def test_normalizer(self):
a = get_unary_score(0.5)
norm_op = Normalizer1()
b = norm_op(a)
pprint(json.loads(b.explained))
a = get_unary_score(0.5)
norm_op = Normalizer2(2)
b = norm_op(a)
pprint(json.loads(b.explained))
self.assertAlmostEqual(b.value, 0.8)
a = get_unary_score(0.5)
norm_op = Normalizer3(2)
b = norm_op(a)
pprint(json.loads(b.explained))
self.assertAlmostEqual(b.value, 0.7387961283389092)
a = get_unary_score(0.5)
norm_op = Normalizer4(2)
b = norm_op(a)
pprint(json.loads(b.explained))
self.assertEqual(b.value, 0.75)
norm_op = ModifierScoreFn('none')
b = norm_op(a)
pprint(json.loads(b.explained))
self.assertEqual(b.value, 0.5)
q_chunk = gnes_pb2.Chunk()
q_chunk.weight = 0.5
q_chunk.offset = 1
d_chunk = gnes_pb2.Chunk()
d_chunk.weight = 0.7
d_chunk.offset = 2
rel_score = get_unary_score(2)
_op = WeightedChunkScoreFn()
c = _op(rel_score, q_chunk, d_chunk)
pprint(json.loads(c.explained))
self.assertAlmostEqual(c.value, 0.7)
def test_combine_score_fn(self):
from gnes.indexer.chunk.helper import ListKeyIndexer
from gnes.indexer.chunk.numpy import NumpyIndexer
from gnes.proto import array2blob
import numpy as np
q_chunk = gnes_pb2.Chunk()
q_chunk.doc_id = 2
q_chunk.weight = 0.3
q_chunk.offset = 0
q_chunk.embedding.CopyFrom(array2blob(np.array([3, 3, 3])))
for _fn in [
WeightedChunkOffsetScoreFn, CoordChunkScoreFn,
TFIDFChunkScoreFn, BM25ChunkScoreFn
]:
indexer = NumpyIndexer(helper_indexer=ListKeyIndexer(),
score_fn=_fn())
indexer.add(keys=[(0, 1), (1, 2)],
vectors=np.array([[1, 1, 1], [2, 2, 2]]),
weights=[0.5, 0.8])
queried_result = indexer.query_and_score(q_chunks=[q_chunk],
top_k=2)