[skip ci] Add comment for the accuracy test (#8092)

Signed-off-by: Nwangting0128 <ting.wang@zilliz.com>

tests/benchmark/milvus_benchmark/runners/accuracy.py

@@ -120,6 +120,7 @@ class AccAccuracyRunner(AccuracyRunner):
     def extract_cases(self, collection):
         collection_name = collection["collection_name"] if "collection_name" in collection else None
         (data_type, dimension, metric_type) = parser.parse_ann_collection_name(collection_name)
+        # hdf5_source_file: The path of the source data file saved on the NAS
         hdf5_source_file = collection["source_file"]
         index_types = collection["index_types"]
         index_params = collection["index_params"]
@@ -136,11 +137,14 @@ class AccAccuracyRunner(AccuracyRunner):
         }
         filters = collection["filters"] if "filters" in collection else []
         filter_query = []
+        # Convert list data into a set of dictionary data
         search_params = utils.generate_combinations(search_params)
         index_params = utils.generate_combinations(index_params)
         cases = list()
         case_metrics = list()
         self.init_metric(self.name, collection_info, {}, search_info=None)
+
+        # true_ids: The data set used to verify the results returned by query
         true_ids = np.array(dataset["neighbors"])
         for index_type in index_types:
             for index_param in index_params:
@@ -192,11 +196,14 @@ class AccAccuracyRunner(AccuracyRunner):
                                     "vector_query": vector_query,
                                     "true_ids": true_ids
                                 }
+                                # Obtain the parameters of the use case to be tested
                                 cases.append(case)
                                 case_metrics.append(case_metric)
         return cases, case_metrics
 
     def prepare(self, **case_param):
+        """ According to the test case parameters, initialize the test """
+
         collection_name = case_param["collection_name"]
         metric_type = case_param["metric_type"]
         dimension = case_param["dimension"]
@@ -211,6 +218,7 @@ class AccAccuracyRunner(AccuracyRunner):
             self.milvus.drop()
         dataset = case_param["dataset"]
         self.milvus.create_collection(dimension, data_type=vector_type)
+        # Get the data set train for inserting into the collection
         insert_vectors = utils.normalize(metric_type, np.array(dataset["train"]))
         if len(insert_vectors) != dataset["train"].shape[0]:
             raise Exception("Row count of insert vectors: %d is not equal to dataset size: %d" % (
@@ -224,6 +232,7 @@ class AccAccuracyRunner(AccuracyRunner):
             start = i * INSERT_INTERVAL
             end = min((i + 1) * INSERT_INTERVAL, len(insert_vectors))
             if start < end:
+                # Insert up to INSERT_INTERVAL=50000 at a time
                 tmp_vectors = insert_vectors[start:end]
                 ids = [i for i in range(start, end)]
                 if not isinstance(tmp_vectors, list):
@@ -256,7 +265,9 @@ class AccAccuracyRunner(AccuracyRunner):
         top_k = case_metric.search["topk"]
         query_res = self.milvus.query(case_param["vector_query"], filter_query=case_param["filter_query"])
         result_ids = self.milvus.get_ids(query_res)
+        # Calculate the accuracy of the result of query
         acc_value = utils.get_recall_value(true_ids[:nq, :top_k].tolist(), result_ids)
         tmp_result = {"acc": acc_value}
+        # Return accuracy results for reporting
         return tmp_result