remove tractate device info

src/models/esmm/fe/tractate_fe.py

@@ -295,13 +295,13 @@ CATEGORICAL_COLUMNS = [
     # "device_fp2",
     # "device_sp2",
     # "device_p2",
-    "device_fd3",
-    "device_sd3",
-    "device_fs3",
-    "device_ss3",
-    "device_fp3",
-    "device_sp3",
-    "device_p3",
+    # "device_fd3",
+    # "device_sd3",
+    # "device_fs3",
+    # "device_ss3",
+    # "device_fp3",
+    # "device_sp3",
+    # "device_p3",
 ]
 CROSS_COLUMNS = [
     ["device_fd", "content_fd"],
@@ -442,13 +442,13 @@ def join_features(device_df, tractate_df, cc_df):
     # df["device_sp2"] = df["second_positions_x"].apply(lambda x: nth_element(x, 1))
     # df["device_p2"] = df["projects_x"].apply(lambda x: nth_element(x, 1))
 
-    df["device_fd3"] = df["first_demands_x"].apply(lambda x: nth_element(x, 2))
-    df["device_sd3"] = df["second_demands_x"].apply(lambda x: nth_element(x, 2))
-    df["device_fs3"] = df["first_solutions_x"].apply(lambda x: nth_element(x, 2))
-    df["device_ss3"] = df["second_solutions_x"].apply(lambda x: nth_element(x, 2))
-    df["device_fp3"] = df["first_positions_x"].apply(lambda x: nth_element(x, 2))
-    df["device_sp3"] = df["second_positions_x"].apply(lambda x: nth_element(x, 2))
-    df["device_p3"] = df["projects_x"].apply(lambda x: nth_element(x, 2))
+    # df["device_fd3"] = df["first_demands_x"].apply(lambda x: nth_element(x, 2))
+    # df["device_sd3"] = df["second_demands_x"].apply(lambda x: nth_element(x, 2))
+    # df["device_fs3"] = df["first_solutions_x"].apply(lambda x: nth_element(x, 2))
+    # df["device_ss3"] = df["second_solutions_x"].apply(lambda x: nth_element(x, 2))
+    # df["device_fp3"] = df["first_positions_x"].apply(lambda x: nth_element(x, 2))
+    # df["device_sp3"] = df["second_positions_x"].apply(lambda x: nth_element(x, 2))
+    # df["device_p3"] = df["projects_x"].apply(lambda x: nth_element(x, 2))
 
     df["content_fd"] = df["first_demands_y"].apply(lambda x: nth_element(x, 0))
     df["content_sd"] = df["second_demands_y"].apply(lambda x: nth_element(x, 0))
@@ -545,13 +545,13 @@ def device_tractate_fe(device_id, tractate_ids, device_dict, tractate_dict):
     # device_info["device_fp2"] = nth_element(device_fp, 1)
     # device_info["device_sp2"] = nth_element(device_sp, 1)
     # device_info["device_p2"] = nth_element(device_p, 1)
-    device_info["device_fd3"] = nth_element(device_fd, 2)
-    device_info["device_sd3"] = nth_element(device_sd, 2)
-    device_info["device_fs3"] = nth_element(device_fs, 2)
-    device_info["device_ss3"] = nth_element(device_ss, 2)
-    device_info["device_fp3"] = nth_element(device_fp, 2)
-    device_info["device_sp3"] = nth_element(device_sp, 2)
-    device_info["device_p3"] = nth_element(device_p, 2)
+    # device_info["device_fd3"] = nth_element(device_fd, 2)
+    # device_info["device_sd3"] = nth_element(device_sd, 2)
+    # device_info["device_fs3"] = nth_element(device_fs, 2)
+    # device_info["device_ss3"] = nth_element(device_ss, 2)
+    # device_info["device_fp3"] = nth_element(device_fp, 2)
+    # device_info["device_sp3"] = nth_element(device_sp, 2)
+    # device_info["device_p3"] = nth_element(device_p, 2)
     tractate_lst = []
     tractate_ids_res = []
     for id in tractate_ids:

src/models/esmm/tractate_model.py

@@ -174,13 +174,13 @@ _categorical_columns = [
     # "device_fp2",
     # "device_sp2",
     # "device_p2",
-    "device_fd3",
-    "device_sd3",
-    "device_fs3",
-    "device_ss3",
-    "device_fp3",
-    "device_sp3",
-    "device_p3",
+    # "device_fd3",
+    # "device_sd3",
+    # "device_fs3",
+    # "device_ss3",
+    # "device_fp3",
+    # "device_sp3",
+    # "device_p3",
 ]
 
 PREDICTION_ALL_COLUMNS = _int_columns + _float_columns + _categorical_columns

src/train_tractate.py

@@ -62,8 +62,7 @@ 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)
-    # TODO 50000
-    train_spec = tf.estimator.TrainSpec(input_fn=lambda: esmm_input_fn(train_df, shuffle=True), max_steps=15000)
+    train_spec = tf.estimator.TrainSpec(input_fn=lambda: esmm_input_fn(train_df, shuffle=True), max_steps=50000)
     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("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
@@ -78,9 +77,8 @@ 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)
-    # 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)
+    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")