try tractate cross features

src/models/esmm/fe/tractate_fe.py

@@ -289,6 +289,15 @@ CATEGORICAL_COLUMNS = [
     "is_related_service",
     "service_city",
 ]
+CROSS_COLUMNS = [
+    ["device_fd", "content_fd"],
+    ["device_sd", "content_sd"],
+    ["device_fs", "content_fs"],
+    ["device_ss", "content_ss"],
+    ["device_fp", "content_fp"],
+    ["device_sp", "content_sp"],
+    ["device_p", "content_p"],
+]
 
 
 def read_csv_data(dataset_path):

src/train_tractate.py

@@ -47,7 +47,8 @@ def main():
     train_df, test_df = train_test_split(df, test_size=0.2)
     train_df, val_df = train_test_split(train_df, test_size=0.2)
 
-    all_features = fe.build_features(df, tractate_fe.INT_COLUMNS, tractate_fe.FLOAT_COLUMNS, tractate_fe.CATEGORICAL_COLUMNS)
+    all_features = fe.build_features(df, tractate_fe.INT_COLUMNS, tractate_fe.FLOAT_COLUMNS, tractate_fe.CATEGORICAL_COLUMNS,
+                                     tractate_fe.CROSS_COLUMNS)
     params = {"feature_columns": all_features, "hidden_units": [360, 200, 80, 2], "learning_rate": 0.2}
     model_path = str(Path("/data/files/model_tmp/tractate/").expanduser())
     if os.path.exists(model_path):
@@ -59,7 +60,8 @@ 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("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
@@ -74,8 +76,9 @@ 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")