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DPHelmet_softmax.py
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DPHelmet_softmax.py
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#!/usr/bin/env python
# coding: utf-8
import functools
import itertools
import os
import time
import numpy as np
import pandas as pd
import tensorflow.compat.v2 as tf
from joblib import Parallel, delayed
from sklearn.metrics import accuracy_score, f1_score
from sklearn.model_selection import PredefinedSplit, RepeatedStratifiedKFold
tf.compat.v1.enable_v2_behavior()
print(tf.__version__)
# NB, This code does not work with multi-process GPU.
os.environ["CUDA_VISIBLE_DEVICES"] = "" # DO NOT MODIFY.
DATASET = "CIFAR10"
SAVED_EMBEDDINGS_PTH = "./"
SAVED_EMBEDDINGS_FILENAME = (
"code_space_cifar10.npy"
if DATASET == "CIFAR10"
else "code_space_cifar100.npy"
if DATASET == "CIFAR100"
else "code_space_federated_emnist.npy"
)
SAVED_LABELS_FILENAME = (
"labels_cifar10.npy"
if DATASET == "CIFAR10"
else "labels_cifar100.npy"
if DATASET == "CIFAR100"
else "labels_federated_emnist.npy"
)
SAVED_USERID_FILENAME = (
""
if DATASET == "CIFAR10"
else ""
if DATASET == "CIFAR100"
else "userid_federated_emnist.npy"
)
####################
### 1. SimCLR Embedding Extraction (execute `extract_embeddings.py` first)
####################
N_CLASSES = 10 if DATASET == "CIFAR10" else 100 if DATASET == "CIFAR100" else 62
# you need to execute `extract_embeddings.py` first
code_space = np.load(os.path.join(SAVED_EMBEDDINGS_PTH, SAVED_EMBEDDINGS_FILENAME))
labels = np.load(os.path.join(SAVED_EMBEDDINGS_PTH, SAVED_LABELS_FILENAME))
userid = None
if SAVED_USERID_FILENAME:
userid = np.load(os.path.join(SAVED_EMBEDDINGS_PTH, SAVED_USERID_FILENAME))
# clip inputs
X_norm = np.linalg.norm(code_space, ord=2, axis=1)
if DATASET == "CIFAR10":
clip_bound = 34.854 - 1e-5 # 95.5-percentile of CIFAR-100 embeddings
elif DATASET == "CIFAR100":
clip_bound = 34.157 - 1e-5 # 95.5-percentile of CIFAR-10 embeddings
else:
clip_bound = 34.854 - 1e-5 # 95.5-percentile of CIFAR-100 embeddings
X_clip = (
code_space / np.where(X_norm > clip_bound, X_norm / clip_bound, 1)[:, np.newaxis]
)
clip_bound += 1e-5
print(f"{np.linalg.norm(X_clip, ord=2, axis=1).max():.6f} <~= {clip_bound}")
####################
### 2. Distributed DP-Helmet
####################
def evaluate_distributed_psgd(
X_train,
y_train,
uid_train,
n_classes,
clip_bound,
lambda_=100,
bs=20,
l2=0.07,
epochs=90,
n_users=100,
n_per_user=500,
):
"""Train DP_SGD_SVM. This is the version used in the paper (Algorithm 2).
Args:
X_train (np.array): input dataset (features).
y_train (np.array): input dataset (labels).
uid_train (np.array): input dataset (user ids).
n_classes (int): number of classes.
clip_bound (float): norm clipping bound of X_train.
lambda_ (float, optional): regularization parameter of the SVM. Defaults to 100.
bs (int, optional): batch size of SGD update. Defaults to 20.
l2 (float, optional): model clipping bound: "l2-projection" (called R in the paper). Defaults to 0.07.
epochs (int, optional): number of training epochs. Defaults to 90.
n_users (int, optional): number of users. Defaults to 100.
n_per_user (int, optional): number of data points per user. Defaults to 500.
Returns:
(list, list, float): Triple of (1) the SVM coefficients with shape (n_users, (n_classes, n_features)),
(2) the SVM intercept (i.e. bias) with shape (n_users, (n_classes)) and
(3) the maximal actual radius (i.e. l2 norm of the SVM parameters)
which is NON-PRIVATE but useful for debug purposes.
"""
d = X_train.shape[1] # dimensions
beta = lambda_ + clip_bound**2 # beta smoothness
beta = np.sqrt(0.5 * beta**2 + n_classes * (d + 1) * lambda_**2) # correct for higher dimensions
# prepare inputs
y_train_onehot = tf.constant(np.eye(n_classes)[y_train].T, dtype=tf.float32)
inputs = tf.constant(X_train, dtype=tf.float32)
lambda_ = tf.constant(lambda_, dtype=tf.float32)
if uid_train is not None:
uid_train = tf.constant(uid_train)
@tf.function
def J(c, i, x, y, l):
"""The SVM training objective.
Args:
c (np.array): SVM coefficients.
i (np.array): SVM intercept.
x (np.array): input dataset (features).
y (np.array): input dataset (one-hot-encoded labels).
l (float): regularization parameter $\Lambda$.
Returns:
np.array: the loss.
"""
z = tf.matmul(c, x, transpose_b=True) + i[:, None]
return 0.5 * l * tf.reduce_sum(
tf.linalg.diag_part(tf.matmul(c, c, transpose_b=True)) + i**2
) + tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
labels=tf.transpose(y), logits=tf.transpose(z)
)
)
if n_users == 1:
uid_train = None
n_per_user = len(inputs)
if uid_train is None:
users = np.arange(0, n_users)
else:
users = np.unique(uid_train)
coefs, intercepts, radius = [], [], []
for n in users:
# initialize hyperplane + intercept
coef = tf.Variable(
tf.keras.initializers.Zeros()((n_classes, d)),
dtype=tf.float32,
trainable=True,
) # zeros init
intercept = tf.Variable(
tf.keras.initializers.Zeros()((n_classes,)),
dtype=tf.float32,
trainable=True,
) # zeros init
# assign data to users
if uid_train is None:
inputs_, y_train_onehot_ = (
inputs[n * n_per_user : (n + 1) * n_per_user],
y_train_onehot[:, n * n_per_user : (n + 1) * n_per_user],
)
else:
uid_train_ = tf.where(uid_train == n)[:, 0]
inputs_ = tf.gather(inputs, uid_train_)
y_train_onehot_ = tf.gather(y_train_onehot, uid_train_, axis=1)
n_per_user = len(inputs_)
n_iter_per_epoch = n_per_user // bs + (0 if n_per_user % bs == 0 else 1)
for i in range(epochs):
# shuffle data
new_idx = tf.random.shuffle(tf.range(n_per_user))
inputs_, y_train_onehot_ = tf.gather(inputs_, new_idx), tf.gather(
y_train_onehot_, new_idx, axis=1
)
for j in range(n_iter_per_epoch):
# select batch data
batch_idx = tf.range(n_per_user)[j * bs : (j + 1) * bs]
inputs__, y_train_onehot__ = tf.gather(inputs_, batch_idx), tf.gather(
y_train_onehot_, batch_idx, axis=1
)
# calculate loss
with tf.GradientTape() as tape:
tape.watch([coef, intercept])
loss = tf.reduce_mean(
J(coef, intercept, inputs__, y_train_onehot__, l=lambda_)
)
# SGD update step
delta_J_c, delta_J_i = tape.gradient(loss, [coef, intercept])
lr_ = tf.minimum(
1 / lambda_ * 1 / (i * n_iter_per_epoch + j + 1), 1 / beta
)
coef = coef - lr_ * delta_J_c
intercept = intercept - lr_ * delta_J_i
# make l2-projection with radius `l2`
actual_l2 = tf.maximum(
l2, tf.sqrt(tf.norm(coef) ** 2 + tf.norm(intercept) ** 2)
)
coef = coef / (actual_l2 / l2)
intercept = intercept / (actual_l2 / l2)
coefs.append(n_per_user * coef.numpy())
intercepts.append(n_per_user * intercept.numpy())
radius.append(
tf.reduce_max(tf.sqrt(tf.norm(coef) ** 2 + tf.norm(intercept) ** 2))
) # (optionally) track non-DP radius
return coefs, intercepts, np.max(radius)
####################
### 3. Cross-Validation
####################
# First train the hyperplanes, then noise them depending on `eps`.
### CV-PARAMS ###
NB_SPLITS = 6
NB_REPEATS = 2
N_RUNS = NB_SPLITS * NB_REPEATS
N_PROCESSES = 10
### CV-PARAMS (END) ###
tests_dphelmet_pre = pd.DataFrame(
columns=[
"variant",
"coefs",
"intercepts",
"test_indices",
"unnoised_radius",
"lambda",
"bs",
"l2",
"epochs",
"n_users",
"n_per_user",
]
)
def multi_eval(configuration, n_classes, clip_bound, X_clip, labels, userid, noniid):
"""wrapper for multi-process evaluation
Args:
configuration (((np.array, np.array), list)): selected training configuration incl. training as well as
testing indicies and also model parameters.
Model parameters are: (regularization lambda, batch_size,
smoothness h, radius R, n_epochs, n_users, n_per_user).
n_classes (int): number of classes.
clip_bound (float): norm clipping bound of X_clip.
X_clip (np.array): clipped input dataset (features).
labels (np.array): input dataset (labels).
userid (np.array): input dataset (user ids).
noniid (bool): setup data among users in a strongly-biased non-iid setting.
Returns:
dict: A dictionary containing the training configuration as well as the trained SVM.
"""
(train_index, test_index), params = configuration
X_train, y_train = X_clip[train_index], labels[train_index]
uid_train = None
if userid is not None:
uid_train = userid[train_index]
if noniid:
idx = np.argsort(y_train)
X_train, y_train = X_train[idx], y_train[idx]
coefs, intercepts, radius = evaluate_distributed_psgd(
X_train,
y_train,
uid_train,
n_classes=n_classes,
clip_bound=clip_bound,
lambda_=params[0],
bs=int(params[1]),
l2=params[2],
epochs=int(params[3]),
n_users=int(params[4]),
n_per_user=int(params[5]),
)
return {
"coefs": coefs,
"intercepts": intercepts,
"radius": radius, # NON-PRIVATE, debug purposes only.
"test_indices": test_index,
"lambda": params[0],
"bs": int(params[1]),
"l2": params[2],
"epochs": int(params[3]),
"n_users": int(params[4]),
"n_per_user": int(params[5]),
}
### HYPERPARAMS ###
LAMBDA = [1, 3, 10, 30] # regularization parameter
BS = [20] # batch size
L2 = [0.1, 0.4, 0.6, 1.0] # radius R, non-dep. on LAMBDA
EPOCHS = [100] # epochs
N_USERS = [100] # number of users
N_PER_USER = [500] # number of data points per user
NONIID = False
### HYPERPARAMS (END) ###
# prepare hyperparams search space
param_test = np.array(
list(itertools.product(LAMBDA, BS, L2, EPOCHS, N_USERS, N_PER_USER))
)
# > make sure that not more datapoints are used than there are accessible
if userid is None:
param_test = param_test[
param_test[:, 4] * param_test[:, 5] <= len(code_space) * (NB_SPLITS - 1) / NB_SPLITS
]
print(f">> testing {len(param_test)} parameter combination(s)")
# cross-validation technique
if userid is None:
vali = RepeatedStratifiedKFold(
n_splits=NB_SPLITS, n_repeats=NB_REPEATS * len(param_test)
)
validator = lambda x, y: vali.split(x, y)
else:
vali = PredefinedSplit(
test_fold=np.concatenate([-np.ones(671585), np.zeros(77483)])
)
validator = lambda x, y: (NB_REPEATS * len(param_test)) * list(vali.split())
# pre-instanciate training routine
my_multi_eval = functools.partial(
multi_eval,
n_classes=N_CLASSES,
clip_bound=clip_bound,
X_clip=X_clip,
labels=labels,
userid=userid,
noniid=NONIID,
)
with Parallel(n_jobs=N_PROCESSES, verbose=40) as p:
# run DP_Softmax_SLP_SGD in parallel for the hyperparams search space
scores = p(delayed(my_multi_eval)(conf)
for conf in zip(
validator(X_clip, labels),
param_test[None].repeat(N_RUNS, axis=0).reshape(-1, 6),
)
)
# store the experiment results
tests_dphelmet_pre = pd.concat([tests_dphelmet_pre,
pd.DataFrame([
{
"variant": "dist_dphelmet",
"bs": score["bs"],
"lambda": score["lambda"],
"l2": score["l2"],
"epochs": score["epochs"],
"coefs": score["coefs"],
"intercepts": score["intercepts"],
"unnoised_radius": score["radius"],
"test_indices": score["test_indices"],
"n_users": score["n_users"],
"n_per_user": score["n_per_user"],
}
for score in scores
], columns=tests_dphelmet_pre.columns)],
ignore_index=True,
)
tests_dphelmet = pd.DataFrame(
columns=[
"variant",
"test_acc",
"test_f1",
"unnoised_radius",
"dp_eps",
"dp_delta",
"lambda",
"bs",
"h",
"l2",
"epochs",
"n_users",
"n_per_user",
]
)
### PRIVACY PARAMETERS ###
EPS = [0.1, 0.2, 0.5, 0.8, 1, 1.5, 2, 5, 10, 100] # these are only eps estimates
DELTA = 1e-5 # changing this requires a re-run of privacy buckets
### PRIVACY PARAMETERS (END) ###
for eps in EPS:
# for each hyperplane add noise and predict, dependent on eps and delta
test_accs, test_f1s = [], []
for i, x in tests_dphelmet_pre.iterrows():
# > This is only a noise scale estimate.
# > For a correct eps refer to the `Gaussian mechanism` (Lemma 3.14) or `DPHelmet_tight_adp.py`
noise_scale = (
(
2 / x["lambda"] * (np.sqrt(2)*clip_bound + x["l2"] * x["lambda"])
# 2 * x['l2']
) # sensitivity
# for Corollary 5.5 use `2 * x['l2']` as a sensitivity instead
* np.sqrt(2 * np.log(1.25 / DELTA)) # estimate c for Gaussian leakage
/ (eps * np.sqrt(x["n_users"])) # cf. Main Theorem 5.3
)
# 50%-non-colluding assumption
if x["n_users"] > 1: # does not make sense for 1 user...
noise_scale *= np.sqrt(2) # t=0.5
coefs, intercepts = [], []
for u in range(int(x["n_users"])):
# noise the hyperplane plus intercept
this_coef, this_intercept = x["coefs"][u], x["intercepts"][u]
coef_noised = this_coef + np.random.normal(
loc=0, scale=noise_scale, size=this_coef.shape
)
intercept_noised = this_intercept + np.random.normal(
loc=0, scale=noise_scale, size=this_intercept.shape
)
# make l2-projection with radius `l2`
actual_l2 = tf.maximum(
x["l2"],
tf.sqrt(tf.norm(coef_noised) ** 2 + tf.norm(intercept_noised) ** 2),
)
coef_noised = coef_noised / (actual_l2 / x["l2"])
intercept_noised = intercept_noised / (actual_l2 / x["l2"])
coefs.append(coef_noised)
intercepts.append(intercept_noised)
# take the averaged hyperplanes across users + predict
coef_, intercept_ = tf.reduce_mean(tf.stack(coefs), axis=0), tf.reduce_mean(
intercepts, axis=0
)
y_pred = tf.argmax(
tf.matmul(coef_, X_clip[x["test_indices"]], transpose_b=True)
+ intercept_[:, None],
axis=0,
).numpy()
test_acc = accuracy_score(labels[x["test_indices"]], y_pred)
test_f1 = f1_score(labels[x["test_indices"]], y_pred, average="macro")
test_accs.append(test_acc)
test_f1s.append(test_f1)
# store the experiment results incl. test accuracy and f1-score (macro)
tests_dphelmet = pd.concat([tests_dphelmet,
pd.DataFrame([
{
"variant": x["variant"],
"bs": x["bs"],
"lambda": x["lambda"],
"dp_eps": eps,
"dp_delta": DELTA,
"h": -1,
"l2": x["l2"],
"epochs": x["epochs"],
"test_acc": test_accs[i],
"test_f1": test_f1s[i],
"unnoised_radius": x["unnoised_radius"],
"n_users": x["n_users"],
"n_per_user": x["n_per_user"],
}
for i, x in tests_dphelmet_pre.iterrows()
], columns=tests_dphelmet.columns)],
ignore_index=True,
)
# save prediction to .csv file
filename = f"tests_dphelmet_{time.strftime('%Y%m%d_%H%M%S')}.csv"
tests_dphelmet.to_csv(filename, index=False)
print(
"Written output to",
filename,
"with scenario noniid",
NONIID,
"and dataset",
DATASET,
)