# Finally, note that the ranking tools also support the use of sparse vectors in # addition to dense vectors (which we used above). So if we wanted to do # exactly what we did in the first part of the example program above but using # sparse vectors we would do it like so: data = dlib.sparse_ranking_pair() samp = dlib.sparse_vector() # Make samp represent the same vector as dlib.vector([1, 0]). In dlib, a sparse # vector is just an array of pair objects. Each pair stores an index and a # value. Moreover, the svm-ranking tools require sparse vectors to be sorted # and to have unique indices. This means that the indices are listed in # increasing order and no index value shows up more than once. If necessary, # you can use the dlib.make_sparse_vector() routine to make a sparse vector # object properly sorted and contain unique indices. samp.append(dlib.pair(0, 1)) data.relevant.append(samp) # Now make samp represent the same vector as dlib.vector([0, 1]) samp.clear() samp.append(dlib.pair(1, 1)) data.nonrelevant.append(samp) trainer = dlib.svm_rank_trainer_sparse() rank = trainer.train(data) print "ranking score for a relevant vector: ", rank(data.relevant[0]) print "ranking score for a non-relevant vector: ", rank(data.nonrelevant[0]) # Just as before, the output is the following: # ranking score for a relevant vector: 0.5 # ranking score for a non-relevant vector: -0.5
# exactly what we did in the first part of the example program above but using
# sparse vectors we would do it like so:
data = dlib.sparse_ranking_pair()
samp = dlib.sparse_vector()
# Make samp represent the same vector as dlib.vector([1, 0]). In dlib, a sparse
# vector is just an array of pair objects. Each pair stores an index and a
# value. Moreover, the svm-ranking tools require sparse vectors to be sorted
# and to have unique indices. This means that the indices are listed in
# increasing order and no index value shows up more than once. If necessary,
# you can use the dlib.make_sparse_vector() routine to make a sparse vector
# object properly sorted and contain unique indices.
samp.append(dlib.pair(0, 1))
data.relevant.append(samp)
# Now make samp represent the same vector as dlib.vector([0, 1])
samp.clear()
samp.append(dlib.pair(1, 1))
data.nonrelevant.append(samp)
trainer = dlib.svm_rank_trainer_sparse()
rank = trainer.train(data)
print("Ranking score for a relevant vector: {}".format(
rank(data.relevant[0])))
print("Ranking score for a non-relevant vector: {}".format(
rank(data.nonrelevant[0])))
# Just as before, the output is the following:
# ranking score for a relevant vector: 0.5
# ranking score for a non-relevant vector: -0.5