Hypothesis-networkx

This module provides a Hypothesis strategy for generating networkx graphs. This can be used to efficiently and thoroughly test your code.

Installation

This module can be installed via pip:

pip install hypothesis-networkx

User guide

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 usefull. 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. Of particular note are the following arguments:

• graph_type: This function (or class) will be called to create an empty initial graph.
• connected: If True, the generated graph is garuanteed 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.

Known limitations

There are a few (minor) outstanding issues with this module:

• Graph generation may be slow for large graphs.
• The min_edges argument is not always respected when the produced graph is too small.
• It currently works for Python 2.7, but this is considered deprecated and may stop working without notice.

See also

Networkx Hypothesis