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)
# non-zero regs to avoid super long convergence time.
nnls = inference.NonNegativeLeastSquares(l1_reg=1e-6, l2_reg=1e-6)
measuredQueries = []
M_history = []
y_history = []
noise_scales = []
if self.total_noise_scale != 0:
M_history.append(workload.Total(domain_size))
y_history.append(np.array([self.data_scale]))
noise_scales.append(self.total_noise_scale)
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()
laplace = measurement.Laplace(M, eps_round * (1-self.ratio))
y = laplace.measure(x, prng)
# default use history
M_history.append(M)
y_history.append(y)
noise_scales.append(laplace_scale_factor(M, eps_round * (1-self.ratio)))
x_hat = nnls.infer(M_history, y_history, noise_scales)
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)
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 = []
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()
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):
prng = np.random.RandomState(seed)
x_hat = prng.rand(*x.shape)
W_partial = sparse.csr_matrix(W.get_matrix().shape)
nnls = inference.NonNegativeLeastSquares()
measured_queries = []
for i in range(1, self.rounds + 1):
eps_round = eps / float(self.rounds)
# SW + SH2
worst_approx = pselection.WorstApprox(
sparse.csr_matrix(W.get_matrix()), 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 * (1 - self.ratio))
y = laplace.measure(x, prng)
x_hat = nnls.infer(M, y)
return x_hat