predict from file

src/main.py

 import os
+import pickle
 import random
 import shutil
 import time
@@ -49,7 +50,6 @@ def main():
     train_df, val_df = train_test_split(train_df, test_size=0.2)
 
     # all_features = build_features(df)
-
     # params = {"feature_columns": all_features, "hidden_units": [64, 32], "learning_rate": 0.1}
     # model_path = str(Path("~/data/model_tmp/").expanduser())
     # if os.path.exists(model_path):
@@ -66,8 +66,13 @@ def main():
     # print("save to: " + save_path)
 
     save_path = "/home/gmuser/data/models/1595317247"
+
     # save_path = str(Path("~/Desktop/models/1595297428").expanduser())
-    predict_fn = tf.contrib.predictor.from_saved_model(save_path)
+    filename = save_path + "/saved_model.pb"
+
+    # tf.saved_model.load
+
+    predict_fn = tf.contrib.predictor.from_saved_model(filename)
 
     # for i in range(5):
     #     test_300 = test_df.sample(300)
@@ -79,13 +84,6 @@ def main():
     #     "16195283", "16838351", "17161073", "17297878", "17307484", "17396235", "16418737", "16995481", "17312201", "12237988"
     # ]
 
-    # df = get_device_df_from_redis()
-    # df2 = get_diary_df_from_redis()
-    # redis_device_df = device_feature_engineering(df)
-    # redis_diary_df = diary_feature_engineering(df2, from_redis=True)
-    # device_ids = list(redis_device_df["device_id"].values)[:20]
-    # diary_ids = list(redis_diary_df["card_id"].values)
-
     device_dict = get_device_dict_from_redis()
     diary_dict = get_diary_dict_from_redis()
 

src/models/esmm/model.py

@@ -132,8 +132,8 @@ def model_predict_diary(device_id, diary_ids, device_dict, diary_dict, predict_f
     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(id)
-    print(res)
+        res.append(int(id))
+    # print(res)
     total_1 = (timeit.default_timer() - time_1)
     print("prediction cost {:.5f}s".format(total_1))
     return res