# Source code for deeprobust.graph.data.dataset

    import numpy as np
    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 deeprobust.graph.utils import get_train_val_test, get_train_val_test_gcn
    import zipfile
    import json

    class Dataset():
        """Dataset class contains four citation network datasets "cora", "cora-ml", "citeseer" and "pubmed",
        and one blog dataset "Polblogs". Datasets "ACM", "BlogCatalog", "Flickr", "UAI",
        "Flickr" are also available. See more details in https://github.com/DSE-MSU/DeepRobust/tree/master/deeprobust/graph#supported-datasets.
        The 'cora', 'cora-ml', 'polblogs' 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 : string
            root directory where the dataset should be saved.
        name : string
            dataset name, it can be chosen from ['cora', 'citeseer', 'cora_ml', 'polblogs',
            'pubmed', 'acm', 'blogcatalog', 'uai', 'flickr']
        setting : string
            there are two data splits settings. It can be chosen from ['nettack', 'gcn', 'prognn']
            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 the full graph and 20 samples
            in each class for traing, 500 nodes for validation, and 1000
            nodes for test. (Note here 'netack' and 'gcn' setting do not provide fixed split, i.e.,
            different random seed would return different data splits)
        seed : int
            random seed for splitting training/validation/test.
        require_mask : bool
            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', seed=15)
    	>>> 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', 'acm', 'blogcatalog', 'uai', 'flickr'], \
                    'Currently only support cora, citeseer, cora_ml, ' + \
                    'polblogs, pubmed, acm, blogcatalog, flickr'
            assert self.setting in ['gcn', 'nettack', 'prognn'], "Settings should be" + \
                            " choosen from ['gcn', 'nettack', 'prognn']"

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

            if setting == 'prognn':
                assert name in ['cora', 'citeseer', 'pubmed', 'cora_ml'], "ProGNN splits only " + \
                            "cora, citeseer, pubmed, cora_ml"
                self.idx_train, self.idx_val, self.idx_test = self.get_prognn_splits()
            else:
                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 get_prognn_splits(self):
            """Get target nodes incides, which is the nodes with degree > 10 in the test set."""
            url = 'https://raw.githubusercontent.com/ChandlerBang/Pro-GNN/' + \
                         'master/splits/{}_prognn_splits.json'.format(self.name)
            json_file = osp.join(self.root,
                    '{}_prognn_splits.json'.format(self.name))

            if not osp.exists(json_file):
                self.download_file(url, json_file)
            # with open(f'/mnt/home/jinwei2/Projects/nettack/{dataset}_nettacked_nodes.json', 'r') as f:
            with open(json_file, 'r') as f:
                idx = json.loads(f.read())
            return np.array(idx['idx_train']), \
                   np.array(idx['idx_val']), np.array(idx['idx_test'])

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

            if self.name in ['acm', 'blogcatalog', 'uai', 'flickr']:
                return self.load_zip()

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

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

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

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

        def download_zip(self, name):
            url = 'https://raw.githubusercontent.com/ChandlerBang/Pro-GNN/master/other_datasets/{}.zip'.\
                    format(name)
            try:
                print('Downlading', url)
                urllib.request.urlretrieve(url, osp.join(self.root, name+'.zip'))
                print('Done!')
            except:
                raise Exception('''Download failed! Make sure you have stable Internet connection and enter the right name''')

        def load_zip(self):
            data_filename = self.data_folder + '.zip'
            name = self.name
            if not osp.exists(data_filename):
                self.download_zip(name)
                with zipfile.ZipFile(data_filename, 'r') as zip_ref:
                    zip_ref.extractall(self.root)

            feature_path = osp.join(self.data_folder, '{0}.feature'.format(name))
            label_path = osp.join(self.data_folder, '{0}.label'.format(name))
            graph_path = osp.join(self.data_folder, '{0}.edge'.format(name))

            f = np.loadtxt(feature_path, dtype = float)
            l = np.loadtxt(label_path, dtype = int)
            features = sp.csr_matrix(f, dtype=np.float32)
            # features = torch.FloatTensor(np.array(features.todense()))
            struct_edges = np.genfromtxt(graph_path, dtype=np.int32)
            sedges = np.array(list(struct_edges), dtype=np.int32).reshape(struct_edges.shape)
            n = features.shape[0]
            sadj = sp.coo_matrix((np.ones(sedges.shape[0]), (sedges[:, 0], sedges[:, 1])), shape=(n, n), dtype=np.float32)
            sadj = sadj + sadj.T.multiply(sadj.T > sadj) - sadj.multiply(sadj.T > sadj)
            label = np.array(l)

            return sadj, features, label

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

    if __name__ == '__main__':
        from deeprobust.graph.data import Dataset
        for name in ['cora', 'citeseer', 'pubmed', 'cora_ml']:
            data = Dataset(root='/tmp/', name=name, setting="prognn")
            idx_train = data.idx_train
            data2 = Dataset(root='/tmp/', name=name, setting="nettack", seed=15)
            idx_train2 = data2.idx_train
            assert (idx_train != idx_train2).sum() == 0

        data = Dataset(root='/tmp/', name='flickr')
        adj, features, labels = data.adj, data.features, data.labels
        idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test