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utils_data.py
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utils_data.py
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: CC-BY-NC-4.0
import jax.numpy as np
from jax import random, jit, nn, vmap, partial
import numpy as onp
from scipy.stats import norm
import math
import datasets
from jax.ops import index_update
data_sources = {
"toy_binary": datasets.toy_binary.ToyBinary,
"adult": datasets.adult.Adult,
"loans": datasets.loans.Loans,
}
def init_D_prime(selection, n_prime, d, D=False, interval=None):
"""
selection: text
n_prime: int, number of samples for Dprime
d: int, number of features in Dprime
D: true data, only needed if near_origin is selected
"""
if selection == "random":
Dprime = 2 * (onp.random.random((n_prime, d)) - 0.5)
elif selection == "rand_interval":
a, b = interval
Dprime = (b - a) * onp.random.random((n_prime, d)) + a
elif selection == "near_origin":
Dprime = D + 0.05 * onp.random.randn(n_prime, d)
else:
raise ValueError(
"Supported selections are 'random', 'randomunit', and 'near_origin'"
)
return Dprime
# Takes as input a GDP parameter mu and a target parameter delta < 1, and returns eps such that a mu-GDP
# algorithm is (eps,delta)-DP.
def GDP_to_DP(mu, delta):
def deltaval(eps):
return norm.cdf(-eps / mu + mu / 2) - math.exp(eps) * norm.cdf(
-eps / mu - mu / 2
)
if delta <= 0: # No finite epsilon value is possible
return math.inf
lower = 0.0
upper = 500.0
while (
abs(deltaval((lower + upper) / 2) - delta) >= delta / 10
and (lower + upper / 2) > 0
): # binary search to get within (1+-1/10)*delta
# print("Delta: ", deltaval((lower+upper)/2), "Eps value: ", (lower+upper)/2)
if deltaval((lower + upper) / 2) < delta:
upper = (lower + upper) / 2
else:
lower = (lower + upper) / 2
return (lower + upper) / 2
def DP_to_GDP(epsilon, delta):
"""
Given a target (epsilon-delta), returns mu such that a mu-GDP algorithm is (eps,delta)-DP
"""
if epsilon <= 0: # No finite mu value is possible
return math.inf
lower = 10 ** -6
upper = 50.0
mid = (lower + upper) / 2
while (
abs(GDP_to_DP(mu=mid, delta=delta) - epsilon) >= (epsilon * 10 ** -2)
and mid > 0
):
target_mu = GDP_to_DP(mu=mid, delta=delta)
if target_mu < epsilon:
lower = mid
else:
upper = mid
mid = (lower + upper) / 2
print("Mu-GDP value", mid)
return mid
def l2_loss_fn(Dprime, target_statistics, statistic_fn):
# np.linalg.norm returns the Frobenius norm for matrix input or L2 norm of the vector when ord is not provided
return np.linalg.norm(statistic_fn(Dprime) - target_statistics)
def jit_loss_fn(statistic_fn, norm=None, lambda_l1=0):
if norm == "L2":
ord_norm = 2
elif norm == "Linfty":
ord_norm = np.inf
else:
ord_norm = 5
@jit
def compute_loss_fn(Dprime, target_statistics):
if norm == "LogExp":
return np.log(
np.exp(statistic_fn(Dprime) - target_statistics).sum()
) + lambda_l1 * np.linalg.norm(Dprime, 1)
else:
return np.linalg.norm(
statistic_fn(Dprime) - target_statistics, ord=ord_norm
) + lambda_l1 * np.linalg.norm(Dprime, 1)
return compute_loss_fn
@jit
def gumbel_softmax_sample(logits, key, temp=1):
"""
Draw a sample from the Gumbel-Softmax distribution
"""
y = logits + random.gumbel(key=key, shape=logits.shape)
return nn.softmax(y / temp, axis=-1)
@jit
def gumbel_softmax(logits, key, temperature=1.0):
"""
Sample from the Gumbel-Softmax distribution and optionally discretize.
"""
logits = nn.log_softmax(logits, axis=-1)
y = gumbel_softmax_sample(logits, key=key, temp=temperature)
# if hard:
# shape = y.shape
# ind = y.argmax(-1)
# y_hard = index_update(np.zeros_like(y), [np.arange(shape[0]), ind], 1)
# y = y_hard
return y
@jit
def project_gumbel_softmax(D, feats_idx, key, temperature=1.0):
# D = np.log(D)
return np.hstack(gumbel_softmax(D[:, q], key, temperature) for q in feats_idx)
@jit
def sparsemax(logits):
"""forward pass for sparsemax
this will process a 2d-array $logits, where axis 1 (each row) is assumed to be
the logits-vector.
"""
# sort logits
z_sorted = np.sort(logits, axis=1)[:, ::-1]
# calculate k(z)
z_cumsum = np.cumsum(z_sorted, axis=1)
k = np.arange(1, logits.shape[1] + 1)
z_check = 1 + k * z_sorted > z_cumsum
k_z = logits.shape[1] - np.argmax(z_check[:, ::-1], axis=1)
# calculate tau(logits)
tau_sum = z_cumsum[np.arange(0, logits.shape[0]), k_z - 1]
tau_z = ((tau_sum - 1) / k_z).reshape(-1, 1)
return np.maximum(0, logits - tau_z)
@jit
def sparsemax_project(D, feats_idx):
return np.hstack(sparsemax(D[:, q]) for q in feats_idx)
def ohe_to_categorical(D, feats_idx):
return np.vstack(np.argwhere(D[:, feat] == 1)[:, 1] for feat in feats_idx).T
def randomized_rounding(D, feats_idx, key, oversample=1):
return np.hstack(
np.vstack(
nn.one_hot(
random.choice(
key, a=len(probs), shape=(oversample, 1), p=probs
).squeeze(),
len(probs),
)
for probs in D[:, feat]
)
for feat in feats_idx
)
def compute_sigma(k, sens, epsilon, delta):
if delta == 0:
return (2 * k * sens) / epsilon
else:
return (np.sqrt(32 * (2 * k) * np.log(delta ** -1)) * sens) / epsilon
def numeric_sparse(queries, skip_idxs, k, T, epsilon, sens, delta=0):
count = 0
idxs = onp.array([], onp.int)
pos = 0
sigma = compute_sigma(k, epsilon, sens, delta)
T_hat = T + onp.random.laplace(loc=0, scale=sigma)
while pos < len(queries) and len(idxs) < k:
if pos in skip_idxs:
pos += 1
continue
v_i = onp.random.laplace(loc=0, scale=2 * sigma)
if queries[pos] + v_i >= T_hat:
idxs = onp.append(idxs, pos)
count += 1
T_hat = T + onp.random.laplace(loc=0, scale=sigma)
pos += 1
return idxs