def Run(self, W, x, eps, seed): x = x.flatten() prng = np.random.RandomState(seed) 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 = W.get_matrix() * 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 = W.get_matrix() 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() 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) 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) 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, 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) 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