mirror of https://github.com/milvus-io/milvus.git
Add cases for invalid DataFrame data and binary collection (#5426)
- test invalid DataFrame data - test binary create and data - add test_insert Cover: #5404 #5405 #5407 #5414 #5421 Signed-off-by: ThreadDao yufen.zong@zilliz.compull/5432/head
ThreadDao
GitHub
parent
200801271d
commit
5feb9ae9c4
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@ -2,6 +2,7 @@ import os
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import random
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import string
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import numpy as np
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import pandas as pd
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from sklearn import preprocessing
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from pymilvus_orm.types import DataType
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@ -53,7 +54,7 @@ def gen_collection_schema(fields, primary_field=None, description=ct.default_des
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return schema
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def gen_default_binary_collection_schema(description=ct.default_binary_desc, primary_field=None):
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def gen_default_binary_collection_schema(description=ct.default_desc, primary_field=None):
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fields = [gen_int64_field(), gen_float_field(), gen_binary_vec_field()]
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binary_schema = CollectionSchema(fields=fields, description=description, primary_field=primary_field)
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return binary_schema
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@ -65,8 +66,17 @@ def gen_vectors(nb, dim):
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return vectors.tolist()
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def gen_binary_vectors(num, dim):
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raw_vectors = []
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binary_vectors = []
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for _ in range(num):
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raw_vector = [random.randint(0, 1) for _ in range(dim)]
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raw_vectors.append(raw_vector)
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binary_vectors.append(bytes(np.packbits(raw_vector, axis=-1).tolist()))
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return raw_vectors, binary_vectors
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def gen_default_dataframe_data(nb=ct.default_nb):
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import pandas as pd
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int_values = pd.Series(data=[i for i in range(nb)])
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float_values = pd.Series(data=[float(i) for i in range(nb)], dtype="float32")
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float_vec_values = gen_vectors(nb, ct.default_dim)
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@ -78,14 +88,42 @@ def gen_default_dataframe_data(nb=ct.default_nb):
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return df
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def gen_default_binary_dataframe_data(nb=ct.default_nb):
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int_values = pd.Series(data=[i for i in range(nb)])
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float_values = pd.Series(data=[float(i) for i in range(nb)], dtype="float32")
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_, binary_vec_values = gen_binary_vectors(nb, ct.default_dim)
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df = pd.DataFrame({
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ct.default_int64_field: int_values,
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ct.default_float_field: float_values,
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ct.default_binary_vec_field_name: binary_vec_values
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})
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return df
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def gen_default_list_data(nb=ct.default_nb):
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int_values = [i for i in range(nb)]
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float_values = [float(i) for i in range(nb)]
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float_values = [np.float32(i) for i in range(nb)]
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float_vec_values = gen_vectors(nb, ct.default_dim)
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data = [int_values, float_values, float_vec_values]
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return data
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def gen_numpy_data(nb=ct.default_nb):
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int_values = np.arange(nb, dtype='int64')
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float_values = np.arange(nb, dtype='float32')
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float_vec_values = gen_vectors(nb, ct.default_dim)
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data = [int_values, float_values, float_vec_values]
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return data
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def gen_default_binary_list_data(nb=ct.default_nb):
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int_values = [i for i in range(nb)]
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float_values = [np.float32(i) for i in range(nb)]
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_, binary_vec_values = gen_binary_vectors(nb, ct.default_dim)
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data = [int_values, float_values, binary_vec_values]
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return data
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def gen_simple_index():
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index_params = []
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for i in range(len(ct.all_index_types)):
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@ -97,22 +135,6 @@ def gen_simple_index():
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return index_params
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def get_vectors(num, dim, is_normal=True):
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vectors = [[random.random() for _ in range(dim)] for _ in range(num)]
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vectors = preprocessing.normalize(vectors, axis=1, norm='l2')
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return vectors.tolist()
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def gen_binary_vectors(num, dim):
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raw_vectors = []
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binary_vectors = []
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for i in range(num):
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raw_vector = [random.randint(0, 1) for i in range(dim)]
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raw_vectors.append(raw_vector)
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binary_vectors.append(bytes(np.packbits(raw_vector, axis=-1).tolist()))
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return raw_vectors, binary_vectors
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def gen_invalid_field_types():
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field_types = [
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6,
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@ -134,6 +156,21 @@ def gen_all_type_fields():
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return fields
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def gen_invalid_dataframe():
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vec = gen_vectors(3, 2)
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dfs = [
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# just columns df
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pd.DataFrame(columns=[ct.default_int64_field, ct.default_float_vec_field_name]),
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# no column just data df
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pd.DataFrame({' ': vec}),
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# datetime df
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pd.DataFrame({"date": pd.date_range('20210101', periods=3)}),
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# invalid column df
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pd.DataFrame({'%$#': vec}),
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]
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return dfs
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def modify_file(file_name_list, input_content=""):
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if not isinstance(file_name_list, list):
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log.error("[modify_file] file is not a list.")
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@ -1,3 +1,4 @@
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import pandas as pd
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import pytest
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from milvus import DataType
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from pymilvus_orm import FieldSchema
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@ -10,6 +11,7 @@ from common.common_type import CaseLabel
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prefix = "collection"
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default_schema = cf.gen_default_collection_schema()
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default_binary_schema = cf.gen_default_binary_collection_schema()
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def assert_default_collection(collection, exp_name=None, exp_schema=default_schema, exp_num=0, exp_primary=None):
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@ -36,59 +38,40 @@ class TestCollectionParams(ApiReq):
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def setup_method(self):
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pass
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@pytest.fixture(
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scope="function",
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params=ct.get_invalid_strs
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)
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@pytest.fixture(scope="function", params=ct.get_invalid_strs)
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def get_invalid_string(self, request):
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yield request.param
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@pytest.fixture(
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scope="function",
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params=ct.get_invalid_strs
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)
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@pytest.fixture(scope="function", params=ct.get_invalid_strs)
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def get_invalid_type_schema(self, request):
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if request.param is None:
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pytest.skip("None schema is valid")
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yield request.param
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@pytest.fixture(
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scope="function",
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params=ct.get_invalid_strs
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)
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@pytest.fixture(scope="function", params=ct.get_invalid_strs)
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def get_invalid_type_fields(self, request):
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skip_param = []
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if request.param == skip_param:
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pytest.skip("skip []")
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yield request.param
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@pytest.fixture(
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scope="function",
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params=cf.gen_invalid_field_types()
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)
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@pytest.fixture(scope="function", params=cf.gen_invalid_field_types())
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def get_invalid_field_type(self, request):
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yield request.param
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@pytest.fixture(
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scope="function",
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params=cf.gen_all_type_fields()
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)
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@pytest.fixture(scope="function", params=cf.gen_all_type_fields())
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def get_unsupported_primary_field(self, request):
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if request.param.dtype == DataType.INT64:
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pytest.skip("int64 type is valid primary key")
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yield request.param
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@pytest.fixture(
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scope="function",
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params=ct.get_invalid_strs
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)
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@pytest.fixture(scope="function", params=ct.get_invalid_strs)
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def get_invalid_dim(self, request):
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if request.param == 1:
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request.param = 0
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yield request.param
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@pytest.mark.tags(CaseLabel.L0)
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@pytest.mark.xfail(reason="issue #5224")
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def test_collection(self):
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"""
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target: test collection with default schema
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@ -99,7 +82,7 @@ class TestCollectionParams(ApiReq):
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c_name = cf.gen_unique_str(prefix)
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collection, _ = self.collection.collection_init(c_name, data=None, schema=default_schema)
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assert_default_collection(collection, c_name)
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assert c_name in self.utility.list_collections()
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assert c_name in self.utility.list_collections()[0]
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@pytest.mark.tags(CaseLabel.L0)
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def test_collection_empty_name(self):
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@ -321,6 +304,23 @@ class TestCollectionParams(ApiReq):
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ex, _ = self.collection.collection_init(c_name, schema=schema)
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log.error(str(ex))
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.xfail(reason="issue #5407")
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def test_collection_with_unknown_type(self):
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"""
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target: test collection with unknown type
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method: create with DataType.UNKNOWN
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expected: raise exception
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"""
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self._connect()
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c_name = cf.gen_unique_str(prefix)
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unknown_field = FieldSchema("unknown", DataType.UNKNOWN)
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fields = [unknown_field, cf.gen_float_vec_field()]
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schema = cf.gen_collection_schema(fields=fields)
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ex, _ = self.collection.collection_init(c_name, schema=schema)
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log.error(str(ex))
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# TODO assert
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@pytest.mark.tags(CaseLabel.L1)
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def test_collection_invalid_field_name(self, get_invalid_string):
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"""
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@ -385,15 +385,16 @@ class TestCollectionParams(ApiReq):
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assert not has
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@pytest.mark.tags(CaseLabel.L0)
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def test_collection_only_float_vector(self):
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@pytest.mark.parametrize("field", [cf.gen_float_vec_field(), cf.gen_binary_vec_field()])
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def test_collection_only_vector(self, field):
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"""
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target: test collection just with float-vec field
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target: test collection just with vec field
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method: create with float-vec fields
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expected: no exception
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"""
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self._connect()
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c_name = cf.gen_unique_str(prefix)
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schema = cf.gen_collection_schema(fields=[cf.gen_float_vec_field()])
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schema = cf.gen_collection_schema(fields=[field])
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collection, _ = self.collection.collection_init(c_name, schema=schema)
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assert_default_collection(collection, c_name, exp_schema=schema)
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@ -534,7 +535,8 @@ class TestCollectionParams(ApiReq):
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log.info(str(ex))
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@pytest.mark.tags(CaseLabel.L0)
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def test_collection_vector_without_dim(self):
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@pytest.mark.parametrize("dtype", [DataType.FLOAT_VECTOR, DataType.BINARY_VECTOR])
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def test_collection_vector_without_dim(self, dtype):
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"""
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target: test collection without dimension
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method: define vector field without dim
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@ -542,7 +544,7 @@ class TestCollectionParams(ApiReq):
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"""
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self._connect()
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c_name = cf.gen_unique_str(prefix)
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float_vec_field = FieldSchema(name=ct.default_float_vec_field_name, dtype=DataType.FLOAT_VECTOR)
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float_vec_field = FieldSchema(name="vec", dtype=dtype)
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schema = cf.gen_collection_schema(fields=[float_vec_field])
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ex, _ = self.collection.collection_init(c_name, schema=schema)
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assert "dimension is not defined in field type params" in str(ex)
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@ -605,7 +607,6 @@ class TestCollectionParams(ApiReq):
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collection, _ = self.collection.collection_init(c_name, schema=schema)
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assert_default_collection(collection, c_name, exp_schema=schema)
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# TODO
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@pytest.mark.tags(CaseLabel.L1)
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def test_collection_long_desc(self):
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"""
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@ -613,7 +614,12 @@ class TestCollectionParams(ApiReq):
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method: create with long desc
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expected:
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"""
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pass
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self._connect()
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c_name = cf.gen_unique_str(prefix)
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desc = "a".join("a" for _ in range(256))
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schema = cf.gen_default_collection_schema(description=desc)
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collection, _ = self.collection.collection_init(c_name, schema=schema)
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assert_default_collection(collection, c_name, exp_schema=schema)
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@pytest.mark.tags(CaseLabel.L0)
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@pytest.mark.xfail(reason="issue #5302")
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@ -645,6 +651,49 @@ class TestCollectionParams(ApiReq):
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collection, _ = self.collection.collection_init(c_name, schema=default_schema, data=data)
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assert_default_collection(collection, c_name, exp_num=nb)
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@pytest.mark.tags(CaseLabel.L0)
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def test_collection_binary(self):
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"""
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target: test collection with binary-vec
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method: create collection with binary field
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expected: assert binary field
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"""
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self._connect()
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c_name = cf.gen_unique_str(prefix)
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collection, _ = self.collection.collection_init(c_name, data=None, schema=default_binary_schema)
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assert_default_collection(collection, c_name, exp_schema=default_binary_schema)
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assert c_name in self.utility.list_collections()[0]
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@pytest.mark.tags(CaseLabel.L0)
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@pytest.mark.xfail(reason="issue #5414")
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def test_collection_binary_with_dataframe(self):
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"""
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target: test binary collection with dataframe
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method: create binary collection with dataframe
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expected: collection num entities equal to nb
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"""
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self._connect()
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nb = ct.default_nb
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c_name = cf.gen_unique_str(prefix)
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data = cf.gen_default_binary_dataframe_data(3)
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log.debug(data)
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collection, _ = self.collection.collection_init(c_name, schema=default_binary_schema, data=data)
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assert_default_collection(collection, c_name, exp_schema=default_binary_schema, exp_num=nb)
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@pytest.mark.tags(CaseLabel.L0)
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def test_collection_binary_with_data_list(self):
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"""
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target: test collection with data (list-like)
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method: create binary collection with data (list-like)
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expected: collection num entities equal to nb
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"""
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self._connect()
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nb = ct.default_nb
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c_name = cf.gen_unique_str(prefix)
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data = cf.gen_default_binary_list_data(nb)
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collection, _ = self.collection.collection_init(c_name, schema=default_binary_schema, data=data)
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assert_default_collection(collection, c_name, exp_schema=default_binary_schema, exp_num=nb)
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class TestCollectionOperation(ApiReq):
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"""
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@ -660,15 +709,16 @@ class TestCollectionOperation(ApiReq):
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def setup_method(self):
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pass
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@pytest.fixture(
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scope="function",
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params=ct.get_invalid_strs
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)
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@pytest.fixture(scope="function", params=ct.get_invalid_strs)
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def get_non_df(self, request):
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if request.param is None:
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pytest.skip("skip None")
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yield request.param
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@pytest.fixture(scope="function", params=cf.gen_invalid_dataframe())
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def get_invalid_df(self, request):
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yield request.param
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@pytest.mark.tags(CaseLabel.L1)
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def test_collection_without_connection(self):
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"""
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@ -702,7 +752,7 @@ class TestCollectionOperation(ApiReq):
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assert c_name not in self.utility.list_collections()
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.xfail(reason="issue #xxx")
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@pytest.mark.xfail(reason="issue #5367")
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def test_collection_dup_name_drop(self):
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"""
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target: test collection with dup name, and drop
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@ -754,15 +804,51 @@ class TestCollectionOperation(ApiReq):
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collection, _ = self.collection.collection_init(name=c_name, data=df)
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assert_default_collection(collection, exp_name=c_name, exp_num=nb, exp_schema=schema)
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# TODO
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@pytest.mark.tags(CaseLabel.L0)
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def _test_collection_created_by_invalid_dataframe(self, get_invalid_df):
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@pytest.mark.xfail(reason="issue #5404")
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def test_collection_created_by_empty_dataframe(self):
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"""
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target: test create collection by invalid dataframe
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target: test create collection by empty dataframe
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method: invalid dataframe type create collection
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expected: raise exception
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"""
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pass
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self._connect()
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c_name = cf.gen_unique_str(prefix)
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data = pd.DataFrame()
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ex, _ = self.collection.collection_init(name=c_name, schema=None, data=data)
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# TODO assert
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@pytest.mark.tags(CaseLabel.L1)
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def test_collection_created_by_invalid_dataframe(self, get_invalid_df):
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"""
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target: test collection with invalid dataframe
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method: create with invalid dataframe
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expected: raise exception
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"""
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self._connect()
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c_name = cf.gen_unique_str(prefix)
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ex, _ = self.collection.collection_init(name=c_name, schema=None, data=get_invalid_df)
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message_one = "Cannot infer schema from empty dataframe"
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message_two = "Field name should not be empty"
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message_three = "Invalid field name"
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assert message_one or message_two or message_three in str(ex)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.xfail(reason="issue #5405")
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def test_collection_created_by_inconsistent_dataframe(self):
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"""
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target: test collection with data inconsistent
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method: create and insert with inconsistent data
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expected: raise exception
|
||||
"""
|
||||
self._connect()
|
||||
c_name = cf.gen_unique_str(prefix)
|
||||
# one field different type df
|
||||
mix_data = [(1, 2., [0.1, 0.2]), (2, 3., 4)]
|
||||
df = pd.DataFrame(data=mix_data, columns=list("ABC"))
|
||||
ex, _ = self.collection.collection_init(name=c_name, schema=None, data=df)
|
||||
log.info(str(ex))
|
||||
# TODO assert
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L0)
|
||||
def test_collection_created_by_non_dataframe(self, get_non_df):
|
||||
|
|
@ -819,4 +905,65 @@ class TestCollectionOperation(ApiReq):
|
|||
assert not self.utility.has_collection(c_name)[0]
|
||||
re_collection = self._collection(name=c_name)
|
||||
assert_default_collection(re_collection, c_name)
|
||||
assert self.utility.has_collection(c_name)[0]
|
||||
assert self.utility.has_collection(c_name)[0]
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L1)
|
||||
@pytest.mark.xfail(reason="issue #5302")
|
||||
def test_collection_binary_insert_dataframe(self):
|
||||
"""
|
||||
target: test collection create and insert dataframe
|
||||
method: 1. create by schema 2. insert dataframe
|
||||
expected: assert num_entities
|
||||
"""
|
||||
self._connect()
|
||||
nb = ct.default_nb
|
||||
collection = self._collection(schema=default_binary_schema)
|
||||
assert_default_collection(collection, exp_schema=default_binary_schema)
|
||||
df = cf.gen_default_binary_dataframe_data(nb)
|
||||
self.collection.insert(data=df)
|
||||
assert collection.num_entities == nb
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L1)
|
||||
@pytest.mark.xfail(reason="issue #5414")
|
||||
def test_collection_binary_created_by_dataframe(self):
|
||||
"""
|
||||
target: test collection with dataframe
|
||||
method: create collection with dataframe
|
||||
expected: create successfully
|
||||
"""
|
||||
self._connect()
|
||||
nb = ct.default_nb
|
||||
c_name = cf.gen_unique_str(prefix)
|
||||
df = cf.gen_default_binary_dataframe_data(nb)
|
||||
schema = cf.gen_default_binary_collection_schema()
|
||||
collection, _ = self.collection.collection_init(name=c_name, data=df)
|
||||
assert_default_collection(collection, exp_name=c_name, exp_num=nb, exp_schema=schema)
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L0)
|
||||
def test_collection_binary_created_by_data_list(self):
|
||||
"""
|
||||
target: test create collection by data list
|
||||
method: data type is list-like
|
||||
expected: raise exception
|
||||
"""
|
||||
self._connect()
|
||||
c_name = cf.gen_unique_str(prefix)
|
||||
data = cf.gen_default_binary_list_data(nb=100)
|
||||
ex, _ = self.collection.collection_init(name=c_name, schema=None, data=data)
|
||||
assert "Data of not pandas.DataFrame type should bepassed into the schema" in str(ex)
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L0)
|
||||
@pytest.mark.xfail(reason="issue #5414")
|
||||
def test_collection_binary_insert_data(self):
|
||||
"""
|
||||
target: test collection create and insert list-like data
|
||||
method: 1. create by schema 2. insert data
|
||||
expected: assert num_entities
|
||||
"""
|
||||
self._connect()
|
||||
nb = ct.default_nb
|
||||
collection = self._collection(schema=default_binary_schema)
|
||||
assert_default_collection(collection, exp_schema=default_binary_schema)
|
||||
data = cf.gen_default_binary_list_data(nb)
|
||||
self.collection.insert(data=data)
|
||||
assert collection.num_entities == nb
|
||||
|
|
|
|||
|
|
@ -0,0 +1,26 @@
|
|||
import pytest
|
||||
|
||||
from base.client_request import ApiReq
|
||||
from utils.util_log import test_log as log
|
||||
from common import common_func as cf
|
||||
from common import common_type as ct
|
||||
from common.common_type import CaseLabel
|
||||
|
||||
|
||||
class TestInsertParams(ApiReq):
|
||||
""" Test case of Insert interface """
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L0)
|
||||
@pytest.mark.xfail(reason="issue #5421")
|
||||
def test_collection_numpy_insert_data(self):
|
||||
"""
|
||||
target: test collection create and insert list-like data
|
||||
method: 1. create by schema 2. insert data
|
||||
expected: assert num_entities
|
||||
"""
|
||||
self._connect()
|
||||
nb = 10
|
||||
collection = self._collection()
|
||||
data = cf.gen_numpy_data(nb)
|
||||
ex, _ = self.collection.insert(data=data)
|
||||
log.error(str(ex))
|
||||
Loading…
Reference in New Issue