update columns

src/models/esmm/fe/diary_fe.py

@@ -12,6 +12,15 @@ DIARY_COLUMNS = [
     "one_ctr", "three_ctr", "seven_ctr", "fifteen_ctr", "first_demands", "second_demands", "first_solutions", "second_solutions",
     "first_positions", "second_positions", "projects"
 ]
+_int_columns = ["active_days", "topic_num", "favor_num", "vote_num"]
+_float_columns = ["one_ctr", "three_ctr", "seven_ctr", "fifteen_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", "device_fd", "content_fd", "fd1", "fd2",
+    "fd3", "device_sd", "content_sd", "sd1", "sd2", "sd3", "device_fs", "content_fs", "fs1", "fs2", "fs3", "device_ss",
+    "content_ss", "ss1", "ss2", "ss3", "device_fp", "content_fp", "fp1", "fp2", "fp3", "device_sp", "content_sp", "sp1", "sp2",
+    "sp3", "device_p", "content_p", "p1", "p2", "p3"
+]
 
 
 def read_csv_data(dataset_path):
@@ -152,31 +161,16 @@ def join_features(device_df, diary_df, cc_df):
 
 
 def build_features(df):
-    # TODO
-    int_columns = ["active_days", "topic_num", "favor_num", "vote_num"]
-    float_columns = ["one_ctr", "three_ctr", "seven_ctr", "fifteen_ctr"]
     numeric_features = []
-    for col in (int_columns + float_columns):
-        if col in int_columns:
+    for col in (_int_columns + _float_columns):
+        if col in _int_columns:
             numeric_features.append(
                 fc.bucketized_column(fc.numeric_column(col, dtype=tf.int64), boundaries=create_boundaries(df, col)))
         else:
             numeric_features.append(fc.bucketized_column(fc.numeric_column(col), boundaries=create_boundaries(df, col)))
 
-    # TODO
-    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",
-        "device_fd", "content_fd", "fd1", "fd2", "fd3", "device_sd", "content_sd", "sd1", "sd2", "sd3", "device_fs", "content_fs",
-        "fs1", "fs2", "fs3", "device_ss", "content_ss", "ss1", "ss2", "ss3", "device_fp", "content_fp", "fp1", "fp2", "fp3",
-        "device_sp", "content_sp", "sp1", "sp2", "sp3", "device_p", "content_p", "p1", "p2", "p3"
-    ]
-
-    categorical_ignore_columns = []
-
     categorical_features = []
-    for col in categorical_columns:
-        if col not in categorical_ignore_columns:
+    for col in _categorical_columns:
         if col == "card_id":
             categorical_features.append(
                 fc.embedding_column(fc.categorical_column_with_hash_bucket(col, 20000, dtype=tf.int64),

src/models/esmm/fe/tractate_fe.py

@@ -11,6 +11,16 @@ 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 = [
+    "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",
+    "device_ss", "content_ss", "ss1", "ss2", "ss3", "device_fp", "content_fp", "fp1", "fp2", "fp3", "device_sp", "content_sp",
+    "sp1", "sp2", "sp3", "device_p", "content_p", "p1", "p2", "p3"
+]
+
 
 def read_csv_data(dataset_path):
     tractate_df = pd.read_csv(dataset_path.joinpath("tractate.csv"), sep="|")
@@ -120,27 +130,20 @@ def join_features(device_df, tractate_df, cc_df):
         "second_solutions", "first_positions_x", "first_positions_y", "first_positions", "second_positions_x",
         "second_positions_y", "second_positions", "projects_x", "projects_y", "projects"
     ]
-    # for col in drop_columns:
-    #     if col in df.columns:
-    #         df.drop(col, inplace=True, axis=1)
     df.drop(drop_columns, inplace=True, axis=1)
     return df
 
 
 def build_features(df):
-    # TODO
-    int_columns = ["active_days", "topic_num", "favor_num", "vote_num"]
-    float_columns = ["one_ctr", "three_ctr", "seven_ctr", "fifteen_ctr"]
     numeric_features = []
-    for col in (int_columns + float_columns):
-        if col in int_columns:
+    for col in (_int_columns + _float_columns):
+        if col in _int_columns:
             numeric_features.append(
                 fc.bucketized_column(fc.numeric_column(col, dtype=tf.int64), boundaries=create_boundaries(df, col)))
         else:
             numeric_features.append(fc.bucketized_column(fc.numeric_column(col), boundaries=create_boundaries(df, col)))
 
-    # TODO
-    categorical_columns = [
+    _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",
         "device_fd", "content_fd", "fd1", "fd2", "fd3", "device_sd", "content_sd", "sd1", "sd2", "sd3", "device_fs", "content_fs",
@@ -148,11 +151,8 @@ def build_features(df):
         "device_sp", "content_sp", "sp1", "sp2", "sp3", "device_p", "content_p", "p1", "p2", "p3"
     ]
 
-    categorical_ignore_columns = []
-
     categorical_features = []
-    for col in categorical_columns:
-        if col not in categorical_ignore_columns:
+    for col in _categorical_columns:
         if col == "card_id":
             categorical_features.append(
                 fc.embedding_column(fc.categorical_column_with_hash_bucket(col, 20000, dtype=tf.int64),

src/train_tractate.py

@@ -25,6 +25,8 @@ def main():
 
     cc_df = click_fe.click_feature_engineering(tractate_click_df, tractate_conversion_df)
     df = tractate_fe.join_features(device_df, tractate_df, cc_df)
+    for i in df.columns:
+        print(i)
     # print(df.dtypes)
 
     train_df, test_df = train_test_split(df, test_size=0.2)