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ffm-baseline
Commit b6dcae66 authored by 高雅喆's avatar 高雅喆
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exponential=0

parent 8212b4a2
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Showing with 5 additions and 5 deletions
eda/smart_rank/dist_update_user_portrait_service.py
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...@@ -60,11 +60,11 @@ def get_user_service_portrait(cl_id, all_word_tags, all_tag_tag_type, all_3tag_2 ...@@ -60,11 +60,11 @@ def get_user_service_portrait(cl_id, all_word_tags, all_tag_tag_type, all_3tag_2
user_df_service["tag2_type"] = user_df_service.apply(lambda x: all_tag_tag_type.get(x["tag2"]), axis=1) user_df_service["tag2_type"] = user_df_service.apply(lambda x: all_tag_tag_type.get(x["tag2"]), axis=1)
# 算分及比例 # 算分及比例
user_df_service["tag_score"] = user_df_service.apply( user_df_service["tag_score"] = user_df_service.apply(
lambda x: compute_henqiang(x.days_diff_now, exponential=1)/get_action_tag_count(user_df_service, x.time) if x.score_type == "henqiang" else ( lambda x: compute_henqiang(x.days_diff_now, exponential=0)/get_action_tag_count(user_df_service, x.time) if x.score_type == "henqiang" else (
compute_jiaoqiang(x.days_diff_now, exponential=1)/get_action_tag_count(user_df_service, x.time) if x.score_type == "jiaoqiang" else ( compute_jiaoqiang(x.days_diff_now, exponential=0)/get_action_tag_count(user_df_service, x.time) if x.score_type == "jiaoqiang" else (
compute_ai_scan(x.days_diff_now, exponential=1)/get_action_tag_count(user_df_service, x.time) if x.score_type == "ai_scan" else ( compute_ai_scan(x.days_diff_now, exponential=0)/get_action_tag_count(user_df_service, x.time) if x.score_type == "ai_scan" else (
compute_ruoyixiang(x.days_diff_now, exponential=1)/get_action_tag_count(user_df_service, x.time) if x.score_type == "ruoyixiang" else compute_ruoyixiang(x.days_diff_now, exponential=0)/get_action_tag_count(user_df_service, x.time) if x.score_type == "ruoyixiang" else
compute_validate(x.days_diff_now, exponential=1)/get_action_tag_count(user_df_service, x.time)))), axis=1) compute_validate(x.days_diff_now, exponential=0)/get_action_tag_count(user_df_service, x.time)))), axis=1)
tag_score_sum = user_df_service.groupby(by=["tag2", "tag2_type"]).agg( tag_score_sum = user_df_service.groupby(by=["tag2", "tag2_type"]).agg(
{'tag_score': 'sum', 'cl_id': 'first', 'action': 'first'}).reset_index().sort_values(by=["tag_score"], {'tag_score': 'sum', 'cl_id': 'first', 'action': 'first'}).reset_index().sort_values(by=["tag_score"],
ascending=False) ascending=False)
......
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