update model path

src/change_city_prediction.py

@@ -33,13 +33,13 @@ if __name__ == "__main__":
 
     diary_save_path = get_essm_model_save_path("diary")
     if not diary_save_path:
-        diary_save_path = "/home/gmuser/data/models/diary/1597050209"
+        diary_save_path = "/data/files/models/diary/1597050209"
         print(diary_save_path + "!!!!!!!!!!!!!!!!!!!!!!!!!!!")
     diary_predict_fn = tf.contrib.predictor.from_saved_model(diary_save_path)
 
     tractate_save_path = get_essm_model_save_path("tractate")
     if not tractate_save_path:
-        tractate_save_path = "/home/gmuser/data/models/tractate/1596509299"
+        tractate_save_path = "/data/files/models/tractate/1596509299"
         print(tractate_save_path + "!!!!!!!!!!!!!!!!!!!!!!!!!!!")
     tractate_predict_fn = tf.contrib.predictor.from_saved_model(tractate_save_path)
 

src/main_portrait.py

@@ -80,13 +80,13 @@ def main():
 
     diary_save_path = get_essm_model_save_path("diary")
     if not diary_save_path:
-        diary_save_path = "/home/gmuser/data/models/diary/1597379800"
+        diary_save_path = "/data/files/models/diary/1597379800"
         print(diary_save_path + "!!!!!!!!!!!!!!!!!!!!!!!!!!!")
     diary_predict_fn = tf.contrib.predictor.from_saved_model(diary_save_path)
 
     tractate_save_path = get_essm_model_save_path("tractate")
     if not tractate_save_path:
-        tractate_save_path = "/home/gmuser/data/models/tractate/1597378202"
+        tractate_save_path = "/data/files/models/tractate/1597378202"
         print(tractate_save_path + "!!!!!!!!!!!!!!!!!!!!!!!!!!!")
     tractate_predict_fn = tf.contrib.predictor.from_saved_model(tractate_save_path)
 

src/online_prediction.py

@@ -45,13 +45,13 @@ if __name__ == "__main__":
 
     diary_save_path = get_essm_model_save_path("diary")
     if not diary_save_path:
-        diary_save_path = "/home/gmuser/data/models/diary/1597379800"
+        diary_save_path = "/data/files/models/diary/1597379800"
         print(diary_save_path + "!!!!!!!!!!!!!!!!!!!!!!!!!!!")
     diary_predict_fn = tf.contrib.predictor.from_saved_model(diary_save_path)
 
     tractate_save_path = get_essm_model_save_path("tractate")
     if not tractate_save_path:
-        tractate_save_path = "/home/gmuser/data/models/tractate/1597378202"
+        tractate_save_path = "/data/files/models/tractate/1597378202"
         print(tractate_save_path + "!!!!!!!!!!!!!!!!!!!!!!!!!!!")
     tractate_predict_fn = tf.contrib.predictor.from_saved_model(tractate_save_path)
 

src/test.py

-import datetime
-import time
-
-import tensorflow as tf
-
-from models.esmm.fe import device_fe, diary_fe, tractate_fe
-from models.esmm.tractate_model import model_predict_tractate
-from utils.cache import redis_client2
-from utils.grey import recommed_service_category_device_id_by_tail
-from utils.portrait import (get_user_portrait_tag3_read_v2, user_portrait_tag3_get_candidate_dict,
-                            user_portrait_tag3_get_candidate_unread_list, user_portrait_tag3_write_ctcvr_data)
-
-if __name__ == "__main__":
-    time_begin = time.time()
-
-    device_dict = device_fe.get_device_dict_from_redis()
-    tractate_dict = tractate_fe.get_tractate_dict_from_redis()
-    print("redis data: " + str(len(device_dict)) + " " + str(len(tractate_dict)))
-
-    device_id = "androidid_a25a1129c0b38f7b"
-
-    tractate_save_path = "/home/gmuser/data/models/tractate/1596092061"
-    tractate_predict_fn = tf.contrib.predictor.from_saved_model(tractate_save_path)
-
-    tractate_ids = user_portrait_tag3_get_candidate_unread_list(device_id, "tractate")
-    print("tractate_ids: " + str(len(tractate_ids)))
-    res = model_predict_tractate(device_id, tractate_ids, device_dict, tractate_dict, tractate_predict_fn)
-    print("res: " + str(len(res)))
-    print(res[:10])
-    user_portrait_tag3_write_ctcvr_data(device_id, "tractate", res[:500])
-
-    total_time = (time.time() - time_begin) / 60
-    print("total cost {:.2f} mins at {}".format(total_time, datetime.datetime.now()))

src/train_diary.py

@@ -51,7 +51,7 @@ def main():
 
     all_features = fe.build_features(df, diary_fe.INT_COLUMNS, diary_fe.FLOAT_COLUMNS, diary_fe.CATEGORICAL_COLUMNS)
     params = {"feature_columns": all_features, "hidden_units": [64, 32], "learning_rate": 0.1}
-    model_path = str(Path("~/data/model_tmp/diary/").expanduser())
+    model_path = str(Path("/data/files/model_tmp/diary/").expanduser())
     if os.path.exists(model_path):
         shutil.rmtree(model_path)
 
@@ -70,14 +70,14 @@ def main():
     print("ctcvr_auc: " + str(res[0]["ctcvr_auc"]))
     print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
 
-    model_export_path = str(Path("~/data/models/diary").expanduser())
+    model_export_path = str(Path("/data/files/models/diary").expanduser())
     save_path = model_export(model, all_features, model_export_path)
     print("save to: " + save_path)
     set_essm_model_save_path("diary", save_path)
     print("============================================================")
 
     # save_path = str(Path("~/Desktop/models/1596012827").expanduser())  # local
-    # save_path = "/home/gmuser/data/models/diary/1596083349"  # server
+    # save_path = "/data/files/models/diary/1596083349"  # server
 
     # tf.saved_model.load
 

src/train_tractate.py

@@ -48,7 +48,7 @@ def main():
 
     all_features = fe.build_features(df, tractate_fe.INT_COLUMNS, tractate_fe.FLOAT_COLUMNS, tractate_fe.CATEGORICAL_COLUMNS)
     params = {"feature_columns": all_features, "hidden_units": [64, 32], "learning_rate": 0.1}
-    model_path = str(Path("~/data/model_tmp/tractate/").expanduser())
+    model_path = str(Path("/data/files/model_tmp/tractate/").expanduser())
     if os.path.exists(model_path):
         shutil.rmtree(model_path)
 
@@ -67,14 +67,14 @@ def main():
     print("ctcvr_auc: " + str(res[0]["ctcvr_auc"]))
     print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
 
-    model_export_path = str(Path("~/data/models/tractate/").expanduser())
+    model_export_path = str(Path("/data/files/models/tractate/").expanduser())
     save_path = model_export(model, all_features, model_export_path)
     print("save to: " + save_path)
     set_essm_model_save_path("tractate", save_path)
     print("============================================================")
 
     # # save_path = str(Path("~/data/models/tractate/1596089465").expanduser())  # local
-    # save_path = "/home/gmuser/data/models/tractate/1596092061"  # server
+    # save_path = "/data/files/models/tractate/1596092061"  # server
     predict_fn = tf.contrib.predictor.from_saved_model(save_path)
 
     device_dict = device_fe.get_device_dict_from_redis()