# Notes about NetworkX namespace objects set up here:
#
# nx.utils.backends.backends:
# dict keyed by backend name to the backend entry point object.
# Filled using ``_get_backends("networkx.backends")`` during import of this module.
#
# nx.utils.backends.backend_info:
# dict keyed by backend name to the metadata returned by the function indicated
# by the "networkx.backend_info" entry point.
# Created as an empty dict while importing this module, but later filled using
# ``_set_configs_from_environment()`` at end of importing ``networkx/__init__.py``.
#
# nx.config:
# Config object for NetworkX config setting. Created using
# ``_set_configs_from_environment()`` at end of importing ``networkx/__init__.py``.
#
# private dicts:
# nx.utils.backends._loaded_backends:
# dict used to memoize loaded backends. Keyed by backend name to loaded backends.
#
# nx.utils.backends._registered_algorithms:
# dict of all the dispatchable functions in networkx, keyed by _dispatchable
# function name to the wrapped function object.
import inspect
import itertools
import logging
import os
import typing
import warnings
from functools import partial
from importlib.metadata import entry_points
import networkx as nx
from .configs import BackendPriorities, Config, NetworkXConfig
from .decorators import argmap
__all__ = ["_dispatchable"]
_logger = logging.getLogger(__name__)
FAILED_TO_CONVERT = "FAILED_TO_CONVERT"
def _get_backends(group, *, load_and_call=False):
"""
Retrieve NetworkX ``backends`` and ``backend_info`` from the entry points.
Parameters
-----------
group : str
The entry_point to be retrieved.
load_and_call : bool, optional
If True, load and call the backend. Defaults to False.
Returns
--------
dict
A dictionary mapping backend names to their respective backend objects.
Notes
------
If a backend is defined more than once, a warning is issued.
The "nx_loopback" backend is removed if it exists, as it is only available during testing.
A warning is displayed if an error occurs while loading a backend.
"""
items = entry_points(group=group)
rv = {}
for ep in items:
if ep.name in rv:
warnings.warn(
f"networkx backend defined more than once: {ep.name}",
RuntimeWarning,
stacklevel=2,
)
elif load_and_call:
try:
rv[ep.name] = ep.load()()
except Exception as exc:
warnings.warn(
f"Error encountered when loading info for backend {ep.name}: {exc}",
RuntimeWarning,
stacklevel=2,
)
else:
rv[ep.name] = ep
rv.pop("nx_loopback", None)
return rv
# Note: "networkx" is in `backend_info` but ignored in `backends` and `config.backends`.
# It is valid to use "networkx" as a backend argument and in `config.backend_priority`.
# If we make "networkx" a "proper" backend, put it in `backends` and `config.backends`.
backends = _get_backends("networkx.backends")
# Use _set_configs_from_environment() below to fill backend_info dict as
# the last step in importing networkx
backend_info = {}
# Load and cache backends on-demand
_loaded_backends = {} # type: ignore[var-annotated]
_registered_algorithms = {}
# Get default configuration from environment variables at import time
def _comma_sep_to_list(string):
return [x_strip for x in string.strip().split(",") if (x_strip := x.strip())]
def _set_configs_from_environment():
"""Initialize ``config.backend_priority``, load backend_info and config.
This gets default values from environment variables (see ``nx.config`` for details).
This function is run at the very end of importing networkx. It is run at this time
to avoid loading backend_info before the rest of networkx is imported in case a
backend uses networkx for its backend_info (e.g. subclassing the Config class.)
"""
# backend_info is defined above as empty dict. Fill it after import finishes.
backend_info.update(_get_backends("networkx.backend_info", load_and_call=True))
backend_info.update(
(backend, {}) for backend in backends.keys() - backend_info.keys()
)
# set up config based on backend_info and environment
backend_config = {}
for backend, info in backend_info.items():
if "default_config" not in info:
cfg = Config()
else:
cfg = info["default_config"]
if not isinstance(cfg, Config):
cfg = Config(**cfg)
backend_config[backend] = cfg
backend_config = Config(**backend_config)
# Setting doc of backends_config type is not setting doc of Config
# Config has __new__ method that returns instance with a unique type!
type(backend_config).__doc__ = "All installed NetworkX backends and their configs."
backend_priority = BackendPriorities(algos=[], generators=[])
config = NetworkXConfig(
backend_priority=backend_priority,
backends=backend_config,
cache_converted_graphs=bool(
os.environ.get("NETWORKX_CACHE_CONVERTED_GRAPHS", True)
),
fallback_to_nx=bool(os.environ.get("NETWORKX_FALLBACK_TO_NX", False)),
warnings_to_ignore=set(
_comma_sep_to_list(os.environ.get("NETWORKX_WARNINGS_TO_IGNORE", ""))
),
)
# Add "networkx" item to backend_info now b/c backend_config is set up
backend_info["networkx"] = {}
# NETWORKX_BACKEND_PRIORITY is the same as NETWORKX_BACKEND_PRIORITY_ALGOS
priorities = {
key[26:].lower(): val
for key, val in os.environ.items()
if key.startswith("NETWORKX_BACKEND_PRIORITY_")
}
backend_priority = config.backend_priority
backend_priority.algos = (
_comma_sep_to_list(priorities.pop("algos"))
if "algos" in priorities
else _comma_sep_to_list(
os.environ.get(
"NETWORKX_BACKEND_PRIORITY",
os.environ.get("NETWORKX_AUTOMATIC_BACKENDS", ""),
)
)
)
backend_priority.generators = _comma_sep_to_list(priorities.pop("generators", ""))
for key in sorted(priorities):
backend_priority[key] = _comma_sep_to_list(priorities[key])
return config
def _do_nothing():
"""This does nothing at all, yet it helps turn ``_dispatchable`` into functions.
Use this with the ``argmap`` decorator to turn ``self`` into a function. It results
in some small additional overhead compared to calling ``_dispatchable`` directly,
but ``argmap`` has the property that it can stack with other ``argmap``
decorators "for free". Being a function is better for REPRs and type-checkers.
"""
def _always_run(name, args, kwargs):
return True
def _load_backend(backend_name):
if backend_name in _loaded_backends:
return _loaded_backends[backend_name]
if backend_name not in backends:
raise ImportError(f"'{backend_name}' backend is not installed")
rv = _loaded_backends[backend_name] = backends[backend_name].load()
if not hasattr(rv, "can_run"):
rv.can_run = _always_run
if not hasattr(rv, "should_run"):
rv.should_run = _always_run
return rv
class _dispatchable:
_is_testing = False
def __new__(
cls,
func=None,
*,
name=None,
graphs="G",
edge_attrs=None,
node_attrs=None,
preserve_edge_attrs=False,
preserve_node_attrs=False,
preserve_graph_attrs=False,
preserve_all_attrs=False,
mutates_input=False,
returns_graph=False,
implemented_by_nx=True,
):
"""A decorator function that is used to redirect the execution of ``func``
function to its backend implementation.
This decorator allows the function to dispatch to different backend
implementations based on the input graph types, and also manages the
extra keywords ``backend`` and ``**backend_kwargs``.
Usage can be any of the following decorator forms:
- ``@_dispatchable``
- ``@_dispatchable()``
- ``@_dispatchable(name="override_name")``
- ``@_dispatchable(graphs="graph_var_name")``
- ``@_dispatchable(edge_attrs="weight")``
- ``@_dispatchable(graphs={"G": 0, "H": 1}, edge_attrs={"weight": "default"})``
with 0 and 1 giving the position in the signature function for graph
objects. When ``edge_attrs`` is a dict, keys are keyword names and values
are defaults.
Parameters
----------
func : callable, optional (default: None)
The function to be decorated. If None, ``_dispatchable`` returns a
partial object that can be used to decorate a function later. If ``func``
is a callable, returns a new callable object that dispatches to a backend
function based on input graph types.
name : str, optional (default: name of `func`)
The name for the function as used for dispatching. If not provided,
the name of ``func`` will be used. ``name`` is useful to avoid name
conflicts, as all dispatched functions live in a single namespace.
For example, ``nx.tournament.is_strongly_connected`` had a name
conflict with the standard ``nx.is_strongly_connected``, so we used
``@_dispatchable(name="tournament_is_strongly_connected")``.
graphs : str or dict or None, optional (default: "G")
If a string, the parameter name of the graph, which must be the first
argument of the wrapped function. If more than one graph is required
for the function (or if the graph is not the first argument), provide
a dict keyed by graph parameter name to the value parameter position.
A question mark in the name indicates an optional argument.
For example, ``@_dispatchable(graphs={"G": 0, "auxiliary?": 4})``
indicates the 0th parameter ``G`` of the function is a required graph,
and the 4th parameter ``auxiliary?`` is an optional graph.
To indicate that an argument is a list of graphs, do ``"[graphs]"``.
Use ``graphs=None``, if *no* arguments are NetworkX graphs such as for
graph generators, readers, and conversion functions.
edge_attrs : str or dict, optional (default: None)
``edge_attrs`` holds information about edge attribute arguments
and default values for those edge attributes.
If a string, ``edge_attrs`` holds the function argument name that
indicates a single edge attribute to include in the converted graph.
The default value for this attribute is 1. To indicate that an argument
is a list of attributes (all with default value 1), use e.g. ``"[attrs]"``.
If a dict, ``edge_attrs`` holds a dict keyed by argument names, with
values that are either the default value or, if a string, the argument
name that indicates the default value.
If None, function does not use edge attributes.
node_attrs : str or dict, optional
Like ``edge_attrs``, but for node attributes.
preserve_edge_attrs : bool or str or dict, optional (default: False)
If bool, whether to preserve all edge attributes.
If a string, the parameter name that may indicate (with ``True`` or a
callable argument) whether all edge attributes should be preserved
when converting graphs to a backend graph type.
If a dict of form ``{graph_name: {attr: default}}``, indicate
pre-determined edge attributes (and defaults) to preserve for the
indicated input graph.
preserve_node_attrs : bool or str or dict, optional (default: False)
Like ``preserve_edge_attrs``, but for node attributes.
preserve_graph_attrs : bool or set, optional (default: False)
If bool, whether to preserve all graph attributes.
If set, which input graph arguments to preserve graph attributes.
preserve_all_attrs : bool, optional (default: False)
Whether to preserve all edge, node and graph attributes.
If True, this overrides all the other preserve_*_attrs.
mutates_input : bool or dict, optional (default: False)
If bool, whether the function mutates an input graph argument.
If dict of ``{arg_name: arg_pos}``, name and position of bool arguments
that indicate whether an input graph will be mutated, and ``arg_name``
may begin with ``"not "`` to negate the logic (for example, ``"not copy"``
means we mutate the input graph when the ``copy`` argument is False).
By default, dispatching doesn't convert input graphs to a different
backend for functions that mutate input graphs.
returns_graph : bool, optional (default: False)
Whether the function can return or yield a graph object. By default,
dispatching doesn't convert input graphs to a different backend for
functions that return graphs.
implemented_by_nx : bool, optional (default: True)
Whether the function is implemented by NetworkX. If it is not, then the
function is included in NetworkX only as an API to dispatch to backends.
Default is True.
"""
if func is None:
return partial(
_dispatchable,
name=name,
graphs=graphs,
edge_attrs=edge_attrs,
node_attrs=node_attrs,
preserve_edge_attrs=preserve_edge_attrs,
preserve_node_attrs=preserve_node_attrs,
preserve_graph_attrs=preserve_graph_attrs,
preserve_all_attrs=preserve_all_attrs,
mutates_input=mutates_input,
returns_graph=returns_graph,
implemented_by_nx=implemented_by_nx,
)
if isinstance(func, str):
raise TypeError("'name' and 'graphs' must be passed by keyword") from None
# If name not provided, use the name of the function
if name is None:
name = func.__name__
self = object.__new__(cls)
# standard function-wrapping stuff
# __annotations__ not used
self.__name__ = func.__name__
# self.__doc__ = func.__doc__ # __doc__ handled as cached property
self.__defaults__ = func.__defaults__
# Add `backend=` keyword argument to allow backend choice at call-time
if func.__kwdefaults__:
self.__kwdefaults__ = {**func.__kwdefaults__, "backend": None}
else:
self.__kwdefaults__ = {"backend": None}
self.__module__ = func.__module__
self.__qualname__ = func.__qualname__
self.__dict__.update(func.__dict__)
self.__wrapped__ = func
# Supplement docstring with backend info; compute and cache when needed
self._orig_doc = func.__doc__
self._cached_doc = None
self.orig_func = func
self.name = name
self.edge_attrs = edge_attrs
self.node_attrs = node_attrs
self.preserve_edge_attrs = preserve_edge_attrs or preserve_all_attrs
self.preserve_node_attrs = preserve_node_attrs or preserve_all_attrs
self.preserve_graph_attrs = preserve_graph_attrs or preserve_all_attrs
self.mutates_input = mutates_input
# Keep `returns_graph` private for now, b/c we may extend info on return types
self._returns_graph = returns_graph
if edge_attrs is not None and not isinstance(edge_attrs, str | dict):
raise TypeError(
f"Bad type for edge_attrs: {type(edge_attrs)}. Expected str or dict."
) from None
if node_attrs is not None and not isinstance(node_attrs, str | dict):
raise TypeError(
f"Bad type for node_attrs: {type(node_attrs)}. Expected str or dict."
) from None
if not isinstance(self.preserve_edge_attrs, bool | str | dict):
raise TypeError(
f"Bad type for preserve_edge_attrs: {type(self.preserve_edge_attrs)}."
" Expected bool, str, or dict."
) from None
if not isinstance(self.preserve_node_attrs, bool | str | dict):
raise TypeError(
f"Bad type for preserve_node_attrs: {type(self.preserve_node_attrs)}."
" Expected bool, str, or dict."
) from None
if not isinstance(self.preserve_graph_attrs, bool | set):
raise TypeError(
f"Bad type for preserve_graph_attrs: {type(self.preserve_graph_attrs)}."
" Expected bool or set."
) from None
if not isinstance(self.mutates_input, bool | dict):
raise TypeError(
f"Bad type for mutates_input: {type(self.mutates_input)}."
" Expected bool or dict."
) from None
if not isinstance(self._returns_graph, bool):
raise TypeError(
f"Bad type for returns_graph: {type(self._returns_graph)}."
" Expected bool."
) from None
if isinstance(graphs, str):
graphs = {graphs: 0}
elif graphs is None:
pass
elif not isinstance(graphs, dict):
raise TypeError(
f"Bad type for graphs: {type(graphs)}. Expected str or dict."
) from None
elif len(graphs) == 0:
raise KeyError("'graphs' must contain at least one variable name") from None
# This dict comprehension is complicated for better performance; equivalent shown below.
self.optional_graphs = set()
self.list_graphs = set()
if graphs is None:
self.graphs = {}
else:
self.graphs = {
self.optional_graphs.add(val := k[:-1]) or val
if (last := k[-1]) == "?"
else self.list_graphs.add(val := k[1:-1]) or val
if last == "]"
else k: v
for k, v in graphs.items()
}
# The above is equivalent to:
# self.optional_graphs = {k[:-1] for k in graphs if k[-1] == "?"}
# self.list_graphs = {k[1:-1] for k in graphs if k[-1] == "]"}
# self.graphs = {k[:-1] if k[-1] == "?" else k: v for k, v in graphs.items()}
# Compute and cache the signature on-demand
self._sig = None
# Which backends implement this function?
self.backends = {
backend
for backend, info in backend_info.items()
if "functions" in info and name in info["functions"]
}
if implemented_by_nx:
self.backends.add("networkx")
if name in _registered_algorithms:
raise KeyError(
f"Algorithm already exists in dispatch registry: {name}"
) from None
# Use the `argmap` decorator to turn `self` into a function. This does result
# in small additional overhead compared to calling `_dispatchable` directly,
# but `argmap` has the property that it can stack with other `argmap`
# decorators "for free". Being a function is better for REPRs and type-checkers.
self = argmap(_do_nothing)(self)
_registered_algorithms[name] = self
return self
@property
def __doc__(self):
"""If the cached documentation exists, it is returned.
Otherwise, the documentation is generated using _make_doc() method,
cached, and then returned."""
rv = self._cached_doc
if rv is None:
rv = self._cached_doc = self._make_doc()
return rv
@__doc__.setter
def __doc__(self, val):
"""Sets the original documentation to the given value and resets the
cached documentation."""
self._orig_doc = val
self._cached_doc = None
@property
def __signature__(self):
"""Return the signature of the original function, with the addition of
the `backend` and `backend_kwargs` parameters."""
if self._sig is None:
sig = inspect.signature(self.orig_func)
# `backend` is now a reserved argument used by dispatching.
# assert "backend" not in sig.parameters
if not any(
p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()
):
sig = sig.replace(
parameters=[
*sig.parameters.values(),
inspect.Parameter(
"backend", inspect.Parameter.KEYWORD_ONLY, default=None
),
inspect.Parameter(
"backend_kwargs", inspect.Parameter.VAR_KEYWORD
),
]
)
else:
*parameters, var_keyword = sig.parameters.values()
sig = sig.replace(
parameters=[
*parameters,
inspect.Parameter(
"backend", inspect.Parameter.KEYWORD_ONLY, default=None
),
var_keyword,
]
)
self._sig = sig
return self._sig
# Fast, simple path if no backends are installed
def _call_if_no_backends_installed(self, /, *args, backend=None, **kwargs):
"""Returns the result of the original function (no backends installed)."""
if backend is not None and backend != "networkx":
raise ImportError(f"'{backend}' backend is not installed")
if "networkx" not in self.backends:
raise NotImplementedError(
f"'{self.name}' is not implemented by 'networkx' backend. "
"This function is included in NetworkX as an API to dispatch to "
"other backends."
)
return self.orig_func(*args, **kwargs)
# Dispatch to backends based on inputs, `backend=` arg, or configuration
def _call_if_any_backends_installed(self, /, *args, backend=None, **kwargs):
"""Returns the result of the original function, or the backend function if
the backend is specified and that backend implements `func`."""
# Use `backend_name` in this function instead of `backend`.
# This is purely for aesthetics and to make it easier to search for this
# variable since "backend" is used in many comments and log/error messages.
backend_name = backend
if backend_name is not None and backend_name not in backend_info:
raise ImportError(f"'{backend_name}' backend is not installed")
graphs_resolved = {}
for gname, pos in self.graphs.items():
if pos < len(args):
if gname in kwargs:
raise TypeError(f"{self.name}() got multiple values for {gname!r}")
graph = args[pos]
elif gname in kwargs:
graph = kwargs[gname]
elif gname not in self.optional_graphs:
raise TypeError(
f"{self.name}() missing required graph argument: {gname}"
)
else:
continue
if graph is None:
if gname not in self.optional_graphs:
raise TypeError(
f"{self.name}() required graph argument {gname!r} is None; must be a graph"
)
else:
graphs_resolved[gname] = graph
# Alternative to the above that does not check duplicated args or missing required graphs.
# graphs_resolved = {
# gname: graph
# for gname, pos in self.graphs.items()
# if (graph := args[pos] if pos < len(args) else kwargs.get(gname)) is not None
# }
# Check if any graph comes from a backend
if self.list_graphs:
# Make sure we don't lose values by consuming an iterator
args = list(args)
for gname in self.list_graphs & graphs_resolved.keys():
list_of_graphs = list(graphs_resolved[gname])
graphs_resolved[gname] = list_of_graphs
if gname in kwargs:
kwargs[gname] = list_of_graphs
else:
args[self.graphs[gname]] = list_of_graphs
graph_backend_names = {
getattr(g, "__networkx_backend__", None)
for gname, g in graphs_resolved.items()
if gname not in self.list_graphs
}
for gname in self.list_graphs & graphs_resolved.keys():
graph_backend_names.update(
getattr(g, "__networkx_backend__", None)
for g in graphs_resolved[gname]
)
else:
graph_backend_names = {
getattr(g, "__networkx_backend__", None)
for g in graphs_resolved.values()
}
backend_priority = nx.config.backend_priority.get(
self.name,
nx.config.backend_priority.generators
if self._returns_graph
else nx.config.backend_priority.algos,
)
fallback_to_nx = nx.config.fallback_to_nx and "networkx" in self.backends
if self._is_testing and backend_priority and backend_name is None:
# Special path if we are running networkx tests with a backend.
# This even runs for (and handles) functions that mutate input graphs.
return self._convert_and_call_for_tests(
backend_priority[0],
args,
kwargs,
fallback_to_nx=fallback_to_nx,
)
graph_backend_names.discard(None)
if backend_name is not None:
# Must run with the given backend.
# `can_run` only used for better log and error messages.
# Check `mutates_input` for logging, not behavior.
backend_kwarg_msg = (
"No other backends will be attempted, because the backend was "
f"specified with the `backend='{backend_name}'` keyword argument."
)
extra_message = (
f"'{backend_name}' backend raised NotImplementedError when calling "
f"'{self.name}'. {backend_kwarg_msg}"
)
if not graph_backend_names or graph_backend_names == {backend_name}:
# All graphs are backend graphs--no need to convert!
if self._can_backend_run(backend_name, args, kwargs):
return self._call_with_backend(
backend_name, args, kwargs, extra_message=extra_message
)
if self._does_backend_have(backend_name):
extra = " for the given arguments"
else:
extra = ""
raise NotImplementedError(
f"'{self.name}' is not implemented by '{backend_name}' backend"
f"{extra}. {backend_kwarg_msg}"
)
if self._can_convert(backend_name, graph_backend_names):
if self._can_backend_run(backend_name, args, kwargs):
if self._will_call_mutate_input(args, kwargs):
_logger.debug(
"'%s' will mutate an input graph. This prevents automatic conversion "
"to, and use of, backends listed in `nx.config.backend_priority`. "
"Using backend specified by the "
"`backend='%s'` keyword argument. This may change behavior by not "
"mutating inputs.",
self.name,
backend_name,
)
mutations = []
else:
mutations = None
rv = self._convert_and_call(
backend_name,
graph_backend_names,
args,
kwargs,
extra_message=extra_message,
mutations=mutations,
)
if mutations:
for cache, key in mutations:
# If the call mutates inputs, then remove all inputs gotten
# from cache. We do this after all conversions (and call) so
# that a graph can be gotten from a cache multiple times.
cache.pop(key, None)
return rv
if self._does_backend_have(backend_name):
extra = " for the given arguments"
else:
extra = ""
raise NotImplementedError(
f"'{self.name}' is not implemented by '{backend_name}' backend"
f"{extra}. {backend_kwarg_msg}"
)
if len(graph_backend_names) == 1:
maybe_s = ""
graph_backend_names = f"'{next(iter(graph_backend_names))}'"
else:
maybe_s = "s"
raise TypeError(
f"'{self.name}' is unable to convert graph from backend{maybe_s} "
f"{graph_backend_names} to '{backend_name}' backend, which was "
f"specified with the `backend='{backend_name}'` keyword argument. "
f"{backend_kwarg_msg}"
)
if self._will_call_mutate_input(args, kwargs):
# The current behavior for functions that mutate input graphs:
#
# 1. If backend is specified by `backend=` keyword, use it (done above).
# 2. If inputs are from one backend, try to use it.
# 3. If all input graphs are instances of `nx.Graph`, then run with the
# default "networkx" implementation.
#
# Do not automatically convert if a call will mutate inputs, because doing
# so would change behavior. Hence, we should fail if there are multiple input
# backends or if the input backend does not implement the function. However,
# we offer a way for backends to circumvent this if they do not implement
# this function: we will fall back to the default "networkx" implementation
# without using conversions if all input graphs are subclasses of `nx.Graph`.
mutate_msg = (
"conversions between backends (if configured) will not be attempted "
"because the original input graph would not be mutated. Using the "
"backend keyword e.g. `backend='some_backend'` will force conversions "
"and not mutate the original input graph."
)
fallback_msg = (
"This call will mutate inputs, so fall back to 'networkx' "
"backend (without converting) since all input graphs are "
"instances of nx.Graph and are hopefully compatible."
)
if len(graph_backend_names) == 1:
[backend_name] = graph_backend_names
msg_template = (
f"Backend '{backend_name}' does not implement '{self.name}'%s. "
f"This call will mutate an input, so automatic {mutate_msg}"
)
# `can_run` is only used for better log and error messages
try:
if self._can_backend_run(backend_name, args, kwargs):
return self._call_with_backend(
backend_name,
args,
kwargs,
extra_message=msg_template % " with these arguments",
)
except NotImplementedError as exc:
if all(isinstance(g, nx.Graph) for g in graphs_resolved.values()):
_logger.debug(
"Backend '%s' raised when calling '%s': %s. %s",
backend_name,
self.name,
exc,
fallback_msg,
)
else:
raise
else:
if fallback_to_nx and all(
# Consider dropping the `isinstance` check here to allow
# duck-type graphs, but let's wait for a backend to ask us.
isinstance(g, nx.Graph)
for g in graphs_resolved.values()
):
# Log that we are falling back to networkx
_logger.debug(
"Backend '%s' can't run '%s'. %s",
backend_name,
self.name,
fallback_msg,
)
else:
if self._does_backend_have(backend_name):
extra = " with these arguments"
else:
extra = ""
raise NotImplementedError(msg_template % extra)
elif fallback_to_nx and all(
# Consider dropping the `isinstance` check here to allow
# duck-type graphs, but let's wait for a backend to ask us.
isinstance(g, nx.Graph)
for g in graphs_resolved.values()
):
# Log that we are falling back to networkx
_logger.debug(
"'%s' was called with inputs from multiple backends: %s. %s",
self.name,
graph_backend_names,
fallback_msg,
)
else:
raise RuntimeError(
f"'{self.name}' will mutate an input, but it was called with "
f"inputs from multiple backends: {graph_backend_names}. "
f"Automatic {mutate_msg}"
)
# At this point, no backends are available to handle the call with
# the input graph types, but if the input graphs are compatible
# nx.Graph instances, fall back to networkx without converting.
return self.orig_func(*args, **kwargs)
# We may generalize fallback configuration as e.g. `nx.config.backend_fallback`
if fallback_to_nx or not graph_backend_names:
# Use "networkx" by default if there are no inputs from backends.
# For example, graph generators should probably return NetworkX graphs
# instead of raising NotImplementedError.
backend_fallback = ["networkx"]
else:
backend_fallback = []
# ##########################
# # How this behaves today #
# ##########################
#
# The prose below describes the implementation and a *possible* way to
# generalize "networkx" as "just another backend". The code is structured
# to perhaps someday support backend-to-backend conversions (including
# simply passing objects from one backend directly to another backend;
# the dispatch machinery does not necessarily need to perform conversions),
# but since backend-to-backend matching is not yet supported, the following
# code is merely a convenient way to implement dispatch behaviors that have
# been carefully developed since NetworkX 3.0 and to include falling back
# to the default NetworkX implementation.
#
# The current behavior for functions that don't mutate input graphs:
#
# 1. If backend is specified by `backend=` keyword, use it (done above).
# 2. If input is from a backend other than "networkx", try to use it.
# - Note: if present, "networkx" graphs will be converted to the backend.
# 3. If input is from "networkx" (or no backend), try to use backends from
# `backend_priority` before running with the default "networkx" implementation.
# 4. If configured, "fall back" and run with the default "networkx" implementation.
#
# ################################################
# # How this is implemented and may work someday #
# ################################################
#
# Let's determine the order of backends we should try according
# to `backend_priority`, `backend_fallback`, and input backends.
# There are two† dimensions of priorities to consider:
# backend_priority > unspecified > backend_fallback
# and
# backend of an input > not a backend of an input
# These are combined to form five groups of priorities as such:
#
# input ~input
# +-------+-------+
# backend_priority | 1 | 2 |
# unspecified | 3 | N/A | (if only 1)
# backend_fallback | 4 | 5 |
# +-------+-------+
#
# This matches the behaviors we developed in versions 3.0 to 3.2, it
# ought to cover virtually all use cases we expect, and I (@eriknw) don't
# think it can be done any simpler (although it can be generalized further
# and made to be more complicated to capture 100% of *possible* use cases).
# Some observations:
#
# 1. If an input is in `backend_priority`, it will be used before trying a
# backend that is higher priority in `backend_priority` and not an input.
# 2. To prioritize converting from one backend to another even if both implement
# a function, list one in `backend_priority` and one in `backend_fallback`.
# 3. To disable conversions, set `backend_priority` and `backend_fallback` to [].
#
# †: There is actually a third dimension of priorities:
# should_run == True > should_run == False
# Backends with `can_run == True` and `should_run == False` are tried last.
#
seen = set()
group1 = [] # In backend_priority, and an input
group2 = [] # In backend_priority, but not an input
for name in backend_priority:
if name in seen:
continue
seen.add(name)
if name in graph_backend_names:
group1.append(name)
else:
group2.append(name)
group4 = [] # In backend_fallback, and an input
group5 = [] # In backend_fallback, but not an input
for name in backend_fallback:
if name in seen:
continue
seen.add(name)
if name in graph_backend_names:
group4.append(name)
else:
group5.append(name)
# An input, but not in backend_priority or backend_fallback.
group3 = graph_backend_names - seen
if len(group3) > 1:
# `group3` backends are not configured for automatic conversion or fallback.
# There are at least two issues if this group contains multiple backends:
#
# 1. How should we prioritize them? We have no good way to break ties.
# Although we could arbitrarily choose alphabetical or left-most,
# let's follow the Zen of Python and refuse the temptation to guess.
# 2. We probably shouldn't automatically convert to these backends,
# because we are not configured to do so.
#
# (2) is important to allow disabling all conversions by setting both
# `nx.config.backend_priority` and `nx.config.backend_fallback` to [].
#
# If there is a single backend in `group3`, then giving it priority over
# the fallback backends is what is generally expected. For example, this
# allows input graphs of `backend_fallback` backends (such as "networkx")
# to be converted to, and run with, the unspecified backend.
_logger.debug(
"Call to '%s' has inputs from multiple backends, %s, that "
"have no priority set in `nx.config.backend_priority`, "
"so automatic conversions to "
"these backends will not be attempted.",
self.name,
group3,
)
group3 = ()
try_order = list(itertools.chain(group1, group2, group3, group4, group5))
if len(try_order) > 1:
# Should we consider adding an option for more verbose logging?
# For example, we could explain the order of `try_order` in detail.
_logger.debug(
"Call to '%s' has inputs from %s backends, and will try to use "
"backends in the following order: %s",
self.name,
graph_backend_names or "no",
try_order,
)
backends_to_try_again = []
for is_not_first, backend_name in enumerate(try_order):
if is_not_first:
_logger.debug("Trying next backend: '%s'", backend_name)
try:
if not graph_backend_names or graph_backend_names == {backend_name}:
if self._can_backend_run(backend_name, args, kwargs):
return self._call_with_backend(backend_name, args, kwargs)
elif self._can_convert(
backend_name, graph_backend_names
) and self._can_backend_run(backend_name, args, kwargs):
if self._should_backend_run(backend_name, args, kwargs):
rv = self._convert_and_call(
backend_name, graph_backend_names, args, kwargs
)
if (
self._returns_graph
and graph_backend_names
and backend_name not in graph_backend_names
):
# If the function has graph inputs and graph output, we try
# to make it so the backend of the return type will match the
# backend of the input types. In case this is not possible,
# let's tell the user that the backend of the return graph
# has changed. Perhaps we could try to convert back, but
# "fallback" backends for graph generators should typically
# be compatible with NetworkX graphs.
_logger.debug(
"Call to '%s' is returning a graph from a different "
"backend! It has inputs from %s backends, but ran with "
"'%s' backend and is returning graph from '%s' backend",
self.name,
graph_backend_names,
backend_name,
backend_name,
)
return rv
# `should_run` is False, but `can_run` is True, so try again later
backends_to_try_again.append(backend_name)
except NotImplementedError as exc:
_logger.debug(
"Backend '%s' raised when calling '%s': %s",
backend_name,
self.name,
exc,
)
# We are about to fail. Let's try backends with can_run=True and should_run=False.
# This is unlikely to help today since we try to run with "networkx" before this.
for backend_name in backends_to_try_again:
_logger.debug(
"Trying backend: '%s' (ignoring `should_run=False`)", backend_name
)
try:
rv = self._convert_and_call(
backend_name, graph_backend_names, args, kwargs
)
if (
self._returns_graph
and graph_backend_names
and backend_name not in graph_backend_names
):
_logger.debug(
"Call to '%s' is returning a graph from a different "
"backend! It has inputs from %s backends, but ran with "
"'%s' backend and is returning graph from '%s' backend",
self.name,
graph_backend_names,
backend_name,
backend_name,
)
return rv
except NotImplementedError as exc:
_logger.debug(
"Backend '%s' raised when calling '%s': %s",
backend_name,
self.name,
exc,
)
# As a final effort, we could try to convert and run with `group3` backends
# that we discarded when `len(group3) > 1`, but let's not consider doing
# so until there is a reasonable request for it.
if len(unspecified_backends := graph_backend_names - seen) > 1:
raise TypeError(
f"Unable to convert inputs from {graph_backend_names} backends and "
f"run '{self.name}'. NetworkX is configured to automatically convert "
f"to {try_order} backends. To remedy this, you may enable automatic "
f"conversion to {unspecified_backends} backends by adding them to "
"`nx.config.backend_priority`, or you "
"may specify a backend to use with the `backend=` keyword argument."
)
if "networkx" not in self.backends:
extra = (
" This function is included in NetworkX as an API to dispatch to "
"other backends."
)
else:
extra = ""
raise NotImplementedError(
f"'{self.name}' is not implemented by {try_order} backends. To remedy "
"this, you may enable automatic conversion to more backends (including "
"'networkx') by adding them to `nx.config.backend_priority`, "
"or you may specify a backend to use with "
f"the `backend=` keyword argument.{extra}"
)
# Dispatch only if there exist any installed backend(s)
__call__: typing.Callable = (
_call_if_any_backends_installed if backends else _call_if_no_backends_installed
)
def _will_call_mutate_input(self, args, kwargs):
# Fairly few nx functions mutate the input graph. Most that do, always do.
# So a boolean input indicates "always" or "never".
if isinstance((mutates_input := self.mutates_input), bool):
return mutates_input
# The ~10 other nx functions either use "copy=True" to control mutation or
# an arg naming an edge/node attribute to mutate (None means no mutation).
# Now `mutates_input` is a dict keyed by arg_name to its func-sig position.
# The `copy=` args are keyed as "not copy" to mean "negate the copy argument".
# Keys w/o "not " mean the call mutates only when the arg value `is not None`.
#
# This section might need different code if new functions mutate in new ways.
#
# NetworkX doesn't have any `mutates_input` dicts with more than 1 item.
# But we treat it like it might have more than 1 item for generality.
n = len(args)
return any(
(args[arg_pos] if n > arg_pos else kwargs.get(arg_name)) is not None
if not arg_name.startswith("not ")
# This assumes that e.g. `copy=True` is the default
else not (args[arg_pos] if n > arg_pos else kwargs.get(arg_name[4:], True))
for arg_name, arg_pos in mutates_input.items()
)
def _can_convert(self, backend_name, graph_backend_names):
# Backend-to-backend conversion not supported yet.
# We can only convert to and from networkx.
rv = backend_name == "networkx" or graph_backend_names.issubset(
{"networkx", backend_name}
)
if not rv:
_logger.debug(
"Unable to convert from %s backends to '%s' backend",
graph_backend_names,
backend_name,
)
return rv
def _does_backend_have(self, backend_name):
"""Does the specified backend have this algorithm?"""
if backend_name == "networkx":
return "networkx" in self.backends
# Inspect the backend; don't trust metadata used to create `self.backends`
backend = _load_backend(backend_name)
return hasattr(backend, self.name)
def _can_backend_run(self, backend_name, args, kwargs):
"""Can the specified backend run this algorithm with these arguments?"""
if backend_name == "networkx":
return "networkx" in self.backends
backend = _load_backend(backend_name)
# `backend.can_run` and `backend.should_run` may return strings that describe
# why they can't or shouldn't be run.
if not hasattr(backend, self.name):
_logger.debug(
"Backend '%s' does not implement '%s'", backend_name, self.name
)
return False
can_run = backend.can_run(self.name, args, kwargs)
if isinstance(can_run, str) or not can_run:
reason = f", because: {can_run}" if isinstance(can_run, str) else ""
_logger.debug(
"Backend '%s' can't run `%s` with arguments: %s%s",
backend_name,
self.name,
_LazyArgsRepr(self, args, kwargs),
reason,
)
return False
return True
def _should_backend_run(self, backend_name, args, kwargs):
"""Should the specified backend run this algorithm with these arguments?
Note that this does not check ``backend.can_run``.
"""
# `backend.can_run` and `backend.should_run` may return strings that describe
# why they can't or shouldn't be run.
# `_should_backend_run` may assume that `_can_backend_run` returned True.
if backend_name == "networkx":
return True
backend = _load_backend(backend_name)
should_run = backend.should_run(self.name, args, kwargs)
if isinstance(should_run, str) or not should_run:
reason = f", because: {should_run}" if isinstance(should_run, str) else ""
_logger.debug(
"Backend '%s' shouldn't run `%s` with arguments: %s%s",
backend_name,
self.name,
_LazyArgsRepr(self, args, kwargs),
reason,
)
return False
return True
def _convert_arguments(self, backend_name, args, kwargs, *, use_cache, mutations):
"""Convert graph arguments to the specified backend.
Returns
-------
args tuple and kwargs dict
"""
bound = self.__signature__.bind(*args, **kwargs)
bound.apply_defaults()
if not self.graphs:
bound_kwargs = bound.kwargs
del bound_kwargs["backend"]
return bound.args, bound_kwargs
if backend_name == "networkx":
# `backend_interface.convert_from_nx` preserves everything
preserve_edge_attrs = preserve_node_attrs = preserve_graph_attrs = True
else:
preserve_edge_attrs = self.preserve_edge_attrs
preserve_node_attrs = self.preserve_node_attrs
preserve_graph_attrs = self.preserve_graph_attrs
edge_attrs = self.edge_attrs
node_attrs = self.node_attrs
# Convert graphs into backend graph-like object
# Include the edge and/or node labels if provided to the algorithm
if preserve_edge_attrs is False:
# e.g. `preserve_edge_attrs=False`
pass
elif preserve_edge_attrs is True:
# e.g. `preserve_edge_attrs=True`
edge_attrs = None
elif isinstance(preserve_edge_attrs, str):
if bound.arguments[preserve_edge_attrs] is True or callable(
bound.arguments[preserve_edge_attrs]
):
# e.g. `preserve_edge_attrs="attr"` and `func(attr=True)`
# e.g. `preserve_edge_attrs="attr"` and `func(attr=myfunc)`
preserve_edge_attrs = True
edge_attrs = None
elif bound.arguments[preserve_edge_attrs] is False and (
isinstance(edge_attrs, str)
and edge_attrs == preserve_edge_attrs
or isinstance(edge_attrs, dict)
and preserve_edge_attrs in edge_attrs
):
# e.g. `preserve_edge_attrs="attr"` and `func(attr=False)`
# Treat `False` argument as meaning "preserve_edge_data=False"
# and not `False` as the edge attribute to use.
preserve_edge_attrs = False
edge_attrs = None
else:
# e.g. `preserve_edge_attrs="attr"` and `func(attr="weight")`
preserve_edge_attrs = False
# Else: e.g. `preserve_edge_attrs={"G": {"weight": 1}}`
if edge_attrs is None:
# May have been set to None above b/c all attributes are preserved
pass
elif isinstance(edge_attrs, str):
if edge_attrs[0] == "[":
# e.g. `edge_attrs="[edge_attributes]"` (argument of list of attributes)
# e.g. `func(edge_attributes=["foo", "bar"])`
edge_attrs = {
edge_attr: 1 for edge_attr in bound.arguments[edge_attrs[1:-1]]
}
elif callable(bound.arguments[edge_attrs]):
# e.g. `edge_attrs="weight"` and `func(weight=myfunc)`
preserve_edge_attrs = True
edge_attrs = None
elif bound.arguments[edge_attrs] is not None:
# e.g. `edge_attrs="weight"` and `func(weight="foo")` (default of 1)
edge_attrs = {bound.arguments[edge_attrs]: 1}
elif self.name == "to_numpy_array" and hasattr(
bound.arguments["dtype"], "names"
):
# Custom handling: attributes may be obtained from `dtype`
edge_attrs = {
edge_attr: 1 for edge_attr in bound.arguments["dtype"].names
}
else:
# e.g. `edge_attrs="weight"` and `func(weight=None)`
edge_attrs = None
else:
# e.g. `edge_attrs={"attr": "default"}` and `func(attr="foo", default=7)`
# e.g. `edge_attrs={"attr": 0}` and `func(attr="foo")`
edge_attrs = {
edge_attr: bound.arguments.get(val, 1) if isinstance(val, str) else val
for key, val in edge_attrs.items()
if (edge_attr := bound.arguments[key]) is not None
}
if preserve_node_attrs is False:
# e.g. `preserve_node_attrs=False`
pass
elif preserve_node_attrs is True:
# e.g. `preserve_node_attrs=True`
node_attrs = None
elif isinstance(preserve_node_attrs, str):
if bound.arguments[preserve_node_attrs] is True or callable(
bound.arguments[preserve_node_attrs]
):
# e.g. `preserve_node_attrs="attr"` and `func(attr=True)`
# e.g. `preserve_node_attrs="attr"` and `func(attr=myfunc)`
preserve_node_attrs = True
node_attrs = None
elif bound.arguments[preserve_node_attrs] is False and (
isinstance(node_attrs, str)
and node_attrs == preserve_node_attrs
or isinstance(node_attrs, dict)
and preserve_node_attrs in node_attrs
):
# e.g. `preserve_node_attrs="attr"` and `func(attr=False)`
# Treat `False` argument as meaning "preserve_node_data=False"
# and not `False` as the node attribute to use. Is this used?
preserve_node_attrs = False
node_attrs = None
else:
# e.g. `preserve_node_attrs="attr"` and `func(attr="weight")`
preserve_node_attrs = False
# Else: e.g. `preserve_node_attrs={"G": {"pos": None}}`
if node_attrs is None:
# May have been set to None above b/c all attributes are preserved
pass
elif isinstance(node_attrs, str):
if node_attrs[0] == "[":
# e.g. `node_attrs="[node_attributes]"` (argument of list of attributes)
# e.g. `func(node_attributes=["foo", "bar"])`
node_attrs = {
node_attr: None for node_attr in bound.arguments[node_attrs[1:-1]]
}
elif callable(bound.arguments[node_attrs]):
# e.g. `node_attrs="weight"` and `func(weight=myfunc)`
preserve_node_attrs = True
node_attrs = None
elif bound.arguments[node_attrs] is not None:
# e.g. `node_attrs="weight"` and `func(weight="foo")`
node_attrs = {bound.arguments[node_attrs]: None}
else:
# e.g. `node_attrs="weight"` and `func(weight=None)`
node_attrs = None
else:
# e.g. `node_attrs={"attr": "default"}` and `func(attr="foo", default=7)`
# e.g. `node_attrs={"attr": 0}` and `func(attr="foo")`
node_attrs = {
node_attr: bound.arguments.get(val) if isinstance(val, str) else val
for key, val in node_attrs.items()
if (node_attr := bound.arguments[key]) is not None
}
# It should be safe to assume that we either have networkx graphs or backend graphs.
# Future work: allow conversions between backends.
for gname in self.graphs:
if gname in self.list_graphs:
bound.arguments[gname] = [
self._convert_graph(
backend_name,
g,
edge_attrs=edge_attrs,
node_attrs=node_attrs,
preserve_edge_attrs=preserve_edge_attrs,
preserve_node_attrs=preserve_node_attrs,
preserve_graph_attrs=preserve_graph_attrs,
graph_name=gname,
use_cache=use_cache,
mutations=mutations,
)
if getattr(g, "__networkx_backend__", "networkx") != backend_name
else g
for g in bound.arguments[gname]
]
else:
graph = bound.arguments[gname]
if graph is None:
if gname in self.optional_graphs:
continue
raise TypeError(
f"Missing required graph argument `{gname}` in {self.name} function"
)
if isinstance(preserve_edge_attrs, dict):
preserve_edges = False
edges = preserve_edge_attrs.get(gname, edge_attrs)
else:
preserve_edges = preserve_edge_attrs
edges = edge_attrs
if isinstance(preserve_node_attrs, dict):
preserve_nodes = False
nodes = preserve_node_attrs.get(gname, node_attrs)
else:
preserve_nodes = preserve_node_attrs
nodes = node_attrs
if isinstance(preserve_graph_attrs, set):
preserve_graph = gname in preserve_graph_attrs
else:
preserve_graph = preserve_graph_attrs
if getattr(graph, "__networkx_backend__", "networkx") != backend_name:
bound.arguments[gname] = self._convert_graph(
backend_name,
graph,
edge_attrs=edges,
node_attrs=nodes,
preserve_edge_attrs=preserve_edges,
preserve_node_attrs=preserve_nodes,
preserve_graph_attrs=preserve_graph,
graph_name=gname,
use_cache=use_cache,
mutations=mutations,
)
bound_kwargs = bound.kwargs
del bound_kwargs["backend"]
return bound.args, bound_kwargs
def _convert_graph(
self,
backend_name,
graph,
*,
edge_attrs,
node_attrs,
preserve_edge_attrs,
preserve_node_attrs,
preserve_graph_attrs,
graph_name,
use_cache,
mutations,
):
nx_cache = getattr(graph, "__networkx_cache__", None) if use_cache else None
if nx_cache is not None:
cache = nx_cache.setdefault("backends", {}).setdefault(backend_name, {})
key = _get_cache_key(
edge_attrs=edge_attrs,
node_attrs=node_attrs,
preserve_edge_attrs=preserve_edge_attrs,
preserve_node_attrs=preserve_node_attrs,
preserve_graph_attrs=preserve_graph_attrs,
)
compat_key, rv = _get_from_cache(cache, key, mutations=mutations)
if rv is not None:
if "cache" not in nx.config.warnings_to_ignore:
warnings.warn(
"Note: conversions to backend graphs are saved to cache "
"(`G.__networkx_cache__` on the original graph) by default."
"\n\nThis warning means the cached graph is being used "
f"for the {backend_name!r} backend in the "
f"call to {self.name}.\n\nFor the cache to be consistent "
"(i.e., correct), the input graph must not have been "
"manually mutated since the cached graph was created. "
"Examples of manually mutating the graph data structures "
"resulting in an inconsistent cache include:\n\n"
" >>> G[u][v][key] = val\n\n"
"and\n\n"
" >>> for u, v, d in G.edges(data=True):\n"
" ... d[key] = val\n\n"
"Using methods such as `G.add_edge(u, v, weight=val)` "
"will correctly clear the cache to keep it consistent. "
"You may also use `G.__networkx_cache__.clear()` to "
"manually clear the cache, or set `G.__networkx_cache__` "
"to None to disable caching for G. Enable or disable caching "
"globally via `nx.config.cache_converted_graphs` config.\n\n"
"To disable this warning:\n\n"
' >>> nx.config.warnings_to_ignore.add("cache")\n'
)
if rv == FAILED_TO_CONVERT:
# NotImplementedError is reasonable to use since the backend doesn't
# implement this conversion. However, this will be different than
# the original exception that the backend raised when it failed.
# Using NotImplementedError allows the next backend to be attempted.
raise NotImplementedError(
"Graph conversion aborted: unable to convert graph to "
f"'{backend_name}' backend in call to `{self.name}', "
"because this conversion has previously failed."
)
_logger.debug(
"Using cached converted graph (from '%s' to '%s' backend) "
"in call to '%s' for '%s' argument",
getattr(graph, "__networkx_backend__", None),
backend_name,
self.name,
graph_name,
)
return rv
if backend_name == "networkx":
# Perhaps we should check that "__networkx_backend__" attribute exists
# and return the original object if not.
if not hasattr(graph, "__networkx_backend__"):
_logger.debug(
"Unable to convert input to 'networkx' backend in call to '%s' for "
"'%s argument, because it is not from a backend (i.e., it does not "
"have `G.__networkx_backend__` attribute). Using the original "
"object: %s",
self.name,
graph_name,
graph,
)
# This may fail, but let it fail in the networkx function
return graph
backend = _load_backend(graph.__networkx_backend__)
try:
rv = backend.convert_to_nx(graph)
except Exception:
if nx_cache is not None:
_set_to_cache(cache, key, FAILED_TO_CONVERT)
raise
else:
backend = _load_backend(backend_name)
try:
rv = backend.convert_from_nx(
graph,
edge_attrs=edge_attrs,
node_attrs=node_attrs,
preserve_edge_attrs=preserve_edge_attrs,
preserve_node_attrs=preserve_node_attrs,
# Always preserve graph attrs when we are caching b/c this should be
# cheap and may help prevent extra (unnecessary) conversions. Because
# we do this, we don't need `preserve_graph_attrs` in the cache key.
preserve_graph_attrs=preserve_graph_attrs or nx_cache is not None,
name=self.name,
graph_name=graph_name,
)
except Exception:
if nx_cache is not None:
_set_to_cache(cache, key, FAILED_TO_CONVERT)
raise
if nx_cache is not None:
_set_to_cache(cache, key, rv)
_logger.debug(
"Caching converted graph (from '%s' to '%s' backend) "
"in call to '%s' for '%s' argument",
getattr(graph, "__networkx_backend__", None),
backend_name,
self.name,
graph_name,
)
return rv
def _call_with_backend(self, backend_name, args, kwargs, *, extra_message=None):
"""Call this dispatchable function with a backend without converting inputs."""
if backend_name == "networkx":
return self.orig_func(*args, **kwargs)
backend = _load_backend(backend_name)
_logger.debug(
"Using backend '%s' for call to '%s' with arguments: %s",
backend_name,
self.name,
_LazyArgsRepr(self, args, kwargs),
)
try:
return getattr(backend, self.name)(*args, **kwargs)
except NotImplementedError as exc:
if extra_message is not None:
_logger.debug(
"Backend '%s' raised when calling '%s': %s",
backend_name,
self.name,
exc,
)
raise NotImplementedError(extra_message) from exc
raise
def _convert_and_call(
self,
backend_name,
input_backend_names,
args,
kwargs,
*,
extra_message=None,
mutations=None,
):
"""Call this dispatchable function with a backend after converting inputs.
Parameters
----------
backend_name : str
input_backend_names : set[str]
args : arguments tuple
kwargs : keywords dict
extra_message : str, optional
Additional message to log if NotImplementedError is raised by backend.
mutations : list, optional
Used to clear objects gotten from cache if inputs will be mutated.
"""
if backend_name == "networkx":
func = self.orig_func
else:
backend = _load_backend(backend_name)
func = getattr(backend, self.name)
other_backend_names = input_backend_names - {backend_name}
_logger.debug(
"Converting input graphs from %s backend%s to '%s' backend for call to '%s'",
other_backend_names
if len(other_backend_names) > 1
else f"'{next(iter(other_backend_names))}'",
"s" if len(other_backend_names) > 1 else "",
backend_name,
self.name,
)
try:
converted_args, converted_kwargs = self._convert_arguments(
backend_name,
args,
kwargs,
use_cache=nx.config.cache_converted_graphs,
mutations=mutations,
)
except NotImplementedError as exc:
# Only log the exception if we are adding an extra message
# because we don't want to lose any information.
_logger.debug(
"Failed to convert graphs from %s to '%s' backend for call to '%s'"
+ ("" if extra_message is None else ": %s"),
input_backend_names,
backend_name,
self.name,
*(() if extra_message is None else (exc,)),
)
if extra_message is not None:
raise NotImplementedError(extra_message) from exc
raise
if backend_name != "networkx":
_logger.debug(
"Using backend '%s' for call to '%s' with arguments: %s",
backend_name,
self.name,
_LazyArgsRepr(self, converted_args, converted_kwargs),
)
try:
return func(*converted_args, **converted_kwargs)
except NotImplementedError as exc:
if extra_message is not None:
_logger.debug(
"Backend '%s' raised when calling '%s': %s",
backend_name,
self.name,
exc,
)
raise NotImplementedError(extra_message) from exc
raise
def _convert_and_call_for_tests(
self, backend_name, args, kwargs, *, fallback_to_nx=False
):
"""Call this dispatchable function with a backend; for use with testing."""
backend = _load_backend(backend_name)
if not self._can_backend_run(backend_name, args, kwargs):
if fallback_to_nx or not self.graphs:
if fallback_to_nx:
_logger.debug(
"Falling back to use 'networkx' instead of '%s' backend "
"for call to '%s' with arguments: %s",
backend_name,
self.name,
_LazyArgsRepr(self, args, kwargs),
)
return self.orig_func(*args, **kwargs)
import pytest
msg = f"'{self.name}' not implemented by {backend_name}"
if hasattr(backend, self.name):
msg += " with the given arguments"
pytest.xfail(msg)
from collections.abc import Iterable, Iterator, Mapping
from copy import copy, deepcopy
from io import BufferedReader, BytesIO, StringIO, TextIOWrapper
from itertools import tee
from random import Random
import numpy as np
from numpy.random import Generator, RandomState
from scipy.sparse import sparray
# We sometimes compare the backend result to the original result,
# so we need two sets of arguments. We tee iterators and copy
# random state so that they may be used twice.
if not args:
args1 = args2 = args
else:
args1, args2 = zip(
*(
(arg, deepcopy(arg))
if isinstance(arg, RandomState)
else (arg, copy(arg))
if isinstance(arg, BytesIO | StringIO | Random | Generator)
else tee(arg)
if isinstance(arg, Iterator)
and not isinstance(arg, BufferedReader | TextIOWrapper)
else (arg, arg)
for arg in args
)
)
if not kwargs:
kwargs1 = kwargs2 = kwargs
else:
kwargs1, kwargs2 = zip(
*(
((k, v), (k, deepcopy(v)))
if isinstance(v, RandomState)
else ((k, v), (k, copy(v)))
if isinstance(v, BytesIO | StringIO | Random | Generator)
else ((k, (teed := tee(v))[0]), (k, teed[1]))
if isinstance(v, Iterator)
and not isinstance(v, BufferedReader | TextIOWrapper)
else ((k, v), (k, v))
for k, v in kwargs.items()
)
)
kwargs1 = dict(kwargs1)
kwargs2 = dict(kwargs2)
try:
converted_args, converted_kwargs = self._convert_arguments(
backend_name, args1, kwargs1, use_cache=False, mutations=None
)
_logger.debug(
"Using backend '%s' for call to '%s' with arguments: %s",
backend_name,
self.name,
_LazyArgsRepr(self, converted_args, converted_kwargs),
)
result = getattr(backend, self.name)(*converted_args, **converted_kwargs)
except NotImplementedError as exc:
if fallback_to_nx:
_logger.debug(
"Graph conversion failed; falling back to use 'networkx' instead "
"of '%s' backend for call to '%s'",
backend_name,
self.name,
)
return self.orig_func(*args2, **kwargs2)
import pytest
pytest.xfail(
exc.args[0] if exc.args else f"{self.name} raised {type(exc).__name__}"
)
# Verify that `self._returns_graph` is correct. This compares the return type
# to the type expected from `self._returns_graph`. This handles tuple and list
# return types, but *does not* catch functions that yield graphs.
if (
self._returns_graph
!= (
isinstance(result, nx.Graph)
or hasattr(result, "__networkx_backend__")
or isinstance(result, tuple | list)
and any(
isinstance(x, nx.Graph) or hasattr(x, "__networkx_backend__")
for x in result
)
)
and not (
# May return Graph or None
self.name in {"check_planarity", "check_planarity_recursive"}
and any(x is None for x in result)
)
and not (
# May return Graph or dict
self.name in {"held_karp_ascent"}
and any(isinstance(x, dict) for x in result)
)
and self.name
not in {
# yields graphs
"all_triads",
"general_k_edge_subgraphs",
# yields graphs or arrays
"nonisomorphic_trees",
}
):
raise RuntimeError(f"`returns_graph` is incorrect for {self.name}")
def check_result(val, depth=0):
if isinstance(val, np.number):
raise RuntimeError(
f"{self.name} returned a numpy scalar {val} ({type(val)}, depth={depth})"
)
if isinstance(val, np.ndarray | sparray):
return
if isinstance(val, nx.Graph):
check_result(val._node, depth=depth + 1)
check_result(val._adj, depth=depth + 1)
return
if isinstance(val, Iterator):
raise NotImplementedError
if isinstance(val, Iterable) and not isinstance(val, str):
for x in val:
check_result(x, depth=depth + 1)
if isinstance(val, Mapping):
for x in val.values():
check_result(x, depth=depth + 1)
def check_iterator(it):
for val in it:
try:
check_result(val)
except RuntimeError as exc:
raise RuntimeError(
f"{self.name} returned a numpy scalar {val} ({type(val)})"
) from exc
yield val
if self.name in {"from_edgelist"}:
# numpy scalars are explicitly given as values in some tests
pass
elif isinstance(result, Iterator):
result = check_iterator(result)
else:
try:
check_result(result)
except RuntimeError as exc:
raise RuntimeError(
f"{self.name} returned a numpy scalar {result} ({type(result)})"
) from exc
check_result(result)
if self.name in {
"edmonds_karp",
"barycenter",
"contracted_edge",
"contracted_nodes",
"stochastic_graph",
"relabel_nodes",
"maximum_branching",
"incremental_closeness_centrality",
"minimal_branching",
"minimum_spanning_arborescence",
"recursive_simple_cycles",
"connected_double_edge_swap",
"set_node_attributes",
"remove_node_attributes",
"set_edge_attributes",
"remove_edge_attributes",
}:
# Special-case algorithms that mutate input graphs
bound = self.__signature__.bind(*converted_args, **converted_kwargs)
bound.apply_defaults()
bound2 = self.__signature__.bind(*args2, **kwargs2)
bound2.apply_defaults()
if self.name in {
"minimal_branching",
"minimum_spanning_arborescence",
"recursive_simple_cycles",
"connected_double_edge_swap",
"set_edge_attributes",
"remove_edge_attributes",
}:
G1 = backend.convert_to_nx(bound.arguments["G"])
G2 = bound2.arguments["G"]
G2._adj = G1._adj
if G2.is_directed():
G2._pred = G1._pred
nx._clear_cache(G2)
elif self.name in {
"set_node_attributes",
"remove_node_attributes",
}:
G1 = backend.convert_to_nx(bound.arguments["G"])
G2 = bound2.arguments["G"]
G2._node = G1._node
nx._clear_cache(G2)
elif self.name == "edmonds_karp":
R1 = backend.convert_to_nx(bound.arguments["residual"])
R2 = bound2.arguments["residual"]
if R1 is not None and R2 is not None:
for k, v in R1.edges.items():
R2.edges[k]["flow"] = v["flow"]
R2.graph.update(R1.graph)
nx._clear_cache(R2)
elif self.name == "barycenter" and bound.arguments["attr"] is not None:
G1 = backend.convert_to_nx(bound.arguments["G"])
G2 = bound2.arguments["G"]
attr = bound.arguments["attr"]
for k, v in G1.nodes.items():
G2.nodes[k][attr] = v[attr]
nx._clear_cache(G2)
elif (
self.name in {"contracted_nodes", "contracted_edge"}
and not bound.arguments["copy"]
):
# Edges and nodes changed; node "contraction" and edge "weight" attrs
G1 = backend.convert_to_nx(bound.arguments["G"])
G2 = bound2.arguments["G"]
G2.__dict__.update(G1.__dict__)
nx._clear_cache(G2)
elif self.name == "stochastic_graph" and not bound.arguments["copy"]:
G1 = backend.convert_to_nx(bound.arguments["G"])
G2 = bound2.arguments["G"]
for k, v in G1.edges.items():
G2.edges[k]["weight"] = v["weight"]
nx._clear_cache(G2)
elif (
self.name == "relabel_nodes"
and not bound.arguments["copy"]
or self.name in {"incremental_closeness_centrality"}
):
G1 = backend.convert_to_nx(bound.arguments["G"])
G2 = bound2.arguments["G"]
if G1 is G2:
return G2
G2._node.clear()
G2._node.update(G1._node)
G2._adj.clear()
G2._adj.update(G1._adj)
if hasattr(G1, "_pred") and hasattr(G2, "_pred"):
G2._pred.clear()
G2._pred.update(G1._pred)
if hasattr(G1, "_succ") and hasattr(G2, "_succ"):
G2._succ.clear()
G2._succ.update(G1._succ)
nx._clear_cache(G2)
if self.name == "relabel_nodes":
return G2
return backend.convert_to_nx(result)
converted_result = backend.convert_to_nx(result)
if (
isinstance(converted_result, nx.Graph)
and "networkx" in self.backends
and self.name
not in {
"boykov_kolmogorov",
"preflow_push",
"quotient_graph",
"shortest_augmenting_path",
"spectral_graph_forge",
# We don't handle tempfile.NamedTemporaryFile arguments
"read_gml",
"read_graph6",
"read_sparse6",
# We don't handle io.BufferedReader or io.TextIOWrapper arguments
"bipartite_read_edgelist",
"read_adjlist",
"read_edgelist",
"read_graphml",
"read_multiline_adjlist",
"read_pajek",
"from_pydot",
"pydot_read_dot",
"agraph_read_dot",
# graph comparison fails b/c of nan values
"read_gexf",
}
):
# For graph return types (e.g. generators), we compare that results are
# the same between the backend and networkx, then return the original
# networkx result so the iteration order will be consistent in tests.
G = self.orig_func(*args2, **kwargs2)
if not nx.utils.graphs_equal(G, converted_result):
assert G.number_of_nodes() == converted_result.number_of_nodes()
assert G.number_of_edges() == converted_result.number_of_edges()
assert G.graph == converted_result.graph
assert G.nodes == converted_result.nodes
assert G.adj == converted_result.adj
assert type(G) is type(converted_result)
raise AssertionError("Graphs are not equal")
return G
return converted_result
def _make_doc(self):
"""Generate the backends section at the end for functions having an alternate
backend implementation(s) using the `backend_info` entry-point."""
if self.backends == {"networkx"}:
return self._orig_doc
# Add "Backends" section to the bottom of the docstring (if there are backends)
lines = [
"Backends",
"--------",
]
for backend in sorted(self.backends - {"networkx"}):
info = backend_info[backend]
if "short_summary" in info:
lines.append(f"{backend} : {info['short_summary']}")
else:
lines.append(backend)
if "functions" not in info or self.name not in info["functions"]:
lines.append("")
continue
func_info = info["functions"][self.name]
# Renaming extra_docstring to additional_docs
if func_docs := (
func_info.get("additional_docs") or func_info.get("extra_docstring")
):
lines.extend(
f" {line}" if line else line for line in func_docs.split("\n")
)
add_gap = True
else:
add_gap = False
# Renaming extra_parameters to additional_parameters
if extra_parameters := (
func_info.get("extra_parameters")
or func_info.get("additional_parameters")
):
if add_gap:
lines.append("")
lines.append(" Additional parameters:")
for param in sorted(extra_parameters):
lines.append(f" {param}")
if desc := extra_parameters[param]:
lines.append(f" {desc}")
lines.append("")
else:
lines.append("")
if func_url := func_info.get("url"):
lines.append(f"[`Source <{func_url}>`_]")
lines.append("")
# We assume the docstrings are indented by four spaces (true for now)
new_doc = self._orig_doc or ""
if not new_doc.rstrip():
new_doc = f"The original docstring for {self.name} was empty."
if self.backends:
lines.pop() # Remove last empty line
to_add = "\n ".join(lines)
new_doc = f"{new_doc.rstrip()}\n\n {to_add}"
# For backend-only funcs, add "Attention" admonishment after the one line summary
if "networkx" not in self.backends:
lines = new_doc.split("\n")
index = 0
while not lines[index].strip():
index += 1
while index < len(lines) and lines[index].strip():
index += 1
backends = sorted(self.backends)
if len(backends) == 0:
example = ""
elif len(backends) == 1:
example = f' such as "{backends[0]}"'
elif len(backends) == 2:
example = f' such as "{backends[0]} or "{backends[1]}"'
else:
example = (
" such as "
+ ", ".join(f'"{x}"' for x in backends[:-1])
+ f', or "{backends[-1]}"' # Oxford comma
)
to_add = (
"\n .. attention:: This function does not have a default NetworkX implementation.\n"
" It may only be run with an installable :doc:`backend </backends>` that\n"
f" supports it{example}.\n\n"
" Hint: use ``backend=...`` keyword argument to specify a backend or add\n"
" backends to ``nx.config.backend_priority``."
)
lines.insert(index, to_add)
new_doc = "\n".join(lines)
return new_doc
def __reduce__(self):
"""Allow this object to be serialized with pickle.
This uses the global registry `_registered_algorithms` to deserialize.
"""
return _restore_dispatchable, (self.name,)
def _restore_dispatchable(name):
return _registered_algorithms[name].__wrapped__
def _get_cache_key(
*,
edge_attrs,
node_attrs,
preserve_edge_attrs,
preserve_node_attrs,
preserve_graph_attrs,
):
"""Return key used by networkx caching given arguments for ``convert_from_nx``."""
# edge_attrs: dict | None
# node_attrs: dict | None
# preserve_edge_attrs: bool (False if edge_attrs is not None)
# preserve_node_attrs: bool (False if node_attrs is not None)
return (
frozenset(edge_attrs.items())
if edge_attrs is not None
else preserve_edge_attrs,
frozenset(node_attrs.items())
if node_attrs is not None
else preserve_node_attrs,
)
def _get_from_cache(cache, key, *, backend_name=None, mutations=None):
"""Search the networkx cache for a graph that is compatible with ``key``.
Parameters
----------
cache : dict
If ``backend_name`` is given, then this is treated as ``G.__networkx_cache__``,
but if ``backend_name`` is None, then this is treated as the resolved inner
cache such as ``G.__networkx_cache__["backends"][backend_name]``.
key : tuple
Cache key from ``_get_cache_key``.
backend_name : str, optional
Name of the backend to control how ``cache`` is interpreted.
mutations : list, optional
Used internally to clear objects gotten from cache if inputs will be mutated.
Returns
-------
tuple or None
The key of the compatible graph found in the cache.
graph or "FAILED_TO_CONVERT" or None
A compatible graph if possible. "FAILED_TO_CONVERT" indicates that a previous
conversion attempt failed for this cache key.
"""
if backend_name is not None:
cache = cache.get("backends", {}).get(backend_name, {})
if not cache:
return None, None
# Do a simple search for a cached graph with compatible data.
# For example, if we need a single attribute, then it's okay
# to use a cached graph that preserved all attributes.
# This looks for an exact match first.
edge_key, node_key = key
for compat_key in itertools.product(
(edge_key, True) if edge_key is not True else (True,),
(node_key, True) if node_key is not True else (True,),
):
if (rv := cache.get(compat_key)) is not None and (
rv != FAILED_TO_CONVERT or key == compat_key
):
if mutations is not None:
# Remove this item from the cache (after all conversions) if
# the call to this dispatchable function will mutate an input.
mutations.append((cache, compat_key))
return compat_key, rv
# Iterate over the items in `cache` to see if any are compatible.
# For example, if no edge attributes are needed, then a graph
# with any edge attribute will suffice. We use the same logic
# below (but switched) to clear unnecessary items from the cache.
# Use `list(cache.items())` to be thread-safe.
for (ekey, nkey), graph in list(cache.items()):
if graph == FAILED_TO_CONVERT:
# Return FAILED_TO_CONVERT if any cache key that requires a subset
# of the edge/node attributes of the given cache key has previously
# failed to convert. This logic is similar to `_set_to_cache`.
if ekey is False or edge_key is True:
pass
elif ekey is True or edge_key is False or not ekey.issubset(edge_key):
continue
if nkey is False or node_key is True: # or nkey == node_key:
pass
elif nkey is True or node_key is False or not nkey.issubset(node_key):
continue
# Save to cache for faster subsequent lookups
cache[key] = FAILED_TO_CONVERT
elif edge_key is False or ekey is True:
pass # Cache works for edge data!
elif edge_key is True or ekey is False or not edge_key.issubset(ekey):
continue # Cache missing required edge data; does not work
if node_key is False or nkey is True:
pass # Cache works for node data!
elif node_key is True or nkey is False or not node_key.issubset(nkey):
continue # Cache missing required node data; does not work
if mutations is not None:
# Remove this item from the cache (after all conversions) if
# the call to this dispatchable function will mutate an input.
mutations.append((cache, (ekey, nkey)))
return (ekey, nkey), graph
return None, None
def _set_to_cache(cache, key, graph, *, backend_name=None):
"""Set a backend graph to the cache, and remove unnecessary cached items.
Parameters
----------
cache : dict
If ``backend_name`` is given, then this is treated as ``G.__networkx_cache__``,
but if ``backend_name`` is None, then this is treated as the resolved inner
cache such as ``G.__networkx_cache__["backends"][backend_name]``.
key : tuple
Cache key from ``_get_cache_key``.
graph : graph or "FAILED_TO_CONVERT"
Setting value to "FAILED_TO_CONVERT" prevents this conversion from being
attempted in future calls.
backend_name : str, optional
Name of the backend to control how ``cache`` is interpreted.
Returns
-------
dict
The items that were removed from the cache.
"""
if backend_name is not None:
cache = cache.setdefault("backends", {}).setdefault(backend_name, {})
# Remove old cached items that are no longer necessary since they
# are dominated/subsumed/outdated by what was just calculated.
# This uses the same logic as above, but with keys switched.
# Also, don't update the cache here if the call will mutate an input.
removed = {}
edge_key, node_key = key
cache[key] = graph # Set at beginning to be thread-safe
if graph == FAILED_TO_CONVERT:
return removed
for cur_key in list(cache):
if cur_key == key:
continue
ekey, nkey = cur_key
if ekey is False or edge_key is True:
pass
elif ekey is True or edge_key is False or not ekey.issubset(edge_key):
continue
if nkey is False or node_key is True:
pass
elif nkey is True or node_key is False or not nkey.issubset(node_key):
continue
# Use pop instead of del to try to be thread-safe
if (graph := cache.pop(cur_key, None)) is not None:
removed[cur_key] = graph
return removed
class _LazyArgsRepr:
"""Simple wrapper to display arguments of dispatchable functions in logging calls."""
def __init__(self, func, args, kwargs):
self.func = func
self.args = args
self.kwargs = kwargs
self.value = None
def __repr__(self):
if self.value is None:
bound = self.func.__signature__.bind_partial(*self.args, **self.kwargs)
inner = ", ".join(f"{key}={val!r}" for key, val in bound.arguments.items())
self.value = f"({inner})"
return self.value
if os.environ.get("_NETWORKX_BUILDING_DOCS_"):
# When building docs with Sphinx, use the original function with the
# dispatched __doc__, b/c Sphinx renders normal Python functions better.
# This doesn't show e.g. `*, backend=None, **backend_kwargs` in the
# signatures, which is probably okay. It does allow the docstring to be
# updated based on the installed backends.
_orig_dispatchable = _dispatchable
[docs]
def _dispatchable(func=None, **kwargs): # type: ignore[no-redef]
if func is None:
return partial(_dispatchable, **kwargs)
dispatched_func = _orig_dispatchable(func, **kwargs)
func.__doc__ = dispatched_func.__doc__
return func
_dispatchable.__doc__ = _orig_dispatchable.__new__.__doc__ # type: ignore[method-assign,assignment]
_sig = inspect.signature(_orig_dispatchable.__new__)
_dispatchable.__signature__ = _sig.replace( # type: ignore[method-assign,assignment]
parameters=[v for k, v in _sig.parameters.items() if k != "cls"]
)
