Source code for networkx.linalg.attrmatrix

"""
    Functions for constructing matrix-like objects from graph attributes.
"""

__all__ = ['attr_matrix', 'attr_sparse_matrix']

import networkx as nx


def _node_value(G, node_attr):
    """Returns a function that returns a value from G.nodes[u].

    We return a function expecting a node as its sole argument. Then, in the
    simplest scenario, the returned function will return G.nodes[u][node_attr].
    However, we also handle the case when `node_attr` is None or when it is a
    function itself.

    Parameters
    ----------
    G : graph
        A NetworkX graph

    node_attr : {None, str, callable}
        Specification of how the value of the node attribute should be obtained
        from the node attribute dictionary.

    Returns
    -------
    value : function
        A function expecting a node as its sole argument. The function will
        returns a value from G.nodes[u] that depends on `edge_attr`.

    """
    if node_attr is None:
        def value(u): return u
    elif not hasattr(node_attr, '__call__'):
        # assume it is a key for the node attribute dictionary
        def value(u): return G.nodes[u][node_attr]
    else:
        # Advanced:  Allow users to specify something else.
        #
        # For example,
        #     node_attr = lambda u: G.nodes[u].get('size', .5) * 3
        #
        value = node_attr

    return value


def _edge_value(G, edge_attr):
    """Returns a function that returns a value from G[u][v].

    Suppose there exists an edge between u and v.  Then we return a function
    expecting u and v as arguments.  For Graph and DiGraph, G[u][v] is
    the edge attribute dictionary, and the function (essentially) returns
    G[u][v][edge_attr].  However, we also handle cases when `edge_attr` is None
    and when it is a function itself. For MultiGraph and MultiDiGraph, G[u][v]
    is a dictionary of all edges between u and v.  In this case, the returned
    function sums the value of `edge_attr` for every edge between u and v.

    Parameters
    ----------
    G : graph
       A NetworkX graph

    edge_attr : {None, str, callable}
        Specification of how the value of the edge attribute should be obtained
        from the edge attribute dictionary, G[u][v].  For multigraphs, G[u][v]
        is a dictionary of all the edges between u and v.  This allows for
        special treatment of multiedges.

    Returns
    -------
    value : function
        A function expecting two nodes as parameters. The nodes should
        represent the from- and to- node of an edge. The function will
        return a value from G[u][v] that depends on `edge_attr`.

    """

    if edge_attr is None:
        # topological count of edges

        if G.is_multigraph():
            def value(u, v): return len(G[u][v])
        else:
            def value(u, v): return 1

    elif not hasattr(edge_attr, '__call__'):
        # assume it is a key for the edge attribute dictionary

        if edge_attr == 'weight':
            # provide a default value
            if G.is_multigraph():
                def value(u, v): return sum([d.get(edge_attr, 1) for d in G[u][v].values()])
            else:
                def value(u, v): return G[u][v].get(edge_attr, 1)
        else:
            # otherwise, the edge attribute MUST exist for each edge
            if G.is_multigraph():
                def value(u, v): return sum([d[edge_attr] for d in G[u][v].values()])
            else:
                def value(u, v): return G[u][v][edge_attr]

    else:
        # Advanced:  Allow users to specify something else.
        #
        # Alternative default value:
        #     edge_attr = lambda u,v: G[u][v].get('thickness', .5)
        #
        # Function on an attribute:
        #     edge_attr = lambda u,v: abs(G[u][v]['weight'])
        #
        # Handle Multi(Di)Graphs differently:
        #     edge_attr = lambda u,v: numpy.prod([d['size'] for d in G[u][v].values()])
        #
        # Ignore multiple edges
        #     edge_attr = lambda u,v: 1 if len(G[u][v]) else 0
        #
        value = edge_attr

    return value


[docs]def attr_matrix(G, edge_attr=None, node_attr=None, normalized=False, rc_order=None, dtype=None, order=None): """Returns a NumPy matrix using attributes from G. If only `G` is passed in, then the adjacency matrix is constructed. Let A be a discrete set of values for the node attribute `node_attr`. Then the elements of A represent the rows and columns of the constructed matrix. Now, iterate through every edge e=(u,v) in `G` and consider the value of the edge attribute `edge_attr`. If ua and va are the values of the node attribute `node_attr` for u and v, respectively, then the value of the edge attribute is added to the matrix element at (ua, va). Parameters ---------- G : graph The NetworkX graph used to construct the NumPy matrix. edge_attr : str, optional Each element of the matrix represents a running total of the specified edge attribute for edges whose node attributes correspond to the rows/cols of the matirx. The attribute must be present for all edges in the graph. If no attribute is specified, then we just count the number of edges whose node attributes correspond to the matrix element. node_attr : str, optional Each row and column in the matrix represents a particular value of the node attribute. The attribute must be present for all nodes in the graph. Note, the values of this attribute should be reliably hashable. So, float values are not recommended. If no attribute is specified, then the rows and columns will be the nodes of the graph. normalized : bool, optional If True, then each row is normalized by the summation of its values. rc_order : list, optional A list of the node attribute values. This list specifies the ordering of rows and columns of the array. If no ordering is provided, then the ordering will be random (and also, a return value). Other Parameters ---------------- dtype : NumPy data-type, optional A valid NumPy dtype used to initialize the array. Keep in mind certain dtypes can yield unexpected results if the array is to be normalized. The parameter is passed to numpy.zeros(). If unspecified, the NumPy default is used. order : {'C', 'F'}, optional Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory. This parameter is passed to numpy.zeros(). If unspecified, the NumPy default is used. Returns ------- M : NumPy matrix The attribute matrix. ordering : list If `rc_order` was specified, then only the matrix is returned. However, if `rc_order` was None, then the ordering used to construct the matrix is returned as well. Examples -------- Construct an adjacency matrix: >>> import numpy as np >>> try: ... np.set_printoptions(legacy="1.13") ... except TypeError: ... pass >>> G = nx.Graph() >>> G.add_edge(0, 1, thickness=1, weight=3) >>> G.add_edge(0, 2, thickness=2) >>> G.add_edge(1, 2, thickness=3) >>> nx.attr_matrix(G, rc_order=[0, 1, 2]) matrix([[ 0., 1., 1.], [ 1., 0., 1.], [ 1., 1., 0.]]) Alternatively, we can obtain the matrix describing edge thickness. >>> nx.attr_matrix(G, edge_attr='thickness', rc_order=[0, 1, 2]) matrix([[ 0., 1., 2.], [ 1., 0., 3.], [ 2., 3., 0.]]) We can also color the nodes and ask for the probability distribution over all edges (u,v) describing: Pr(v has color Y | u has color X) >>> G.nodes[0]['color'] = 'red' >>> G.nodes[1]['color'] = 'red' >>> G.nodes[2]['color'] = 'blue' >>> rc = ['red', 'blue'] >>> nx.attr_matrix(G, node_attr='color', normalized=True, rc_order=rc) matrix([[ 0.33333333, 0.66666667], [ 1. , 0. ]]) For example, the above tells us that for all edges (u,v): Pr( v is red | u is red) = 1/3 Pr( v is blue | u is red) = 2/3 Pr( v is red | u is blue) = 1 Pr( v is blue | u is blue) = 0 Finally, we can obtain the total weights listed by the node colors. >>> nx.attr_matrix(G, edge_attr='weight', node_attr='color', rc_order=rc) matrix([[ 3., 2.], [ 2., 0.]]) Thus, the total weight over all edges (u,v) with u and v having colors: (red, red) is 3 # the sole contribution is from edge (0,1) (red, blue) is 2 # contributions from edges (0,2) and (1,2) (blue, red) is 2 # same as (red, blue) since graph is undirected (blue, blue) is 0 # there are no edges with blue endpoints """ try: import numpy as np except ImportError: raise ImportError( "attr_matrix() requires numpy: http://scipy.org/ ") edge_value = _edge_value(G, edge_attr) node_value = _node_value(G, node_attr) if rc_order is None: ordering = list(set([node_value(n) for n in G])) else: ordering = rc_order N = len(ordering) undirected = not G.is_directed() index = dict(zip(ordering, range(N))) M = np.zeros((N, N), dtype=dtype, order=order) seen = set([]) for u, nbrdict in G.adjacency(): for v in nbrdict: # Obtain the node attribute values. i, j = index[node_value(u)], index[node_value(v)] if v not in seen: M[i, j] += edge_value(u, v) if undirected: M[j, i] = M[i, j] if undirected: seen.add(u) if normalized: M /= M.sum(axis=1).reshape((N, 1)) M = np.asmatrix(M) if rc_order is None: return M, ordering else: return M
[docs]def attr_sparse_matrix(G, edge_attr=None, node_attr=None, normalized=False, rc_order=None, dtype=None): """Returns a SciPy sparse matrix using attributes from G. If only `G` is passed in, then the adjacency matrix is constructed. Let A be a discrete set of values for the node attribute `node_attr`. Then the elements of A represent the rows and columns of the constructed matrix. Now, iterate through every edge e=(u,v) in `G` and consider the value of the edge attribute `edge_attr`. If ua and va are the values of the node attribute `node_attr` for u and v, respectively, then the value of the edge attribute is added to the matrix element at (ua, va). Parameters ---------- G : graph The NetworkX graph used to construct the NumPy matrix. edge_attr : str, optional Each element of the matrix represents a running total of the specified edge attribute for edges whose node attributes correspond to the rows/cols of the matirx. The attribute must be present for all edges in the graph. If no attribute is specified, then we just count the number of edges whose node attributes correspond to the matrix element. node_attr : str, optional Each row and column in the matrix represents a particular value of the node attribute. The attribute must be present for all nodes in the graph. Note, the values of this attribute should be reliably hashable. So, float values are not recommended. If no attribute is specified, then the rows and columns will be the nodes of the graph. normalized : bool, optional If True, then each row is normalized by the summation of its values. rc_order : list, optional A list of the node attribute values. This list specifies the ordering of rows and columns of the array. If no ordering is provided, then the ordering will be random (and also, a return value). Other Parameters ---------------- dtype : NumPy data-type, optional A valid NumPy dtype used to initialize the array. Keep in mind certain dtypes can yield unexpected results if the array is to be normalized. The parameter is passed to numpy.zeros(). If unspecified, the NumPy default is used. Returns ------- M : SciPy sparse matrix The attribute matrix. ordering : list If `rc_order` was specified, then only the matrix is returned. However, if `rc_order` was None, then the ordering used to construct the matrix is returned as well. Examples -------- Construct an adjacency matrix: >>> import numpy as np >>> try: ... np.set_printoptions(legacy="1.13") ... except TypeError: ... pass >>> G = nx.Graph() >>> G.add_edge(0,1,thickness=1,weight=3) >>> G.add_edge(0,2,thickness=2) >>> G.add_edge(1,2,thickness=3) >>> M = nx.attr_sparse_matrix(G, rc_order=[0,1,2]) >>> M.todense() matrix([[ 0., 1., 1.], [ 1., 0., 1.], [ 1., 1., 0.]]) Alternatively, we can obtain the matrix describing edge thickness. >>> M = nx.attr_sparse_matrix(G, edge_attr='thickness', rc_order=[0,1,2]) >>> M.todense() matrix([[ 0., 1., 2.], [ 1., 0., 3.], [ 2., 3., 0.]]) We can also color the nodes and ask for the probability distribution over all edges (u,v) describing: Pr(v has color Y | u has color X) >>> G.nodes[0]['color'] = 'red' >>> G.nodes[1]['color'] = 'red' >>> G.nodes[2]['color'] = 'blue' >>> rc = ['red', 'blue'] >>> M = nx.attr_sparse_matrix(G, node_attr='color', \ normalized=True, rc_order=rc) >>> M.todense() matrix([[ 0.33333333, 0.66666667], [ 1. , 0. ]]) For example, the above tells us that for all edges (u,v): Pr( v is red | u is red) = 1/3 Pr( v is blue | u is red) = 2/3 Pr( v is red | u is blue) = 1 Pr( v is blue | u is blue) = 0 Finally, we can obtain the total weights listed by the node colors. >>> M = nx.attr_sparse_matrix(G, edge_attr='weight',\ node_attr='color', rc_order=rc) >>> M.todense() matrix([[ 3., 2.], [ 2., 0.]]) Thus, the total weight over all edges (u,v) with u and v having colors: (red, red) is 3 # the sole contribution is from edge (0,1) (red, blue) is 2 # contributions from edges (0,2) and (1,2) (blue, red) is 2 # same as (red, blue) since graph is undirected (blue, blue) is 0 # there are no edges with blue endpoints """ try: import numpy as np from scipy import sparse except ImportError: raise ImportError( "attr_sparse_matrix() requires scipy: http://scipy.org/ ") edge_value = _edge_value(G, edge_attr) node_value = _node_value(G, node_attr) if rc_order is None: ordering = list(set([node_value(n) for n in G])) else: ordering = rc_order N = len(ordering) undirected = not G.is_directed() index = dict(zip(ordering, range(N))) M = sparse.lil_matrix((N, N), dtype=dtype) seen = set([]) for u, nbrdict in G.adjacency(): for v in nbrdict: # Obtain the node attribute values. i, j = index[node_value(u)], index[node_value(v)] if v not in seen: M[i, j] += edge_value(u, v) if undirected: M[j, i] = M[i, j] if undirected: seen.add(u) if normalized: norms = np.asarray(M.sum(axis=1)).ravel() for i, norm in enumerate(norms): M[i, :] /= norm if rc_order is None: return M, ordering else: return M
# fixture for nose tests def setup_module(module): from nose import SkipTest try: import numpy except: raise SkipTest("NumPy not available") try: import scipy except: raise SkipTest("SciPy not available")