def union1d(arr1, arr2, toarr=False):
arr1 = asintarr(arr1)
arr2 = asintarr(arr2)
arr = list(arr1.intersection(arr2))
if toarr:
arr = np.asarray(arr)
return arr
def setdiff1d(arr1, arr2, toarr=False):
arr1 = asintarr(arr1)
arr2 = asintarr(arr2)
arr = list(arr1.difference(arr2))
if toarr:
arr = np.asarray(arr)
return arr
def __init__(self,
adj,
x,
labels,
idx_train,
idx_unlabeled,
hidden_layers,
use_relu,
self_training_labels=None,
seed=None,
name=None,
device='CPU:0',
**kwargs):
super().__init__(adj=adj,
x=x,
labels=labels,
seed=seed,
name=name,
device=device,
**kwargs)
adj, x, labels = self.adj, self.x, self.labels
idx_train = asintarr(idx_train)
idx_unlabeled = asintarr(idx_unlabeled)
if self_training_labels is None:
surrogate = DenseGCN(adj,
x,
labels,
device='GPU',
norm_x=None,
seed=None)
surrogate.build(16, activations='relu' if use_relu else None)
his = surrogate.train(idx_train,
verbose=0,
epochs=200,
save_best=False)
self_training_labels = surrogate.predict(idx_unlabeled).argmax(1)
self.ll_ratio = None
# mettack can also conduct feature attack
self.allow_feature_attack = True
with tf.device(self.device):
self.idx_train = astensor(idx_train, dtype=self.intx)
self.idx_unlabeled = astensor(idx_unlabeled, dtype=self.intx)
self.labels_train = astensor(self.labels[idx_train],
dtype=self.floatx)
self.self_training_labels = astensor(self_training_labels,
dtype=self.floatx)
self.tf_adj = astensor(adj.A, dtype=self.floatx)
self.tf_x = astensor(x, dtype=self.floatx)
self.build(hidden_layers=hidden_layers)
self.use_relu = use_relu
self.loss_fn = SparseCategoricalCrossentropy(from_logits=True)
self.adj_changes = tf.Variable(tf.zeros_like(self.tf_adj))
self.x_changes = tf.Variable(tf.zeros_like(self.tf_x))
def __init__(self,
adj,
x,
labels,
idx_train,
surrogate=None,
surrogate_args={},
seed=None,
name=None,
device='CPU:0',
**kwargs):
super().__init__(adj=adj,
x=x,
labels=labels,
seed=seed,
name=name,
device=device,
**kwargs)
adj, x = self.adj, self.x
if surrogate is None:
surrogate = train_a_surrogate(self, 'DenseGCN', idx_train,
**surrogate_args)
elif not isinstance(surrogate, DenseGCN):
raise RuntimeError(
"surrogate model should be the instance of `graphgallery.nn.DenseGCN`."
)
idx_attack = asintarr(idx_train)
with tf.device(self.device):
self.idx_attack = astensor(idx_attack)
self.labels_attack = astensor(labels[idx_attack])
self.tf_adj = astensor(self.adj.A)
self.tf_x = astensor(x)
self.complementary = tf.ones_like(self.tf_adj) - tf.eye(
self.n_nodes) - 2. * self.tf_adj
self.loss_fn = sparse_categorical_crossentropy
self.adj_changes = tf.Variable(
tf.zeros(adj.shape, dtype=self.floatx))
self.surrogate = surrogate
# used for CW_loss=True
self.label_matrix = tf.gather(tf.eye(self.n_classes),
self.labels_attack)
def __init__(self,
adj,
x,
labels,
idx_train,
idx_unlabeled=None,
surrogate=None,
surrogate_args={},
seed=None,
name=None,
device='CPU:0',
**kwargs):
super().__init__(adj=adj,
x=x,
labels=labels,
seed=seed,
name=name,
device=device,
**kwargs)
adj, x = self.adj, self.x
if surrogate is None:
surrogate = train_a_surrogate(self, 'DenseGCN', idx_train,
**surrogate_args)
elif not isinstance(surrogate, DenseGCN):
raise RuntimeError(
"surrogate model should be the instance of `graphgallery.nn.DenseGCN`."
)
# poisoning attack in DeepRobust
if idx_unlabeled is None:
idx_attack = idx_train
labels_attack = labels[idx_train]
else: # Evasion attack in original paper
idx_unlabeled = asintarr(idx_unlabeled)
self_training_labels = self.estimate_self_training_labels(
surrogate, idx_unlabeled)
idx_attack = np.hstack([idx_train, idx_unlabeled])
labels_attack = np.hstack(
[labels[idx_train], self_training_labels])
with tf.device(self.device):
self.idx_attack = astensor(idx_attack)
self.labels_attack = astensor(labels_attack)
self.tf_adj = astensor(self.adj.A)
self.tf_x = astensor(x)
self.complementary = tf.ones_like(self.tf_adj) - tf.eye(
self.n_nodes) - 2. * self.tf_adj
self.loss_fn = SparseCategoricalCrossentropy()
self.adj_changes = tf.Variable(
tf.zeros(adj.shape, dtype=self.floatx))
self.surrogate = surrogate
# used for `CW_loss=True`
self.label_matrix = tf.gather(tf.eye(self.n_classes),
self.labels_attack)
self.range_idx = tf.range(idx_attack.size, dtype=self.intx)
self.indices_real = tf.stack([self.range_idx, self.labels_attack],
axis=1)