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import unittest
import numpy as np
import hnswlib
class RandomSelfTestCase(unittest.TestCase):
def testGettingItems(self):
print("\n**** Getting the data by label test ****\n")
dim = 16
num_elements = 10000
# Generating sample data
data = np.float32(np.random.random((num_elements, dim)))
labels = np.arange(0, num_elements)
# Declaring index
p = hnswlib.Index(space='l2', dim=dim) # possible options are l2, cosine or ip
# Initiating index
# max_elements - the maximum number of elements, should be known beforehand
# (probably will be made optional in the future)
#
# ef_construction - controls index search speed/build speed tradeoff
# M - is tightly connected with internal dimensionality of the data
# strongly affects the memory consumption
p.init_index(max_elements=num_elements, ef_construction=100, M=16)
# Controlling the recall by setting ef:
# higher ef leads to better accuracy, but slower search
p.set_ef(100)
p.set_num_threads(4) # by default using all available cores
# Before adding anything, getting any labels should fail
self.assertRaises(Exception, lambda: p.get_items(labels))
print("Adding all elements (%d)" % (len(data)))
p.add_items(data, labels)
# Getting data by label should raise an exception if a scalar is passed:
self.assertRaises(ValueError, lambda: p.get_items(labels[0]))
# After adding them, all labels should be retrievable
returned_items_np = p.get_items(labels)
self.assertTrue((data == returned_items_np).all())
# check returned type of get_items
self.assertTrue(isinstance(returned_items_np, np.ndarray))
returned_items_list = p.get_items(labels, return_type="list")
self.assertTrue(isinstance(returned_items_list, list))
self.assertTrue(isinstance(returned_items_list[0], list))
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