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use denser random graphs in distance tests #240

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Merged
merged 5 commits into from
Jun 24, 2019
Merged

use denser random graphs in distance tests #240

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dd4bdda
use denser random graphs in distance tests
sdmccabe Jun 20, 2019
b7c6f80
Merge branch 'master' into test-tweaks
sdmccabe Jun 20, 2019
0a481a1
ensure undirected graphs in QuantumJSD
sdmccabe Jun 24, 2019
a128b35
Merge branch 'master' of https://github.com/netsiphd/netrd into test-…
sdmccabe Jun 24, 2019
a34525a
Merge branch 'test-tweaks' of https://github.com/sdmccabe/netrd into …
sdmccabe Jun 24, 2019
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4 changes: 4 additions & 0 deletions netrd/distance/quantum_jsd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@
import numpy as np
from scipy.linalg import expm
from .base import BaseDistance
from ..utilities import ensure_undirected


class QuantumJSD(BaseDistance):
Expand Down Expand Up @@ -110,6 +111,9 @@ def dist(self, G1, G2, beta=0.1, q=None):
if q and q >= 2:
warnings.warn("JSD is only a metric for 0 ≤ q < 2.", RuntimeWarning)

G1 = ensure_undirected(G1)
G2 = ensure_undirected(G2)

def density_matrix(A, beta):
"""
Create the density matrix encoding probabilities for entropies.
Expand Down
14 changes: 7 additions & 7 deletions tests/test_distance.py
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Expand Up @@ -29,7 +29,7 @@ def test_different_graphs():
## networks may have the same eigenvalues, thus a method that compares
## their eigenvalues would result in distance 0. However, this is very
## unlikely in the constructed case, so we rely on it for now.
G1 = nx.fast_gnp_random_graph(100, 0.1)
G1 = nx.fast_gnp_random_graph(100, 0.3)
G2 = nx.barabasi_albert_graph(100, 5)

for obj in distance.__dict__.values():
Expand All @@ -41,7 +41,7 @@ def test_different_graphs():
def test_symmetry():
"""The distance between two graphs must be symmetric."""
G1 = nx.barabasi_albert_graph(100, 4)
G2 = nx.fast_gnp_random_graph(100, 0.1)
G2 = nx.fast_gnp_random_graph(100, 0.3)

for label, obj in distance.__dict__.items():
if isinstance(obj, type) and BaseDistance in obj.__bases__:
Expand All @@ -63,13 +63,13 @@ def test_quantum_jsd():
dist = JSD.dist(G, G, beta=0.1, q=2)
assert np.isclose(dist, 0.0)

G1 = nx.fast_gnp_random_graph(100, 0.1)
G1 = nx.fast_gnp_random_graph(100, 0.3)
G2 = nx.barabasi_albert_graph(100, 5)
dist = JSD.dist(G1, G2, beta=0.1, q=2)
assert dist > 0.0

G1 = nx.barabasi_albert_graph(100, 4)
G2 = nx.fast_gnp_random_graph(100, 0.1)
G2 = nx.fast_gnp_random_graph(100, 0.3)
dist1 = JSD.dist(G1, G2, beta=0.1, q=2)
dist2 = JSD.dist(G2, G1, beta=0.1, q=2)
assert np.isclose(dist1, dist2)
Expand All @@ -80,15 +80,15 @@ def test_directed_input():
warnings.filterwarnings(
"ignore", message="Coercing directed graph to undirected."
)
G = nx.fast_gnp_random_graph(100, 0.1, directed=True)
G = nx.fast_gnp_random_graph(100, 0.3, directed=True)

for label, obj in distance.__dict__.items():
if isinstance(obj, type) and BaseDistance in obj.__bases__:
dist = obj().dist(G, G)
assert np.isclose(dist, 0.0)

G1 = nx.fast_gnp_random_graph(100, 0.1, directed=True)
G2 = nx.fast_gnp_random_graph(100, 0.1, directed=True)
G1 = nx.fast_gnp_random_graph(100, 0.3, directed=True)
G2 = nx.fast_gnp_random_graph(100, 0.3, directed=True)

for label, obj in distance.__dict__.items():
if isinstance(obj, type) and BaseDistance in obj.__bases__:
Expand Down