Vehicle-cpp/hkpc/software/networkx/linalg/attrmatrix.py

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

__all__ = ["attr_matrix", "attr_sparse_matrix"]


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 callable(node_attr):
        # 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 callable(edge_attr):
        # 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


def attr_matrix(
    G,
    edge_attr=None,
    node_attr=None,
    normalized=False,
    rc_order=None,
    dtype=None,
    order=None,
):
    """Returns the attribute matrix using attributes from `G` as a numpy array.

    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 attribute 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 matrix. 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 : 2D NumPy ndarray
        The attribute matrix.

    ordering : list
        If `rc_order` was specified, then only the attribute 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:

    >>> 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])
    array([[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])
    array([[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)
    array([[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)
    array([[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

    """
    import numpy as np

    edge_value = _edge_value(G, edge_attr)
    node_value = _node_value(G, node_attr)

    if rc_order is None:
        ordering = list({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))

    if rc_order is None:
        return M, ordering
    else:
        return M


def attr_sparse_matrix(
    G, edge_attr=None, node_attr=None, normalized=False, rc_order=None, dtype=None
):
    """Returns a SciPy sparse array 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 matrix. 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 array
        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:

    >>> 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.toarray()
    array([[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.toarray()
    array([[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.toarray()
    array([[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.toarray()
    array([[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

    """
    import numpy as np
    import scipy as sp
    import scipy.sparse  # call as sp.sparse

    edge_value = _edge_value(G, edge_attr)
    node_value = _node_value(G, node_attr)

    if rc_order is None:
        ordering = list({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 = sp.sparse.lil_array((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:
        M *= 1 / M.sum(axis=1)[:, np.newaxis]  # in-place mult preserves sparse

    if rc_order is None:
        return M, ordering
    else:
        return M