Source code for networkx.classes.backends

Code to support various backends in a plugin dispatch architecture.

Create a Dispatcher

To be a valid plugin, a package must register an entry_point
of `networkx.plugins` with a key pointing to the handler.

For example::

    entry_points={'networkx.plugins': 'sparse = networkx_plugin_sparse'}

The plugin must create a Graph-like object which contains an attribute
``__networkx_plugin__`` with a value of the entry point name.

Continuing the example above::

    class WrappedSparse:
        __networkx_plugin__ = "sparse"

When a dispatchable NetworkX algorithm encounters a Graph-like object
with a ``__networkx_plugin__`` attribute, it will look for the associated
dispatch object in the entry_points, load it, and dispatch the work to it.

To assist in validating the backend algorithm implementations, if an
environment variable ``NETWORKX_GRAPH_CONVERT`` is set to a registered
plugin keys, the dispatch machinery will automatically convert regular
networkx Graphs and DiGraphs to the backend equivalent by calling
``<backend dispatcher>.convert_from_nx(G, weight=weight, name=name)``.

The converted object is then passed to the backend implementation of
the algorithm. The result is then passed to
``<backend dispatcher>.convert_to_nx(result, name=name)`` to convert back
to a form expected by the NetworkX tests.

By defining ``convert_from_nx`` and ``convert_to_nx`` methods and setting
the environment variable, NetworkX will automatically route tests on
dispatchable algorithms to the backend, allowing the full networkx test
suite to be run against the backend implementation.

Example pytest invocation::

    NETWORKX_GRAPH_CONVERT=sparse pytest --pyargs networkx

Dispatchable algorithms which are not implemented by the backend
will cause a ``pytest.xfail()``, giving some indication that not all
tests are working, while avoiding causing an explicit failure.

A special ``on_start_tests(items)`` function may be defined by the backend.
It will be called with the list of NetworkX tests discovered. Each item
is a test object that can be marked as xfail if the backend does not support
the test using `item.add_marker(pytest.mark.xfail(reason=...))`.
import functools
import inspect
import os
import sys
from importlib.metadata import entry_points

from ..exception import NetworkXNotImplemented

__all__ = ["_dispatch", "_mark_tests"]

class PluginInfo:
    """Lazily loaded entry_points plugin information"""

    def __init__(self):
        self._items = None

    def __bool__(self):
        return len(self.items) > 0

    def items(self):
        if self._items is None:
            if sys.version_info < (3, 10):
                self._items = entry_points()["networkx.plugins"]
                self._items = entry_points(group="networkx.plugins")
        return self._items

    def __contains__(self, name):
        if sys.version_info < (3, 10):
            return len([ep for ep in self.items if == name]) > 0
        return name in self.items.names

    def __getitem__(self, name):
        if sys.version_info < (3, 10):
            return [ep for ep in self.items if == name][0]
        return self.items[name]

plugins = PluginInfo()
_registered_algorithms = {}

def _register_algo(name, wrapped_func):
    if name in _registered_algorithms:
        raise KeyError(f"Algorithm already exists in dispatch registry: {name}")
    _registered_algorithms[name] = wrapped_func
    wrapped_func.dispatchname = name

[docs]def _dispatch(func=None, *, name=None): """Dispatches to a backend algorithm when the first argument is a backend graph-like object. """ # Allow any of the following decorator forms: # - @_dispatch # - @_dispatch() # - @_dispatch("override_name") # - @_dispatch(name="override_name") if func is None: if name is None: return _dispatch return functools.partial(_dispatch, name=name) if isinstance(func, str): return functools.partial(_dispatch, name=func) # If name not provided, use the name of the function if name is None: name = func.__name__ @functools.wraps(func) def wrapper(*args, **kwds): if args: graph = args[0] else: try: graph = kwds["G"] except KeyError: raise TypeError(f"{name}() missing positional argument: 'G'") from None if hasattr(graph, "__networkx_plugin__") and plugins: plugin_name = graph.__networkx_plugin__ if plugin_name in plugins: backend = plugins[plugin_name].load() if hasattr(backend, name): return getattr(backend, name).__call__(*args, **kwds) else: raise NetworkXNotImplemented( f"'{name}' not implemented by {plugin_name}" ) return func(*args, **kwds) # Keep a handle to the original function to use when testing # the dispatch mechanism internally wrapper._orig_func = func _register_algo(name, wrapper) return wrapper
def test_override_dispatch(func=None, *, name=None): """Auto-converts the first argument into the backend equivalent, causing the dispatching mechanism to trigger for every decorated algorithm.""" if func is None: if name is None: return test_override_dispatch return functools.partial(test_override_dispatch, name=name) if isinstance(func, str): return functools.partial(test_override_dispatch, name=func) # If name not provided, use the name of the function if name is None: name = func.__name__ sig = inspect.signature(func) @functools.wraps(func) def wrapper(*args, **kwds): backend = plugins[plugin_name].load() if not hasattr(backend, name): if plugin_name == "nx-loopback": raise NetworkXNotImplemented( f"'{name}' not found in {backend.__class__.__name__}" ) pytest.xfail(f"'{name}' not implemented by {plugin_name}") bound = sig.bind(*args, **kwds) bound.apply_defaults() if args: graph, *args = args else: try: graph = kwds.pop("G") except KeyError: raise TypeError(f"{name}() missing positional argument: 'G'") from None # Convert graph into backend graph-like object # Include the weight label, if provided to the algorithm weight = None if "weight" in bound.arguments: weight = bound.arguments["weight"] elif "data" in bound.arguments and "default" in bound.arguments: # This case exists for several MultiGraph edge algorithms if isinstance(bound.arguments["data"], str): weight = bound.arguments["data"] elif bound.arguments["data"]: weight = "weight" graph = backend.convert_from_nx(graph, weight=weight, name=name) result = getattr(backend, name).__call__(graph, *args, **kwds) return backend.convert_to_nx(result, name=name) wrapper._orig_func = func _register_algo(name, wrapper) return wrapper # Check for auto-convert testing # This allows existing NetworkX tests to be run against a backend # implementation without any changes to the testing code. The only # required change is to set an environment variable prior to running # pytest. if os.environ.get("NETWORKX_GRAPH_CONVERT"): plugin_name = os.environ["NETWORKX_GRAPH_CONVERT"] if not plugins: raise Exception("No registered networkx.plugins entry_points") if plugin_name not in plugins: raise Exception( f"No registered networkx.plugins entry_point named {plugin_name}" ) try: import pytest except ImportError: raise ImportError( f"Missing pytest, which is required when using NETWORKX_GRAPH_CONVERT" ) # Override `dispatch` for testing _dispatch = test_override_dispatch def _mark_tests(items): """Allow backend to mark tests (skip or xfail) if they aren't able to correctly handle them""" if os.environ.get("NETWORKX_GRAPH_CONVERT"): plugin_name = os.environ["NETWORKX_GRAPH_CONVERT"] backend = plugins[plugin_name].load() if hasattr(backend, "on_start_tests"): getattr(backend, "on_start_tests")(items)