add prediction

src/models/esmm/model.py

@@ -95,45 +95,49 @@ def _bytes_feature(value):
 
 
 def model_predict_diary(device_id, diary_ids, device_dict, diary_dict, predict_fn):
-    time_1 = timeit.default_timer()
-    device_info, diary_lst, diary_ids_res = device_diary_fe(device_id, diary_ids, device_dict, diary_dict)
-    print("predict check: " + str(len(diary_lst)) + " " + str(len(diary_ids_res)))
-    int_columns = [
-        "active_type", "active_days", "card_id", "is_pure_author", "is_have_reply", "is_have_pure_reply", "content_level",
-        "topic_num", "favor_num", "vote_num"
-    ]
-    float_columns = ["one_ctr", "three_ctr", "seven_ctr", "fifteen_ctr"]
-    str_columns = [
-        "device_id", "past_consume_ability_history", "potential_consume_ability_history", "price_sensitive_history", "device_fd",
-        "device_sd", "device_fs", "device_ss", "device_fp", "device_sp", "device_p", "content_fd", "content_sd", "content_fs",
-        "content_ss", "content_fp", "content_sp", "content_p", "fd1", "fd2", "fd3", "sd1", "sd2", "sd3", "fs1", "fs2", "fs3",
-        "ss1", "ss2", "ss3", "fp1", "fp2", "fp3", "sp1", "sp2", "sp3", "p1", "p2", "p3"
-    ]
-    examples = []
-    for diary_info in diary_lst:
-        tmp = {}
-        tmp.update(device_info)
-        tmp.update(diary_info)
-        features = {}
-        for col in int_columns:
-            features[col] = _int64_feature(int(tmp[col]))
-        for col in float_columns:
-            features[col] = _float_feature(float(tmp[col]))
-        for col in str_columns:
-            features[col] = _bytes_feature(str(tmp[col]).encode(encoding="utf-8"))
-        example = tf.train.Example(features=tf.train.Features(feature=features))
-        examples.append(example.SerializeToString())
-
-    total_1 = (timeit.default_timer() - time_1)
-    print("make example cost {:.5f}s".format(total_1))
-
-    time_1 = timeit.default_timer()
-    predictions = predict_fn({"examples": examples})
-    res_tuple = sorted(zip(diary_ids_res, predictions["output"].tolist()), key=lambda x: x[1], reverse=True)
-    res = []
-    for (id, _) in res_tuple:
-        res.append(int(id))
-    # print(res)
-    total_1 = (timeit.default_timer() - time_1)
-    print("prediction cost {:.5f}s".format(total_1))
-    return res
+    try:
+        time_1 = timeit.default_timer()
+        device_info, diary_lst, diary_ids_res = device_diary_fe(device_id, diary_ids, device_dict, diary_dict)
+        print("predict check: " + str(len(diary_lst)) + " " + str(len(diary_ids_res)))
+        int_columns = [
+            "active_type", "active_days", "card_id", "is_pure_author", "is_have_reply", "is_have_pure_reply", "content_level",
+            "topic_num", "favor_num", "vote_num"
+        ]
+        float_columns = ["one_ctr", "three_ctr", "seven_ctr", "fifteen_ctr"]
+        str_columns = [
+            "device_id", "past_consume_ability_history", "potential_consume_ability_history", "price_sensitive_history", "device_fd",
+            "device_sd", "device_fs", "device_ss", "device_fp", "device_sp", "device_p", "content_fd", "content_sd", "content_fs",
+            "content_ss", "content_fp", "content_sp", "content_p", "fd1", "fd2", "fd3", "sd1", "sd2", "sd3", "fs1", "fs2", "fs3",
+            "ss1", "ss2", "ss3", "fp1", "fp2", "fp3", "sp1", "sp2", "sp3", "p1", "p2", "p3"
+        ]
+        examples = []
+        for diary_info in diary_lst:
+            tmp = {}
+            tmp.update(device_info)
+            tmp.update(diary_info)
+            features = {}
+            for col in int_columns:
+                features[col] = _int64_feature(int(tmp[col]))
+            for col in float_columns:
+                features[col] = _float_feature(float(tmp[col]))
+            for col in str_columns:
+                features[col] = _bytes_feature(str(tmp[col]).encode(encoding="utf-8"))
+            example = tf.train.Example(features=tf.train.Features(feature=features))
+            examples.append(example.SerializeToString())
+
+        total_1 = (timeit.default_timer() - time_1)
+        print("make example cost {:.5f}s".format(total_1))
+
+        time_1 = timeit.default_timer()
+        predictions = predict_fn({"examples": examples})
+        res_tuple = sorted(zip(diary_ids_res, predictions["output"].tolist()), key=lambda x: x[1], reverse=True)
+        res = []
+        for (id, _) in res_tuple:
+            res.append(int(id))
+        # print(res)
+        total_1 = (timeit.default_timer() - time_1)
+        print("prediction cost {:.5f}s".format(total_1))
+        return res
+    except Exception as e:
+        print(e)
+        return []