test_df

eda/smart_rank/gyz_test.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import pymysql
+import smtplib
+from email.mime.text import MIMEText
+from email.utils import formataddr
+from email.mime.multipart import MIMEMultipart
+from email.mime.application import MIMEApplication
+import redis
+import datetime
+from pyspark import SparkConf
+import time
+from pyspark.sql import SparkSession
+import json
+import numpy as np
+import pandas as pd
+from pyspark.sql.functions import lit
+from pyspark.sql.functions import concat_ws
+from tool import *
+
+
+def get_user_service_portrait(x, all_word_tags, all_tag_tag_type, all_3tag_2tag, all_tags_name, size=None, pay_time=0):
+    cl_id = x[0]
+    search_info = x[1]
+    user_df_service = get_user_log(cl_id, all_word_tags, pay_time=pay_time)
+
+    # 增加df字段(days_diff_now, tag_type, tag2)
+    if not user_df_service.empty:
+        user_df_service["days_diff_now"] = round((int(time.time()) - user_df_service["time"].astype(float)) / (24 * 60 * 60))
+        user_df_service["tag_type"] = user_df_service.apply(lambda x: all_tag_tag_type.get(x["tag_id"]), axis=1)
+        user_df_service = user_df_service[user_df_service['tag_type'].isin(['2','3'])]
+        if not user_df_service.empty:
+            user_log_df_tag2_list = user_df_service[user_df_service['tag_type'] == '2']['tag_id'].unique().tolist()
+            user_df_service["tag2"] = user_df_service.apply(lambda x:
+                                                    get_tag2_from_tag3(x.tag_id, all_3tag_2tag, user_log_df_tag2_list)
+                                                    if x.tag_type == '3' else x.tag_id, 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
+            user_df_service["tag_score"] = user_df_service.apply(
+                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=0)/get_action_tag_count(user_df_service, x.time) if x.score_type == "jiaoqiang" 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=0)/get_action_tag_count(user_df_service, x.time) if x.score_type == "ruoyixiang" else
+                        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', 'cl_id': 'first', 'action': 'first'}).reset_index().sort_values(by=["tag_score"],
+                                                                                                     ascending=False)
+            tag_score_sum['weight'] = 100 * tag_score_sum['tag_score'] / tag_score_sum['tag_score'].sum()
+            tag_score_sum["pay_type"] = tag_score_sum.apply(
+                lambda x: 3 if x.action == "api/order/validate" else (
+                    2 if x.action == "api/settlement/alipay_callback" else 1
+                ), axis=1
+            )
+            gmkv_tag_score_sum = tag_score_sum[["tag2", "tag_score", "weight"]][:size].to_dict('record')
+            gmkv_tag_score2_sum = tag_score_sum[["tag2", "tag_score"]][:size].to_dict('record')
+            gmkv_tag_score2_sum_dict = {i["tag2"]: i["tag_score"] for i in gmkv_tag_score2_sum}
+            gmkv_tag_score3_sum_dict = {all_tags_name[i]: gmkv_tag_score2_sum_dict[i] for i in gmkv_tag_score2_sum_dict}
+            gmkv_tag_score3_sum_dict_sort_list = sorted(gmkv_tag_score3_sum_dict.items(), key=lambda x:x[1],reverse=True)
+            portrait_result = [i[0] for i in gmkv_tag_score3_sum_dict_sort_list]
+            return [cl_id, search_info, portrait_result]
+
+# data
+device_info = []
+with open("/home/gmuser/gyz/log/have_search_device_20191105.csv", "r") as f:
+    for line in f.readlines():
+        data = line.strip().split("=")
+        device = data[0]
+        search_words = eval(data[1])
+        device_info.append([device, search_words])
+pay_time = 1572883200
+
+# rdd
+sparkConf = SparkConf().set("spark.hive.mapred.supports.subdirectories", "true") \
+    .set("spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive", "true") \
+    .set("spark.tispark.plan.allow_index_double_read", "false") \
+    .set("spark.tispark.plan.allow_index_read", "true") \
+    .set("spark.sql.extensions", "org.apache.spark.sql.TiExtensions") \
+    .set("spark.tispark.pd.addresses", "172.16.40.170:2379").set("spark.io.compression.codec", "lzf") \
+    .set("spark.driver.maxResultSize", "8g").set("spark.sql.avro.compression.codec", "snappy")
+
+spark = SparkSession.builder.config(conf=sparkConf).enableHiveSupport().getOrCreate()
+spark.sparkContext.setLogLevel("WARN")
+spark.sparkContext.addPyFile("/srv/apps/ffm-baseline_git/eda/smart_rank/tool.py")
+device_ids_lst_rdd = spark.sparkContext.parallelize(device_info)
+result = device_ids_lst_rdd.repartition(100).map(lambda x: get_user_service_portrait(x, all_word_tags, all_tag_tag_type, all_3tag_2tag, all_tags_name, size=None, pay_time=pay_time))
+# result.foreach(print)
+result_df = result.toDF()
+result_df.to_csv("~/test_df.csv")
+spark.stop()