add timer

personas_vector/match_api.py

@@ -22,27 +22,29 @@ def match_tractate_by_device(device_id, n=10):
     try:
         if device_id not in ["0", "unknown", "87654", ""]:
             time_begin = time.time()
-            portrait_time_begin = time.time()
             business_tags = get_user_portrait_tag3_business_tags(device_id, tags_num=3)
-            portrait_time_end = time.time() - portrait_time_begin
             res = []
             if business_tags:
-                vectors_time_begin = time.time()
                 vectors = []
                 for tag in business_tags:
                     lst = json.loads(TAG_EMBEDDING_DICT.get(bytes(tag, "utf-8"), b"[]"))
                     if lst:
                         vectors.append(np.array(lst).astype("float32"))
-                vectors_time_end = time.time() - vectors_time_begin
-                search_time_begin = time.time()
+
                 if vectors:
-                    D, I = FAISS_TAGS_INDEX.search(np.array([np.average(vectors, axis=0)]).astype("float32"), n)
+                    average_time_begin = time.time()
+                    average_vectors = np.array([np.average(vectors, axis=0)]).astype("float32")
+                    average_time_end = time.time() - average_time_begin
+                    search_time_begin = time.time()
+                    D, I = FAISS_TAGS_INDEX.search(average_vectors, n)
+                    search_time_end = time.time() - search_time_begin
+                    to_list_time_begin = time.time()
                     distances = D.tolist()[0]
                     ids = I.tolist()[0]
+                    to_list_time_end = time.time() - to_list_time_begin
                     for (index, i) in enumerate(distances):
                         if i <= 5.0:
                             res.append(ids[index])
-                search_time_end = time.time() - search_time_begin
                 time_end = time.time() - time_begin
                 if time_end > 0.04:
                     timer_dict = {
@@ -50,8 +52,8 @@ def match_tractate_by_device(device_id, n=10):
                         "api": "strategy_embedding/personas_vector/match",
                         "device_id": device_id,
                         "n": n,
-                        "get_business_tags": "{:.3f}ms".format(portrait_time_end * 1000),
-                        "get_vectors": "{:.3f}ms".format(vectors_time_end * 1000),
+                        "average_time": "{:.3f}ms".format(average_time_end * 1000),
+                        "to_list_time": "{:.3f}ms".format(to_list_time_end * 1000),
                         "search": "{:.3f}ms".format(search_time_end * 1000),
                         "total_time": "{:.3f}ms".format(time_end * 1000)
                     }