def compute_relklinker(G, relsim, subs, preds, objs):
"""
Parameters:
-----------
G: rgraph
See `datastructures`.
relsim: ndarray
A square matrix containing relational similarity scores.
subs, preds, objs: sequence
Sequences representing the subject, predicate and object of
input triples.
Returns:
--------
scores, paths, rpaths, times: sequence
One sequence each for the proximity scores, shortest path in terms of
nodes, shortest path in terms of relation sequence, and times taken.
"""
# set weights
indegsim = weighted_degree(G.indeg_vec, weight=WTFN).reshape((1, G.N))
indegsim = indegsim.ravel()
targets = G.csr.indices % G.N
specificity_wt = indegsim[targets] # specificity
G.csr.data = specificity_wt.copy()
# relation vector
relations = (G.csr.indices - targets) / G.N
relations_int = relations.astype(int) # convert to int for indexing
# back up
data = G.csr.data.copy()
indices = G.csr.indices.copy()
indptr = G.csr.indptr.copy()
scores, paths, rpaths, times = [], [], [], []
for idx, (s, p, o) in enumerate(zip(subs, preds, objs)):
print('{}. Working on {}..'.format(idx + 1, (s, p, o)), end=' ')
ts = time()
# set relational weight
G.csr.data[targets ==
o] = 1 # no cost for target t => max. specificity.
relsimvec = relsim[p, :] # specific to predicate p
relsim_wt = relsimvec[relations_int] # graph weight
G.csr.data = np.multiply(relsim_wt, G.csr.data)
rp = relclosure(G, s, p, o, kind='metric', linkpred=True)
tend = time()
print('time: {:.2f}s'.format(tend - ts))
times.append(tend - ts)
scores.append(rp.score)
paths.append(rp.path)
rpaths.append(rp.relational_path)
# reset graph
G.csr.data = data.copy()
G.csr.indices = indices.copy()
G.csr.indptr = indptr.copy()
sys.stdout.flush()
log.info('')
return scores, paths, rpaths, times
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()