This module provides a Hypothesis strategy for generating networkx graphs. This can be used to efficiently and thoroughly test your code.
This module can be installed via pip:
pip install hypothesis-networkx
The module exposes a single function: graph_builder. This function is a
hypothesis composite strategy for building graphs. You can use it as follows:
from hypothesis_networkx import graph_builder
from hypothesis import strategies as st
import networkx as nx
node_data = st.fixed_dictionaries({'name': st.text(),
'number': st.integers()})
edge_data = st.fixed_dictionaries({'weight': st.floats(allow_nan=False,
allow_infinity=False)})
builder = graph_builder(graph_type=nx.Graph,
node_keys=st.integers(),
node_data=node_data,
edge_data=edge_data,
min_nodes=2, max_nodes=10,
min_edges=1, max_edges=None,
self_loops=False,
connected=True)
graph = builder.example()
print(graph.nodes(data=True))
print(graph.edges(data=True))Of course this builder is a valid hypothesis strategy, and using it to just make examples is not super useful. Instead, you can (and should) use it in your testing framework:
from hypothesis import given
@given(graph=builder)
def test_my_function(graph):
assert my_function(graph) == known_function(graph)The meaning of the arguments given to graph_builder are pretty
self-explanatory, but they must be given as keyword arguments.
node_data: The strategy from which node attributes will be drawn.edge_data: The strategy from which edge attributes will be drawn.node_keys: Either the strategy from which node keys will be draw, or None. If None, node keys will be integers from the range (0, number of nodes).min_nodesandmax_nodes: The minimum and maximum number of nodes the produced graphs will contain.min_edgesandmax_edges: The minimum and maximum number of edges the produced graphs will contain. Note that less edges thanmin_edgesmay be added if there are not enough nodes, and more thanmax_edgesifconnectedis True.graph_type: This function (or class) will be called without arguments to create an empty initial graph.connected: If True, the generated graph is guaranteed to be a single connected component.self_loops: If False, there will be no self-loops in the generated graph. Self-loops are edges between a node and itself.
There are a few (minor) outstanding issues with this module:
- Graph generation may be slow for large graphs.
- The
min_edgesargument is not always respected when the produced graph is too small. - The
max_edgesargument is not always respected ifconnectedis True. - It currently works for Python 2.7, but this is considered deprecated and may stop working without notice.
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