def main():
args = cli_parser().parse_args()
config = utility_main_helper(default_config, args)
log = logging.getLogger(__name__)
output_filepath = args.output_map
if not output_filepath:
raise ValueError("No path given for output map file (pickle).")
#: :type: smqtk.representation.DescriptorIndex
index = from_plugin_config(config['descriptor_index'],
get_descriptor_index_impls())
mbkm = MiniBatchKMeans(verbose=args.verbose,
compute_labels=False,
**config['minibatch_kmeans_params'])
initial_fit_size = int(config['initial_fit_size'])
d_classes = mb_kmeans_build_apply(index, mbkm, initial_fit_size)
log.info("Saving KMeans centroids to: %s",
config['centroids_output_filepath_npy'])
numpy.save(config['centroids_output_filepath_npy'], mbkm.cluster_centers_)
log.info("Saving result classification map to: %s", output_filepath)
safe_create_dir(os.path.dirname(output_filepath))
with open(output_filepath, 'w') as f:
cPickle.dump(d_classes, f, -1)
log.info("Done")
def main():
args = cli_parser().parse_args()
config = utility_main_helper(default_config, args)
log = logging.getLogger(__name__)
output_filepath = args.output_map
if not output_filepath:
raise ValueError("No path given for output map file (pickle).")
#: :type: smqtk.representation.DescriptorIndex
index = from_plugin_config(config['descriptor_index'],
get_descriptor_index_impls())
mbkm = MiniBatchKMeans(verbose=args.verbose,
compute_labels=False,
**config['minibatch_kmeans_params'])
initial_fit_size = int(config['initial_fit_size'])
d_classes = mb_kmeans_build_apply(index, mbkm, initial_fit_size)
log.info("Saving KMeans centroids to: %s",
config['centroids_output_filepath_npy'])
numpy.save(config['centroids_output_filepath_npy'], mbkm.cluster_centers_)
log.info("Saving result classification map to: %s", output_filepath)
safe_create_dir(os.path.dirname(output_filepath))
with open(output_filepath, 'w') as f:
cPickle.dump(d_classes, f, -1)
log.info("Done")
def test_clustering_equal_descriptors(self):
# Test that clusters of descriptor of size n-features are correctly
# clustered together.
print("Creating dummy descriptors")
n_features = 8
n_descriptors = 20
index = MemoryDescriptorIndex()
c = 0
for i in range(n_features):
v = numpy.ndarray((8,))
v[...] = 0
v[i] = 1
for j in range(n_descriptors):
d = DescriptorMemoryElement('test', c)
d.set_vector(v)
index.add_descriptor(d)
c += 1
print("Creating test MBKM")
mbkm = MiniBatchKMeans(n_features, batch_size=12, verbose=True,
compute_labels=False, random_state=0)
# Initial fit with half of index
d_classes = mb_kmeans_build_apply(index, mbkm, n_descriptors)
# There should be 20 descriptors per class
for c in d_classes:
self.assertEqual(
len(d_classes[c]),
n_descriptors,
"Cluster %s did not have expected number of descriptors "
"(%d != %d)"
% (c, n_descriptors, len(d_classes[c]))
)
# Each descriptor in each cluster should be equal to the other
# descriptors in that cluster
uuids = list(d_classes[c])
v = index[uuids[0]].vector()
for uuid in uuids[1:]:
v2 = index[uuid].vector()
numpy.testing.assert_array_equal(v, v2,
"vector in cluster %d did not "
"match other vectors "
"(%s != %s)"
% (c, v, v2))
def test_clustering_equal_descriptors(self):
# Test that clusters of descriptor of size n-features are correctly
# clustered together.
print "Creating dummy descriptors"
n_features = 8
n_descriptors = 20
index = MemoryDescriptorIndex()
c = 0
for i in range(n_features):
v = numpy.ndarray(8)
v[...] = 0
v[i] = 1
for j in range(n_descriptors):
d = DescriptorMemoryElement('test', c)
d.set_vector(v)
index.add_descriptor(d)
c += 1
print "Creating test MBKM"
mbkm = MiniBatchKMeans(n_features,
batch_size=12,
verbose=True,
compute_labels=False,
random_state=0)
# Initial fit with half of index
d_classes = mb_kmeans_build_apply(index, mbkm, n_descriptors)
# There should be 20 descriptors per class
for c in d_classes:
nose.tools.assert_equal(
len(d_classes[c]), n_descriptors,
"Cluster %s did not have expected number of descriptors "
"(%d != %d)" % (c, n_descriptors, len(d_classes[c])))
# Each descriptor in each cluster should be equal to the other
# descriptors in that cluster
uuids = list(d_classes[c])
v = index[uuids[0]].vector()
for uuid in uuids[1:]:
v2 = index[uuid].vector()
numpy.testing.assert_array_equal(
v, v2, "vector in cluster %d did not "
"match other vectors "
"(%s != %s)" % (c, v, v2))
def test_empty_index(self):
# what happens when function given an empty descriptor index
index = MemoryDescriptorIndex()
mbkm = MiniBatchKMeans()
d = mb_kmeans_build_apply(index, mbkm, 0)
nose.tools.assert_false(d)
def test_empty_index(self):
# what happens when function given an empty descriptor index
index = MemoryDescriptorIndex()
mbkm = MiniBatchKMeans()
d = mb_kmeans_build_apply(index, mbkm, 0)
self.assertFalse(d)
def test_emptyset(self):
# what happens when function given an empty descriptor set
descr_set = MemoryDescriptorSet()
mbkm = MiniBatchKMeans()
d = mb_kmeans_build_apply(descr_set, mbkm, 0)
self.assertFalse(d)