chore: import upstream snapshot with attribution

tests/python_client/deploy/scripts/second_recall_test.py

@@ -0,0 +1,103 @@
+import h5py
+import numpy as np
+import time
+import sys
+import threading
+from pathlib import Path
+from loguru import logger
+from pymilvus import connections, Collection
+
+
+all_index_types = ["IVF_FLAT", "IVF_SQ8", "HNSW"]
+
+
+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
+
+
+def gen_search_param(index_type, metric_type="L2"):
+    search_params = []
+    if index_type in ["FLAT", "IVF_FLAT", "IVF_SQ8", "IVF_PQ"]:
+        for nprobe in [10]:
+            ivf_search_params = {"metric_type": metric_type, "params": {"nprobe": nprobe}}
+            search_params.append(ivf_search_params)
+    elif index_type in ["BIN_FLAT", "BIN_IVF_FLAT"]:
+        for nprobe in [10]:
+            bin_search_params = {"metric_type": "HAMMING", "params": {"nprobe": nprobe}}
+            search_params.append(bin_search_params)
+    elif index_type in ["HNSW"]:
+        for ef in [150]:
+            hnsw_search_param = {"metric_type": metric_type, "params": {"ef": ef}}
+            search_params.append(hnsw_search_param)
+    elif index_type == "ANNOY":
+        for search_k in [1000]:
+            annoy_search_param = {"metric_type": metric_type, "params": {"search_k": search_k}}
+            search_params.append(annoy_search_param)
+    else:
+        logger.info("Invalid index_type.")
+        raise Exception("Invalid index_type.")
+    return search_params[0]
+
+
+dim = 128
+TIMEOUT = 200
+
+
+def search_test(host="127.0.0.1", index_type="HNSW"):
+    logger.info(f"recall test for index type {index_type}")
+    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=f"sift_128_euclidean_{index_type}")
+    nq = 10000
+    topK = 100
+    search_params = gen_search_param(index_type)
+    for i in range(3):
+        t0 = time.time()
+        logger.info(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()
+        logger.info(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]), f"get {len(item)} but expect {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), 6)
+        logger.info(f"recall={recall}")
+        if index_type in ["IVF_PQ", "ANNOY"]:
+            assert recall >= 0.6, f"recall={recall} < 0.6"
+        else:
+            assert 0.95 <= recall < 1.0, f"recall is {recall}, less than 0.95, greater than or equal to 1.0"
+
+
+
+if __name__ == "__main__":
+    import argparse
+    import threading
+    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
+    tasks = []
+    for index_type in ["HNSW"]:
+        search_test(host, index_type)