def view_edge_list(self):
"""
Display the edge list. Compute it if needed.
Returns
-------
source_col : cudf.Series
This cudf.Series wraps a gdf_column of size E (E: number of edges).
The gdf column contains the source index for each edge.
Source indices are in the range [0, V) (V: number of vertices).
Source indices must be 32 bit integers.
dest_col : cudf.Series
This cudf.Series wraps a gdf_column of size E (E: number of edges).
The gdf column contains the destination index for each edge.
Destination indices are in the range [0, V) (V: number of
vertices).
Destination indices must be 32 bit integers.
value_col : cudf.Series or ``None``
This pointer is ``None`` for unweighted graphs.
For weighted graphs, this cudf.Series wraps a gdf_column of size E
(E: number of edges).
The gdf column contains the weight value for each edge.
The expected type of the gdf_column element is floating point
number.
"""
source_col, dest_col, value_col = \
graph_wrapper.view_edge_list(self.graph_ptr)
return source_col, dest_col, value_col
def view_edge_list(self):
"""
Display the edge list. Compute it if needed.
Returns
-------
edgelist_df : cudf.DataFrame
This cudf.DataFrame wraps source, destination and weight
gdf_column of size E (E: number of edges)
The 'src' column contains the source index for each edge.
Source indices are in the range [0, V) (V: number of vertices).
The 'dst' column contains the destination index for each edge.
Destination indices are in the range [0, V) (V: number of
vertices).
For weighted graphs, dataframe contains 'weight' column
containing the weight value for each edge.
"""
if self.edgelist is None:
graph_wrapper.view_edge_list(self)
edgelist_df = self.edgelist.edgelist_df
if self.renumbered:
if isinstance(self.edgelist.renumber_map, cudf.DataFrame):
df = cudf.DataFrame()
ncols = len(self.edgelist.edgelist_df) - 2
unrnb_df_ = edgelist_df.merge(self.edgelist.renumber_map,
left_on='src',
right_on='id',
how='left').drop(['id', 'src'])
unrnb_df = unrnb_df_.merge(self.edgelist.renumber_map,
left_on='dst',
right_on='id',
how='left').drop(['id', 'dst'])
cols = unrnb_df.columns
df = unrnb_df[[cols[ncols:], cols[0:ncols]]]
else:
df = cudf.DataFrame()
for c in edgelist_df.columns:
if c in ['src', 'dst']:
df[c] = self.edgelist.renumber_map[edgelist_df[c]].\
reset_index(drop=True)
else:
df[c] = edgelist_df[c]
return df
else:
return edgelist_df
def view_edge_list(self):
"""
Display the edge list. Compute it if needed.
Returns
-------
edgelist_df : cudf.DataFrame
This cudf.DataFrame wraps source, destination and weight
gdf_column of size E (E: number of edges)
The 'src' column contains the source index for each edge.
Source indices are in the range [0, V) (V: number of vertices).
The 'dst' column contains the destination index for each edge.
Destination indices are in the range [0, V) (V: number of
vertices).
For weighted graphs, dataframe contains 'weight' column
containing the weight value for each edge.
"""
if self.edgelist is None:
graph_wrapper.view_edge_list(self)
df = self.edgelist.edgelist_df
if self.renumbered:
df['src'] = self.edgelist.renumber_map[df['src']]
df['dst'] = self.edgelist.renumber_map[df['dst']]
return df
def view_edge_list(self):
"""
Display the edge list. Compute it if needed.
NOTE: If the graph is of type Graph() then the displayed undirected
edges are the same as displayed by networkx Graph(), but the direction
could be different i.e. an edge displayed by cugraph as (src, dst)
could be displayed as (dst, src) by networkx.
cugraph.Graph stores symmetrized edgelist internally. For displaying
undirected edgelist for a Graph the upper trianglar matrix of the
symmetrized edgelist is returned.
networkx.Graph renumbers the input and stores the upper triangle of
this renumbered input. Since the internal renumbering of networx and
cugraph is different, the upper triangular matrix of networkx
renumbered input may not be the same as cugraph's upper trianglar
matrix of the symmetrized edgelist. Hence the displayed source and
destination pairs in both will represent the same edge but node values
could be swapped.
Returns
-------
edgelist_df : cudf.DataFrame
This cudf.DataFrame wraps source, destination and weight
gdf_column of size E (E: number of edges)
The 'src' column contains the source index for each edge.
Source indices are in the range [0, V) (V: number of vertices).
The 'dst' column contains the destination index for each edge.
Destination indices are in the range [0, V) (V: number of
vertices).
For weighted graphs, dataframe contains 'weight' column
containing the weight value for each edge.
"""
if self.edgelist is None:
graph_wrapper.view_edge_list(self)
if type(self) is Graph:
edgelist_df = self.edgelist.edgelist_df[self.edgelist.edgelist_df[
'src'] <= self.edgelist.edgelist_df['dst']].\
reset_index(drop=True)
self.edge_count = len(edgelist_df)
else:
edgelist_df = self.edgelist.edgelist_df
if self.renumbered:
if isinstance(self.edgelist.renumber_map, cudf.DataFrame):
df = cudf.DataFrame()
ncols = len(edgelist_df.columns) - 2
unrnb_df_ = edgelist_df.merge(self.edgelist.renumber_map,
left_on='src',
right_on='id',
how='left').drop(['id', 'src'])
unrnb_df = unrnb_df_.merge(self.edgelist.renumber_map,
left_on='dst',
right_on='id',
how='left').drop(['id', 'dst'])
cols = unrnb_df.columns.to_list()
df = unrnb_df[cols[ncols:] + cols[0:ncols]]
else:
df = cudf.DataFrame()
for c in edgelist_df.columns:
if c in ['src', 'dst']:
df[c] = self.edgelist.renumber_map[edgelist_df[c]].\
reset_index(drop=True)
else:
df[c] = edgelist_df[c]
return df
else:
return edgelist_df