A Hypothesis strategy for generating NetworkX graphs
Project description
Hypothesisnetworkx
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 hypothesisnetworkx
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 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
selfexplanatory, 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_nodes
andmax_nodes
: The minimum and maximum number of nodes the produced graphs will contain.min_edges
andmax_edges
: The minimum and maximum number of edges the produced graphs will contain. Note that less edges thanmin_edges
may be added if there are not enough nodes, and more thanmax_edges
ifconnected
is 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 selfloops in the generated graph. Selfloops 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.  The
max_edges
argument is not always respected ifconnected
is True.  It currently works for Python 2.7, but this is considered deprecated and may stop working without notice.
See also
Project details
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