Commit d3f7206c authored by 赵威's avatar 赵威

update trace price type

parent 2817f05c
......@@ -174,7 +174,11 @@ _categorical_columns = [
"click_diary_id3",
"click_diary_id4",
"click_diary_id5",
"service_id",
"service_city",
"recommend_service_id",
"recommend_service_city",
"recommend_service_price",
"device_fd2",
"device_sd2",
"device_fs2",
......
......@@ -129,9 +129,13 @@ DIARY_COLUMNS = [
"business_second_skip_num",
"effect_second_skip_rate",
"business_second_skip_rate",
"service_id",
"service_price",
"service_sold_num",
"service_city",
"recommend_service_id",
"recommend_service_city",
"recommend_service_price",
]
INT_COLUMNS = [
"active_days",
......@@ -302,7 +306,11 @@ CATEGORICAL_COLUMNS = [
"click_diary_id4",
"click_diary_id5",
"is_related_service",
"service_id",
"service_city",
"recommend_service_id",
"recommend_service_city",
"recommend_service_price",
"device_fd2",
"device_sd2",
"device_fs2",
......@@ -415,7 +423,14 @@ def diary_feature_engineering(df):
diary_df["is_have_reply"] = diary_df["is_have_reply"].astype(int)
diary_df["is_related_service"] = diary_df["is_related_service"].astype(int)
diary_df["service_id"] = diary_df["service_id"].astype(str)
diary_df["recommend_service_id"] = diary_df["recommend_service_id"].astype(str)
diary_df["recommend_service_price"] = diary_df["recommend_service_price"].astype(str)
diary_df["service_id"] = diary_df["service_city"].fillna("-1")
diary_df["service_city"] = diary_df["service_city"].fillna("")
diary_df["recommend_service_id"] = diary_df["recommend_service_id"].fillna("-1")
diary_df["recommend_service_city"] = diary_df["recommend_service_city"].fillna("")
diary_df = diary_df[DIARY_COLUMNS]
......
......@@ -407,13 +407,14 @@ def tractate_feature_engineering(tractate_df):
df["is_have_pure_reply"] = df["is_have_pure_reply"].astype(int)
df["is_have_reply"] = df["is_have_reply"].astype(int)
df["show_tag_id"] = df["show_tag_id"].astype(str)
df["service_id"] = df["service_id"].astype(str)
df["recommend_service_id"] = df["recommend_service_id"].astype(str)
df["is_related_service"] = df["is_related_service"].astype(int)
df["service_city"] = df["service_city"].fillna("")
df["service_id"] = df["service_id"].astype(str)
df["recommend_service_id"] = df["recommend_service_id"].astype(str)
df["recommend_service_price"] = df["recommend_service_price"].astype(str)
df["service_id"] = df["service_city"].fillna("-1")
df["service_city"] = df["service_city"].fillna("")
df["recommend_service_id"] = df["recommend_service_id"].fillna("-1")
df["recommend_service_city"] = df["recommend_service_city"].fillna("")
......
......@@ -46,6 +46,12 @@ if __name__ == "__main__":
tractate_dict = tractate_fe.get_tractate_dict_from_redis()
print("redis data: " + str(len(device_dict)) + " " + str(len(diary_dict)) + " " + str(len(tractate_dict)))
# device_ids = list(device_dict.keys())[:20]
# tractate_ids = list(tractate_dict.keys())[:20]
# print(device_dict[device_ids[0]], "\n")
# print(tractate_dict[tractate_ids[0]], "\n")
diary_save_path = get_essm_model_save_path("diary")
if not diary_save_path:
diary_save_path = "/data/files/models/diary/1597390452"
......
......@@ -65,7 +65,8 @@ 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=13000)
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("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
......@@ -80,8 +81,9 @@ def main():
model_export_path = str(Path("/data/files/models/diary").expanduser())
save_path = model_export(model, all_features, model_export_path)
print("save to: " + save_path)
set_essm_model_save_path("diary", save_path)
record_esmm_auc_to_db("diary", ctr_auc, ctcvr_auc, total_time, save_path)
# TODO save model
# set_essm_model_save_path("diary", save_path)
# record_esmm_auc_to_db("diary", ctr_auc, ctcvr_auc, total_time, save_path)
print("============================================================")
# save_path = str(Path("~/Desktop/models/1596012827").expanduser()) # local
......
......@@ -63,7 +63,7 @@ def main():
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=12000)
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("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
......@@ -87,7 +87,7 @@ def main():
# print("load path: " + save_path)
# save_path = str(Path("~/data/models/tractate/1598236893").expanduser()) # local
# save_path = "/data/files/models/tractate/1598254242" # server
save_path = "/data/files/models/tractate/1599123885" # server
predict_fn = tf.contrib.predictor.from_saved_model(save_path)
device_dict = device_fe.get_device_dict_from_redis()
......
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