def test_similar_output_to_naive_big(self):
G = nx.erdos_renyi_graph(1001, 0.02)
G2 = graphpca.reduce_graph_efficiently(G, 2)
G2n = graphpca.reduce_graph_naively(G, 2)
self.assertTrue(
np.allclose(G2, G2n, rtol=1e-03, atol=1e-05),
'Regular result:\n{}\nNaive result:\n{}\n'.format(G2, G2n))
def test_add_supernode_similar_output_to_naive_small(self):
G = nx.erdos_renyi_graph(10, 0.5)
G2 = graphpca.reduce_graph_efficiently(G, 2, add_supernode=True)
G2n = graphpca.reduce_graph_naively(G, 2)
self.assertTrue(
np.allclose(G2, G2n, rtol=1e-02, atol=1e-06),
'Regular result:\n{}\nNaive result:\n{}\n'.format(G2, G2n))
def test_similar_output_to_naive_peterson(self):
G = nx.petersen_graph()
G2 = graphpca.reduce_graph_efficiently(G, 2)
G2n = graphpca.reduce_graph_naively(G, 2)
self.assertTrue(
np.allclose(G2, G2n, rtol=1e-04, atol=1e-06),
'Regular result:\n{}\nNaive result:\n{}\n'.format(G2, G2n))
def test_add_supernode_similar_output_to_naive_big(self):
G = nx.watts_strogatz_graph(1001, 10, 0.05)
G2 = graphpca.reduce_graph_efficiently(G, 2, add_supernode=True)
G2n = graphpca.reduce_graph_naively(G, 2)
self.assertTrue(
np.allclose(G2, G2n, rtol=1e-01, atol=1e-02),
'Regular result:\n{}\nNaive result:\n{}\n'.format(G2, G2n))
def test_returns_plausible_results(self):
g = nx.erdos_renyi_graph(100, 0.3)
g_5 = graphpca.reduce_graph_efficiently(g, 5)
self.assertEqual(len(g_5), 5)
self.assertEqual(len(g_5[0]), 100)
for i in range(5):
max_val = max(abs(g_5[i]))
self.assertGreater(max_val, 0.01)
def test_add_supernode_similar_output_to_naive_mat_3(self):
mat = scipy.io.loadmat('bcspwr01.mat')
A = mat['Problem'][0][0][1].todense()
G = nx.from_numpy_matrix(A)
G3 = graphpca.reduce_graph_efficiently(G, 3, add_supernode=True)
G3n = graphpca.reduce_graph_naively(G, 3)
self.assertTrue(np.allclose(G3, G3n, rtol=1e-02, atol=1e-06),
'Regular result:\n{}\nNaive result:\n{}\n'.format(G3, G3n))
def test_returns_plausible_results(self):
g = nx.erdos_renyi_graph(100, 0.3)
g_5 = graphpca.reduce_graph_efficiently(g, 5)
self.assertEqual(len(g_5), 5)
self.assertEqual(len(g_5[0]), 100)
for i in range(5):
max_val = max(abs(g_5[i]))
self.assertGreater(max_val, 0.01)
def test_add_supernode_similar_output_to_naive_mat_3(self):
mat = scipy.io.loadmat('bcspwr01.mat')
A = mat['Problem'][0][0][1].todense()
G = nx.from_numpy_matrix(A)
G3 = graphpca.reduce_graph_efficiently(G, 3, add_supernode=True)
G3n = graphpca.reduce_graph_naively(G, 3)
self.assertTrue(
np.allclose(G3, G3n, rtol=1e-02, atol=1e-06),
'Regular result:\n{}\nNaive result:\n{}\n'.format(G3, G3n))
def test_add_supernode_similar_output_to_naive_mat_3(self):
mat = get_fixture_mat("bcspwr01.mat")
A = mat["Problem"][0][0][1].todense()
G = nx.from_numpy_matrix(A)
G3 = graphpca.reduce_graph_efficiently(G, 3, add_supernode=True)
G3n = graphpca.reduce_graph_naively(G, 3)
self.assertTrue(
np.allclose(G3, G3n, rtol=1e-02, atol=1e-06),
"Regular result:\n{}\nNaive result:\n{}\n".format(G3, G3n),
)
def test_similar_output_to_naive_mat_3(self):
mat = scipy.io.loadmat('bcspwr01.mat')
# I love the UFSMC (https://www.cise.ufl.edu/research/sparse/matrices/)
# but wow they really buried the matrix in this .mat
A = mat['Problem'][0][0][1].todense()
G = nx.from_numpy_matrix(A)
G3 = graphpca.reduce_graph_efficiently(G, 3)
G3n = graphpca.reduce_graph_naively(G, 3)
self.assertTrue(np.allclose(G3, G3n, rtol=1e-04, atol=1e-06),
'Regular result:\n{}\nNaive result:\n{}\n'.format(G3, G3n))
def test_ok_if_multiple_zero_eigens(self):
g = nx.erdos_renyi_graph(100, 0.3)
node = next(g.nodes_iter())
for neighbor in g.neighbors(node):
g.remove_edge(node, neighbor)
g_5 = graphpca.reduce_graph_efficiently(g, 5)
self.assertEqual(len(g_5), 5)
self.assertEqual(len(g_5[0]), 100)
for i in range(5):
max_val = max(abs(g_5[i]))
self.assertGreater(max_val, 0.01)
def test_ok_if_multiple_zero_eigens(self):
g = nx.erdos_renyi_graph(100, 0.3)
node = next(g.nodes_iter())
for neighbor in g.neighbors(node):
g.remove_edge(node, neighbor)
g_5 = graphpca.reduce_graph_efficiently(g, 5)
self.assertEqual(len(g_5), 5)
self.assertEqual(len(g_5[0]), 100)
for i in range(5):
max_val = max(abs(g_5[i]))
self.assertGreater(max_val, 0.01)
def test_similar_output_to_naive_mat_3(self):
mat = scipy.io.loadmat('bcspwr01.mat')
# I love the UFSMC (https://www.cise.ufl.edu/research/sparse/matrices/)
# but wow they really buried the matrix in this .mat
A = mat['Problem'][0][0][1].todense()
G = nx.from_numpy_matrix(A)
G3 = graphpca.reduce_graph_efficiently(G, 3)
G3n = graphpca.reduce_graph_naively(G, 3)
self.assertTrue(
np.allclose(G3, G3n, rtol=1e-04, atol=1e-06),
'Regular result:\n{}\nNaive result:\n{}\n'.format(G3, G3n))
def test_add_supernode_similar_output_to_naive_big(self):
G = nx.watts_strogatz_graph(1001, 10, 0.05)
G2 = graphpca.reduce_graph_efficiently(G, 2, add_supernode=True)
G2n = graphpca.reduce_graph_naively(G, 2)
self.assertTrue(np.allclose(G2, G2n, rtol=1e-01, atol=1e-02),
'Regular result:\n{}\nNaive result:\n{}\n'.format(G2, G2n))
def test_add_supernode_similar_output_to_naive_small(self):
G = nx.erdos_renyi_graph(10, 0.5)
G2 = graphpca.reduce_graph_efficiently(G, 2, add_supernode=True)
G2n = graphpca.reduce_graph_naively(G, 2)
self.assertTrue(np.allclose(G2, G2n, rtol=1e-02, atol=1e-06),
'Regular result:\n{}\nNaive result:\n{}\n'.format(G2, G2n))
def test_similar_output_to_naive_big(self):
G = nx.erdos_renyi_graph(1001, 0.02)
G2 = graphpca.reduce_graph_efficiently(G, 2)
G2n = graphpca.reduce_graph_naively(G, 2)
self.assertTrue(np.allclose(G2, G2n, rtol=1e-03, atol=1e-05),
'Regular result:\n{}\nNaive result:\n{}\n'.format(G2, G2n))
def test_similar_output_to_naive_peterson(self):
G = nx.petersen_graph()
G2 = graphpca.reduce_graph_efficiently(G, 2)
G2n = graphpca.reduce_graph_naively(G, 2)
self.assertTrue(np.allclose(G2, G2n, rtol=1e-04, atol=1e-06),
'Regular result:\n{}\nNaive result:\n{}\n'.format(G2, G2n))
