update columns

src/models/esmm/fe/tractate_fe.py

@@ -9,9 +9,9 @@ TRACTATE_COLUMNS = [
     "first_demands", "second_demands", "first_solutions", "second_solutions", "first_positions", "second_positions", "projects"
 ]
 
-_int_columns = ["active_days", "reply_num", "reply_pure_num"]
-_float_columns = ["one_ctr", "three_ctr", "seven_ctr", "fifteen_ctr", "thirty_ctr", "sixty_ctr", "ninety_ctr", "history_ctr"]
-_categorical_columns = [
+INT_COLUMNS = ["active_days", "reply_num", "reply_pure_num"]
+FLOAT_COLUMNS = ["one_ctr", "three_ctr", "seven_ctr", "fifteen_ctr", "thirty_ctr", "sixty_ctr", "ninety_ctr", "history_ctr"]
+CATEGORICAL_COLUMNS = [
     "device_id", "active_type", "past_consume_ability_history", "potential_consume_ability_history", "price_sensitive_history",
     "card_id", "is_pure_author", "is_have_reply", "is_have_pure_reply", "content_level", "show_tag_id", "device_fd", "content_fd",
     "fd1", "fd2", "fd3", "device_sd", "content_sd", "sd1", "sd2", "sd3", "device_fs", "content_fs", "fs1", "fs2", "fs3",

src/train_diary.py

@@ -10,10 +10,7 @@ import tensorflow as tf
 from sklearn.model_selection import train_test_split
 
 from models.esmm.diary_model import model_predict_diary
-from models.esmm.fe import click_fe as click_fe
-from models.esmm.fe import device_fe as device_fe
-from models.esmm.fe import diary_fe as diary_fe
-from models.esmm.fe import fe as fe
+from models.esmm.fe import click_fe, device_fe, diary_fe, fe
 from models.esmm.input_fn import esmm_input_fn
 from models.esmm.model import esmm_model_fn, model_export
 

src/train_tractate.py

@@ -7,7 +7,7 @@ from pathlib import Path
 import tensorflow as tf
 from sklearn.model_selection import train_test_split
 
-from models.esmm.fe import click_fe, device_fe, tractate_fe
+from models.esmm.fe import click_fe, device_fe, fe, tractate_fe
 from models.esmm.input_fn import esmm_input_fn
 
 
@@ -32,7 +32,7 @@ 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 = tractate_fe.build_features(df)
+    all_features = fe.build_features(df, tractate_fe.INT_COLUMNS, tractate_fe.FLOAT_COLUMNS, tractate_fe.CATEGORICAL_COLUMNS)
     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):