milvus/tests/python_client/deploy/scripts/second_recall_test.py
zhuwenxing fcd71930c8
[skip e2e]Add recall test (#18562)
Signed-off-by: zhuwenxing <wenxing.zhu@zilliz.com>
2022-08-09 13:50:36 +08:00

65 lines
2.0 KiB
Python

import h5py
import numpy as np
import time
from pathlib import Path
from pymilvus import connections, Collection
def read_benchmark_hdf5(file_path):
f = h5py.File(file_path, 'r')
train = np.array(f["train"])
test = np.array(f["test"])
neighbors = np.array(f["neighbors"])
f.close()
return train, test, neighbors
dim = 128
TIMEOUT = 200
def search_test(host="127.0.0.1"):
file_path = f"{str(Path(__file__).absolute().parent.parent.parent)}/assets/ann_hdf5/sift-128-euclidean.hdf5"
train, test, neighbors = read_benchmark_hdf5(file_path)
connections.connect(host=host, port="19530")
collection = Collection(name="sift_128_euclidean")
nq = 10000
topK = 100
search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
t0 = time.time()
print(f"\nSearch...")
# define output_fields of search result
res = collection.search(
test[:nq], "float_vector", search_params, topK, output_fields=["int64"], timeout=TIMEOUT
)
t1 = time.time()
print(f"search cost {t1 - t0:.4f} seconds")
result_ids = []
for hits in res:
result_id = []
for hit in hits:
result_id.append(hit.entity.get("int64"))
result_ids.append(result_id)
# calculate recall
true_ids = neighbors[:nq,:topK]
sum_radio = 0.0
for index, item in enumerate(result_ids):
# tmp = set(item).intersection(set(flat_id_list[index]))
assert len(item) == len(true_ids[index])
tmp = set(true_ids[index]).intersection(set(item))
sum_radio = sum_radio + len(tmp) / len(item)
recall = round(sum_radio / len(result_ids), 3)
assert recall >= 0.95
print(f"recall={recall}")
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='config for recall test')
parser.add_argument('--host', type=str, default="127.0.0.1", help='milvus server ip')
args = parser.parse_args()
host = args.host
search_test(host)