try third device portrait

src/models/esmm/fe/tractate_fe.py

@@ -288,20 +288,20 @@ CATEGORICAL_COLUMNS = [
     "click_tractate_id5",
     "is_related_service",
     "service_city",
-    "device_fd2",
-    "device_sd2",
-    "device_fs2",
-    "device_ss2",
-    "device_fp2",
-    "device_sp2",
-    "device_p2",
-    # "device_fd3",
-    # "device_sd3",
-    # "device_fs3",
-    # "device_ss3",
-    # "device_fp3",
-    # "device_sp3",
-    # "device_p3",
+    # "device_fd2",
+    # "device_sd2",
+    # "device_fs2",
+    # "device_ss2",
+    # "device_fp2",
+    # "device_sp2",
+    # "device_p2",
+    "device_fd3",
+    "device_sd3",
+    "device_fs3",
+    "device_ss3",
+    "device_fp3",
+    "device_sp3",
+    "device_p3",
 ]
 CROSS_COLUMNS = [
     ["device_fd", "content_fd"],
@@ -434,21 +434,21 @@ def join_features(device_df, tractate_df, cc_df):
     df["device_sp"] = df["second_positions_x"].apply(lambda x: nth_element(x, 0))
     df["device_p"] = df["projects_x"].apply(lambda x: nth_element(x, 0))
 
-    df["device_fd2"] = df["first_demands_x"].apply(lambda x: nth_element(x, 1))
-    df["device_sd2"] = df["second_demands_x"].apply(lambda x: nth_element(x, 1))
-    df["device_fs2"] = df["first_solutions_x"].apply(lambda x: nth_element(x, 1))
-    df["device_ss2"] = df["second_solutions_x"].apply(lambda x: nth_element(x, 1))
-    df["device_fp2"] = df["first_positions_x"].apply(lambda x: nth_element(x, 1))
-    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_fd2"] = df["first_demands_x"].apply(lambda x: nth_element(x, 1))
+    # df["device_sd2"] = df["second_demands_x"].apply(lambda x: nth_element(x, 1))
+    # df["device_fs2"] = df["first_solutions_x"].apply(lambda x: nth_element(x, 1))
+    # df["device_ss2"] = df["second_solutions_x"].apply(lambda x: nth_element(x, 1))
+    # df["device_fp2"] = df["first_positions_x"].apply(lambda x: nth_element(x, 1))
+    # 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))
@@ -538,20 +538,20 @@ def device_tractate_fe(device_id, tractate_ids, device_dict, tractate_dict):
     device_info["device_fp"] = nth_element(device_fp, 0)
     device_info["device_sp"] = nth_element(device_sp, 0)
     device_info["device_p"] = nth_element(device_p, 0)
-    device_info["device_fd2"] = nth_element(device_fd, 1)
-    device_info["device_sd2"] = nth_element(device_sd, 1)
-    device_info["device_fs2"] = nth_element(device_fs, 1)
-    device_info["device_ss2"] = nth_element(device_ss, 1)
-    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_fd2"] = nth_element(device_fd, 1)
+    # device_info["device_sd2"] = nth_element(device_sd, 1)
+    # device_info["device_fs2"] = nth_element(device_fs, 1)
+    # device_info["device_ss2"] = nth_element(device_ss, 1)
+    # 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)
     tractate_lst = []
     tractate_ids_res = []
     for id in tractate_ids:

src/models/esmm/tractate_model.py

@@ -167,20 +167,20 @@ _categorical_columns = [
     "click_tractate_id4",
     "click_tractate_id5",
     "service_city",
-    "device_fd2",
-    "device_sd2",
-    "device_fs2",
-    "device_ss2",
-    "device_fp2",
-    "device_sp2",
-    "device_p2",
-    # "device_fd3",
-    # "device_sd3",
-    # "device_fs3",
-    # "device_ss3",
-    # "device_fp3",
-    # "device_sp3",
-    # "device_p3",
+    # "device_fd2",
+    # "device_sd2",
+    # "device_fs2",
+    # "device_ss2",
+    # "device_fp2",
+    # "device_sp2",
+    # "device_p2",
+    "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_diary.py

@@ -60,8 +60,8 @@ def main():
     session_config = tf.compat.v1.ConfigProto()
     session_config.gpu_options.allow_growth = True
     session_config.gpu_options.per_process_gpu_memory_fraction = 0.7
-    session_config.inter_op_parallelism_threads = 1
-    session_config.intra_op_parallelism_threads = 1
+    # session_config.inter_op_parallelism_threads = 1
+    # session_config.intra_op_parallelism_threads = 1
     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)