def test_graph1():
"""Test graph."""
shape = (4, 4, 3)
adj = np.array([[0, 1, 0], [1, 2, 0], [2, 3, 0], [0, 2, 1], [1, 2, 1],
[1, 3, 1], [2, 1, 1], [2, 3, 1], [0, 1, 2], [1, 2, 2],
[1, 3, 2]])
revadj = adj.copy()
ii = revadj[:, 0].copy()
revadj[:, 0] = revadj[:, 1]
revadj[:, 1] = ii
# UNDIRECTED
G = make_graph(adj, shape, sym=True, display=False)
revG = make_graph(revadj, shape, sym=True, display=False)
expect_cooc_mat_sym = np.array([[10, 13, 10], [13, 18, 12], [10, 12, 12]])
cooc_mat_sym = compute_cooccurrence(G, revG)
assert np.array_equal(expect_cooc_mat_sym, cooc_mat_sym)
print ''
# DIRECTED
G = make_graph(adj, shape, sym=False, display=False)
revG = make_graph(revadj, shape, sym=False, display=False)
expect_cooc_mat_asym = np.array([[2, 4, 2], [3, 6, 2], [2, 4, 2]])
cooc_mat_asym = compute_cooccurrence(G, revG)
assert np.array_equal(expect_cooc_mat_asym, cooc_mat_asym)
def test_graph1_creation():
shape = np.asarray([4, 4, 2], dtype=np.int32)
adj = np.asarray([
[0, 1, 0],
[0, 2, 1],
[1, 2, 0],
[1, 2, 1],
[1, 3, 1],
[2, 3, 0],
[2, 1, 1],
[2, 3, 1],
],
dtype=np.int32)
values = np.arange(adj.shape[0]) + 10.
# create graph
expect_G = np.asarray([[0., 1., 0., 0., 0., 0., 1., 0.],
[1., 0., 1., 0., 0., 0., 1., 1.],
[0., 1., 0., 1., 1., 1., 0., 1.],
[0., 0., 1., 0., 0., 1., 1., 0.]])
G = make_graph(adj, shape, sym=True, save_csc=True)
assert np.array_equal(G.csr.toarray(), G.csc.toarray())
dirpath = join(abspath(expanduser(os.curdir)), '_undir')
if not exists(dirpath):
os.mkdir(dirpath)
G.save_graph(dirpath)
assert np.array_equal(G.indeg_vec, np.asarray([2, 3, 3, 2]))
assert np.array_equal(expect_G, G.csr.toarray())
# rebuild graph
G = Graph.reconstruct(dirpath, shape, sym=True, save_csc=True)
assert np.array_equal(expect_G, G.csr.toarray())
if exists(dirpath):
shutil.rmtree(dirpath)
print('Removed: %s' % dirpath)
def test_graph2(): sym = True adj = np.array([ [0, 1, 0, 16], [2, 4, 0, 14], [4, 5, 0, 4], [0, 2, 1, 13], [2, 1, 1, 4], [3, 5, 1, 20], [1, 3, 2, 12], [3, 2, 2, 9], [4, 3, 2, 7] ]) shape = (6, 6, 3) G = make_graph(adj[:,:3], shape, values=adj[:,3], sym=sym, display=False) G.sources = np.repeat(np.arange(G.N), np.diff(G.csr.indptr)) G.targets = G.csr.indices % G.N cost_vec = G.indeg_vec print "Original graph:\n", G # Successive shortest path algorithm s, p, o = 0, 2, 5 expect = 2.88888888889 mincostflow = succ_shortest_path(G, cost_vec, s, p, o) print mincostflow assert np.allclose(mincostflow.flow, expect) print 'Recovered max-flow edges (i, j, r, flow)..' adj = np.zeros((len(mincostflow.edges), 4)) for i, (k, v) in enumerate(mincostflow.edges.iteritems()): adj[i, :] = np.array([k[0], k[1], k[2], v]) adj = adj[np.lexsort((adj[:,2], adj[:,1], adj[:,0])),:] print adj print ''
def test_graph1():
sym = True
adj = np.array([[0, 2, 0, 10], [1, 2, 0, 30], [0, 1, 1, 20], [1, 3, 1, 10],
[2, 3, 1, 20]])
shape = (4, 4, 2)
G = make_graph(adj[:, :3], shape, values=adj[:, 3], sym=sym, display=False)
G.sources = np.repeat(np.arange(G.N), np.diff(G.csr.indptr))
G.targets = G.csr.indices % G.N
cost_vec = G.indeg_vec
print("Original graph:\n", G)
# Successive shortest path algorithm
s, p, o = 0, 1, 3
expect = 6.42857142857
mincostflow = succ_shortest_path(G, cost_vec, s, p, o)
print(mincostflow)
assert np.allclose(mincostflow.flow, expect)
print('Recovered max-flow edges (i, j, r, flow)..')
adj = np.zeros((len(mincostflow.edges), 4))
for i, (k, v) in enumerate(mincostflow.edges.items()):
adj[i, :] = np.array([k[0], k[1], k[2], v])
adj = adj[np.lexsort((adj[:, 2], adj[:, 1], adj[:, 0])), :]
print(adj)
print('')
def test_graph2():
"""
Co-occurrence count on a small graph, e.g. CNetS/IU graph.
"""
shape = (8, 8, 6)
# s, o, p
adj = np.array([[3, 5, 0], [4, 5, 0], [5, 3, 0], [5, 4, 0], [6, 7, 0],
[7, 6, 0], [3, 0, 1], [3, 1, 1], [3, 2, 1], [4, 0, 1],
[4, 1, 1], [5, 0, 1], [5, 1, 1], [5, 2, 1], [6, 0, 1],
[7, 0, 1], [3, 2, 2], [3, 6, 3], [3, 7, 3], [4, 6, 3],
[4, 7, 3], [1, 0, 4], [2, 0, 4], [0, 3, 5], [0, 4, 5],
[0, 5, 5], [0, 6, 5], [0, 7, 5], [1, 3, 5], [1, 4, 5],
[1, 5, 5], [2, 3, 5], [2, 4, 5], [2, 5, 5], [2, 6, 5],
[2, 7, 5]])
revadj = adj.copy()
ii = revadj[:, 0].copy()
revadj[:, 0] = revadj[:, 1]
revadj[:, 1] = ii
# UNDIRECTED cooccurrence matrix
G = make_graph(adj, shape, sym=True, display=False)
revG = make_graph(revadj, shape, sym=True, display=False)
expect_cooc_mat_sym = np.array([[8, 13, 1, 8, 0, 16],
[13, 62, 5, 14, 15, 72],
[1, 5, 2, 2, 1, 8], [8, 14, 2, 16, 0, 20],
[0, 15, 1, 0, 6, 18],
[16, 72, 8, 20, 18, 94]])
cooc_mat_sym = compute_cooccurrence(G, revG)
assert np.array_equal(expect_cooc_mat_sym, cooc_mat_sym)
print '\n', '=' * 25
# DIRECTED cooccurrence matrix
expect_cooc_mat_asym = np.array([[8, 13, 1, 4, 0, 0], [0, 0, 0, 0, 5, 44],
[0, 0, 0, 0, 1, 5], [4, 4, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 10],
[16, 28, 3, 12, 0, 0]])
G = make_graph(adj, shape, sym=False, display=False)
revG = make_graph(revadj, shape, sym=False, display=False)
cooc_mat_asym = compute_cooccurrence(G, revG)
assert np.array_equal(expect_cooc_mat_asym, cooc_mat_asym)
def test_graph2_creation():
# shape of an example graph.
shape = (8, 8, 6)
# adjacency
adj = np.array([[3, 5, 0], [4, 5, 0], [5, 3, 0], [5, 4, 0], [6, 7, 0],
[7, 6, 0], [3, 0, 1], [3, 1, 1], [3, 2, 1], [4, 0, 1],
[4, 1, 1], [5, 0, 1], [5, 1, 1], [5, 2, 1], [6, 0, 1],
[7, 0, 1], [3, 2, 2], [3, 6, 3], [3, 7, 3], [4, 6, 3],
[4, 7, 3], [1, 0, 4], [2, 0, 4], [0, 3, 5], [0, 4, 5],
[0, 5, 5], [0, 6, 5], [0, 7, 5], [1, 3, 5], [1, 4, 5],
[1, 5, 5], [2, 3, 5], [2, 4, 5], [2, 5, 5], [2, 6, 5],
[2, 7, 5]])
A = np.asarray([[[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 1., 0., 0.],
[0., 0., 0., 0., 0., 1., 0., 0.],
[0., 0., 0., 1., 1., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 1.],
[0., 0., 0., 0., 0., 0., 1., 0.]],
[[0., 0., 0., 1., 1., 1., 1., 1.],
[0., 0., 0., 1., 1., 1., 0., 0.],
[0., 0., 0., 1., 0., 1., 0., 0.],
[1., 1., 1., 0., 0., 0., 0., 0.],
[1., 1., 0., 0., 0., 0., 0., 0.],
[1., 1., 1., 0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 1., 0., 0., 0., 0.],
[0., 0., 1., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 1., 1.],
[0., 0., 0., 0., 0., 0., 1., 1.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 1., 1., 0., 0., 0.],
[0., 0., 0., 1., 1., 0., 0., 0.]],
[[0., 1., 1., 0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 1., 1., 1., 1., 1.],
[0., 0., 0., 1., 1., 1., 0., 0.],
[0., 0., 0., 1., 1., 1., 1., 1.],
[1., 1., 1., 0., 0., 0., 0., 0.],
[1., 1., 1., 0., 0., 0., 0., 0.],
[1., 1., 1., 0., 0., 0., 0., 0.],
[1., 0., 1., 0., 0., 0., 0., 0.],
[1., 0., 1., 0., 0., 0., 0., 0.]]])
G = make_graph(adj, shape, sym=True, save_csc=True)
assert np.array_equal(G.csr.toarray(), G.csc.toarray())
assert np.array_equal(G.indeg_vec, np.asarray([7, 4, 6, 6, 6, 5, 5, 5]))
for k in xrange(shape[2]):
assert np.array_equal(G.getslice(k).toarray(), A[k, :, :])
def test_graph2():
sym = True
adj = np.array([[0, 1, 0, 16], [2, 4, 0, 14], [4, 5, 0, 4], [0, 2, 1, 13],
[2, 1, 1, 4], [3, 5, 1, 20], [1, 3, 2, 12], [3, 2, 2, 9],
[4, 3, 2, 7]])
shape = (6, 6, 3)
G = make_graph(adj[:, :3], shape, values=adj[:, 3], sym=sym, display=False)
print "Original graph:\n", G
# set weights
indegsim = weighted_degree(G.indeg_vec, weight='degree').reshape((1, G.N))
indegsim = indegsim.ravel()
targets = G.csr.indices % G.N
specificity_wt = indegsim[targets] # specificity
G.csr.data = specificity_wt.copy()
# back up
data = G.csr.data.copy()
indices = G.csr.indices.copy()
indptr = G.csr.indptr.copy()
# Closure
expect = [[0, 0, 1, 0.20000000000000001, [0, 2, 1], [-1, 1, 1]],
[0, 0, 2, 1.0, [0, 2], [-1, 1]],
[0, 0, 3, 0.25, [0, 1, 3], [-1, 0, 2]],
[0, 0, 4, 0.20000000000000001, [0, 2, 4], [-1, 1, 0]],
[0, 0, 5, 0.125, [0, 1, 3, 5], [-1, 0, 2, 1]],
[0, 1, 1, 1.0, [0, 1], [-1, 0]],
[0, 1, 2, 0.25, [0, 1, 2], [-1, 0, 1]],
[0, 1, 3, 0.25, [0, 1, 3], [-1, 0, 2]],
[0, 1, 4, 0.20000000000000001, [0, 2, 4], [-1, 1, 0]],
[0, 1, 5, 0.125, [0, 1, 3, 5], [-1, 0, 2, 1]],
[0, 2, 1, 1.0, [0, 1], [-1, 0]], [0, 2, 2, 1.0, [0, 2], [-1, 1]],
[0, 2, 3, 0.25, [0, 1, 3], [-1, 0, 2]],
[0, 2, 4, 0.20000000000000001, [0, 2, 4], [-1, 1, 0]],
[0, 2, 5, 0.125, [0, 1, 3, 5], [-1, 0, 2, 1]],
[1, 0, 0, 0.20000000000000001, [1, 2, 0], [-1, 1, 1]],
[1, 0, 2, 1.0, [1, 2], [-1, 1]], [1, 0, 3, 1.0, [1, 3], [-1, 2]],
[1, 0, 4, 0.20000000000000001, [1, 3, 4], [-1, 2, 2]],
[1, 0, 5, 0.20000000000000001, [1, 3, 5], [-1, 2, 1]],
[1, 1, 0, 1.0, [1, 0], [-1, 0]],
[1, 1, 2, 0.33333333333333331, [1, 0, 2], [-1, 0, 1]],
[1, 1, 3, 1.0, [1, 3], [-1, 2]],
[1, 1, 4, 0.20000000000000001, [1, 3, 4], [-1, 2, 2]],
[1, 1, 5, 0.20000000000000001, [1, 3, 5], [-1, 2, 1]],
[1, 2, 0, 1.0, [1, 0], [-1, 0]], [1, 2, 2, 1.0, [1, 2], [-1, 1]],
[1, 2, 3, 0.20000000000000001, [1, 2, 3], [-1, 1, 2]],
[1, 2, 4, 0.20000000000000001, [1, 3, 4], [-1, 2, 2]],
[1, 2, 5, 0.20000000000000001, [1, 3, 5], [-1, 2, 1]],
[2, 0, 0, 1.0, [2, 0], [-1, 1]], [2, 0, 1, 1.0, [2, 1], [-1, 1]],
[2, 0, 3, 1.0, [2, 3], [-1, 2]],
[2, 0, 4, 0.20000000000000001, [2, 3, 4], [-1, 2, 2]],
[2, 0, 5, 0.25, [2, 4, 5], [-1, 0, 0]],
[2, 1, 0, 0.25, [2, 1, 0], [-1, 1, 0]],
[2, 1, 1, 0.33333333333333331, [2, 0, 1], [-1, 1, 0]],
[2, 1, 3, 1.0, [2, 3], [-1, 2]], [2, 1, 4, 1.0, [2, 4], [-1, 0]],
[2, 1, 5, 0.25, [2, 4, 5], [-1, 0, 0]],
[2, 2, 0, 1.0, [2, 0], [-1, 1]], [2, 2, 1, 1.0, [2, 1], [-1, 1]],
[2, 2, 3, 0.25, [2, 1, 3], [-1, 1, 2]],
[2, 2, 4, 1.0, [2, 4], [-1, 0]],
[2, 2, 5, 0.25, [2, 4, 5], [-1, 0, 0]],
[3, 0, 0, 0.25, [3, 1, 0], [-1, 2, 0]],
[3, 0, 1, 1.0, [3, 1], [-1, 2]], [3, 0, 2, 1.0, [3, 2], [-1, 2]],
[3, 0, 4, 1.0, [3, 4], [-1, 2]], [3, 0, 5, 1.0, [3, 5], [-1, 1]],
[3, 1, 0, 0.25, [3, 1, 0], [-1, 2, 0]],
[3, 1, 1, 1.0, [3, 1], [-1, 2]], [3, 1, 2, 1.0, [3, 2], [-1, 2]],
[3, 1, 4, 1.0, [3, 4], [-1, 2]],
[3, 1, 5, 0.25, [3, 4, 5], [-1, 2, 0]],
[3, 2, 0, 0.25, [3, 1, 0], [-1, 2, 0]],
[3, 2, 1, 0.20000000000000001, [3, 2, 1], [-1, 2, 1]],
[3, 2, 2, 0.25, [3, 4, 2], [-1, 2, 0]],
[3, 2, 4, 0.33333333333333331, [3, 5, 4], [-1, 1, 0]],
[3, 2, 5, 1.0, [3, 5], [-1, 1]],
[4, 0, 0, 0.20000000000000001, [4, 2, 0], [-1, 0, 1]],
[4, 0, 1, 0.20000000000000001, [4, 3, 1], [-1, 2, 2]],
[4, 0, 2, 0.20000000000000001, [4, 3, 2], [-1, 2, 2]],
[4, 0, 3, 1.0, [4, 3], [-1, 2]],
[4, 0, 5, 0.20000000000000001, [4, 3, 5], [-1, 2, 1]],
[4, 1, 0, 0.20000000000000001, [4, 2, 0], [-1, 0, 1]],
[4, 1, 1, 0.20000000000000001, [4, 3, 1], [-1, 2, 2]],
[4, 1, 2, 1.0, [4, 2], [-1, 0]], [4, 1, 3, 1.0, [4, 3], [-1, 2]],
[4, 1, 5, 1.0, [4, 5], [-1, 0]],
[4, 2, 0, 0.20000000000000001, [4, 2, 0], [-1, 0, 1]],
[4, 2, 1, 0.20000000000000001, [4, 3, 1], [-1, 2, 2]],
[4, 2, 2, 1.0, [4, 2], [-1, 0]],
[4, 2, 3, 0.33333333333333331, [4, 5, 3], [-1, 0, 1]],
[4, 2, 5, 1.0, [4, 5], [-1, 0]],
[5, 0, 0, 0.125, [5, 4, 2, 0], [-1, 0, 0, 1]],
[5, 0, 1, 0.20000000000000001, [5, 3, 1], [-1, 1, 2]],
[5, 0, 2, 0.25, [5, 4, 2], [-1, 0, 0]],
[5, 0, 3, 1.0, [5, 3], [-1, 1]],
[5, 0, 4, 0.20000000000000001, [5, 3, 4], [-1, 1, 2]],
[5, 1, 0, 0.125, [5, 4, 2, 0], [-1, 0, 0, 1]],
[5, 1, 1, 0.20000000000000001, [5, 3, 1], [-1, 1, 2]],
[5, 1, 2, 0.25, [5, 4, 2], [-1, 0, 0]],
[5, 1, 3, 0.25, [5, 4, 3], [-1, 0, 2]],
[5, 1, 4, 1.0, [5, 4], [-1, 0]],
[5, 2, 0, 0.125, [5, 4, 2, 0], [-1, 0, 0, 1]],
[5, 2, 1, 0.20000000000000001, [5, 3, 1], [-1, 1, 2]],
[5, 2, 2, 0.25, [5, 4, 2], [-1, 0, 0]],
[5, 2, 3, 1.0, [5, 3], [-1, 1]], [5, 2, 4, 1.0, [5, 4], [-1, 0]]]
results = []
itr = 0
for s in xrange(G.N):
for p in xrange(G.R):
for o in xrange(G.N):
if s == o:
continue
G.csr.data[targets == o] = 1
rp = relclosure(G, s, p, o, kind='metric', linkpred=True)
tmp = [
rp.source, rp.relation, rp.target, rp.score, rp.path,
rp.relational_path
]
results.append(tmp)
assert allclose(expect[itr], tmp)
itr += 1
G.csr.data = data.copy()
G.csr.indices = indices.copy()
G.csr.indptr = indptr.copy()
