def test_model_reload(self):
fd, fp = tempfile.mkstemp('.npz')
os.close(fd)
os.remove(fp) # shouldn't exist before construction
try:
bt = SkLearnBallTreeHashIndex(fp)
m = numpy.random.randint(0, 2, 1000 * 256).reshape(1000, 256)
bt.build_index(m)
q = numpy.random.randint(0, 2, 256).astype(bool)
bt_neighbors, bt_dists = bt.nn(q, 10)
bt2 = SkLearnBallTreeHashIndex(fp)
bt2_neighbors, bt2_dists = bt2.nn(q, 10)
nose.tools.assert_is_not(bt, bt2)
nose.tools.assert_is_not(bt.bt, bt2.bt)
numpy.testing.assert_equal(bt2_neighbors, bt_neighbors)
numpy.testing.assert_equal(bt2_dists, bt_dists)
finally:
os.remove(fp)
def test_load_model(self):
# Create two index instances, building model with one, and loading
# the other with the cache of the first instance. Each should have
# distinct model instances, but should otherwise have equal model
# values and parameters.
cache_element = DataMemoryElement()
bt1 = SkLearnBallTreeHashIndex(cache_element, random_seed=0)
m = np.random.randint(0, 2, 1000 * 256).reshape(1000, 256)
bt1.build_index(m)
bt2 = SkLearnBallTreeHashIndex(cache_element)
self.assertIsNotNone(bt2.bt)
q = np.random.randint(0, 2, 256).astype(bool)
bt_neighbors, bt_dists = bt1.nn(q, 10)
bt2_neighbors, bt2_dists = bt2.nn(q, 10)
self.assertIsNot(bt1, bt2)
self.assertIsNot(bt1.bt, bt2.bt)
np.testing.assert_equal(bt2_neighbors, bt_neighbors)
np.testing.assert_equal(bt2_dists, bt_dists)