Python networkx.convert_node_labels_to_integers方法代碼示例

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def __getitem__(self, item):
    """
    Returns an rdkit mol object
    :param item:
    :return:
    """
    smiles = self.df['smiles'][item]
    mol = Chem.MolFromSmiles(smiles)
    return mol

# # TESTS
# path = 'gdb13.rand1M.smi.gz'
# dataset = gdb_dataset(path)
#
# print(len(dataset))
# mol,_ = dataset[0]
# graph = mol_to_nx(mol)
# graph_sub = graph.subgraph([0,3,5,7,9])
# graph_sub_new = nx.convert_node_labels_to_integers(graph_sub,label_attribute='old')
# graph_sub_node = graph_sub.nodes()
# graph_sub_new_node = graph_sub_new.nodes()
# matrix = nx.adjacency_matrix(graph_sub)
# np_matrix = matrix.toarray()
# print(np_matrix)
# print('end') 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def _get_9q_square_qvm(
    name: str, noisy: bool, connection: Optional[ForestConnection] = None, qvm_type: str = "qvm"
) -> QuantumComputer:
    """
    A nine-qubit 3x3 square lattice.

    This uses a "generic" lattice not tied to any specific device. 9 qubits is large enough
    to do vaguely interesting algorithms and small enough to simulate quickly.

    :param name: The name of this QVM
    :param connection: The connection to use to talk to external services
    :param noisy: Whether to construct a noisy quantum computer
    :param qvm_type: The type of QVM. Either 'qvm' or 'pyqvm'.
    :return: A pre-configured QuantumComputer
    """
    topology = nx.convert_node_labels_to_integers(nx.grid_2d_graph(3, 3))
    return _get_qvm_with_topology(
        name=name,
        connection=connection,
        topology=topology,
        noisy=noisy,
        requires_executable=True,
        qvm_type=qvm_type,
    ) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def load_nofeatures(dataset, version, n = None):
    '''
    Loads a dataset that is just an edgelist, creating sparse one-hot features. 

    n: total number of nodes in the graph. This is the number of nodes present
    in the edgelist unless otherwise specified    
    '''
#    g = nx.read_edgelist('data/{}/{}{}.txt'.format(dataset, dataset, version))
#    g = nx.convert_node_labels_to_integers(g)
#    edges = np.array([(x[0], x[1]) for x in nx.to_edgelist(g)])
    edges = np.loadtxt('data/{}/{}{}.cites'.format(dataset, dataset, version), dtype=np.int)
    if n == None:
        n = int(edges.max()) + 1
    adj = make_normalized_adj(torch.tensor(edges).long(), n)
    features = torch.eye(n).to_sparse()
    return adj, features, None 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def tensorize_graph(graph_batch, vocab):
        fnode,fmess = [None],[(0,0,0,0)] 
        agraph,bgraph = [[]], [[]] 
        scope = []
        edge_dict = {}
        all_G = []

        for bid,G in enumerate(graph_batch):
            offset = len(fnode)
            scope.append( (offset, len(G)) )
            G = nx.convert_node_labels_to_integers(G, first_label=offset)
            all_G.append(G)
            fnode.extend( [None for v in G.nodes] )

            for v, attr in G.nodes(data='label'):
                G.nodes[v]['batch_id'] = bid
                fnode[v] = vocab[attr]
                agraph.append([])

            for u, v, attr in G.edges(data='label'):
                if type(attr) is tuple:
                    fmess.append( (u, v, attr[0], attr[1]) )
                else:
                    fmess.append( (u, v, attr, 0) )
                edge_dict[(u, v)] = eid = len(edge_dict) + 1
                G[u][v]['mess_idx'] = eid
                agraph[v].append(eid)
                bgraph.append([])

            for u, v in G.edges:
                eid = edge_dict[(u, v)]
                for w in G.predecessors(u):
                    if w == v: continue
                    bgraph[eid].append( edge_dict[(w, u)] )

        fnode[0] = fnode[1]
        fnode = torch.IntTensor(fnode)
        fmess = torch.IntTensor(fmess)
        agraph = create_pad_tensor(agraph)
        bgraph = create_pad_tensor(bgraph)
        return (fnode, fmess, agraph, bgraph, scope), nx.union_all(all_G) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def __getitem__(self, index):

        #TODO: Manually have to check the convert_node_labels_to_integers function
        g = nx.convert_node_labels_to_integers(nx.read_graphml(os.path.join(self.root, self.ids[index])))

        target = self.classes[index]

        h = self.vertex_transform(g)

        g, e = self.edge_transform(g)

        target = self.target_transform(target)

        return (g, h, e), target 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def from_networkx(G):
    r"""Converts a :obj:`networkx.Graph` or :obj:`networkx.DiGraph` to a
    :class:`torch_geometric.data.Data` instance.

    Args:
        G (networkx.Graph or networkx.DiGraph): A networkx graph.
    """

    G = nx.convert_node_labels_to_integers(G)
    G = G.to_directed() if not nx.is_directed(G) else G
    edge_index = torch.tensor(list(G.edges)).t().contiguous()

    data = {}

    for i, (_, feat_dict) in enumerate(G.nodes(data=True)):
        for key, value in feat_dict.items():
            data[key] = [value] if i == 0 else data[key] + [value]

    for i, (_, _, feat_dict) in enumerate(G.edges(data=True)):
        for key, value in feat_dict.items():
            data[key] = [value] if i == 0 else data[key] + [value]

    for key, item in data.items():
        try:
            data[key] = torch.tensor(item)
        except ValueError:
            pass

    data['edge_index'] = edge_index.view(2, -1)
    data = torch_geometric.data.Data.from_dict(data)
    data.num_nodes = G.number_of_nodes()

    return data 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def _gen_hypercube(self, n):
        for _ in range(n):
            num_v = np.random.randint(self.min_num_v, self.max_num_v)
            g = nx.hypercube_graph(int(math.log(num_v, 2)))
            g = nx.convert_node_labels_to_integers(g)
            self.graphs.append(g)
            self.labels.append(4) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def _gen_grid(self, n):
        for _ in range(n):
            num_v = np.random.randint(self.min_num_v, self.max_num_v)
            assert num_v >= 4, 'We require a grid graph to contain at least two ' \
                                   'rows and two columns, thus 4 nodes, got {:d} ' \
                                   'nodes'.format(num_v)
            n_rows = np.random.randint(2, num_v // 2)
            n_cols = num_v // n_rows
            g = nx.grid_graph([n_rows, n_cols])
            g = nx.convert_node_labels_to_integers(g)
            self.graphs.append(g)
            self.labels.append(5) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def compile(self, seq, registers):
        """Class-specific circuit compilation method.

        If additional compilation logic is required, child classes can redefine this method.

        Args:
            seq (Sequence[Command]): quantum circuit to modify
            registers (Sequence[RegRefs]): quantum registers
        Returns:
            List[Command]: modified circuit
        Raises:
            CircuitError: the given circuit cannot be validated to belong to this circuit class
        """
        # registers is not used here, but may be used if the method is overwritten pylint: disable=unused-argument
        if self.graph is not None:
            # check topology
            DAG = pu.list_to_DAG(seq)

            # relabel the DAG nodes to integers, with attributes
            # specifying the operation name. This allows them to be
            # compared, rather than using Command objects.
            mapping = {i: n.op.__class__.__name__ for i, n in enumerate(DAG.nodes())}
            circuit = nx.convert_node_labels_to_integers(DAG)
            nx.set_node_attributes(circuit, mapping, name="name")

            def node_match(n1, n2):
                """Returns True if both nodes have the same name"""
                return n1["name"] == n2["name"]

            # check if topology matches
            if not nx.is_isomorphic(circuit, self.graph, node_match):
                # TODO: try and compile the program to match the topology
                # TODO: add support for parameter range matching/compilation
                raise pu.CircuitError(
                    "Program cannot be used with the CircuitSpec '{}' "
                    "due to incompatible topology.".format(self.short_name)
                )

        return seq 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def citeseer_ego():
    _, _, G = data.Graph_load(dataset='citeseer')
    G = max(nx.connected_component_subgraphs(G), key=len)
    G = nx.convert_node_labels_to_integers(G)
    graphs = []
    for i in range(G.number_of_nodes()):
        G_ego = nx.ego_graph(G, i, radius=3)
        if G_ego.number_of_nodes() >= 50 and (G_ego.number_of_nodes() <= 400):
            graphs.append(G_ego)
    return graphs 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def load_graph_list(fname,is_real=True):
    with open(fname, "rb") as f:
        graph_list = pickle.load(f)
    for i in range(len(graph_list)):
        edges_with_selfloops = graph_list[i].selfloop_edges()
        if len(edges_with_selfloops)>0:
            graph_list[i].remove_edges_from(edges_with_selfloops)
        if is_real:
            graph_list[i] = max(nx.connected_component_subgraphs(graph_list[i]), key=len)
            graph_list[i] = nx.convert_node_labels_to_integers(graph_list[i])
        else:
            graph_list[i] = pick_connected_component_new(graph_list[i])
    return graph_list 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def grid(start, dim=2, role_start=0):
    """ Builds a 2by2 grid
    """
    grid_G = nx.grid_graph([dim, dim])
    grid_G = nx.convert_node_labels_to_integers(grid_G, first_label=start)
    roles = [role_start for i in grid_G.nodes()]
    return grid_G, roles 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def plain_graph_generation(qcs, acs, paths, rels):
    global cpnet, concept2id, relation2id, id2relation, id2concept, cpnet_simple
    # print("qcs", qcs)
    # print("acs", acs)
    # print("paths", paths)
    # print("rels", rels)

    graph = nx.Graph()
    for index, p in enumerate(paths):

        for c_index in range(len(p)-1):
            h = p[c_index]
            t = p[c_index+1]
            # TODO: the weight can computed by concept embeddings and relation embeddings of TransE
            graph.add_edge(h,t, weight=1.0)

    for qc1, qc2 in list(itertools.combinations(qcs, 2)):
        if cpnet_simple.has_edge(qc1, qc2):
            graph.add_edge(qc1, qc2, weight=1.0)

    for ac1, ac2 in list(itertools.combinations(acs, 2)):
        if cpnet_simple.has_edge(ac1, ac2):
            graph.add_edge(ac1, ac2, weight=1.0)

    if len(qcs) == 0:
        qcs.append(-1)

    if len(acs) == 0:
        acs.append(-1)

    if len(paths) == 0:
        for qc in qcs:
            for ac in acs:
                graph.add_edge(qc,ac, rel=-1, weight=0.1)

    g = nx.convert_node_labels_to_integers(graph, label_attribute='cid') # re-index
    g_str = json.dumps(nx.node_link_data(g))
    return g_str


# relational graph generation 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def reddit_to_networkx(dirpath):
    print("Loading graph data")
    coo_adj = scipy.sparse.load_npz(os.path.join(dirpath, edge_file_name))
    G = nx.from_scipy_sparse_matrix(coo_adj)

    print("Loading node feature and label")
    # node feature, edge label
    reddit_data = numpy.load(os.path.join(dirpath, feat_file_name))
    G.graph['x'] = reddit_data['feature'].astype(numpy.float32)
    G.graph['y'] = reddit_data['label'].astype(numpy.int32)

    G.graph['label_num'] = 41
    # G = nx.convert_node_labels_to_integers(G)
    print("Finish loading graph: {}".format(dirpath))
    return G 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import convert_node_labels_to_integers [as 別名]
def citation_to_networkx(dirpath, name):
    G = nx.Graph()
    # node feature, node label
    with open(os.path.join(dirpath, "{}.content".format(name))) as f:
        lines = f.readlines()
        compressor = {}
        acc = 0
        for line in tqdm(lines):
            lis = line.split()
            key, val = lis[0], lis[-1]
            if val in compressor:
                val = compressor[val]
            else:
                compressor[val] = acc
                val = acc
                acc += 1
            G.add_node(key,
                       x=numpy.array(lis[1:-1], dtype=numpy.float32),
                       y=val)
        G.graph['label_num'] = acc

    # edge
    with open(os.path.join(dirpath, "{}.cites".format(name))) as f:
        lines = f.readlines()
        for line in tqdm(lines):
            u, v = line.split()
            if u not in G.nodes.keys():
                print("Warning: {} does not appear in {}{}.content".format(
                    u, dirpath, name))
            elif v not in G.nodes.keys():
                print("Warning: {} does not appear in {}{}.content".format(
                    v, dirpath, name))
            else:
                G.add_edge(u, v)

    G = nx.convert_node_labels_to_integers(G)
    print("Finished loading graph: {}".format(dirpath))
    print("number of nodes: {}, number of edges: {}".format(
        G.number_of_nodes(), G.number_of_edges()
    ))
    return G