import itertools
import easygraph as eg
__all__ = [
"to_numpy_matrix",
"from_numpy_array",
"to_numpy_array",
"from_pandas_adjacency",
"from_pandas_edgelist",
"from_scipy_sparse_matrix",
"to_scipy_sparse_matrix",
"to_scipy_sparse_array",
]
[docs]def to_scipy_sparse_array(G, nodelist=None, dtype=None, weight="weight", format="csr"):
"""Returns the graph adjacency matrix as a SciPy sparse array.
Parameters
----------
G : graph
The EasyGraph graph used to construct the sparse matrix.
nodelist : list, optional
The rows and columns are ordered according to the nodes in `nodelist`.
If `nodelist` is None, then the ordering is produced by G.nodes().
dtype : NumPy data-type, optional
A valid NumPy dtype used to initialize the array. If None, then the
NumPy default is used.
weight : string or None optional (default='weight')
The edge attribute that holds the numerical value used for
the edge weight. If None then all edge weights are 1.
format : str in {'bsr', 'csr', 'csc', 'coo', 'lil', 'dia', 'dok'}
The type of the matrix to be returned (default 'csr'). For
some algorithms different implementations of sparse matrices
can perform better. See [1]_ for details.
Returns
-------
A : SciPy sparse array
Graph adjacency matrix.
Notes
-----
For directed graphs, matrix entry i,j corresponds to an edge from i to j.
The matrix entries are populated using the edge attribute held in
parameter weight. When an edge does not have that attribute, the
value of the entry is 1.
For multiple edges the matrix values are the sums of the edge weights.
When `nodelist` does not contain every node in `G`, the adjacency matrix
is built from the subgraph of `G` that is induced by the nodes in
`nodelist`.
The convention used for self-loop edges in graphs is to assign the
diagonal matrix entry value to the weight attribute of the edge
(or the number 1 if the edge has no weight attribute). If the
alternate convention of doubling the edge weight is desired the
resulting Scipy sparse matrix can be modified as follows:
>>> G = eg.Graph([(1, 1)])
>>> A = eg.to_scipy_sparse_array(G)
>>> print(A.todense())
[[1]]
>>> A.setdiag(A.diagonal() * 2)
>>> print(A.toarray())
[[2]]
Examples
--------
>>> S = eg.to_scipy_sparse_array(G, nodelist=[0, 1, 2])
>>> print(S.toarray())
[[0 2 0]
[1 0 0]
[0 0 4]]
References
----------
.. [1] Scipy Dev. References, "Sparse Matrices",
https://docs.scipy.org/doc/scipy/reference/sparse.html
"""
import scipy as sp
import scipy.sparse # call as sp.sparse
if len(G) == 0:
raise eg.EasyGraphError("Graph has no nodes or edges")
if nodelist is None:
nodelist = list(G)
nlen = len(G)
else:
nlen = len(nodelist)
if nlen == 0:
raise eg.EasyGraphError("nodelist has no nodes")
nodeset = set(G.nbunch_iter(nodelist))
if nlen != len(nodeset):
for n in nodelist:
if n not in G:
raise eg.EasyGraphError(f"Node {n} in nodelist is not in G")
raise eg.EasyGraphError("nodelist contains duplicates.")
if nlen < len(G):
G = G.subgraph(nodelist)
index = dict(zip(nodelist, range(nlen)))
# G.edges(data=weight, default=1)
coefficients = zip(
*((index[u], index[v], wt.get("weight", 1)) for u, v, wt in G.edges)
)
try:
row, col, data = coefficients
except ValueError:
# there is no edge in the subgraph
row, col, data = [], [], []
if G.is_directed():
A = sp.sparse.coo_array((data, (row, col)), shape=(nlen, nlen), dtype=dtype)
else:
# symmetrize matrix
d = data + data
r = row + col
c = col + row
# selfloop entries get double counted when symmetrizing
# so we subtract the data on the diagonal
selfloops = list(eg.selfloop_edges(G, data=weight, default=1))
if selfloops:
diag_index, diag_data = zip(*((index[u], -wt) for u, v, wt in selfloops))
d += diag_data
r += diag_index
c += diag_index
A = sp.sparse.coo_array((d, (r, c)), shape=(nlen, nlen), dtype=dtype)
try:
return A.asformat(format)
except ValueError as err:
raise eg.EasyGraphError(f"Unknown sparse matrix format: {format}") from err
[docs]def to_scipy_sparse_matrix(G, nodelist=None, dtype=None, weight="weight", format="csr"):
"""Returns the graph adjacency matrix as a SciPy sparse matrix.
Parameters
----------
G : graph
The EasyGraph graph used to construct the sparse matrix.
nodelist : list, optional
The rows and columns are ordered according to the nodes in `nodelist`.
If `nodelist` is None, then the ordering is produced by G.nodes().
dtype : NumPy data-type, optional
A valid NumPy dtype used to initialize the array. If None, then the
NumPy default is used.
weight : string or None optional (default='weight')
The edge attribute that holds the numerical value used for
the edge weight. If None then all edge weights are 1.
format : str in {'bsr', 'csr', 'csc', 'coo', 'lil', 'dia', 'dok'}
The type of the matrix to be returned (default 'csr'). For
some algorithms different implementations of sparse matrices
can perform better. See [1]_ for details.
Returns
-------
A : SciPy sparse matrix
Graph adjacency matrix.
Notes
-----
For directed graphs, matrix entry i,j corresponds to an edge from i to j.
The matrix entries are populated using the edge attribute held in
parameter weight. When an edge does not have that attribute, the
value of the entry is 1.
For multiple edges the matrix values are the sums of the edge weights.
When `nodelist` does not contain every node in `G`, the adjacency matrix
is built from the subgraph of `G` that is induced by the nodes in
`nodelist`.
The convention used for self-loop edges in graphs is to assign the
diagonal matrix entry value to the weight attribute of the edge
(or the number 1 if the edge has no weight attribute). If the
alternate convention of doubling the edge weight is desired the
resulting Scipy sparse matrix can be modified as follows:
>>> G = eg.Graph([(1, 1)])
>>> A = eg.to_scipy_sparse_matrix(G)
>>> print(A.todense())
[[1]]
>>> A.setdiag(A.diagonal() * 2)
>>> print(A.todense())
[[2]]
Examples
--------
>>> G.add_edge(1, 0)
0
>>> G.add_edge(2, 2, weight=3)
0
>>> G.add_edge(2, 2)
1
>>> S = eg.to_scipy_sparse_matrix(G, nodelist=[0, 1, 2])
>>> print(S.todense())
[[0 2 0]
[1 0 0]
[0 0 4]]
References
----------
.. [1] Scipy Dev. References, "Sparse Matrices",
https://docs.scipy.org/doc/scipy/reference/sparse.html
"""
import scipy as sp
import scipy.sparse
A = to_scipy_sparse_array(
G, nodelist=nodelist, dtype=dtype, weight=weight, format=format
)
return sp.sparse.csr_matrix(A).asformat(format)
[docs]def to_numpy_matrix(G, edge_sign=1.0, not_edge_sign=0.0):
"""
Returns the graph adjacency matrix as a NumPy matrix.
Parameters
----------
edge_sign : float
Sign for the position of matrix where there is an edge
not_edge_sign : float
Sign for the position of matrix where there is no edge
"""
import numpy as np
index_of_node = dict(zip(G.nodes, range(len(G))))
N = len(G)
M = np.full((N, N), not_edge_sign)
for u, udict in G.adj.items():
for v, data in udict.items():
M[index_of_node[u], index_of_node[v]] = edge_sign
M = np.asmatrix(M)
return M
[docs]def from_numpy_array(A, parallel_edges=False, create_using=None):
"""Returns a graph from a 2D NumPy array.
The 2D NumPy array is interpreted as an adjacency matrix for the graph.
Parameters
----------
A : a 2D numpy.ndarray
An adjacency matrix representation of a graph
parallel_edges : Boolean
If this is True, `create_using` is a multigraph, and `A` is an
integer array, then entry *(i, j)* in the array is interpreted as the
number of parallel edges joining vertices *i* and *j* in the graph.
If it is False, then the entries in the array are interpreted as
the weight of a single edge joining the vertices.
create_using : EasyGraph graph constructor, optional (default=eg.Graph)
Graph type to create. If graph instance, then cleared before populated.
Notes
-----
For directed graphs, explicitly mention create_using=eg.DiGraph,
and entry i,j of A corresponds to an edge from i to j.
If `create_using` is :class:`easygraph.MultiGraph` or
:class:`easygraph.MultiDiGraph`, `parallel_edges` is True, and the
entries of `A` are of type :class:`int`, then this function returns a
multigraph (of the same type as `create_using`) with parallel edges.
If `create_using` indicates an undirected multigraph, then only the edges
indicated by the upper triangle of the array `A` will be added to the
graph.
If the NumPy array has a single data type for each array entry it
will be converted to an appropriate Python data type.
If the NumPy array has a user-specified compound data type the names
of the data fields will be used as attribute keys in the resulting
EasyGraph graph.
See Also
--------
to_numpy_array
Examples
--------
Simple integer weights on edges:
>>> import numpy as np
>>> A = np.array([[1, 1], [2, 1]])
>>> G = eg.from_numpy_array(A)
>>> G.edges(data=True)
EdgeDataView([(0, 0, {'weight': 1}), (0, 1, {'weight': 2}), (1, 1, {'weight': 1})])
If `create_using` indicates a multigraph and the array has only integer
entries and `parallel_edges` is False, then the entries will be treated
as weights for edges joining the nodes (without creating parallel edges):
>>> A = np.array([[1, 1], [1, 2]])
>>> G = eg.from_numpy_array(A, create_using=eg.MultiGraph)
>>> G[1][1]
AtlasView({0: {'weight': 2}})
If `create_using` indicates a multigraph and the array has only integer
entries and `parallel_edges` is True, then the entries will be treated
as the number of parallel edges joining those two vertices:
>>> A = np.array([[1, 1], [1, 2]])
>>> temp = eg.MultiGraph()
>>> G = eg.from_numpy_array(A, parallel_edges=True, create_using=temp)
>>> G[1][1]
AtlasView({0: {'weight': 1}, 1: {'weight': 1}})
User defined compound data type on edges:
>>> dt = [("weight", float), ("cost", int)]
>>> A = np.array([[(1.0, 2)]], dtype=dt)
>>> G = eg.from_numpy_array(A)
>>> G.edges()
EdgeView([(0, 0)])
>>> G[0][0]["cost"]
2
>>> G[0][0]["weight"]
1.0
"""
kind_to_python_type = {
"f": float,
"i": int,
"u": int,
"b": bool,
"c": complex,
"S": str,
"U": str,
"V": "void",
}
G = eg.empty_graph(0, create_using)
if A.ndim != 2:
raise eg.EasyGraphError(f"Input array must be 2D, not {A.ndim}")
n, m = A.shape
if n != m:
raise eg.EasyGraphError(f"Adjacency matrix not square: eg,ny={A.shape}")
dt = A.dtype
try:
python_type = kind_to_python_type[dt.kind]
except Exception as err:
raise TypeError(f"Unknown numpy data type: {dt}") from err
# Make sure we get even the isolated nodes of the graph.
G.add_nodes_from(range(n))
# Get a list of all the entries in the array with nonzero entries. These
# coordinates become edges in the graph. (convert to int from np.int64)
edges = ((int(e[0]), int(e[1])) for e in zip(*A.nonzero()))
# handle numpy constructed data type
if python_type == "void":
# Sort the fields by their offset, then by dtype, then by name.
fields = sorted(
(offset, dtype, name) for name, (dtype, offset) in A.dtype.fields.items()
)
triples = (
(
u,
v,
{
name: kind_to_python_type[dtype.kind](val)
for (_, dtype, name), val in zip(fields, A[u, v])
},
)
for u, v in edges
)
# If the entries in the adjacency matrix are integers, the graph is a
# multigraph, and parallel_edges is True, then create parallel edges, each
# with weight 1, for each entry in the adjacency matrix. Otherwise, create
# one edge for each positive entry in the adjacency matrix and set the
# weight of that edge to be the entry in the matrix.
elif python_type is int and G.is_multigraph() and parallel_edges:
chain = itertools.chain.from_iterable
# The following line is equivalent to:
#
# for (u, v) in edges:
# for d in range(A[u, v]):
# G.add_edge(u, v, weight=1)
#
triples = chain(
((u, v, {"weight": 1}) for d in range(A[u, v])) for (u, v) in edges
)
else: # basic data type
triples = ((u, v, dict(weight=python_type(A[u, v]))) for u, v in edges)
# If we are creating an undirected multigraph, only add the edges from the
# upper triangle of the matrix. Otherwise, add all the edges. This relies
# on the fact that the vertices created in the
# `_generated_weighted_edges()` function are actually the row/column
# indices for the matrix `A`.
#
# Without this check, we run into a problem where each edge is added twice
# when `G.add_edges_from()` is invoked below.
if G.is_multigraph() and not G.is_directed():
triples = ((u, v, d) for u, v, d in triples if u <= v)
G.add_edges_from(triples)
return G
[docs]def to_numpy_array(
G,
nodelist=None,
dtype=None,
order=None,
multigraph_weight=sum,
weight="weight",
nonedge=0.0,
):
"""Returns the graph adjacency matrix as a NumPy array.
Parameters
----------
G : graph
The EasyGraph graph used to construct the NumPy array.
nodelist : list, optional
The rows and columns are ordered according to the nodes in `nodelist`.
If `nodelist` is None, then the ordering is produced by G.nodes().
dtype : NumPy data type, optional
A valid single NumPy data type used to initialize the array.
This must be a simple type such as int or numpy.float64 and
not a compound data type (see to_numpy_recarray)
If None, then 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. If None, then the NumPy default
is used.
multigraph_weight : {sum, min, max}, optional
An operator that determines how weights in multigraphs are handled.
The default is to sum the weights of the multiple edges.
weight : string or None optional (default = 'weight')
The edge attribute that holds the numerical value used for
the edge weight. If an edge does not have that attribute, then the
value 1 is used instead.
nonedge : float (default = 0.0)
The array values corresponding to nonedges are typically set to zero.
However, this could be undesirable if there are array values
corresponding to actual edges that also have the value zero. If so,
one might prefer nonedges to have some other value, such as nan.
Returns
-------
A : NumPy ndarray
Graph adjacency matrix
See Also
--------
from_numpy_array
Notes
-----
For directed graphs, entry i,j corresponds to an edge from i to j.
Entries in the adjacency matrix are assigned to the weight edge attribute.
When an edge does not have a weight attribute, the value of the entry is
set to the number 1. For multiple (parallel) edges, the values of the
entries are determined by the `multigraph_weight` parameter. The default is
to sum the weight attributes for each of the parallel edges.
When `nodelist` does not contain every node in `G`, the adjacency matrix is
built from the subgraph of `G` that is induced by the nodes in `nodelist`.
The convention used for self-loop edges in graphs is to assign the
diagonal array entry value to the weight attribute of the edge
(or the number 1 if the edge has no weight attribute). If the
alternate convention of doubling the edge weight is desired the
resulting NumPy array can be modified as follows:
>>> import numpy as np
>>> G = eg.Graph([(1, 1)])
>>> A = eg.to_numpy_array(G)
>>> A
array([[1.]])
>>> A[np.diag_indices_from(A)] *= 2
>>> A
array([[2.]])
Examples
--------
>>> G = eg.MultiDiGraph()
>>> G.add_edge(0, 1, weight=2)
0
>>> G.add_edge(1, 0)
0
>>> G.add_edge(2, 2, weight=3)
0
>>> G.add_edge(2, 2)
1
>>> eg.to_numpy_array(G, nodelist=[0, 1, 2])
array([[0., 2., 0.],
[1., 0., 0.],
[0., 0., 4.]])
"""
import numpy as np
if nodelist is None:
nodelist = list(G)
nodeset = G
nlen = len(G)
else:
nlen = len(nodelist)
nodeset = set(G.nodes)
if nlen != len(nodeset):
for n in nodelist:
if n not in G:
raise eg.EasyGraphError(f"Node {n} in nodelist is not in G")
raise eg.EasyGraphError("nodelist contains duplicates.")
undirected = not G.is_directed()
index = dict(zip(nodelist, range(nlen)))
# Initially, we start with an array of nans. Then we populate the array
# using data from the graph. Afterwards, any leftover nans will be
# converted to the value of `nonedge`. Note, we use nans initially,
# instead of zero, for two reasons:
#
# 1) It can be important to distinguish a real edge with the value 0
# from a nonedge with the value 0.
#
# 2) When working with multi(di)graphs, we must combine the values of all
# edges between any two nodes in some manner. This often takes the
# form of a sum, min, or max. Using the value 0 for a nonedge would
# have undesirable effects with min and max, but using nanmin and
# nanmax with initially nan values is not problematic at all.
#
# That said, there are still some drawbacks to this approach. Namely, if
# a real edge is nan, then that value is a) not distinguishable from
# nonedges and b) is ignored by the default combinator (nansum, nanmin,
# nanmax) functions used for multi(di)graphs. If this becomes an issue,
# an alternative approach is to use masked arrays. Initially, every
# element is masked and set to some `initial` value. As we populate the
# graph, elements are unmasked (automatically) when we combine the initial
# value with the values given by real edges. At the end, we convert all
# masked values to `nonedge`. Using masked arrays fully addresses reason 1,
# but for reason 2, we would still have the issue with min and max if the
# initial values were 0.0. Note: an initial value of +inf is appropriate
# for min, while an initial value of -inf is appropriate for max. When
# working with sum, an initial value of zero is appropriate. Ideally then,
# we'd want to allow users to specify both a value for nonedges and also
# an initial value. For multi(di)graphs, the choice of the initial value
# will, in general, depend on the combinator function---sensible defaults
# can be provided.
if G.is_multigraph():
# Handle MultiGraphs and MultiDiGraphs
A = np.full((nlen, nlen), np.nan, order=order)
# use numpy nan-aware operations
operator = {sum: np.nansum, min: np.nanmin, max: np.nanmax}
try:
op = operator[multigraph_weight]
except Exception as err:
raise ValueError("multigraph_weight must be sum, min, or max") from err
for u, v, _, attrs in G.edges:
if (u in nodeset) and (v in nodeset):
i, j = index[u], index[v]
e_weight = attrs.get(weight, 1)
A[i, j] = op([e_weight, A[i, j]])
if undirected:
A[j, i] = A[i, j]
else:
# Graph or DiGraph, this is much faster than above
A = np.full((nlen, nlen), np.nan, order=order)
for u, nbrdict in G.adj.items():
for v, d in nbrdict.items():
try:
A[index[u], index[v]] = d.get(weight, 1)
except KeyError:
# This occurs when there are fewer desired nodes than
# there are nodes in the graph: len(nodelist) < len(G)
pass
A[np.isnan(A)] = nonedge
A = np.asarray(A, dtype=dtype)
return A
[docs]def from_pandas_adjacency(df, create_using=None):
r"""Returns a graph from Pandas DataFrame.
The Pandas DataFrame is interpreted as an adjacency matrix for the graph.
Parameters
----------
df : Pandas DataFrame
An adjacency matrix representation of a graph
create_using : EasyGraph graph constructor, optional (default=eg.Graph)
Graph type to create. If graph instance, then cleared before populated.
Notes
-----
For directed graphs, explicitly mention create_using=eg.DiGraph,
and entry i,j of df corresponds to an edge from i to j.
If `df` has a single data type for each entry it will be converted to an
appropriate Python data type.
If `df` has a user-specified compound data type the names
of the data fields will be used as attribute keys in the resulting
EasyGraph graph.
See Also
--------
to_pandas_adjacency
Examples
--------
Simple integer weights on edges:
>>> import pandas as pd
>>> pd.options.display.max_columns = 20
>>> df = pd.DataFrame([[1, 1], [2, 1]])
>>> df
0 1
0 1 1
1 2 1
>>> G = eg.from_pandas_adjacency(df)
>>> G.name = "Graph from pandas adjacency matrix"
"""
try:
df = df[df.index]
except Exception as err:
missing = list(set(df.index).difference(set(df.columns)))
msg = f"{missing} not in columns"
raise eg.EasyGraphError("Columns must match Indices.", msg) from err
A = df.values
G = from_numpy_array(A, create_using=create_using)
G = eg.relabel_nodes(G, dict(enumerate(df.columns)))
return G
[docs]def from_pandas_edgelist(
df,
source="source",
target="target",
edge_attr=None,
create_using=None,
edge_key=None,
):
"""Returns a graph from Pandas DataFrame containing an edge list.
The Pandas DataFrame should contain at least two columns of node names and
zero or more columns of edge attributes. Each row will be processed as one
edge instance.
Note: This function iterates over DataFrame.values, which is not
guaranteed to retain the data type across columns in the row. This is only
a problem if your row is entirely numeric and a mix of ints and floats. In
that case, all values will be returned as floats. See the
DataFrame.iterrows documentation for an example.
Parameters
----------
df : Pandas DataFrame
An edge list representation of a graph
source : str or int
A valid column name (string or integer) for the source nodes (for the
directed case).
target : str or int
A valid column name (string or integer) for the target nodes (for the
directed case).
edge_attr : str or int, iterable, True, or None
A valid column name (str or int) or iterable of column names that are
used to retrieve items and add them to the graph as edge attributes.
If `True`, all of the remaining columns will be added.
If `None`, no edge attributes are added to the graph.
create_using : EasyGraph graph constructor, optional (default=eg.Graph)
Graph type to create. If graph instance, then cleared before populated.
edge_key : str or None, optional (default=None)
A valid column name for the edge keys (for a MultiGraph). The values in
this column are used for the edge keys when adding edges if create_using
is a multigraph.
See Also
--------
to_pandas_edgelist
Examples
--------
Simple integer weights on edges:
>>> import pandas as pd
>>> pd.options.display.max_columns = 20
>>> import numpy as np
>>> rng = np.random.RandomState(seed=5)
>>> ints = rng.randint(1, 11, size=(3, 2))
>>> a = ["A", "B", "C"]
>>> b = ["D", "A", "E"]
>>> df = pd.DataFrame(ints, columns=["weight", "cost"])
>>> df[0] = a
>>> df["b"] = b
>>> df[["weight", "cost", 0, "b"]]
weight cost 0 b
0 4 7 A D
1 7 1 B A
2 10 9 C E
>>> G = eg.from_pandas_edgelist(df, 0, "b", ["weight", "cost"])
>>> G["E"]["C"]["weight"]
10
>>> G["E"]["C"]["cost"]
9
>>> edges = pd.DataFrame(
... {
... "source": [0, 1, 2],
... "target": [2, 2, 3],
... "weight": [3, 4, 5],
... "color": ["red", "blue", "blue"],
... }
... )
>>> G = eg.from_pandas_edgelist(edges, edge_attr=True)
>>> G[0][2]["color"]
'red'
Build multigraph with custom keys:
>>> edges = pd.DataFrame(
... {
... "source": [0, 1, 2, 0],
... "target": [2, 2, 3, 2],
... "my_edge_key": ["A", "B", "C", "D"],
... "weight": [3, 4, 5, 6],
... "color": ["red", "blue", "blue", "blue"],
... }
... )
>>> G = eg.from_pandas_edgelist(
... edges,
... edge_key="my_edge_key",
... edge_attr=["weight", "color"],
... create_using=eg.MultiGraph(),
... )
>>> G[0][2]
AtlasView({'A': {'weight': 3, 'color': 'red'}, 'D': {'weight': 6, 'color': 'blue'}})
"""
g = eg.empty_graph(0, create_using)
if edge_attr is None:
g.add_edges_from(zip(df[source], df[target]))
return g
reserved_columns = [source, target]
# Additional columns requested
attr_col_headings = []
attribute_data = []
if edge_attr is True:
attr_col_headings = [c for c in df.columns if c not in reserved_columns]
elif isinstance(edge_attr, (list, tuple)):
attr_col_headings = edge_attr
else:
attr_col_headings = [edge_attr]
if len(attr_col_headings) == 0:
raise eg.EasyGraphError(
"Invalid edge_attr argument: No columns found with name:"
f" {attr_col_headings}"
)
try:
attribute_data = zip(*[df[col] for col in attr_col_headings])
except (KeyError, TypeError) as err:
msg = f"Invalid edge_attr argument: {edge_attr}"
raise eg.EasyGraphError(msg) from err
if g.is_multigraph():
# => append the edge keys from the df to the bundled data
if edge_key is not None:
try:
multigraph_edge_keys = df[edge_key]
attribute_data = zip(attribute_data, multigraph_edge_keys)
except (KeyError, TypeError) as err:
msg = f"Invalid edge_key argument: {edge_key}"
raise eg.EasyGraphError(msg) from err
for s, t, attrs in zip(df[source], df[target], attribute_data):
if edge_key is not None:
attrs, multigraph_edge_key = attrs
key = g.add_edge(s, t, key=multigraph_edge_key)
else:
key = g.add_edge(s, t)
g[s][t][key].update(zip(attr_col_headings, attrs))
else:
for s, t, attrs in zip(df[source], df[target], attribute_data):
g.add_edge(s, t)
g[s][t].update(zip(attr_col_headings, attrs))
return g
[docs]def from_scipy_sparse_matrix(
A, parallel_edges=False, create_using=None, edge_attribute="weight"
):
"""Creates a new graph from an adjacency matrix given as a SciPy sparse
matrix.
Parameters
----------
A: scipy sparse matrix
An adjacency matrix representation of a graph
parallel_edges : Boolean
If this is True, `create_using` is a multigraph, and `A` is an
integer matrix, then entry *(i, j)* in the matrix is interpreted as the
number of parallel edges joining vertices *i* and *j* in the graph.
If it is False, then the entries in the matrix are interpreted as
the weight of a single edge joining the vertices.
create_using : EasyGraph graph constructor, optional (default=eg.Graph)
Graph type to create. If graph instance, then cleared before populated.
edge_attribute: string
Name of edge attribute to store matrix numeric value. The data will
have the same type as the matrix entry (int, float, (real,imag)).
Notes
-----
For directed graphs, explicitly mention create_using=eg.DiGraph,
and entry i,j of A corresponds to an edge from i to j.
If `create_using` is :class:`easygraph.MultiGraph` or
:class:`easygraph.MultiDiGraph`, `parallel_edges` is True, and the
entries of `A` are of type :class:`int`, then this function returns a
multigraph (constructed from `create_using`) with parallel edges.
In this case, `edge_attribute` will be ignored.
If `create_using` indicates an undirected multigraph, then only the edges
indicated by the upper triangle of the matrix `A` will be added to the
graph.
Examples
--------
>>> import scipy as sp
>>> import scipy.sparse # call as sp.sparse
>>> A = sp.sparse.eye(2, 2, 1)
>>> G = eg.from_scipy_sparse_matrix(A)
If `create_using` indicates a multigraph and the matrix has only integer
entries and `parallel_edges` is Falnxse, then the entries will be treated
as weights for edges joining the nodes (without creating parallel edges):
>>> A = sp.sparse.csr_matrix([[1, 1], [1, 2]])
>>> G = eg.from_scipy_sparse_matrix(A, create_using=eg.MultiGraph)
>>> G[1][1]
AtlasView({0: {'weight': 2}})
If `create_using` indicates a multigraph and the matrix has only integer
entries and `parallel_edges` is True, then the entries will be treated
as the number of parallel edges joining those two vertices:
>>> A = sp.sparse.csr_matrix([[1, 1], [1, 2]])
>>> G = eg.from_scipy_sparse_matrix(
... A, parallel_edges=True, create_using=eg.MultiGraph
... )
>>> G[1][1]
AtlasView({0: {'weight': 1}, 1: {'weight': 1}})
"""
return from_scipy_sparse_array(
A,
parallel_edges=parallel_edges,
create_using=create_using,
edge_attribute=edge_attribute,
)
def from_scipy_sparse_array(
A, parallel_edges=False, create_using=None, edge_attribute="weight"
):
G = eg.empty_graph(0, create_using)
n, m = A.shape
if n != m:
raise eg.EasyGraphError(f"Adjacency matrix not square: nx,ny={A.shape}")
# Make sure we get even the isolated nodes of the graph.
G.add_nodes_from(range(n))
# Create an iterable over (u, v, w) triples and for each triple, add an
# edge from u to v with weight w.
triples = _generate_weighted_edges(A)
# If the entries in the adjacency matrix are integers, the graph is a
# multigraph, and parallel_edges is True, then create parallel edges, each
# with weight 1, for each entry in the adjacency matrix. Otherwise, create
# one edge for each positive entry in the adjacency matrix and set the
# weight of that edge to be the entry in the matrix.
if A.dtype.kind in ("i", "u") and G.is_multigraph() and parallel_edges:
chain = itertools.chain.from_iterable
# The following line is equivalent to:
#
# for (u, v) in edges:
# for d in range(A[u, v]):
# G.add_edge(u, v, weight=1)
#
triples = chain(((u, v, 1) for d in range(w)) for (u, v, w) in triples)
# If we are creating an undirected multigraph, only add the edges from the
# upper triangle of the matrix. Otherwise, add all the edges. This relies
# on the fact that the vertices created in the
# `_generated_weighted_edges()` function are actually the row/column
# indices for the matrix `A`.
#
# Without this check, we run into a problem where each edge is added twice
# when `G.add_weighted_edges_from()` is invoked below.
if G.is_multigraph() and not G.is_directed():
triples = ((u, v, d) for u, v, d in triples if u <= v)
G.add_edges_from(((u, v, {"weight": d}) for u, v, d in triples))
return G
def _generate_weighted_edges(A):
"""Returns an iterable over (u, v, w) triples, where u and v are adjacent
vertices and w is the weight of the edge joining u and v.
`A` is a SciPy sparse matrix (in any format).
"""
if A.format == "csr":
return _csr_gen_triples(A)
if A.format == "csc":
return _csc_gen_triples(A)
if A.format == "dok":
return _dok_gen_triples(A)
# If A is in any other format (including COO), convert it to COO format.
return _coo_gen_triples(A.tocoo())
def _csr_gen_triples(A):
"""Converts a SciPy sparse matrix in **Compressed Sparse Row** format to
an iterable of weighted edge triples.
"""
nrows = A.shape[0]
data, indices, indptr = A.data, A.indices, A.indptr
for i in range(nrows):
for j in range(indptr[i], indptr[i + 1]):
yield i, indices[j], data[j]
def _csc_gen_triples(A):
"""Converts a SciPy sparse matrix in **Compressed Sparse Column** format to
an iterable of weighted edge triples.
"""
ncols = A.shape[1]
data, indices, indptr = A.data, A.indices, A.indptr
for i in range(ncols):
for j in range(indptr[i], indptr[i + 1]):
yield indices[j], i, data[j]
def _coo_gen_triples(A):
"""Converts a SciPy sparse matrix in **Coordinate** format to an iterable
of weighted edge triples.
"""
row, col, data = A.row, A.col, A.data
return zip(row, col, data)
def _dok_gen_triples(A):
"""Converts a SciPy sparse matrix in **Dictionary of Keys** format to an
iterable of weighted edge triples.
"""
for (r, c), v in A.items():
yield r, c, v