update path

src/models/esmm/diary_model.py

@@ -2,7 +2,7 @@ import timeit
 
 import tensorflow as tf
 
-from .diary_fe import device_diary_fe
+from .fe.diary_fe import device_diary_fe
 from .model import _bytes_feature, _float_feature, _int64_feature
 
 

src/models/esmm/model.py

@@ -92,55 +92,3 @@ def _float_feature(value):
 
 def _bytes_feature(value):
     return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
-
-
-def model_predict_diary(device_id, diary_ids, device_dict, diary_dict, predict_fn):
-    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)))
-        # TODO
-        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)
-        # device_info, _, _ = device_diary_fe(device_id, diary_ids, device_dict, diary_dict)
-        # print(device_info)
-        return []