service id for tractate

src/models/esmm/fe/fe.py

@@ -33,7 +33,7 @@ def build_features(df, int_columns, float_columns, categorical_columns, cross_co
         elif col in [
                 "show_tag_id", "click_tractate_id1", "click_tractate_id2", "click_tractate_id3", "click_tractate_id4",
                 "click_tractate_id5", "click_diary_id1", "click_diary_id2", "click_diary_id3", "click_diary_id4",
-                "click_diary_id5"
+                "click_diary_id5", "service_id", "recommend_service_id"
         ]:
             categorical_features.append(
                 fc.embedding_column(fc.categorical_column_with_hash_bucket(col, 20000), dimension=int(df[col].size**0.25)))

src/models/esmm/fe/tractate_fe.py

@@ -121,9 +121,11 @@ TRACTATE_COLUMNS = [
     "business_second_skip_num",
     "effect_second_skip_rate",
     "business_second_skip_rate",
+    "service_id",
     "service_price",
     "service_sold_num",
     "service_city",
+    "recommend_service_id",
 ]
 
 INT_COLUMNS = [
@@ -287,7 +289,9 @@ CATEGORICAL_COLUMNS = [
     "click_tractate_id4",
     "click_tractate_id5",
     "is_related_service",
+    "service_id",
     "service_city",
+    "recommend_service_id",
     # "device_fd2",
     # "device_sd2",
     # "device_fs2",

src/models/esmm/tractate_model.py

@@ -166,7 +166,9 @@ _categorical_columns = [
     "click_tractate_id3",
     "click_tractate_id4",
     "click_tractate_id5",
+    "service_id",
     "service_city",
+    "recommend_service_id",
     # "device_fd2",
     # "device_sd2",
     # "device_fs2",

src/train_tractate.py

@@ -62,7 +62,8 @@ def main():
     estimator_config = tf.estimator.RunConfig(session_config=session_config)
 
     model = tf.estimator.Estimator(model_fn=esmm_model_fn, params=params, model_dir=model_path, config=estimator_config)
-    train_spec = tf.estimator.TrainSpec(input_fn=lambda: esmm_input_fn(train_df, shuffle=True), max_steps=50000)
+    # TODO 50000
+    train_spec = tf.estimator.TrainSpec(input_fn=lambda: esmm_input_fn(train_df, shuffle=True), max_steps=15000)
     eval_spec = tf.estimator.EvalSpec(input_fn=lambda: esmm_input_fn(val_df, shuffle=False))
     res = tf.estimator.train_and_evaluate(model, train_spec, eval_spec)
     print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
@@ -77,8 +78,9 @@ def main():
     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)
-    record_esmm_auc_to_db("tractate", ctr_auc, ctcvr_auc, total_time, save_path)
+    # TODO save model
+    # set_essm_model_save_path("tractate", save_path)
+    # record_esmm_auc_to_db("tractate", ctr_auc, ctcvr_auc, total_time, save_path)
     print("============================================================")
 
     # save_path = get_essm_model_save_path("tractate")