def bfs(G, start, return_sp_counter=False):
"""
Find the distances and predecessors for a breadth first traversal of a
graph.
Parameters
----------
G : cugraph.graph
cuGraph graph descriptor, should contain the connectivity information
as an adjacency list.
start : Integer
The index of the graph vertex from which the traversal begins
return_sp_counter : bool, optional, default=False
Indicates if shortest path counters should be returned
Returns
-------
df : cudf.DataFrame
df['vertex'][i] gives the vertex id of the i'th vertex
df['distance'][i] gives the path distance for the i'th vertex from the
starting vertex
df['predecessor'][i] gives for the i'th vertex the vertex it was
reached from in the traversal
df['sp_counter'][i] gives for the i'th vertex the number of shortest
path leading to it during traversal (Only if retrun_sp_counter is True)
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>> dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> df = cugraph.bfs(G, 0)
"""
if type(G) is Graph:
directed = False
else:
directed = True
if G.renumbered is True:
start = G.lookup_internal_vertex_id(cudf.Series([start]))[0]
df = bfs_wrapper.bfs(G, start, directed, return_sp_counter)
if G.renumbered:
df = G.unrenumber(df, "vertex")
df = G.unrenumber(df, "predecessor")
df["predecessor"].fillna(-1, inplace=True)
return df
def bfs(G, start, directed=True):
"""
Find the distances and predecessors for a breadth first traversal of a
graph.
Parameters
----------
G : cugraph.graph
cuGraph graph descriptor, should contain the connectivity information
as an adjacency list.
start : Integer
The index of the graph vertex from which the traversal begins
directed : bool
Indicates whether the graph in question is a directed graph, or whether
each edge has a corresponding reverse edge. (Allows optimizations if
the graph is undirected)
Returns
-------
df : cudf.DataFrame
df['vertex'][i] gives the vertex id of the i'th vertex
df['distance'][i] gives the path distance for the i'th vertex from the
starting vertex
df['predecessor'][i] gives for the i'th vertex the vertex it was
reached from in the traversal
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>> dtype=['int32', 'int32', 'float32'], header=None)
>>> sources = cudf.Series(M['0'])
>>> destinations = cudf.Series(M['1'])
>>> G = cugraph.Graph()
>>> G.add_edge_list(sources, destinations, None)
>>> df = cugraph.bfs(G, 0)
"""
df = bfs_wrapper.bfs(G, start, directed)
return df
def bfs(G, start):
"""
Find the distances and predecessors for a breadth first traversal of a
graph.
Parameters
----------
G : cugraph.graph
cuGraph graph descriptor, should contain the connectivity information
as an adjacency list.
start : Integer
The index of the graph vertex from which the traversal begins
Returns
-------
df : cudf.DataFrame
df['vertex'][i] gives the vertex id of the i'th vertex
df['distance'][i] gives the path distance for the i'th vertex from the
starting vertex
df['predecessor'][i] gives for the i'th vertex the vertex it was
reached from in the traversal
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>> dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> df = cugraph.bfs(G, 0)
"""
if type(G) is Graph:
directed = False
else:
directed = True
df = bfs_wrapper.bfs(G, start, directed)
return df
def bfs(G,
start=None,
return_sp_counter=None,
i_start=None,
directed=None,
return_predecessors=None):
"""Find the distances and predecessors for a breadth first traversal of a
graph.
Parameters
----------
G : cugraph.Graph, networkx.Graph, CuPy or SciPy sparse matrix
Graph or matrix object, which should contain the connectivity
information. Edge weights, if present, should be single or double
precision floating point values.
start : Integer
The index of the graph vertex from which the traversal begins
return_sp_counter : bool, optional, default=False
Indicates if shortest path counters should be returned
i_start : Integer, optional
Identical to start, added for API compatibility. Only start or i_start
can be set, not both.
directed : bool, optional
NOTE
For non-Graph-type (eg. sparse matrix) values of G only. Raises
TypeError if used with a Graph object.
If True (default), then convert the input matrix to a cugraph.DiGraph,
otherwise a cugraph.Graph object will be used.
Returns
-------
Return value type is based on the input type. If G is a cugraph.Graph,
returns:
cudf.DataFrame
df['vertex'] vertex IDs
df['distance'] path distance for each vertex from the starting vertex
df['predecessor'] for each i'th position in the column, the vertex ID
immediately preceding the vertex at position i in the 'vertex' column
df['sp_counter'] for each i'th position in the column, the number of
shortest paths leading to the vertex at position i in the 'vertex'
column (Only if retrun_sp_counter is True)
If G is a networkx.Graph, returns:
pandas.DataFrame with contents equivalent to the cudf.DataFrame
described above.
If G is a CuPy or SciPy matrix, returns:
a 2-tuple of CuPy ndarrays (if CuPy matrix input) or Numpy ndarrays (if
SciPy matrix input) representing:
distance: cupy or numpy ndarray
ndarray of shortest distances between source and vertex.
predecessor: cupy or numpy ndarray
ndarray of predecessors of a vertex on the path from source, which
can be used to reconstruct the shortest paths.
...or if return_sp_counter is True, returns a 3-tuple with the above two
arrays plus:
sp_counter: cupy or numpy ndarray
ndarray of number of shortest paths leading to each vertex.
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>> dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> df = cugraph.bfs(G, 0)
"""
(start, return_sp_counter, directed) = \
_ensure_args(G, start, return_sp_counter, i_start, directed)
# FIXME: allow nx_weight_attr to be specified
(G, input_type) = ensure_cugraph_obj(
G,
nx_weight_attr="weight",
matrix_graph_type=DiGraph if directed else Graph)
if type(G) is Graph:
is_directed = False
else:
is_directed = True
if G.renumbered is True:
start = G.lookup_internal_vertex_id(cudf.Series([start]))[0]
df = bfs_wrapper.bfs(G, start, is_directed, return_sp_counter)
if G.renumbered:
df = G.unrenumber(df, "vertex")
df = G.unrenumber(df, "predecessor")
df["predecessor"].fillna(-1, inplace=True)
return _convert_df_to_output_type(df, input_type)
def bfs(G, start, return_sp_counter=False):
"""
Find the distances and predecessors for a breadth first traversal of a
graph.
Parameters
----------
G : cuGraph.Graph, NetworkX.Graph, or CuPy sparse COO matrix
cuGraph graph descriptor with connectivity information. Edge weights,
if present, should be single or double precision floating point values.
start : Integer
The index of the graph vertex from which the traversal begins
return_sp_counter : bool, optional, default=False
Indicates if shortest path counters should be returned
Returns
-------
Return value type is based on the input type. If G is a cugraph.Graph,
returns:
cudf.DataFrame
df['vertex'] vertex IDs
df['distance'] path distance for each vertex from the starting vertex
df['predecessor'] for each i'th position in the column, the vertex ID
immediately preceding the vertex at position i in the 'vertex' column
df['sp_counter'] for each i'th position in the column, the number of
shortest paths leading to the vertex at position i in the 'vertex'
column (Only if retrun_sp_counter is True)
If G is a networkx.Graph, returns:
pandas.DataFrame with contents equivalent to the cudf.DataFrame
described above.
If G is a CuPy sparse COO matrix, returns a 2-tuple of cupy.ndarray:
distance: cupy.ndarray
ndarray of shortest distances between source and vertex.
predecessor: cupy.ndarray
ndarray of predecessors of a vertex on the path from source, which
can be used to reconstruct the shortest paths.
sp_counter: cupy.ndarray
ndarray of number of shortest paths leading to each vertex (only if
retrun_sp_counter is True)
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>> dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> df = cugraph.bfs(G, 0)
"""
# FIXME: allow nx_weight_attr to be specified
(G, input_type) = ensure_cugraph_obj(G,
nx_weight_attr="weight",
matrix_graph_type=Graph)
if type(G) is Graph:
directed = False
else:
directed = True
if G.renumbered is True:
start = G.lookup_internal_vertex_id(cudf.Series([start]))[0]
df = bfs_wrapper.bfs(G, start, directed, return_sp_counter)
if G.renumbered:
df = G.unrenumber(df, "vertex")
df = G.unrenumber(df, "predecessor")
df["predecessor"].fillna(-1, inplace=True)
return _convert_df_to_output_type(df, input_type)
