def Run(self, W, x, eps):
domain_size = np.prod(self.domain_shape)
# Start with a unifrom estimation of x
x_hat = np.array([self.data_scale / float(domain_size)] * domain_size)
W_partial = sparse.csr_matrix(get_matrix(W).shape)
M_history = np.empty((0, domain_size))
y_history = []
for i in range(1, self.rounds + 1):
eps_round = eps / float(self.rounds)
# SW + SH2
W_next = x.worst_approx(sparse.csr_matrix(get_matrix(W)),
W_partial, x_hat, eps_round * self.ratio)
M = selection.AddEquiWidthIntervals(W_next, i).select()
W_partial += W_next
y = x.laplace(M, eps_round * (1-self.ratio))\
# default use history
M_history = sparse.vstack([M_history, M])
y_history.extend(y)
if self.total_noise_scale != 0:
total_query = sparse.csr_matrix([1] * domain_size)
noise_scale = laplace_scale_factor(
M, eps_round * (1 - self.ratio))
x_hat = non_negative_least_squares(
[total_query, M_history], [[self.data_scale], y_history],
[self.total_noise_scale, noise_scale])
else:
x_hat = non_negative_least_squares(M, y)
return x_hat
def Run(self, W, x, eps):
if self.workload_based:
x, W = workload_based(x=x, W=W)
W = get_matrix(W)
if len(self.domain_shape) == 2:
# apply hilbert transform to convert 2d domain_shape into 1d
hilbert_mapping = hilbert(self.domain_shape)
x = x.reduce_by_partition(hilbert_mapping)
W = W * support.expansion_matrix(hilbert_mapping)
mapping = x.dawa(self.ratio, self.approx, eps)
x_bar = x.reduce_by_partition(mapping)
W_bar = get_matrix(W) * support.expansion_matrix(mapping)
M_bar = greedyH((len(set(mapping)), ), W_bar)
y = x_bar.laplace(M_bar, eps * (1 - self.ratio))
x_bar_hat = least_squares(M_bar, y)
x_hat = support.expansion_matrix(mapping) * x_bar_hat
if len(self.domain_shape) == 2:
return support.expansion_matrix(hilbert_mapping) * x_hat
return x_hat
def Run(self, W, x, eps):
domain_size = np.prod(self.domain_shape)
# Start with a unifrom estimation of x
x_hat = np.array([self.data_scale / float(domain_size)] * domain_size)
W_partial = sparse.csr_matrix(get_matrix(W).shape)
M_history = np.empty((0, domain_size))
y_history = []
for i in range(1, self.rounds + 1):
eps_round = eps / float(self.rounds)
W_next = x.worst_approx(sparse.csr_matrix(get_matrix(W)),
W_partial, x_hat, eps_round * self.ratio,
'EXPONENTIAL')
M = support.extract_M(W_next)
W_partial += W_next
y = x.laplace(M, eps_round * (1 - self.ratio))
M_history = sparse.vstack([M_history, M])
y_history.extend(y)
if self.use_history:
x_hat = multiplicative_weights(
M_history,
y_history,
x_hat,
update_rounds=self.update_rounds)
else:
x_hat = multiplicative_weights(
M, y, x_hat, update_rounds=self.update_rounds)
return x_hat
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
if self.workload_based:
W = get_matrix(W)
mapping = mapper.WorkloadBased(W).mapping()
reducer = transformation.ReduceByPartition(mapping)
x = reducer.transform(x)
# Reduce workload
# W = support.reduce_queries(mapping, W)
W = W * support.expansion_matrix(mapping)
self.domain_shape = x.shape
if len(self.domain_shape) == 2:
# apply hilbert transform to convert 2d domain into 1d
hilbert_mapping = mapper.HilbertTransform(self.domain_shape).mapping()
domain_reducer = transformation.ReduceByPartition(hilbert_mapping)
x = domain_reducer.transform(x)
W = get_matrix(W)
W = W * support.expansion_matrix(hilbert_mapping)
dawa = pmapper.Dawa(eps, self.ratio, self.approx)
mapping = dawa.mapping(x, prng)
elif len(self.domain_shape) == 1:
W = get_matrix(W)
dawa = pmapper.Dawa(eps, self.ratio, self.approx)
mapping = dawa.mapping(x, prng)
reducer = transformation.ReduceByPartition(mapping)
x_bar = reducer.transform(x)
W_bar = W * 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 = measurement.Laplace(M_bar, eps*(1-self.ratio)).measure(x_bar, prng)
x_bar_hat = inference.LeastSquares().infer(M_bar, y)
x_bar_hat_exp = support.expansion_matrix(mapping) * x_bar_hat
if len(self.domain_shape) == 1:
return x_bar_hat_exp
elif len(self.domain_shape) == 2:
return support.expansion_matrix(hilbert_mapping) * x_bar_hat_exp
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
if self.workload_based:
W = get_matrix(W)
mapping = mapper.WorkloadBased(W).mapping()
reducer = transformation.ReduceByPartition(mapping)
x = reducer.transform(x)
# Reduce workload
# W = support.reduce_queries(mapping, W)
W = W * support.expansion_matrix(mapping)
# Orange AHPparition(PA) operator in paper can be expressed
# as the following sequence of simpler opeartors
M = selection.Identity(x.shape).select()
y = measurement.Laplace(M, self.ratio * eps).measure(x, prng)
xest = inference.AHPThresholding(self.eta, self.ratio).infer(M, y, eps)
mapping = mapper.AHPCluster(xest, (1-self.ratio) * eps).mapping()
# TR
reducer = transformation.ReduceByPartition(mapping)
x_bar = reducer.transform(x)
# SI LM LS
M_bar = selection.Identity(x_bar.shape).select()
y_bar = measurement.Laplace(M_bar, eps*(1-self.ratio)).measure(x_bar, prng)
x_bar_hat = inference.LeastSquares().infer(M_bar, y_bar)
x_hat = support.expansion_matrix(mapping) * x_bar_hat
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 = get_matrix(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):
W = get_matrix(W)
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))
W_bar = W_i * support.expansion_matrix(mapping)
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, seed):
prng = np.random.RandomState(seed)
W = get_matrix(W)
M = selection.GreedyH(x.shape, W).select()
y = measurement.Laplace(M, eps).measure(x, prng)
x_hat = inference.LeastSquares().infer(M, y)
return x_hat
def workload_based(x, W=None):
'''workload-based domain reduction
'''
mapping = mapper.WorkloadBased(W).mapping()
x = x.reduce_by_partition(mapping)
if W is not None:
W = support.get_matrix(W)
W = W * support.expansion_matrix(mapping)
return x, W
def Run(self, W, x, eps, seed):
domain_dimension = len(self.domain_shape)
eps_share = util.old_div(float(eps), domain_dimension)
x = x.flatten()
prng = np.random.RandomState(seed)
Ms = []
ys = []
scale_factors = []
for i in range(domain_dimension):
# Reducde domain to get marginals
marginal_mapping = mapper.MarginalPartition(
domain_shape=self.domain_shape, proj_dim=i).mapping()
reducer = transformation.ReduceByPartition(marginal_mapping)
x_i = reducer.transform(x)
if self.domain_shape[i] < 50:
# run identity subplan
M_i = selection.Identity(x_i.shape).select()
y_i = measurement.Laplace(M_i, eps_share).measure(x_i, prng)
noise_scale_factor = laplace_scale_factor(
M_i, eps_share)
else:
# run dawa subplan
W = get_matrix(W)
W_i = W * support.expansion_matrix(marginal_mapping)
dawa = pmapper.Dawa(eps_share, 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()
y_i = measurement.Laplace(
M_bar, eps_share * (1 - self.ratio)).measure(x_bar, prng)
noise_scale_factor = laplace_scale_factor(
M_bar, eps_share * (1 - self.ratio))
# expand the dawa reduction
M_i = M_bar * support.reduction_matrix(mapping)
MM = M_i * support.reduction_matrix(marginal_mapping)
Ms.append(MM)
ys.append(y_i)
scale_factors.append(noise_scale_factor)
x_hat = inference.LeastSquares(method='lsmr').infer(Ms, ys, scale_factors)
return x_hat
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
domain_size = np.prod(self.domain_shape)
# Start with a unifrom estimation of x
x_hat = np.array([self.data_scale / float(domain_size)] * domain_size)
W = get_matrix(W)
if not isinstance(W, np.ndarray):
W = W.toarray()
measuredQueries = []
nnls = inference.NonNegativeLeastSquares(method='new')
M_history = np.empty((0, domain_size))
y_history = []
for i in range(1, self.rounds+1):
eps_round = eps / float(self.rounds)
# SW + SH2
worst_approx = pselection.WorstApprox(W,
measuredQueries,
x_hat,
eps_round * self.ratio)
W_next = worst_approx.select(x, prng)
measuredQueries.append(W_next.mwem_index)
M = selection.AddEquiWidthIntervals(W_next, i).select()
if not isinstance(M, np.ndarray):
M = M.toarray()
laplace = measurement.Laplace(M, eps_round * (1-self.ratio))
y = laplace.measure(x, prng)
# default use history
M_history = np.vstack([M_history, M])
y_history.extend(y)
if self.total_noise_scale != 0:
total_query = sparse.csr_matrix([1]*domain_size)
noise_scale = laplace_scale_factor(M, eps_round * (1-self.ratio))
x_hat = nnls.infer([total_query, M_history], [[self.data_scale], y_history], [self.total_noise_scale, noise_scale])
else:
x_hat = nnls.infer(M, y)
return x_hat
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
if self.workload_based:
W = get_matrix(W)
mapping = mapper.WorkloadBased(W).mapping()
reducer = transformation.ReduceByPartition(mapping)
x = reducer.transform(x)
# Reduce workload
# W = support.reduce_queries(mapping, W)
W = W * support.expansion_matrix(mapping)
M = selection.Identity(x.shape).select()
y = measurement.Laplace(M, eps).measure(x, prng)
x_hat = inference.LeastSquares().infer(M, y)
return x_hat
def Run(self, W, x, eps):
domain_dimension = len(self.domain_shape)
eps_share = util.old_div(float(eps), domain_dimension)
Ms = []
ys = []
scale_factors = []
for i in range(domain_dimension):
# Reducde domain to get marginals
marginal_mapping = marginal_partition(self.domain_shape, i)
x_i = x.reduce_by_partition(marginal_mapping)
if self.domain_shape[i] < 50:
# run identity subplan
M_i = identity((self.domain_shape[i], ))
y_i = x_i.laplace(M_i, eps_share)
noise_scale_factor = laplace_scale_factor(M_i, eps_share)
else:
# run dawa subplan
W = get_matrix(W)
W_i = W * support.expansion_matrix(marginal_mapping)
mapping = x_i.dawa(self.ratio, self.approx, eps_share)
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_share * (1 - self.ratio))
noise_scale_factor = laplace_scale_factor(
M_bar, eps_share * (1 - self.ratio))
# expand the dawa reduction
M_i = M_bar * support.reduction_matrix(mapping)
MM = M_i * support.reduction_matrix(marginal_mapping)
Ms.append(MM)
ys.append(y_i)
scale_factors.append(noise_scale_factor)
x_hat = least_squares(Ms, ys, scale_factors)
return x_hat
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 Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
domain_size = np.prod(self.domain_shape)
# Start with a uniform estimation of x
x_hat = np.array([self.data_scale / float(domain_size)] * domain_size)
W = get_matrix(W)
if not isinstance(W, np.ndarray):
W = W.toarray()
measuredQueries = []
mult_weight = inference.MultiplicativeWeights(updateRounds = self.update_rounds)
M_history = np.empty((0, domain_size))
y_history = []
for i in range(1, self.rounds+1):
eps_round = eps / float(self.rounds)
# SW
worst_approx = pselection.WorstApprox(W,
measuredQueries,
x_hat,
eps_round * self.ratio,
'EXPONENTIAL')
M = worst_approx.select(x, prng)
measuredQueries.append(M.mwem_index)
if not isinstance(M, np.ndarray):
M = M.toarray()
# LM
laplace = measurement.Laplace(M, eps_round * (1-self.ratio))
y = laplace.measure(x, prng)
M_history = np.vstack([M_history, M])
y_history.extend(y)
# MW
if self.use_history:
x_hat = mult_weight.infer(M_history, y_history, x_hat)
else:
x_hat = mult_weight.infer(M, y, x_hat)
return x_hat
def Run(self, W, x, eps, seed):
prng = np.random.RandomState(seed)
domain_size = np.prod(self.domain_shape)
# Start with a unifrom estimation of x
x_hat = np.array([self.data_scale / float(domain_size)] * domain_size)
W = get_matrix(W)
W_partial = sparse.csr_matrix(W.shape)
nnls = inference.NonNegativeLeastSquares()
M_history = np.empty((0, domain_size))
y_history = []
for i in range(1, self.rounds+1):
eps_round = eps / float(self.rounds)
# SW + SH2
worst_approx = pselection.WorstApprox(sparse.csr_matrix(W),
W_partial,
x_hat,
eps_round * self.ratio)
W_next = worst_approx.select(x, prng)
M = selection.AddEquiWidthIntervals(W_next, i).select()
W_partial += W_next
laplace = measurement.Laplace(M, eps_round * (1-self.ratio))
y = laplace.measure(x, prng)
# default use history
M_history = sparse.vstack([M_history, M])
y_history.extend(y)
if self.total_noise_scale != 0:
total_query = sparse.csr_matrix([1]*domain_size)
noise_scale = laplace_scale_factor(M, eps_round * (1-self.ratio))
x_hat = nnls.infer([total_query, M_history], [[self.data_scale], y_history], [self.total_noise_scale, noise_scale])
else:
x_hat = nnls.infer(M, y)
return x_hat
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
domain_size = np.prod(self.domain_shape)
# Start with a unifrom estimation of x
x_hat = np.array([self.data_scale / float(domain_size)] * domain_size)
W = get_matrix(W)
W_partial = sparse.csr_matrix(W.shape)
mult_weight = inference.MultiplicativeWeights(updateRounds = self.update_rounds)
M_history = np.empty((0, domain_size))
y_history = []
for i in range(1, self.rounds+1):
eps_round = eps / float(self.rounds)
# SW
worst_approx = pselection.WorstApprox(sparse.csr_matrix(W),
W_partial,
x_hat,
eps_round * self.ratio,
'EXPONENTIAL')
W_next = worst_approx.select(x, prng)
M = support.extract_M(W_next)
W_partial += W_next
# LM
laplace = measurement.Laplace(M, eps_round * (1-self.ratio))
y = laplace.measure(x, prng)
M_history = sparse.vstack([M_history, M])
y_history.extend(y)
# MW
if self.use_history:
x_hat = mult_weight.infer(M_history, y_history, x_hat)
else:
x_hat = mult_weight.infer(M, y, x_hat)
return x_hat
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
domain_size = np.prod(self.domain_shape)
# Start with a unifrom estimation of x
x_hat = np.array([self.data_scale / float(domain_size)] * domain_size)
W = get_matrix(W)
measuredQueries = []
mult_weight = inference.MultiplicativeWeights(
updateRounds=self.update_rounds)
M_history = np.empty((0, domain_size))
y_history = []
for i in range(1, self.rounds + 1):
eps_round = eps / float(self.rounds)
# SW + SH2
worst_approx = pselection.WorstApprox(W, measuredQueries, x_hat,
eps_round * self.ratio)
W_next = worst_approx.select(x, prng)
measuredQueries.append(W_next.mwem_index)
M = selection.AddEquiWidthIntervals(W_next, i).select()
# LM
laplace = measurement.Laplace(M, eps_round * (1 - self.ratio))
y = laplace.measure(x, prng)
M_history = sparse.vstack([M_history, M])
y_history.extend(y)
# MW
if self.use_history:
x_hat = mult_weight.infer(M_history, y_history, x_hat)
else:
x_hat = mult_weight.infer(M, y, x_hat)
return x_hat
def std_project_workload(self, w, mapping, groupID):
P_i = support.projection_matrix(mapping, groupID)
w = get_matrix(w)
return w * P_i.T
def Run(self, W, x, eps):
M = greedyH((self.n, ), get_matrix(W))
y = x.laplace(M, eps)
x_hat = least_squares(M, y)
return x_hat
