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test: optimizing variable names (#34035) 

Signed-off-by: elstic <hao.wang@zilliz.com>
pull/34043/head
elstic 2024-06-21 10:14:00 +08:00 committed by GitHub
parent d9f986439d
commit 4e414fb7fc
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GPG Key ID: B5690EEEBB952194
3 changed files with 10 additions and 9 deletions
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14
tests/python_client/common/common_func.py
View File

@ -364,10 +364,10 @@ def gen_collection_schema_all_datatype(description=ct.default_desc,
else:
multiple_dim_array.insert(0, dim)
for i in range(len(multiple_dim_array)):
if ct.all_float_vector_types[i%3] != ct.sparse_vector:
fields.append(gen_float_vec_field(name=f"multiple_vector_{ct.all_float_vector_types[i%3]}",
if ct.append_vector_type[i%3] != ct.sparse_vector:
fields.append(gen_float_vec_field(name=f"multiple_vector_{ct.append_vector_type[i%3]}",
dim=multiple_dim_array[i],
vector_data_type=ct.all_float_vector_types[i%3]))
vector_data_type=ct.append_vector_type[i%3]))
else:
# The field of a sparse vector cannot be dimensioned
fields.append(gen_float_vec_field(name=f"multiple_vector_{ct.sparse_vector}",
@ -719,7 +719,7 @@ def gen_dataframe_all_data_type(nb=ct.default_nb, dim=ct.default_dim, start=0, w
df[ct.default_float_vec_field_name] = float_vec_values
else:
for i in range(len(multiple_dim_array)):
df[multiple_vector_field_name[i]] = gen_vectors(nb, multiple_dim_array[i], ct.all_float_vector_types[i%3])
df[multiple_vector_field_name[i]] = gen_vectors(nb, multiple_dim_array[i], ct.append_vector_type[i%3])
if with_json is False:
df.drop(ct.default_json_field_name, axis=1, inplace=True)
@ -757,7 +757,7 @@ def gen_general_list_all_data_type(nb=ct.default_nb, dim=ct.default_dim, start=0
insert_list.append(float_vec_values)
else:
for i in range(len(multiple_dim_array)):
insert_list.append(gen_vectors(nb, multiple_dim_array[i], ct.all_float_vector_types[i%3]))
insert_list.append(gen_vectors(nb, multiple_dim_array[i], ct.append_vector_type[i%3]))
if with_json is False:
# index = insert_list.index(json_values)
@ -802,7 +802,7 @@ def gen_default_rows_data_all_data_type(nb=ct.default_nb, dim=ct.default_dim, st
else:
for i in range(len(multiple_dim_array)):
dict[multiple_vector_field_name[i]] = gen_vectors(nb, multiple_dim_array[i],
ct.all_float_vector_types[i])[0]
ct.append_vector_type[i])[0]
if len(multiple_dim_array) != 0:
with open(ct.rows_all_data_type_file_path + f'_{partition_id}' + f'_dim{dim}.txt', 'wb') as json_file:
pickle.dump(array, json_file)
@ -1792,7 +1792,7 @@ def insert_data(collection_w, nb=ct.default_nb, is_binary=False, is_all_data_typ
multiple_vector_field_name=vector_name_list,
vector_data_type=vector_data_type,
auto_id=auto_id, primary_field=primary_field)
elif vector_data_type in ct.all_float_vector_types:
elif vector_data_type in ct.append_vector_type:
default_data = gen_general_default_list_data(nb // num, dim=dim, start=start, with_json=with_json,
random_primary_key=random_primary_key,
multiple_dim_array=multiple_dim_array,

3
tests/python_client/common/common_type.py
View File

@ -45,7 +45,8 @@ float_type = "FLOAT_VECTOR"
float16_type = "FLOAT16_VECTOR"
bfloat16_type = "BFLOAT16_VECTOR"
sparse_vector = "SPARSE_FLOAT_VECTOR"
all_float_vector_types = [float16_type, bfloat16_type, sparse_vector]
append_vector_type = [float16_type, bfloat16_type, sparse_vector]
all_dense_vector_types = [float_type, float16_type, bfloat16_type]
default_sparse_vec_field_name = "sparse_vector"
default_partition_name = "_default"
default_resource_group_name = '__default_resource_group'

2
tests/python_client/testcases/test_search.py
View File

@ -6961,7 +6961,7 @@ class TestCollectionRangeSearch(TestcaseBase):
******************************************************************
"""
@pytest.mark.tags(CaseLabel.L0)
@pytest.mark.parametrize("vector_data_type", ["FLOAT_VECTOR", "FLOAT16_VECTOR", "BFLOAT16_VECTOR"])
@pytest.mark.parametrize("vector_data_type", ct.all_dense_vector_types)
def test_range_search_default(self, index_type, metric, vector_data_type):
"""
target: verify the range search returns correct results