try predict

src/models/esmm/tractate_model.py

@@ -2,7 +2,7 @@ import timeit
 
 import tensorflow as tf
 
-from .fe.tractate_fe import (CATEGORICAL_COLUMNS, FLOAT_COLUMNS, INT_COLUMNS, device_tractate_fe)
+from .fe.tractate_fe import device_tractate_fe
 from .model import _bytes_feature, _float_feature, _int64_feature
 
 
@@ -11,9 +11,21 @@ def model_predict_tractate(device_id, tractate_ids, device_dict, tractate_dict,
         time_1 = timeit.default_timer()
         device_info, tractate_lst, tractate_ids_res = device_tractate_fe(device_id, tractate_ids, device_dict, tractate_dict)
         print("predict check: " + str(len(tractate_lst)) + " " + str(len(tractate_ids_res)))
-        int_columns = INT_COLUMNS
-        float_columns = FLOAT_COLUMNS
-        str_columns = CATEGORICAL_COLUMNS
+        # TODO
+        int_columns = [
+            "active_type", "active_days", "card_id", "is_pure_author", "is_have_reply", "is_have_pure_reply", "content_level",
+            "reply_num", "reply_pure_num", "show_tag_id"
+        ]
+        float_columns = [
+            "one_ctr", "three_ctr", "seven_ctr", "fifteen_ctr", "thirty_ctr", "sixty_ctr", "ninety_ctr", "history_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", "click_tractate_id1",
+            "click_tractate_id2", "click_tractate_id3", "click_tractate_id4", "click_tractate_id5"
+        ]
         examples = []
         for tractate_info in tractate_lst:
             tmp = {}