<reponame>smaranjitghose/DeepRobust
import random
import numpy as np
import scipy.sparse as sp
from deeprobust.graph.global_attack import BaseAttack

class DICE(BaseAttack):
    """As is described in ADVERSARIAL ATTACKS ON GRAPH NEURAL NETWORKS VIA META LEARNING (ICLR'19),
    'DICE (delete internally, connect externally) is a baseline where, for each perturbation,
    we randomly choose whether to insert or remove an edge. Edges are only removed between
    nodes from the same classes, and only inserted between nodes from different classes.

    Parameters
    ----------
    model :
        model to attack. Default `None`.
    nnodes : int
        number of nodes in the input graph
    attack_structure : bool
        whether to attack graph structure
    attack_features : bool
        whether to attack node features
    device: str
        'cpu' or 'cuda'


    Examples
    --------

    >>> from deeprobust.graph.data import Dataset
    >>> from deeprobust.graph.global_attack import DICE
    >>> data = Dataset(root='/tmp/', name='cora')
    >>> adj, features, labels = data.adj, data.features, data.labels
    >>> model = DICE()
    >>> model.attack(adj, labels, n_perturbations=10)
    >>> modified_adj = model.modified_adj

    """

    def __init__(self, model=None, nnodes=None, attack_structure=True, attack_features=False, device='cpu'):
        super(DICE, self).__init__(model, nnodes, attack_structure=attack_structure, attack_features=attack_features, device=device)

        assert not self.attack_features, 'DICE does NOT support attacking features'

    def attack(self, ori_adj, labels, n_perturbations, **kwargs):
        """Delete internally, connect externally. This baseline has all true class labels
        (train and test) available.

        Parameters
        ----------
        ori_adj : scipy.sparse.csr_matrix
            Original (unperturbed) adjacency matrix.
        labels:
            node labels
        n_perturbations : int
            Number of edge removals/additions.

        Returns
        -------
        None.

        """

        # ori_adj: sp.csr_matrix

        print('number of pertubations: %s' % n_perturbations)
        modified_adj = ori_adj.tolil()

        remove_or_insert = np.random.choice(2, n_perturbations)
        n_remove = sum(remove_or_insert)

        nonzero = set(zip(*ori_adj.nonzero()))
        indices = sp.triu(modified_adj).nonzero()
        possible_indices = [x for x in zip(indices[0], indices[1])
                            if labels[x[0]] == labels[x[1]]]

        remove_indices = np.random.permutation(possible_indices)[: n_remove]
        modified_adj[remove_indices[:, 0], remove_indices[:, 1]] = 0
        modified_adj[remove_indices[:, 1], remove_indices[:, 0]] = 0

        n_insert = n_perturbations - n_remove

        # # sample edges to add
        # nonzero = nonzero
        # edges = self.random_sample_edges(adj, n_insert, exclude=nonzero)
        # for n1, n2 in edges:
        #     modified_adj[n1, n2] += 1
        #     modified_adj[n2, n1] += 1

        # sample edges to add
        for i in range(n_insert):
            # select a node
            node1 = np.random.randint(ori_adj.shape[0])
            possible_nodes = [x for x in range(ori_adj.shape[0])
                              if labels[x] != labels[node1] and modified_adj[x, node1] == 0]
            # select another node
            node2 = possible_nodes[np.random.randint(len(possible_nodes))]
            modified_adj[node1, node2] = 1
            modified_adj[node2, node1] = 1

        self.check_adj(modified_adj)
        self.modified_adj = modified_adj


    def sample_forever(self, adj, exclude):
        """Randomly random sample edges from adjacency matrix, `exclude` is a set
        which contains the edges we do not want to sample and the ones already sampled
        """
        while True:
            # t = tuple(np.random.randint(0, adj.shape[0], 2))
            t = tuple(random.sample(range(0, adj.shape[0]), 2))
            if t not in exclude:
                yield t
                exclude.add(t)
                exclude.add((t[1], t[0]))

    def random_sample_edges(self, adj, n, exclude):
        itr = self.sample_forever(adj, exclude=exclude)
        return [next(itr) for _ in range(n)]

import random
import numpy as np
import scipy.sparse as sp
from deeprobust.graph.global_attack import BaseAttack

class DICE(BaseAttack):
    """As is described in ADVERSARIAL ATTACKS ON GRAPH NEURAL NETWORKS VIA META LEARNING (ICLR'19),
    'DICE (delete internally, connect externally) is a baseline where, for each perturbation,
    we randomly choose whether to insert or remove an edge. Edges are only removed between
    nodes from the same classes, and only inserted between nodes from different classes.

    Parameters
    ----------
    model :
        model to attack. Default `None`.
    nnodes : int
        number of nodes in the input graph
    attack_structure : bool
        whether to attack graph structure
    attack_features : bool
        whether to attack node features
    device: str
        'cpu' or 'cuda'


    Examples
    --------

    >>> from deeprobust.graph.data import Dataset
    >>> from deeprobust.graph.global_attack import DICE
    >>> data = Dataset(root='/tmp/', name='cora')
    >>> adj, features, labels = data.adj, data.features, data.labels
    >>> model = DICE()
    >>> model.attack(adj, labels, n_perturbations=10)
    >>> modified_adj = model.modified_adj

    """

    def __init__(self, model=None, nnodes=None, attack_structure=True, attack_features=False, device='cpu'):
        super(DICE, self).__init__(model, nnodes, attack_structure=attack_structure, attack_features=attack_features, device=device)

        assert not self.attack_features, 'DICE does NOT support attacking features'

    def attack(self, ori_adj, labels, n_perturbations, **kwargs):
        """Delete internally, connect externally. This baseline has all true class labels
        (train and test) available.

        Parameters
        ----------
        ori_adj : scipy.sparse.csr_matrix
            Original (unperturbed) adjacency matrix.
        labels:
            node labels
        n_perturbations : int
            Number of edge removals/additions.

        Returns
        -------
        None.

        """

        # ori_adj: sp.csr_matrix

        print('number of pertubations: %s' % n_perturbations)
        modified_adj = ori_adj.tolil()

        remove_or_insert = np.random.choice(2, n_perturbations)
        n_remove = sum(remove_or_insert)

        nonzero = set(zip(*ori_adj.nonzero()))
        indices = sp.triu(modified_adj).nonzero()
        possible_indices = [x for x in zip(indices[0], indices[1])
                            if labels[x[0]] == labels[x[1]]]

        remove_indices = np.random.permutation(possible_indices)[: n_remove]
        modified_adj[remove_indices[:, 0], remove_indices[:, 1]] = 0
        modified_adj[remove_indices[:, 1], remove_indices[:, 0]] = 0

        n_insert = n_perturbations - n_remove

        # # sample edges to add
        # nonzero = nonzero
        # edges = self.random_sample_edges(adj, n_insert, exclude=nonzero)
        # for n1, n2 in edges:
        #     modified_adj[n1, n2] += 1
        #     modified_adj[n2, n1] += 1

        # sample edges to add
        for i in range(n_insert):
            # select a node
            node1 = np.random.randint(ori_adj.shape[0])
            possible_nodes = [x for x in range(ori_adj.shape[0])
                              if labels[x] != labels[node1] and modified_adj[x, node1] == 0]
            # select another node
            node2 = possible_nodes[np.random.randint(len(possible_nodes))]
            modified_adj[node1, node2] = 1
            modified_adj[node2, node1] = 1

        self.check_adj(modified_adj)
        self.modified_adj = modified_adj


    def sample_forever(self, adj, exclude):
        """Randomly random sample edges from adjacency matrix, `exclude` is a set
        which contains the edges we do not want to sample and the ones already sampled
        """
        while True:
            # t = tuple(np.random.randint(0, adj.shape[0], 2))
            t = tuple(random.sample(range(0, adj.shape[0]), 2))
            if t not in exclude:
                yield t
                exclude.add(t)
                exclude.add((t[1], t[0]))

    def random_sample_edges(self, adj, n, exclude):
        itr = self.sample_forever(adj, exclude=exclude)
        return [next(itr) for _ in range(n)]
As is described in ADVERSARIAL ATTACKS ON GRAPH NEURAL NETWORKS VIA META LEARNING (ICLR'19),     'DICE (delete internally, connect externally) is a baseline where, for each perturbation,     we randomly choose whether to insert or remove an edge. Edges are only removed between     nodes from the same classes, and only inserted between nodes from different classes.      Parameters     ----------     model :         model to attack. Default `None`.     nnodes : int         number of nodes in the input graph     attack_structure : bool         whether to attack graph structure     attack_features : bool         whether to attack node features     device: str         'cpu' or 'cuda'       Examples     --------      >>> from deeprobust.graph.data import Dataset     >>> from deeprobust.graph.global_attack import DICE     >>> data = Dataset(root='/tmp/', name='cora')     >>> adj, features, labels = data.adj, data.features, data.labels     >>> model = DICE()     >>> model.attack(adj, labels, n_perturbations=10)     >>> modified_adj = model.modified_adj Delete internally, connect externally. This baseline has all true class labels         (train and test) available.          Parameters         ----------         ori_adj : scipy.sparse.csr_matrix             Original (unperturbed) adjacency matrix.         labels:             node labels         n_perturbations : int             Number of edge removals/additions.          Returns         -------         None. # ori_adj: sp.csr_matrix # # sample edges to add # nonzero = nonzero # edges = self.random_sample_edges(adj, n_insert, exclude=nonzero) # for n1, n2 in edges: #     modified_adj[n1, n2] += 1 #     modified_adj[n2, n1] += 1 # sample edges to add # select a node # select another node Randomly random sample edges from adjacency matrix, `exclude` is a set         which contains the edges we do not want to sample and the ones already sampled # t = tuple(np.random.randint(0, adj.shape[0], 2))