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* update index case Signed-off-by: zhenwu <zhenwu@milvus.io> * add index cases Signed-off-by: zw <zw@milvus.io> * update index case Signed-off-by: zw <zw@milvus.io> * update utility cases Signed-off-by: zw <zw@milvus.io> * [skip ci] update index cases Signed-off-by: zw <zw@milvus.io> Co-authored-by: zhenwu <zhenwu@milvus.io> Co-authored-by: zw <zw@milvus.io>
250 lines
9.0 KiB
Python
250 lines
9.0 KiB
Python
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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from base.schema_wrapper import ApiCollectionSchemaWrapper, ApiFieldSchemaWrapper
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from common import common_type as ct
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from utils.util_log import test_log as log
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import threading
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import traceback
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"""" Methods of processing data """
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l2 = lambda x, y: np.linalg.norm(np.array(x) - np.array(y))
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def gen_unique_str(str_value=None):
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prefix = "".join(random.choice(string.ascii_letters + string.digits) for _ in range(8))
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return "test_" + prefix if str_value is None else str_value + "_" + prefix
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def gen_str_by_length(length=8):
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return "".join(random.choice(string.ascii_letters + string.digits) for _ in range(length))
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def gen_int64_field(name=ct.default_int64_field_name, is_primary=False, description=ct.default_desc):
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int64_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.INT64, description=description,
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is_primary=is_primary)
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return int64_field
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def gen_float_field(name=ct.default_float_field_name, is_primary=False, description=ct.default_desc):
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float_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.FLOAT, description=description,
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is_primary=is_primary)
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return float_field
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def gen_float_vec_field(name=ct.default_float_vec_field_name, is_primary=False, dim=ct.default_dim,
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description=ct.default_desc):
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float_vec_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.FLOAT_VECTOR,
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description=description, dim=dim,
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is_primary=is_primary)
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return float_vec_field
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def gen_binary_vec_field(name=ct.default_binary_vec_field_name, is_primary=False, dim=ct.default_dim,
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description=ct.default_desc):
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binary_vec_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.BINARY_VECTOR,
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description=description, dim=dim,
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is_primary=is_primary)
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return binary_vec_field
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def gen_default_collection_schema(description=ct.default_desc, primary_field=None):
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fields = [gen_int64_field(), gen_float_field(), gen_float_vec_field()]
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schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description,
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primary_field=primary_field)
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log.error(schema)
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return schema
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def gen_collection_schema(fields, primary_field=None, description=ct.default_desc):
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schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, primary_field=primary_field,
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description=description)
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return schema
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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, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description,
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primary_field=primary_field)
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return binary_schema
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def gen_vectors(nb, dim):
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vectors = [[random.random() for _ in range(dim)] for _ in range(nb)]
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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 _ 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, dim=ct.default_dim):
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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, dim)
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df = pd.DataFrame({
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ct.default_int64_field_name: int_values,
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ct.default_float_field_name: float_values,
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ct.default_float_vec_field_name: float_vec_values
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})
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return df
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def gen_default_binary_dataframe_data(nb=ct.default_nb, dim=ct.default_dim):
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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_raw_values, binary_vec_values = gen_binary_vectors(nb, dim)
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df = pd.DataFrame({
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ct.default_int64_field_name: int_values,
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ct.default_float_field_name: 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, binary_raw_values
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def gen_default_list_data(nb=ct.default_nb, dim=ct.default_dim):
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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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float_vec_values = gen_vectors(nb, 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_tuple_data(nb=ct.default_nb, dim=ct.default_dim):
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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_vec_values = gen_vectors(nb, 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, dim=ct.default_dim):
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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, 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, dim=ct.default_dim):
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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_raw_values, binary_vec_values = gen_binary_vectors(nb, dim)
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data = [int_values, float_values, binary_vec_values]
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return data, binary_raw_values
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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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if ct.all_index_types[i] in ct.binary_support:
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continue
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dic = {"index_type": ct.all_index_types[i], "metric_type": "L2"}
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dic.update({"params": ct.default_index_params[i]})
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index_params.append(dic)
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return index_params
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def gen_invalid_field_types():
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field_types = [
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6,
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1.0,
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[[]],
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{},
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(),
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"",
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"a"
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]
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return field_types
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def gen_all_type_fields():
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fields = []
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for k, v in DataType.__members__.items():
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if v != DataType.UNKNOWN:
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field, _ = ApiFieldSchemaWrapper().init_field_schema(name=k.lower(), dtype=v)
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fields.append(field)
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return fields
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def jaccard(x, y):
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x = np.asarray(x, np.bool)
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y = np.asarray(y, np.bool)
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return 1 - np.double(np.bitwise_and(x, y).sum()) / np.double(np.bitwise_or(x, y).sum())
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def hamming(x, y):
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x = np.asarray(x, np.bool)
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y = np.asarray(y, np.bool)
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return np.bitwise_xor(x, y).sum()
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def tanimoto(x, y):
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x = np.asarray(x, np.bool)
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y = np.asarray(y, np.bool)
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return -np.log2(np.double(np.bitwise_and(x, y).sum()) / np.double(np.bitwise_or(x, y).sum()))
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def substructure(x, y):
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x = np.asarray(x, np.bool)
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y = np.asarray(y, np.bool)
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return 1 - np.double(np.bitwise_and(x, y).sum()) / np.count_nonzero(y)
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def superstructure(x, y):
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x = np.asarray(x, np.bool)
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y = np.asarray(y, np.bool)
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return 1 - np.double(np.bitwise_and(x, y).sum()) / np.count_nonzero(x)
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def modify_file(file_path_list, is_modify=False, input_content=""):
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"""
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file_path_list : file list -> list[<file_path>]
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is_modify : does the file need to be reset
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input_content ：the content that need to insert to the file
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"""
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if not isinstance(file_path_list, list):
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log.error("[modify_file] file is not a list.")
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for file_path in file_path_list:
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folder_path, file_name = os.path.split(file_path)
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if not os.path.isdir(folder_path):
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log.debug("[modify_file] folder(%s) is not exist." % folder_path)
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os.makedirs(folder_path)
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if not os.path.isfile(file_path):
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log.error("[modify_file] file(%s) is not exist." % file_path)
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else:
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if is_modify is True:
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log.debug("[modify_file] start modifying file(%s)..." % file_path)
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with open(file_path, "r+") as f:
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f.seek(0)
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f.truncate()
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f.write(input_content)
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f.close()
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log.info("[modify_file] file(%s) modification is complete." % file_path_list)
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def index_to_dict(index):
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return {
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"collection_name": index.collection_name,
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"field_name": index.field_name,
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# "name": index.name,
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"params": index.params
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}
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def assert_equal_index(index_1, index_2):
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return index_to_dict(index_1) == index_to_dict(index_2) |