select()
y_i = measurement.Laplace(M_bar, eps).measure(x_i, prng)
noise_scale_factor = laplace_scale_factor(M_bar, eps)
M_i = M_bar * P_i
Ms.append(M_i)
ys.append(y_i)
scale_factors.append(noise_scale_factor)
x_hat = inference.LeastSquares().infer(Ms, ys, scale_factors)
return x_hat
def Run(self, W, x, eps):
striped_mapping = striped(self.domain_shape, self.stripe_dim)
x_sub_list = x.split_by_partition(striped_mapping)
Ms = []
ys = []
scale_factors = []
group_idx = sorted(set(striped_mapping))
for i in group_idx:
x_i = x_sub_list[group_idx.index(i)]
P_i = support.projection_matrix(striped_mapping, i)
W_i = W * P_i.T
mapping = x_i.dawa(self.ratio, self.approx, eps)
x_bar = x_i.reduce_by_partition(mapping)
W_bar = W_i * support.expansion_matrix(mapping)
M_bar = greedyH((len(set(mapping)), ), W_bar)
y_i = x_bar.laplace(M_bar, eps * (1 - self.ratio))
M_i = (M_bar * support.reduction_matrix(mapping)) * P_i
Ms.append(M_i)
ys.append(y_i)
scale_factors.append(laplace_scale_factor(M_bar, eps))
x_hat = least_squares(Ms, ys, scale_factors)
return x_hat
def Run(self, W, x, eps):
striped_mapping = striped(self.domain_shape, self.stripe_dim)
x_sub_list = x.split_by_partition(striped_mapping)
Ms = []
ys = []
scale_factors = []
group_idx = sorted(set(striped_mapping))
for i in group_idx:
x_i = x_sub_list[group_idx.index(i)]
P_i = support.projection_matrix(striped_mapping, i)
W_i = W * P_i.T
M_bar = hb((P_i.shape[0], ))
y_i = x_i.laplace(M_bar, eps)
# TODO: Ideally this would be just M_bar * P_i
# but currently that returns an int type matrix
# because the type of P_i is int
M_i = (P_i.T * M_bar.T).T
Ms.append(M_i)
ys.append(y_i)
scale_factors.append(laplace_scale_factor(M_bar, eps))
x_hat = least_squares(Ms, ys, scale_factors)
return x_hat
def Run(self, W, x, eps):
striped_mapping = striped(self.domain_shape, self.stripe_dim)
x_sub_list = x.split_by_partition(striped_mapping)
Ms = []
ys = []
scale_factors = []
group_idx = sorted(set(striped_mapping))
for i in group_idx:
x_i = x_sub_list[group_idx.index(i)]
P_i = support.projection_matrix(striped_mapping, i)
W_i = W * P_i.T
M_bar = hb((P_i.shape[0],))
y_i = x_i.laplace(M_bar, eps)
M_i = M_bar * P_i
Ms.append(M_i)
ys.append(y_i)
scale_factors.append(laplace_scale_factor(M_bar, eps))
x_hat = least_squares(Ms, ys, scale_factors)
return x_hat
def Run(self, W, x, eps, seed):
assert len(x.shape) == 2, "Adaptive Grid only works for 2D domain"
shape_2d = x.shape
x = x.flatten()
prng = np.random.RandomState(seed)
Ms = []
ys = []
M = selection.UniformGrid(shape_2d,
self.data_scale,
eps,
ag_flag=True,
c=self.c).select()
y = measurement.Laplace(M, self.alpha*eps).measure(x, prng)
x_hat = inference.LeastSquares().infer(M, y)
Ms.append(M)
ys.append(y)
# Prepare parition object for later SplitByParition.
# This Partition selection operator is missing from Figure 2, plan 12 in the paper.
uniform_mapping = mapper.UGridPartition(shape_2d,
self.data_scale,
eps,
ag_flag=True,
c=self.c).mapping()
x_sub_list = meta.SplitByPartition(uniform_mapping).transform(x)
sub_domains = support.get_subdomain_grid(uniform_mapping, shape_2d)
ll, hi =[], []
for i in sorted(set(uniform_mapping)):
x_i = x_sub_list[i]
P_i = support.projection_matrix(uniform_mapping, i)
x_hat_i = P_i * x_hat
sub_domain_shape = sub_domains[i]
M_i = selection.AdaptiveGrid(sub_domain_shape,
x_hat_i,
(1-self.alpha)*eps,
c2=self.c2).select()
y_i = measurement.Laplace(M_i, (1-self.alpha)*eps).measure(x_i, prng)
offset = np.unravel_index(P_i.matrix.nonzero()[1][0], shape_2d)
ll.extend(M_i._lower + np.array(offset))
hi.extend(M_i._higher + np.array(offset))
ys.append(y_i)
Ms.append(workload.RangeQueries(shape_2d, np.array(ll), np.array(hi)))
x_hat = inference.LeastSquares().infer(Ms, ys)
return x_hat
def Run(self, W, x, eps, seed):
assert len(x.shape) == 2, "Adaptive Grid only works for 2D domain"
shape_2d = x.shape
x = x.flatten()
prng = np.random.RandomState(seed)
Ms = []
ys = []
M = selection.UniformGrid(shape_2d,
self.data_scale,
eps,
ag_flag=True,
c=self.c).select()
if not isinstance(M, np.ndarray):
M = M.toarray()
y = measurement.Laplace(M, self.alpha*eps).measure(x, prng)
x_hat = inference.LeastSquares().infer(M, y)
Ms.append(M)
ys.append(y)
# Prepare parition object for later SplitByParition.
# This Partition selection operator is missing from Figure 2, plan 12 in the paper.
uniform_mapping = mapper.UGridPartition(shape_2d,
self.data_scale,
eps,
ag_flag=True,
c=self.c).mapping()
x_sub_list = meta.SplitByPartition(uniform_mapping).transform(x)
sub_domains = support.get_subdomain_grid(uniform_mapping, shape_2d)
for i in sorted(set(uniform_mapping)):
x_i = x_sub_list[i]
P_i = support.projection_matrix(uniform_mapping, i)
x_hat_i = P_i * x_hat
sub_domain_shape = sub_domains[i]
M_i = selection.AdaptiveGrid(sub_domain_shape,
x_hat_i,
(1-self.alpha)*eps,
c2=self.c2).select()
if not isinstance(M, np.ndarray):
M_i = M_i.toarray()
y_i = measurement.Laplace(M_i, (1-self.alpha)*eps).measure(x_i, prng)
M_i_o = M_i * P_i
Ms.append(M_i_o)
ys.append(y_i)
x_hat = inference.LeastSquares().infer(Ms, ys, [1.0]*len(ys))
return x_hat
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
striped_vectors = mapper.Striped(self.domain, self.stripe_dim).partitions()
hd_vector = support.combine_all(striped_vectors)
striped_mapping = hd_vector.flatten()
x_sub_list = meta.SplitByPartition(striped_mapping).transform(x)
Ms = []
ys = []
scale_factors = []
group_idx = sorted(set(striped_mapping))
# Given a group id on the full vector, recover the group id for each partition
# put back in loop to save memory
self.subgroups = {}
for i in group_idx:
selected_idx = np.where(hd_vector == i)
ans = [p[i[0]] for p, i in zip(striped_vectors, selected_idx)]
self.subgroups[i] = ans
for i in group_idx:
x_i = x_sub_list[group_idx.index(i)]
# overwriting standard projection for efficiency
W_i = self.project_workload(W, striped_vectors, hd_vector, i)
dawa = pmapper.Dawa(eps, self.ratio, self.approx)
mapping = dawa.mapping(x_i, prng)
reducer = transformation.ReduceByPartition(mapping)
x_bar = reducer.transform(x_i)
W_bar = W_i * support.expansion_matrix(mapping)
M_bar = selection.GreedyH(x_bar.shape, W_bar).select()
if not isinstance(M_bar, np.ndarray):
M_bar = M_bar.toarray()
y_i = measurement.Laplace(
M_bar, eps * (1 - self.ratio)).measure(x_bar, prng)
noise_scale_factor = laplace_scale_factor(
M_bar, eps * (1 - self.ratio))
# convert the measurement back to the original domain for inference
P_i = support.projection_matrix(striped_mapping, i)
M_i = (M_bar * support.reduction_matrix(mapping)) * P_i
Ms.append(M_i)
ys.append(y_i)
scale_factors.append(noise_scale_factor)
x_hat = inference.LeastSquares().infer(Ms, ys, scale_factors)
return x_hat
def Run(self, W, x, eps):
striped_vectors = striped_partition(self.domain_shape, self.stripe_dim)
hd_vector = support.combine_all(striped_vectors)
striped_mapping = hd_vector.flatten()
x_sub_list = x.split_by_partition(striped_mapping)
Ms = []
ys = []
scale_factors = []
group_idx = sorted(set(striped_mapping))
# Given a group id on the full vector, recover the group id for each partition
# put back in loop to save memory
self.subgroups = {}
for i in group_idx:
selected_idx = np.where(hd_vector == i)
ans = [p[i[0]] for p, i in zip(striped_vectors, selected_idx)]
self.subgroups[i] = ans
for i in group_idx:
x_i = x_sub_list[group_idx.index(i)]
# overwriting standard projection for efficiency
W_i = self.project_workload(W, striped_vectors, hd_vector, i)
mapping = x_i.dawa(self.ratio, self.approx, eps)
x_bar = x_i.reduce_by_partition(mapping)
W_bar = W_i * support.expansion_matrix(mapping)
M_bar = greedyH((len(set(mapping)), ), W_bar)
y_i = x_bar.laplace(M_bar, eps * (1 - self.ratio))
noise_scale_factor = laplace_scale_factor(M_bar,
eps * (1 - self.ratio))
# convert the measurement back to the original domain for inference
P_i = support.projection_matrix(striped_mapping, i)
M_i = (M_bar * support.reduction_matrix(mapping)) * P_i
Ms.append(M_i)
ys.append(y_i)
scale_factors.append(laplace_scale_factor(M_bar, eps))
x_hat = least_squares(Ms, ys, scale_factors)
return x_hat
def Run(self, W, x, eps):
assert len(self.domain_shape
) == 2, "Adaptive Grid only works for 2D domain_shape"
shape_2d = self.domain_shape
Ms = []
ys = []
M = ugrid_select(shape_2d,
self.data_scale,
eps,
ag_flag=True,
c=self.c)
y = x.laplace(M, self.alpha * eps)
x_hat = least_squares(M, y)
Ms.append(M)
ys.append(y)
# Prepare parition object for later SplitByParition.
# This Partition selection operator is missing from Figure 2, plan 12 in the paper.
uniform_mapping = ugrid_mapper(shape_2d,
self.data_scale,
eps,
ag_flag=True,
c=self.c)
x_sub_list = x.split_by_partition(uniform_mapping)
sub_domains = support.get_subdomain_grid(uniform_mapping, shape_2d)
for i in sorted(set(uniform_mapping)):
x_i = x_sub_list[i]
P_i = support.projection_matrix(uniform_mapping, i)
x_hat_i = P_i * x_hat
sub_domain_shape = sub_domains[i]
M_i = agrid_select(sub_domain_shape,
x_hat_i, (1 - self.alpha) * eps,
c2=self.c2)
y_i = x_i.laplace(M_i, (1 - self.alpha) * eps)
M_i_o = M_i * P_i
Ms.append(M_i_o)
ys.append(y_i)
x_hat2 = least_squares(Ms, ys, [1.0] * len(ys))
return x_hat2
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
striped_mapping = mapper.Striped(self.domain, self.stripe_dim).mapping()
x_sub_list = meta.SplitByPartition(striped_mapping).transform(x)
Ms = []
ys = []
scale_factors = []
group_idx = sorted(set(striped_mapping))
W = get_matrix(W)
for i in group_idx:
x_i = x_sub_list[group_idx.index(i)]
P_i = support.projection_matrix(striped_mapping, i)
W_i = W * P_i.T
dawa = pmapper.Dawa(eps, self.ratio, self.approx)
mapping = dawa.mapping(x_i, prng)
reducer = transformation.ReduceByPartition(mapping)
x_bar = reducer.transform(x_i)
W_bar = W_i * support.expansion_matrix(mapping)
M_bar = selection.GreedyH(x_bar.shape, W_bar).select()
if not isinstance(M_bar, np.ndarray):
M_bar = M_bar.toarray()
y_i = measurement.Laplace(
M_bar, eps * (1 - self.ratio)).measure(x_bar, prng)
noise_scale_factor = laplace_scale_factor(
M_bar, eps * (1 - self.ratio))
M_i = (M_bar * support.reduction_matrix(mapping)) * P_i
Ms.append(M_i)
ys.append(y_i)
scale_factors.append(noise_scale_factor)
x_hat = inference.LeastSquares().infer(Ms, ys, scale_factors)
return x_hat
def std_project_workload(self, w, mapping, groupID):
P_i = support.projection_matrix(mapping, groupID)
return w * P_i.T
