142 lines
4.0 KiB
Python
142 lines
4.0 KiB
Python
# Copyright (C) 2018
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# Robert Gmyr <robert@gmyr.net>
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# All rights reserved.
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# BSD license.
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"""Unit tests for the sparsifier computation functions."""
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import pytest
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import networkx as nx
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from networkx.utils import py_random_state
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_seed = 2
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def _test_spanner(G, spanner, stretch, weight=None):
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"""Test whether a spanner is valid.
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This function tests whether the given spanner is a subgraph of the
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given graph G with the same node set. It also tests for all shortest
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paths whether they adhere to the given stretch.
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Parameters
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----------
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G : NetworkX graph
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The original graph for which the spanner was constructed.
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spanner : NetworkX graph
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The spanner to be tested.
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stretch : float
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The proclaimed stretch of the spanner.
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weight : object
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The edge attribute to use as distance.
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"""
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# check node set
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assert set(G.nodes()) == set(spanner.nodes())
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# check edge set and weights
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for u, v in spanner.edges():
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assert G.has_edge(u, v)
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if weight:
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assert spanner[u][v][weight] == G[u][v][weight]
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# check connectivity and stretch
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original_length = dict(nx.shortest_path_length(G, weight=weight))
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spanner_length = dict(nx.shortest_path_length(spanner, weight=weight))
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for u in G.nodes():
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for v in G.nodes():
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if u in original_length and v in original_length[u]:
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assert spanner_length[u][v] <= stretch * original_length[u][v]
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@py_random_state(1)
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def _assign_random_weights(G, seed=None):
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"""Assigns random weights to the edges of a graph.
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Parameters
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----------
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G : NetworkX graph
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The original graph for which the spanner was constructed.
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seed : integer, random_state, or None (default)
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Indicator of random number generation state.
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See :ref:`Randomness<randomness>`.
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"""
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for u, v in G.edges():
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G[u][v]['weight'] = seed.random()
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def test_spanner_trivial():
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"""Test a trivial spanner with stretch 1."""
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G = nx.complete_graph(20)
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spanner = nx.spanner(G, 1, seed=_seed)
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for u, v in G.edges:
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assert(spanner.has_edge(u, v))
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def test_spanner_unweighted_complete_graph():
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"""Test spanner construction on a complete unweighted graph."""
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G = nx.complete_graph(20)
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spanner = nx.spanner(G, 4, seed=_seed)
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_test_spanner(G, spanner, 4)
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spanner = nx.spanner(G, 10, seed=_seed)
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_test_spanner(G, spanner, 10)
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def test_spanner_weighted_complete_graph():
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"""Test spanner construction on a complete weighted graph."""
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G = nx.complete_graph(20)
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_assign_random_weights(G, seed=_seed)
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spanner = nx.spanner(G, 4, weight='weight', seed=_seed)
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_test_spanner(G, spanner, 4, weight='weight')
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spanner = nx.spanner(G, 10, weight='weight', seed=_seed)
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_test_spanner(G, spanner, 10, weight='weight')
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def test_spanner_unweighted_gnp_graph():
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"""Test spanner construction on an unweighted gnp graph."""
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G = nx.gnp_random_graph(20, 0.4, seed=_seed)
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spanner = nx.spanner(G, 4, seed=_seed)
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_test_spanner(G, spanner, 4)
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spanner = nx.spanner(G, 10, seed=_seed)
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_test_spanner(G, spanner, 10)
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def test_spanner_weighted_gnp_graph():
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"""Test spanner construction on an weighted gnp graph."""
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G = nx.gnp_random_graph(20, 0.4, seed=_seed)
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_assign_random_weights(G, seed=_seed)
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spanner = nx.spanner(G, 4, weight='weight', seed=_seed)
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_test_spanner(G, spanner, 4, weight='weight')
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spanner = nx.spanner(G, 10, weight='weight', seed=_seed)
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_test_spanner(G, spanner, 10, weight='weight')
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def test_spanner_unweighted_disconnected_graph():
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"""Test spanner construction on a disconnected graph."""
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G = nx.disjoint_union(nx.complete_graph(10), nx.complete_graph(10))
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spanner = nx.spanner(G, 4, seed=_seed)
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_test_spanner(G, spanner, 4)
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spanner = nx.spanner(G, 10, seed=_seed)
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_test_spanner(G, spanner, 10)
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def test_spanner_invalid_stretch():
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"""Check whether an invalid stretch is caught."""
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with pytest.raises(ValueError):
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G = nx.empty_graph()
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nx.spanner(G, 0)
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