queries.append(data)
queries.append(data)
queries.append(data)
queries.append(data)
# We can train just as before.
rank = trainer.train(queries)
# Now that we have multiple ranking_pair instances, we can also use
# cross_validate_ranking_trainer(). This performs cross-validation by splitting
# the queries up into folds. That is, it lets the trainer train on a subset of
# ranking_pair instances and tests on the rest. It does this over 4 different
# splits and returns the overall ranking accuracy based on the held out data.
# Just like test_ranking_function(), it reports both the ordering accuracy and
# mean average precision.
print "cross validation results: ", dlib.cross_validate_ranking_trainer(
trainer, queries, 4)
# 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
queries.append(data)
queries.append(data)
queries.append(data)
# We can train just as before.
rank = trainer.train(queries)
# Now that we have multiple ranking_pair instances, we can also use
# cross_validate_ranking_trainer(). This performs cross-validation by splitting
# the queries up into folds. That is, it lets the trainer train on a subset of
# ranking_pair instances and tests on the rest. It does this over 4 different
# splits and returns the overall ranking accuracy based on the held out data.
# Just like test_ranking_function(), it reports both the ordering accuracy and
# mean average precision.
print("Cross validation results: {}".format(
dlib.cross_validate_ranking_trainer(trainer, queries, 4)))
# 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
queries.append(data)
queries.append(data)
queries.append(data)
# We can train just as before.
rank = trainer.train(queries)
# Now that we have multiple ranking_pair instances, we can also use
# cross_validate_ranking_trainer(). This performs cross-validation by splitting
# the queries up into folds. That is, it lets the trainer train on a subset of
# ranking_pair instances and tests on the rest. It does this over 4 different
# splits and returns the overall ranking accuracy based on the held out data.
# Just like test_ranking_function(), it reports both the ordering accuracy and
# mean average precision.
print("Cross validation results: {}".format(
dlib.cross_validate_ranking_trainer(trainer, queries, 4)))
# 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
queries.append(data) queries.append(data) queries.append(data) # We can train just as before. rank = trainer.train(queries) # Now that we have multiple ranking_pair instances, we can also use # cross_validate_ranking_trainer(). This performs cross-validation by splitting # the queries up into folds. That is, it lets the trainer train on a subset of # ranking_pair instances and tests on the rest. It does this over 4 different # splits and returns the overall ranking accuracy based on the held out data. # Just like test_ranking_function(), it reports both the ordering accuracy and # mean average precision. print "cross validation results: ", dlib.cross_validate_ranking_trainer(trainer, queries, 4) # 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
