#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# This file digraph.py is referred and derived from project NetworkX,
#
# https://github.com/networkx/networkx/blob/master/networkx/classes/digraph.py
#
# which has the following license:
#
# Copyright (C) 2004-2020, NetworkX Developers
# Aric Hagberg <[email protected]>
# Dan Schult <[email protected]>
# Pieter Swart <[email protected]>
# All rights reserved.
#
# This file is part of NetworkX.
#
# NetworkX is distributed under a BSD license; see LICENSE.txt for more
# information.
#
from copy import deepcopy
from networkx.classes.coreviews import AdjacencyView
from networkx.classes.digraph import DiGraph as RefDiGraph
from networkx.classes.reportviews import DiDegreeView
from networkx.classes.reportviews import InDegreeView
from networkx.classes.reportviews import OutDegreeView
from graphscope.client.session import get_default_session
from graphscope.client.session import get_session_by_id
from graphscope.framework import dag_utils
from graphscope.framework.errors import check_argument
from graphscope.framework.graph_schema import GraphSchema
from graphscope.nx import NetworkXError
from graphscope.nx.classes.graph import Graph
from graphscope.nx.classes.graphviews import reverse_view
from graphscope.nx.classes.reportviews import InEdgeView
from graphscope.nx.classes.reportviews import OutEdgeView
from graphscope.nx.convert import to_networkx_graph
from graphscope.nx.utils.compat import patch_docstring
from graphscope.nx.utils.misc import clear_mutation_cache
from graphscope.nx.utils.misc import init_empty_graph_in_engine
[docs]class DiGraph(Graph):
"""
Base class for directed graphs.
A DiGraph that holds the metadata of a graph, and provides NetworkX-like DiGraph APIs.
It is worth noticing that the graph is actually stored by the Analytical Engine backend.
In other words, the Graph object holds nothing but metadata of a graph
DiGraph support nodes and edges with optional data, or attributes.
DiGraphs support directed edges. Self loops are allowed but multiple
(parallel) edges are not.
Nodes can be arbitrary int/str/float/bool objects with optional
key/value attributes.
Edges are represented as links between nodes with optional
key/value attributes.
DiGraph support node label if it's created from a GraphScope graph object.
nodes are identified by `(label, id)` tuple.
Parameters
----------
incoming_graph_data : input graph (optional, default: None)
Data to initialize graph. If None (default) an empty
graph is created. The data can be any format that is supported
by the to_networkx_graph() function, currently including edge list,
dict of dicts, dict of lists, NetworkX graph, NumPy matrix
or 2d ndarray, Pandas DataFrame, SciPy sparse matrix, or a GraphScope
graph object.
default_label : default node label (optional, default: None)
if incoming_graph_data is a GraphScope graph object, default label means
the nodes of the label can be identified by id directly, other label nodes
need to use `(label, id)` to identify.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See Also
--------
Graph
Examples
--------
Create an empty graph structure (a "null graph") with no nodes and
no edges.
>>> G = nx.DiGraph()
G can be grown in several ways.
**Nodes:**
Add one node at a time:
>>> G.add_node(1)
Add the nodes from any container (a list, dict, set or
even the lines from a file or the nodes from another graph).
>>> G.add_nodes_from([2, 3])
>>> G.add_nodes_from(range(100, 110))
>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)
In addition integers, strings can represent a node.
>>> G.add_node('a node')
**Edges:**
G can also be grown by adding edges.
Add one edge,
>>> G.add_edge(1, 2)
a list of edges,
>>> G.add_edges_from([(1, 2), (1, 3)])
or a collection of edges,
>>> G.add_edges_from(H.edges)
If some edges connect nodes not yet in the graph, the nodes
are added automatically. There are no errors when adding
nodes or edges that already exist.
**Attributes:**
Each graph, node, and edge can hold key/value attribute pairs
in an associated attribute dictionary (the keys must be hashable).
By default these are empty, but can be added or changed using
add_edge, add_node or direct manipulation of the attribute
dictionaries named graph, node and edge respectively.
>>> G = nx.DiGraph(day="Friday")
>>> G.graph
{'day': 'Friday'}
Add node attributes using add_node(), add_nodes_from() or G.nodes
>>> G.add_node(1, time='5pm')
>>> G.add_nodes_from([3], time='2pm')
>>> G.nodes[1]
{'time': '5pm'}
>>> G.nodes[1]['room'] = 714
>>> del G.nodes[1]['room'] # remove attribute
>>> list(G.nodes(data=True))
[(1, {'time': '5pm'}), (3, {'time': '2pm'})]
Add edge attributes using add_edge(), add_edges_from(), subscript
notation, or G.edges.
>>> G.add_edge(1, 2, weight=4.7 )
>>> G.add_edges_from([(3, 4), (4, 5)], color='red')
>>> G.add_edges_from([(1, 2, {'color':'blue'}), (2, 3, {'weight':8})])
>>> G[1][2]['weight'] = 4.7
>>> G.edges[1, 2]['weight'] = 4
Warning: we protect the graph data structure by making `G.edges[1, 2]` a
read-only dict-like structure. However, you can assign to attributes
in e.g. `G.edges[1, 2]`. Thus, use 2 sets of brackets to add/change
data attributes: `G.edges[1, 2]['weight'] = 4`
(For multigraphs: `MG.edges[u, v, key][name] = value`).
**Shortcuts:**
Many common graph features allow python syntax to speed reporting.
>>> 1 in G # check if node in graph
True
>>> [n for n in G if n < 3] # iterate through nodes
[1, 2]
>>> len(G) # number of nodes in graph
5
Often the best way to traverse all edges of a graph is via the neighbors.
The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`
>>> for n, nbrsdict in G.adjacency():
... for nbr, eattr in nbrsdict.items():
... if 'weight' in eattr:
... # Do something useful with the edges
... pass
But the edges reporting object is often more convenient:
>>> for u, v, weight in G.edges(data='weight'):
... if weight is not None:
... # Do something useful with the edges
... pass
**Transformation**
Create a graph with GraphScope graph object. First we init a GraphScope graph
with two node labels: person and comment`
>>> g = graphscope.g(directed=True).add_vertice("persion.csv", label="person").add_vertice("comment.csv", label="comment")
create a graph with g, set default_label to 'person'
>>> G = nx.DiGraph(g, default_label="person")
`person` label nodes can be identified by id directly, for `comment` label,
we has to use tuple `("comment", id)` identify. Like, add a person label
node and a comment label node
>>> G.add_node(0, type="person")
>>> G.add_node(("comment", 0), type="comment")
print property of two nodes
>>> G.nodes[0]
{"type", "person"}
>>> G.nodes[("comment", 0)]
{"type", "comment"}
**Reporting:**
Simple graph information is obtained using object-attributes and methods.
Reporting usually provides views instead of containers to reduce memory
usage. The views update as the graph is updated similarly to dict-views.
The objects `nodes, `edges` and `adj` provide access to data attributes
via lookup (e.g. `nodes[n], `edges[u, v]`, `adj[u][v]`) and iteration
(e.g. `nodes.items()`, `nodes.data('color')`,
`nodes.data('color', default='blue')` and similarly for `edges`)
Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
For details on these and other miscellaneous methods, see below.
"""
[docs] @patch_docstring(Graph.__init__)
def __init__(self, incoming_graph_data=None, default_label=None, **attr):
self.graph_attr_dict_factory = self.graph_attr_dict_factory
self.node_dict_factory = self.node_dict_factory
self.adjlist_outer_dict_factory = self.adjlist_outer_dict_factory
self.cache = self.graph_cache_factory(self)
# init node and adj (must be after cache)
self.graph = self.graph_attr_dict_factory()
self._node = self.node_dict_factory(self)
self._adj = self.adjlist_outer_dict_factory(self)
self._succ = self._adj
self._pred = self.adjlist_outer_dict_factory(self, pred=True)
self._key = None
self._op = None
self._graph_type = self._graph_type
self._schema = GraphSchema()
# cache for add_node and add_edge
self._add_node_cache = []
self._add_edge_cache = []
self._remove_node_cache = []
self._remove_edge_cache = []
create_empty_in_engine = attr.pop(
"create_empty_in_engine", True
) # a hidden parameter
self._distributed = attr.pop("dist", False)
if incoming_graph_data is not None and self._is_gs_graph(incoming_graph_data):
# convert from gs graph always use distributed mode
self._distributed = True
if self._session is None:
self._session = get_session_by_id(incoming_graph_data.session_id)
self._default_label = default_label
self._default_label_id = -1
if self._session is None:
self._session = get_default_session()
if not self._is_gs_graph(incoming_graph_data) and create_empty_in_engine:
graph_def = init_empty_graph_in_engine(
self, self.is_directed(), self._distributed
)
self._key = graph_def.key
# attempt to load graph with data
if incoming_graph_data is not None:
to_networkx_graph(incoming_graph_data, create_using=self)
self.cache.warmup()
# load graph attributes (must be after to_networkx_graph)
self.graph.update(attr)
self._saved_signature = self.signature
self._is_client_view = False
# statically create the unload op
if self.op is None:
self._unload_op = None
else:
self._unload_op = dag_utils.unload_graph(self)
@property
@clear_mutation_cache
@patch_docstring(RefDiGraph.adj)
def adj(self):
return AdjacencyView(self._succ)
succ = adj
@property
@clear_mutation_cache
@patch_docstring(RefDiGraph.pred)
def pred(self):
return AdjacencyView(self._pred)
[docs] @clear_mutation_cache
@patch_docstring(RefDiGraph.has_predecessor)
def has_successor(self, u, v):
return self.has_edge(u, v)
[docs] @clear_mutation_cache
@patch_docstring(RefDiGraph.has_predecessor)
def has_predecessor(self, u, v):
return self.has_edge(v, u)
[docs] @clear_mutation_cache
@patch_docstring(RefDiGraph.successors)
def successors(self, n):
try:
return iter(self._succ[n])
except KeyError:
raise NetworkXError("The node %s is not in the digraph." % (n,))
# digraph definitions
neighbors = successors
[docs] @clear_mutation_cache
@patch_docstring(RefDiGraph.predecessors)
def predecessors(self, n):
try:
return iter(self._pred[n])
except KeyError:
raise NetworkXError("The node %s is not in the digraph." % (n,))
@property
@clear_mutation_cache
def edges(self):
"""An OutEdgeView of the DiGraph as G.edges or G.edges().
edges(self, nbunch=None, data=False, default=None)
The OutEdgeView provides set-like operations on the edge-tuples
as well as edge attribute lookup. When called, it also provides
an EdgeDataView object which allows control of access to edge
attributes (but does not provide set-like operations).
Hence, `G.edges[u, v]['color']` provides the value of the color
attribute for edge `(u, v)` while
`for (u, v, c) in G.edges.data('color', default='red'):`
iterates through all the edges yielding the color attribute
with default `'red'` if no color attribute exists.
Parameters
----------
nbunch : single node, container, or all nodes (default= all nodes)
The view will only report edges incident to these nodes.
data : string or bool, optional (default=False)
The edge attribute returned in 3-tuple (u, v, ddict[data]).
If True, return edge attribute dict in 3-tuple (u, v, ddict).
If False, return 2-tuple (u, v).
default : value, optional (default=None)
Value used for edges that don't have the requested attribute.
Only relevant if data is not True or False.
Returns
-------
edges : OutEdgeView
A view of edge attributes, usually it iterates over (u, v)
or (u, v, d) tuples of edges, but can also be used for
attribute lookup as `edges[u, v]['foo']`.
See Also
--------
in_edges, out_edges
Notes
-----
Nodes in nbunch that are not in the graph will be (quietly) ignored.
For directed graphs this returns the out-edges.
Examples
--------
>>> G = nx.DiGraph()
>>> nx.add_path(G, [0, 1, 2])
>>> G.add_edge(2, 3, weight=5)
>>> [e for e in G.edges]
[(0, 1), (1, 2), (2, 3)]
>>> G.edges.data() # default data is {} (empty dict)
OutEdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})])
>>> G.edges.data("weight", default=1)
OutEdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)])
>>> G.edges([0, 2]) # only edges incident to these nodes
OutEdgeDataView([(0, 1), (2, 3)])
>>> G.edges(0) # only edges incident to a single node (use G.adj[0]?)
OutEdgeDataView([(0, 1)])
"""
return OutEdgeView(self)
# alias out_edges to edges
out_edges = edges
@property
@clear_mutation_cache
@patch_docstring(RefDiGraph.in_edges)
def in_edges(self):
return InEdgeView(self)
@property
@clear_mutation_cache
def degree(self):
"""A DegreeView for the Graph as G.degree or G.degree().
The node degree is the number of edges adjacent to the node.
The weighted node degree is the sum of the edge weights for
edges incident to that node.
This object provides an iterator for (node, degree) as well as
lookup for the degree for a single node.
Parameters
----------
nbunch : single node, container, or all nodes (default= all nodes)
The view will only report edges incident to these nodes.
weight : string or None, optional (default=None)
The name of an edge attribute that holds the numerical value used
as a weight. If None, then each edge has weight 1.
The degree is the sum of the edge weights adjacent to the node.
Returns
-------
If a single node is requested
deg : int
Degree of the node
OR if multiple nodes are requested
nd_iter : iterator
The iterator returns two-tuples of (node, degree).
See Also
--------
in_degree, out_degree
Examples
--------
>>> G = nx.DiGraph()
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.degree(0) # node 0 with degree 1
1
>>> list(G.degree([0, 1, 2]))
[(0, 1), (1, 2), (2, 2)]
"""
return DiDegreeView(self)
@property
@clear_mutation_cache
@patch_docstring(RefDiGraph.in_degree)
def in_degree(self):
return InDegreeView(self)
@property
@clear_mutation_cache
@patch_docstring(RefDiGraph.out_degree)
def out_degree(self):
return OutDegreeView(self)
[docs] @patch_docstring(RefDiGraph.is_directed)
def is_directed(self):
return True
[docs] @patch_docstring(RefDiGraph.is_multigraph)
def is_multigraph(self):
return False
[docs] @clear_mutation_cache
@patch_docstring(RefDiGraph.reverse)
def reverse(self, copy=True):
self._convert_arrow_to_dynamic()
if not copy:
g = reverse_view(self)
g._op = self._op
g._key = self._key
g._schema = deepcopy(self._schema)
g._is_client_view = True
else:
g = self.__class__(create_empty_in_engine=False)
g.graph = self.graph
g.name = self.name
op = dag_utils.copy_graph(self, "reverse")
g._op = op
graph_def = op.eval(leaf=False)
g._key = graph_def.key
g.cache.warmup()
g._session = self._session
return g