dataset.py

import numpy as np
import random
import scipy.sparse as sp
import os.path as osp
import os
import urllib.request
import sys
import pickle as pkl
import networkx as nx
from utils import get_train_val_test, get_train_val_test_gcn
import scipy.io as sio

class Dataset():
    """Dataset class contains four citation network datasets "cora", "cora-ml", "citeseer" and "pubmed",
    and one blog dataset "Polblogs".
    The 'cora', 'cora-ml', 'poblogs' and 'citeseer' are downloaded from https://github.com/danielzuegner/gnn-meta-attack/tree/master/data, and 'pubmed' is from https://github.com/tkipf/gcn/tree/master/gcn/data.

    Parameters
    ----------
    root :
        root directory where the dataset should be saved.
    name :
        dataset name, it can be choosen from ['cora', 'citeseer', 'cora_ml', 'polblogs', 'pubmed']
    setting :
        there are two data splits settings. The 'nettack' setting follows nettack paper where they select the largest connected components of the graph and use 10%/10%/80% nodes for training/validation/test . The 'gcn' setting follows gcn paper where they use 20 samples in each class for traing, 500 nodes for validation, and 1000 nodes for test. (Note here 'gcn' setting is not a fixed split, i.e., different random seed would return different data splits)
    seed :
        random seed for splitting training/validation/test.
    require_mask :
        setting require_mask True to get training, validation and test mask (self.train_mask, self.val_mask, self.test_mask)

    Examples
    --------
	We can first create an instance of the Dataset class and then take out its attributes.

	>>> from deeprobust.graph.data import Dataset
	>>> data = Dataset(root='/tmp/', name='cora')
	>>> adj, features, labels = data.adj, data.features, data.labels
	>>> idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
    """

    def __init__(self, root, name, setting='nettack', seed=None, require_mask=False):
        self.name = name.lower()
        self.setting = setting.lower()

        assert self.name in ['cora', 'citeseer', 'cora_ml', 'polblogs', 'pubmed', 'aids', 'enzyme', 'usair', 'brazil',\
                             'blogcatalog','europe'], \
            'Currently only support cora, citeseer, cora_ml, polblogs, pubmed, aids, enzyme, usair, brazil, europe'
        assert self.setting in ['gcn', 'nettack'], 'Settings should be gcn or nettack'

        self.seed = seed
        # self.url =  'https://raw.githubusercontent.com/danielzuegner/nettack/master/data/%s.npz' % self.name
        self.url =  'https://raw.githubusercontent.com/danielzuegner/gnn-meta-attack/master/data/%s.npz' % self.name
        self.root = osp.expanduser(osp.normpath(root))
        self.data_folder = osp.join(root, self.name)
        self.data_filename = self.data_folder + '.npz'
        self.require_mask = require_mask

        self.require_lcc = True if setting == 'nettack' else False
        self.adj, self.features, self.labels = self.load_data()

        self.init_adj = self.init_matrix(self.adj)
        self.idx_train, self.idx_val, self.idx_test = self.get_train_val_test()
        if self.require_mask:
            self.get_mask()

    def get_train_val_test(self):
        """Get training, validation, test splits according to self.setting (either 'nettack' or 'gcn').
        """
        if self.setting == 'nettack':
            return get_train_val_test(nnodes=self.adj.shape[0], val_size=0.1, test_size=0.8, stratify=self.labels, seed=self.seed)
        if self.setting == 'gcn':
            return get_train_val_test_gcn(self.labels, seed=self.seed)

    def load_data(self):
        print('Loading {} dataset...'.format(self.name))
        if self.name == 'pubmed':
            return self.load_pubmed()

        if self.name == 'aids':
            return self.load_AIDS()

        if self.name == 'usair':
            return self.load_usair()

        if self.name == 'brazil':
            return self.load_brazil()

        if self.name == 'europe':
            return self.load_europe()

        if self.name == 'enzyme':
            return self.load_enzyme()

        if self.name == 'blogcatalog':
            return self.load_blog()

        if not osp.exists(self.data_filename):
            self.download_npz()

        adj, features, labels = self.get_adj()
        return adj, features, labels

    def download_npz(self):
        """Download adjacen matrix npz file from self.url.
        """
        print('Dowloading from {} to {}'.format(self.url, self.data_filename))
        try:
            urllib.request.urlretrieve(self.url, self.data_filename)
        except:
            raise Exception('''Download failed! Make sure you have stable Internet connection and enter the right name''')

    def download_pubmed(self, name):
        url = 'https://raw.githubusercontent.com/tkipf/gcn/master/gcn/data/'
        try:
            urllib.request.urlretrieve(url + name, osp.join(self.root, name))
        except:
            raise Exception('''Download failed! Make sure you have stable Internet connection and enter the right name''')

    def load_pubmed(self):
        dataset = 'pubmed'
        names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']
        objects = []
        for i in range(len(names)):
            name = "ind.{}.{}".format(dataset, names[i])
            data_filename = osp.join(self.root, name)

            if not osp.exists(data_filename):
                self.download_pubmed(name)

            with open(data_filename, 'rb') as f:
                if sys.version_info > (3, 0):
                    objects.append(pkl.load(f, encoding='latin1'))
                else:
                    objects.append(pkl.load(f))

        x, y, tx, ty, allx, ally, graph = tuple(objects)


        test_idx_file = "ind.{}.test.index".format(dataset)
        if not osp.exists(osp.join(self.root, test_idx_file)):
            self.download_pubmed(test_idx_file)

        test_idx_reorder = parse_index_file(osp.join(self.root, test_idx_file))
        test_idx_range = np.sort(test_idx_reorder)

        features = sp.vstack((allx, tx)).tolil()
        features[test_idx_reorder, :] = features[test_idx_range, :]
        adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))
        labels = np.vstack((ally, ty))
        labels[test_idx_reorder, :] = labels[test_idx_range, :]
        labels = np.where(labels)[1]
        return adj, features, labels

    def load_AIDS(self):
        dataset = 'AIDS'
        g = np.zeros((1429, 1429))
        with open('%s/%s_A.txt' % (dataset, dataset), 'r') as f:
            for _ in range(2948):
                row = f.readline().strip().replace(',',' ').split()
                i, j = [int(w) for w in row]
                g[i-1][j-1] = 1
        adj = sp.csr_matrix(g)

        features = []
        with open('%s/%s_node_attributes.txt' % (dataset, dataset), 'r') as f:
            for _ in range(1429):
                row = f.readline().strip().replace(',',' ').split()
                features.append([float(w) for w in row])
        features = sp.csr_matrix(features)

        labels = np.loadtxt('%s/%s_node_labels.txt' % (dataset, dataset))
        labels = np.array(labels, dtype='int8')[:1429]

        return adj, features, labels

    def load_enzyme(self):
        dataset = 'ENZYMES'
        g = np.zeros((6254, 6254))
        with open('%s/%s_A.txt' % (dataset, dataset), 'r') as f:
            for _ in range(23914):
                row = f.readline().strip().replace(',',' ').split()
                i, j = [int(w) for w in row]
                g[i-1][j-1] = 1
        adj = sp.csr_matrix(g)

        features = []
        with open('%s/%s_node_attributes.txt' % (dataset, dataset), 'r') as f:
            for _ in range(6254):
                row = f.readline().strip().replace(',',' ').split()
                features.append([float(w) for w in row])
        features = sp.csr_matrix(features)

        labels = np.loadtxt('%s/%s_node_labels.txt' % (dataset, dataset))
        labels = np.array(labels, dtype='int8')[:6254]

        return adj, features, labels

    def load_usair(self):
        dataset = 'usair'
        node_dict = {}
        f = np.loadtxt('%s/%s_lable.txt' % (dataset, dataset))
        id = f[:, 0]
        labels = f[:, 1]
        for i in range(len(id)):
            node_dict[id[i]] = i
        g = np.zeros((1190, 1190))
        with open('%s/%s_A.txt' % (dataset, dataset), 'r') as f:
            for _ in range(13582):
                row = f.readline().strip().split()
                i, j = [int(w) for w in row]
                g[node_dict[i]][node_dict[j]] = 1
                g[node_dict[j]][node_dict[i]] = 1
        adj = sp.csr_matrix(g)

        features = np.identity(1190)
        features = sp.csr_matrix(features)

        labels = np.array(labels, dtype='int8')

        return adj, features, labels

    def load_europe(self):
        dataset = 'europe'
        node_dict = {}
        f = np.loadtxt('%s/%s_lable.txt' % (dataset, dataset))
        id = f[:, 0]
        labels = f[:, 1]
        for i in range(len(id)):
            node_dict[id[i]] = i
        g = np.zeros((399, 399))
        with open('%s/%s_A.txt' % (dataset, dataset), 'r') as f:
            for _ in range(5995):
                row = f.readline().strip().split()
                i, j = [int(w) for w in row]
                g[node_dict[i]][node_dict[j]] = 1
                g[node_dict[j]][node_dict[i]] = 1
        adj = sp.csr_matrix(g)

        features = np.identity(399)
        features = sp.csr_matrix(features)

        labels = np.array(labels, dtype='int8')

        return adj, features, labels

    def load_brazil(self):
        dataset = 'brazil'
        node_dict = {}
        f = np.loadtxt('%s/%s_lable.txt' % (dataset, dataset))
        id = f[:, 0]
        labels = f[:, 1]
        for i in range(len(id)):
            node_dict[id[i]] = i
        g = np.zeros((131, 131))
        with open('%s/%s_A.txt' % (dataset, dataset), 'r') as f:
            for _ in range(1074):
                row = f.readline().strip().split()
                i, j = [int(w) for w in row]
                g[node_dict[i]][node_dict[j]] = 1
                g[node_dict[j]][node_dict[i]] = 1
        adj = sp.csr_matrix(g)

        features = np.identity(131)
        features = sp.csr_matrix(features)

        labels = np.array(labels, dtype='int8')

        return adj, features, labels

    def load_blog(self):
        data = sio.loadmat('blogcatalog.mat')
        adj = data['network']
        lable = data['group'].todense()
        labels = np.array(np.argmax(lable, 1).squeeze(1)).squeeze()

        features = np.identity(10312)
        features = sp.csr_matrix(features)

        labels = np.array(labels, dtype='int8')

        return adj, features, labels

    def get_adj(self):
        adj, features, labels = self.load_npz(self.data_filename)
        adj = adj + adj.T
        adj = adj.tolil()
        adj[adj > 1] = 1

        if self.require_lcc:
            lcc = self.largest_connected_components(adj)
            adj = adj[lcc][:, lcc]
            features = features[lcc]
            labels = labels[lcc]
            assert adj.sum(0).A1.min() > 0, "Graph contains singleton nodes"

        # whether to set diag=0?
        adj.setdiag(0)
        adj = adj.astype("float32").tocsr()
        adj.eliminate_zeros()

        assert np.abs(adj - adj.T).sum() == 0, "Input graph is not symmetric"
        assert adj.max() == 1 and len(np.unique(adj[adj.nonzero()].A1)) == 1, "Graph must be unweighted"

        return adj, features, labels

    def load_npz(self, file_name, is_sparse=True):
        with np.load(file_name) as loader:
            # loader = dict(loader)
            if is_sparse:
                adj = sp.csr_matrix((loader['adj_data'], loader['adj_indices'],
                                            loader['adj_indptr']), shape=loader['adj_shape'])
                if 'attr_data' in loader:
                    features = sp.csr_matrix((loader['attr_data'], loader['attr_indices'],
                                                 loader['attr_indptr']), shape=loader['attr_shape'])
                else:
                    features = None
                labels = loader.get('labels')
            else:
                adj = loader['adj_data']
                if 'attr_data' in loader:
                    features = loader['attr_data']
                else:
                    features = None
                labels = loader.get('labels')
        if features is None:
            features = np.eye(adj.shape[0])
        features = sp.csr_matrix(features, dtype=np.float32)
        return adj, features, labels

    def largest_connected_components(self, adj, n_components=1):
        """Select k largest connected components.

		Parameters
		----------
		adj : scipy.sparse.csr_matrix
			input adjacency matrix
		n_components : int
			n largest connected components we want to select
		"""

        _, component_indices = sp.csgraph.connected_components(adj)
        component_sizes = np.bincount(component_indices)
        components_to_keep = np.argsort(component_sizes)[::-1][:n_components]  # reverse order to sort descending
        nodes_to_keep = [
            idx for (idx, component) in enumerate(component_indices) if component in components_to_keep]
        print("Selecting {0} largest connected components".format(n_components))
        return nodes_to_keep

    def __repr__(self):
        return '{0}(adj_shape={1}, feature_shape={2})'.format(self.name, self.adj.shape, self.features.shape)

    def get_mask(self):
        idx_train, idx_val, idx_test = self.idx_train, self.idx_val, self.idx_test
        labels = self.onehot(self.labels)

        def get_mask(idx):
            mask = np.zeros(labels.shape[0], dtype=np.bool)
            mask[idx] = 1
            return mask

        def get_y(idx):
            mx = np.zeros(labels.shape)
            mx[idx] = labels[idx]
            return mx

        self.train_mask = get_mask(self.idx_train)
        self.val_mask = get_mask(self.idx_val)
        self.test_mask = get_mask(self.idx_test)
        self.y_train, self.y_val, self.y_test = get_y(idx_train), get_y(idx_val), get_y(idx_test)

    def onehot(self, labels):
        eye = np.identity(labels.max() + 1)
        onehot_mx = eye[labels]
        return onehot_mx

    def init_matrix(self, adj):
        n = adj.shape[0]
        result = np.zeros((n, n))

        return sp.csr_matrix(result)


def parse_index_file(filename):
    index = []
    for line in open(filename):
        index.append(int(line.strip()))
    return index