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[skip e2e] Modify test cases (#18341)

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Signed-off-by: Binbin Lv <binbin.lv@zilliz.com>
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binbin 2022-07-20 15:46:32 +08:00 committed by GitHub
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commit 5e84c2adb0
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1 changed files with 12 additions and 18 deletions
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tests/python_client/testcases/test_search.py
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@ -1631,9 +1631,10 @@ class TestCollectionSearch(TestcaseBase):
"""
target: search binary_collection, and check the result: distance
method: compare the return distance value with value computed with SUBSTRUCTURE.
(1) The returned limit(topK) are impacted by dimension (dim) of data.
(1) The returned limit(topK) are impacted by dimension (dim) of data
(2) Searched topK is smaller than set limit when dim is large
(3) it does not support "BIN_IVF_FLAT" index
(3) It does not support "BIN_IVF_FLAT" index
(4) Only two values for distance: 0 and 1, 0 means hits, 1 means not
expected: the return distance equals to the computed value
"""
# 1. initialize with binary data
@ -1646,12 +1647,8 @@ class TestCollectionSearch(TestcaseBase):
default_index = {"index_type": index, "params": {"nlist": 128}, "metric_type": "SUBSTRUCTURE"}
collection_w.create_index("binary_vector", default_index)
collection_w.load()
# 3. compute the distance
query_raw_vector, binary_vectors = cf.gen_binary_vectors(nq, dim)
distance_min = 1
for binary_raw in binary_raw_vector:
distance = cf.substructure(query_raw_vector[0], binary_raw)
distance_min = min(distance, distance_min)
# 3. generate search vectors
_, binary_vectors = cf.gen_binary_vectors(nq, dim)
# 4. search and compare the distance
search_params = {"metric_type": "SUBSTRUCTURE", "params": {"nprobe": 10}}
res = collection_w.search(binary_vectors[:nq], "binary_vector",
@ -1666,7 +1663,7 @@ class TestCollectionSearch(TestcaseBase):
if _async:
res.done()
res = res.result()
assert abs(res[0].distances[0] - distance_min) <= epsilon
assert res[0].distances[0] == 0.0
@pytest.mark.tags(CaseLabel.L2)
@pytest.mark.parametrize("index", ["BIN_FLAT"])
@ -1674,9 +1671,10 @@ class TestCollectionSearch(TestcaseBase):
"""
target: search binary_collection, and check the result: distance
method: compare the return distance value with value computed with SUPERSTRUCTURE
(1) The returned limit(topK) are impacted by dimension (dim) of data.
(1) The returned limit(topK) are impacted by dimension (dim) of data
(2) Searched topK is smaller than set limit when dim is large
(3) it does not support "BIN_IVF_FLAT" index
(3) It does not support "BIN_IVF_FLAT" index
(4) Only two values for distance: 0 and 1, 0 means hits, 1 means not
expected: the return distance equals to the computed value
"""
# 1. initialize with binary data
@ -1689,12 +1687,8 @@ class TestCollectionSearch(TestcaseBase):
default_index = {"index_type": index, "params": {"nlist": 128}, "metric_type": "SUPERSTRUCTURE"}
collection_w.create_index("binary_vector", default_index)
collection_w.load()
# 3. compute the distance
query_raw_vector, binary_vectors = cf.gen_binary_vectors(nq, dim)
distance_min = 1
for binary_raw in binary_raw_vector:
distance = cf.superstructure(query_raw_vector[0], binary_raw)
distance_min = min(distance, distance_min)
# 3. generate search vectors
_, binary_vectors = cf.gen_binary_vectors(nq, dim)
# 4. search and compare the distance
search_params = {"metric_type": "SUPERSTRUCTURE", "params": {"nprobe": 10}}
res = collection_w.search(binary_vectors[:nq], "binary_vector",
@ -1709,7 +1703,7 @@ class TestCollectionSearch(TestcaseBase):
if _async:
res.done()
res = res.result()
assert abs(res[0].distances[0] - distance_min) <= epsilon
assert res[0].distances[0] == 0.0
@pytest.mark.tags(CaseLabel.L2)
def test_search_binary_without_flush(self, metrics, auto_id):