def main():
root = "./data/"
trans = transforms.Compose([
transforms.ToTensor(),
])
train_set = dset.FashionMNIST(root=root,
train=True,
transform=trans,
download=True)
torch.manual_seed(4)
n_samples = 1600
dt_range = np.linspace(start=0.05, num=20, stop=1)
s_range = np.linspace(start=0.01, num=20, stop=0.6)
X_train_val, Y_train_val = filter_by_label(
x=train_set.data,
y=train_set.targets,
labels=[6, 9],
n_samples=n_samples,
device=DEVICE,
)
X, Y, X_transf, Y_transf = split_train_val(X_train_val, Y_train_val,
n_samples)
k = len(Y.unique())
print(Y.sum(), Y_transf.sum())
models = [
KMeans(n_clusters=k),
SpectralClustering(
n_clusters=k,
lmode="rw",
similarity="gaussian_zp",
assign_labels="kmeans",
),
Hierarchical(n_clusters=k),
]
test_robustness(
X,
Y,
X_transf,
Y_transf,
models=models,
dt_range=dt_range,
s_range=s_range,
lb=1 / 255,
mutation_rate=0.1,
path=PATH + "/TransferfashionMNIST/",
)
def main():
root = "./data/"
trans = transforms.Compose([
transforms.ToTensor(),
])
train_set = dset.FashionMNIST(root=root,
train=True,
transform=trans,
download=True)
torch.manual_seed(4)
n_samples = 800
dt_range = np.linspace(start=0.1, num=19, stop=1)
s_range = np.linspace(start=0.1, num=21, stop=0.6)
X, Y = filter_by_label(
x=train_set.data,
y=train_set.targets,
labels=[6, 9],
n_samples=n_samples,
device=DEVICE,
)
k = len(Y.unique())
models = [
KMeans(n_clusters=k),
SpectralClustering(
n_clusters=k,
lmode="rw",
similarity="gaussian_zp",
assign_labels="kmeans",
),
Hierarchical(n_clusters=k),
]
test_robustness(
X,
Y,
models=models,
dt_range=dt_range,
s_range=s_range,
lb=1 / 255,
path="./exportfashionMNIST/",
)
def main():
device = DEVICE
x = torch.load("./data/cifar10/cifar_features_classes016.pt")
y = torch.load("./data/cifar10/cifar_labels_classes016.pt")
X = x.unsqueeze(2).to(device)
Y = y.to(device)
dt_range = np.linspace(start=0.01, stop=1.5, num=20)
s_range = np.linspace(start=0.01, num=20, stop=0.6)
k = len(Y.unique())
models = [
KMeans(n_clusters=k, max_tol=1e-05, max_iter=500),
SpectralClustering(
n_clusters=k,
lmode="rw",
similarity="gaussian_zp",
assign_labels="kmeans",
),
Hierarchical(n_clusters=k),
]
test_robustness(
X,
Y,
y_target=6,
models=models,
dt_range=dt_range,
box=(0, 6),
s_range=s_range,
lb=1 / 255,
path=PATH + "cifar/",
)
"--dataset",
type=str,
default="89",
help="DIGIT dataset to analyze. Available: '89' or '14']",
)
(opts, args) = op.parse_args(sys.argv[1:])
X = np.load("comparison/DIGIT/X_org_{}.npy".format(opts.dataset))
Xadv_s = np.load("comparison/DIGIT/X_adv_{}.npy".format(opts.dataset))
X = torch.from_numpy(X).unsqueeze(2)
Xadv_s = torch.from_numpy(Xadv_s).unsqueeze(2)
eps_s = Xadv_s - X
h = Hierarchical(n_clusters=2)
model = ClusteringWrapper3Dto2D(h)
yhat = model.fit_predict(X)
yadv_s = model.fit_predict(Xadv_s)
set_seed(55)
T = ConstrainedAdvPoisoningGlobal(
delta=(Xadv_s - X).norm(float("inf")) / 2,
s=1,
clst_model=model,
lb=1.0,
G=150,
mutation_rate=0.2,
crossover_rate=0.85,
zero_rate=0.35,
domain_cons=[0, 16],
X = dt_x[perm].flatten(1)
Y = dt_y[perm].flatten(0)
return X, Y
X = torch.load(
"./src/classification/cifar_features/cifar_features_resnet18.pt"
).detach()
Y = torch.load("./src/classification/cifar_features/cifar_labels.pt").detach()
# best_tp = (6, 1, 7)
# best_tp = (4, 6, 9)
best_tp = (0, 1, 6)
i, j, k = best_tp
set_seed(4)
x, y = filter_data(X, Y, i, j, k, n=1600)
k = len(y.unique())
models = [
KMeans(n_clusters=k, max_tol=1e-05, max_iter=500),
SpectralClustering(
n_clusters=k, lmode="rw", similarity="gaussian_zp", assign_labels="kmeans"
),
Hierarchical(n_clusters=k),
]
for m in models:
cls = m.fit_predict(x)
print(adjusted_mutual_info_score(cls, y))
torch.save(x, "./data/cifar10/cifar_features_classes016.pt")
torch.save(y, "./data/cifar10/cifar_labels_classes016.pt")