Python networkx.read_weighted_edgelist方法代碼示例

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import read_weighted_edgelist [as 別名]
def read_graph(filename,g_type):
	with open('data/'+filename,'rb') as f:
		if g_type == "undirected":
			G = nx.read_weighted_edgelist(f)
  		else:
			G = nx.read_weighted_edgelist(f,create_using=nx.DiGraph())
		node_idx = G.nodes()
	adj_matrix = np.asarray(nx.adjacency_matrix(G, nodelist=None,weight='weight').todense())	
	return adj_matrix, node_idx 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import read_weighted_edgelist [as 別名]
def test_read_edgelist_2(self):
        s = b"""\
# comment line
1 2 2.0
# comment line
2 3 3.0
"""
        bytesIO = io.BytesIO(s)
        G = nx.read_edgelist(bytesIO,nodetype=int,data=False)
        assert_edges_equal(G.edges(),[(1,2),(2,3)])

        bytesIO = io.BytesIO(s)
        G = nx.read_weighted_edgelist(bytesIO,nodetype=int)
        assert_edges_equal(G.edges(data=True),
                            [(1,2,{'weight':2.0}),(2,3,{'weight':3.0})]) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import read_weighted_edgelist [as 別名]
def read_for_SVD(filename, weighted=False):
    if weighted:
        G = nx.read_weighted_edgelist(filename)
    else:
        G = nx.read_edgelist(filename)
    return G 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import read_weighted_edgelist [as 別名]
def split_train_test_graph(input_edgelist, seed, testing_ratio=0.2, weighted=False):
    
    if (weighted):
        G = nx.read_weighted_edgelist(input_edgelist)
    else:
        G = nx.read_edgelist(input_edgelist)
    node_num1, edge_num1 = len(G.nodes), len(G.edges)
    print('Original Graph: nodes:', node_num1, 'edges:', edge_num1)
    testing_edges_num = int(len(G.edges) * testing_ratio)
    random.seed(seed)
    testing_pos_edges = random.sample(G.edges, testing_edges_num)
    G_train = copy.deepcopy(G)
    for edge in testing_pos_edges:
        node_u, node_v = edge
        if (G_train.degree(node_u) > 1 and G_train.degree(node_v) > 1):
            G_train.remove_edge(node_u, node_v)

    G_train.remove_nodes_from(nx.isolates(G_train))
    node_num2, edge_num2 = len(G_train.nodes), len(G_train.edges)
    assert node_num1 == node_num2
    train_graph_filename = 'graph_train.edgelist'
    if weighted:
        nx.write_edgelist(G_train, train_graph_filename, data=['weight'])
    else:
        nx.write_edgelist(G_train, train_graph_filename, data=False)

    node_num1, edge_num1 = len(G_train.nodes), len(G_train.edges)
    print('Training Graph: nodes:', node_num1, 'edges:', edge_num1)
    return G, G_train, testing_pos_edges, train_graph_filename 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import read_weighted_edgelist [as 別名]
def test_read_edgelist_2(self):
        s = b"""\
# comment line
1 2 2.0
# comment line
2 3 3.0
"""
        bytesIO = io.BytesIO(s)
        G = nx.read_edgelist(bytesIO, nodetype=int, data=False)
        assert_edges_equal(G.edges(), [(1, 2), (2, 3)])

        bytesIO = io.BytesIO(s)
        G = nx.read_weighted_edgelist(bytesIO, nodetype=int)
        assert_edges_equal(G.edges(data=True),
                           [(1, 2, {'weight': 2.0}), (2, 3, {'weight': 3.0})]) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import read_weighted_edgelist [as 別名]
def read_weighted_edgelist(path, comments="#", delimiter=None,
                           create_using=None, nodetype=None, encoding='utf-8'):

    """Read a graph as list of edges with numeric weights.

    Parameters
    ----------
    path : file or string
       File or filename to read. If a file is provided, it must be
       opened in 'rb' mode.
       Filenames ending in .gz or .bz2 will be uncompressed.
    comments : string, optional
       The character used to indicate the start of a comment.
    delimiter : string, optional
       The string used to separate values.  The default is whitespace.
    create_using : Graph container, optional,
       Use specified container to build graph.  The default is networkx.Graph,
       an undirected graph.
    nodetype : int, float, str, Python type, optional
       Convert node data from strings to specified type
    encoding: string, optional
       Specify which encoding to use when reading file.

    Returns
    -------
    G : graph
       A networkx Graph or other type specified with create_using

    Notes
    -----
    Since nodes must be hashable, the function nodetype must return hashable
    types (e.g. int, float, str, frozenset - or tuples of those, etc.)

    Example edgelist file format.

    With numeric edge data::

     # read with
     # >>> G=nx.read_weighted_edgelist(fh)
     # source target data
     a b 1
     a c 3.14159
     d e 42
    """
    return read_edgelist(path,
                         comments=comments,
                         delimiter=delimiter,
                         create_using=create_using,
                         nodetype=nodetype,
                         data=(('weight',float),),
                         encoding = encoding
                         )


# fixture for nose tests 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import read_weighted_edgelist [as 別名]
def read_weighted_edgelist(path, comments="#", delimiter=None,
                           create_using=None, nodetype=None, encoding='utf-8'):
    """Read a graph as list of edges with numeric weights.

    Parameters
    ----------
    path : file or string
       File or filename to read. If a file is provided, it must be
       opened in 'rb' mode.
       Filenames ending in .gz or .bz2 will be uncompressed.
    comments : string, optional
       The character used to indicate the start of a comment.
    delimiter : string, optional
       The string used to separate values.  The default is whitespace.
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.
    nodetype : int, float, str, Python type, optional
       Convert node data from strings to specified type
    encoding: string, optional
       Specify which encoding to use when reading file.

    Returns
    -------
    G : graph
       A networkx Graph or other type specified with create_using

    Notes
    -----
    Since nodes must be hashable, the function nodetype must return hashable
    types (e.g. int, float, str, frozenset - or tuples of those, etc.)

    Example edgelist file format.

    With numeric edge data::

     # read with
     # >>> G=nx.read_weighted_edgelist(fh)
     # source target data
     a b 1
     a c 3.14159
     d e 42
    """
    return read_edgelist(path,
                         comments=comments,
                         delimiter=delimiter,
                         create_using=create_using,
                         nodetype=nodetype,
                         data=(('weight', float),),
                         encoding=encoding
                         )


# fixture for nose tests