feature_engineering.py

# -*- coding: utf-8 -*-
import pymysql
from pyspark.conf import SparkConf
import pytispark.pytispark as pti
from pyspark.sql import SparkSession
import datetime
import pandas as pd
import time
from pyspark import StorageLevel


def app_list_func(x,l):
    b = str(x).split(",")
    e = []
    for i in b:
        if i in l.keys():
            e.append(l[i])
        else:
            e.append(0)
    return e


def get_list(db,sql,n):
    cursor = db.cursor()
    cursor.execute(sql)
    result = cursor.fetchall()
    v = list(set([i[0] for i in result]))
    app_list_value = [str(i).split(",") for i in v]
    app_list_unique = []
    for i in app_list_value:
        app_list_unique.extend(i)
    app_list_unique = list(set(app_list_unique))
    number = len(app_list_unique)
    app_list_map = dict(zip(app_list_unique, list(range(n, number + n))))
    db.close()
    return number, app_list_map


def get_map():
    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select app_list from device_app_list"
    a = time.time()
    apps_number, app_list_map = get_list(db,sql,16)
    print("applist")
    print((time.time()-a)/60)
    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select level2_ids from diary_feat"
    b = time.time()
    leve2_number, leve2_map = get_list(db, sql, 16+apps_number)
    print("leve2")
    print((time.time() - b) / 60)
    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select level3_ids from diary_feat"
    c = time.time()
    leve3_number, leve3_map = get_list(db, sql, 16+leve2_number+apps_number)
    print((time.time() - c) / 60)
    return apps_number, app_list_map,leve2_number, leve2_map,leve3_number, leve3_map


def get_unique(db,sql):
    cursor = db.cursor()
    cursor.execute(sql)
    result = cursor.fetchall()
    v = list(set([i[0] for i in result]))
    db.close()
    print(sql)
    print(len(v))
    return v

def con_sql(db,sql):
    cursor = db.cursor()
    cursor.execute(sql)
    result = cursor.fetchall()
    df = pd.DataFrame(list(result))
    db.close()
    return df


def get_pre_number():
    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select count(*) from esmm_pre_data"
    cursor = db.cursor()
    cursor.execute(sql)
    result = cursor.fetchone()[0]
    print("预测集数量：")
    print(result)
    db.close()


def feature_engineer():
    apps_number, app_list_map, level2_number, leve2_map, level3_number, leve3_map = get_map()
    app_list_map["app_list"] = 16
    leve3_map["level3_ids"] = 17
    leve3_map["search_tag3"] = 18
    leve2_map["level2_ids"] = 19
    leve2_map["tag1"] = 20
    leve2_map["tag2"] = 21
    leve2_map["tag3"] = 22
    leve2_map["tag4"] = 23
    leve2_map["tag5"] = 24
    leve2_map["tag6"] = 25
    leve2_map["tag7"] = 26
    leve2_map["search_tag2"] = 27

    unique_values = []
    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct stat_date from esmm_train_data_dwell"
    unique_values.extend(get_unique(db,sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct ucity_id from esmm_train_data_dwell"
    unique_values.extend(get_unique(db, sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct ccity_name from esmm_train_data_dwell"
    unique_values.extend(get_unique(db, sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct time from cid_time_cut"
    unique_values.extend(get_unique(db, sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct device_type from user_feature"
    unique_values.extend(get_unique(db, sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct manufacturer from user_feature"
    unique_values.extend(get_unique(db, sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct channel from user_feature"
    unique_values.extend(get_unique(db, sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct top from cid_type_top"
    unique_values.extend(get_unique(db, sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct price_min from knowledge"
    unique_values.extend(get_unique(db, sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct treatment_method from knowledge"
    unique_values.extend(get_unique(db, sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct price_max from knowledge"
    unique_values.extend(get_unique(db, sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct treatment_time from knowledge"
    unique_values.extend(get_unique(db, sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct maintain_time from knowledge"
    unique_values.extend(get_unique(db, sql))

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select distinct recover_time from knowledge"
    unique_values.extend(get_unique(db, sql))

    # unique_values.append("video")

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select max(stat_date) from esmm_train_data_dwell"
    validate_date = con_sql(db, sql)[0].values.tolist()[0]
    print("validate_date:" + validate_date)
    temp = datetime.datetime.strptime(validate_date, "%Y-%m-%d")
    start = (temp - datetime.timedelta(days=180)).strftime("%Y-%m-%d")
    print(start)

    db = pymysql.connect(host='172.16.40.170', port=4000, user='root', passwd='3SYz54LS9#^9sBvC')
    sql = "select distinct doctor.hospital_id from jerry_test.esmm_train_data_dwell e " \
          "left join eagle.src_zhengxing_api_service service on e.diary_service_id = service.id " \
          "left join eagle.src_zhengxing_api_doctor doctor on service.doctor_id = doctor.id " \
          "where e.stat_date >= '{}'".format(start)
    unique_values.extend(get_unique(db, sql))
    features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
                "channel", "top", "time", "stat_date", "hospital_id",
                "treatment_method", "price_min", "price_max", "treatment_time", "maintain_time", "recover_time",
                "app_list", "level3_ids", "level2_ids", "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
                "search_tag2", "search_tag3"]
    unique_values.extend(features)
    print("unique_values length")
    print(len(unique_values))
    print("特征维度：")
    print(apps_number + level2_number + level3_number + len(unique_values))

    temp = list(range(29 + apps_number + level2_number + level3_number,
                      29 + apps_number + level2_number + level3_number + len(unique_values)))
    value_map = dict(zip(unique_values, temp))

    sql = "select e.y,e.z,e.stat_date,e.ucity_id,feat.level2_ids,e.ccity_name,u.device_type,u.manufacturer," \
          "u.channel,c.top,cut.time,dl.app_list,feat.level3_ids,doctor.hospital_id," \
          "wiki.tag as tag1,question.tag as tag2,search.tag as tag3,budan.tag as tag4," \
          "ot.tag as tag5,sixin.tag as tag6,cart.tag as tag7,doris.search_tag2,doris.search_tag3," \
          "k.treatment_method,k.price_min,k.price_max,k.treatment_time,k.maintain_time,k.recover_time," \
          "e.device_id,e.cid_id " \
          "from jerry_test.esmm_train_data_dwell e left join jerry_test.user_feature u on e.device_id = u.device_id " \
          "left join jerry_test.cid_type_top c on e.device_id = c.device_id " \
          "left join jerry_test.cid_time_cut cut on e.cid_id = cut.cid " \
          "left join jerry_test.device_app_list dl on e.device_id = dl.device_id " \
          "left join jerry_test.diary_feat feat on e.cid_id = feat.diary_id " \
          "left join jerry_test.knowledge k on feat.level2 = k.level2_id " \
          "left join jerry_test.wiki_tag wiki on e.device_id = wiki.device_id " \
          "left join jerry_test.question_tag question on e.device_id = question.device_id " \
          "left join jerry_test.search_tag search on e.device_id = search.device_id " \
          "left join jerry_test.budan_tag budan on e.device_id = budan.device_id " \
          "left join jerry_test.order_tag ot on e.device_id = ot.device_id " \
          "left join jerry_test.sixin_tag sixin on e.device_id = sixin.device_id " \
          "left join jerry_test.cart_tag cart on e.device_id = cart.device_id " \
          "left join eagle.src_zhengxing_api_service service on e.diary_service_id = service.id " \
          "left join eagle.src_zhengxing_api_doctor doctor on service.doctor_id = doctor.id " \
          "left join jerry_test.search_doris doris on e.device_id = doris.device_id and e.stat_date = doris.get_date " \
          "where e.stat_date >= '{}'".format(start)

    df = spark.sql(sql)

    df = df.drop_duplicates(["ucity_id", "level2_ids", "ccity_name", "device_type", "manufacturer",
                             "channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids",
                             "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7"])

    df = df.na.fill(dict(zip(features, features)))

    rdd = df.select("stat_date", "y", "z", "app_list", "level2_ids", "level3_ids",
                    "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
                    "ucity_id", "ccity_name", "device_type", "manufacturer", "channel", "top", "time",
                    "hospital_id", "treatment_method", "price_min", "price_max", "treatment_time",
                    "maintain_time", "recover_time", "search_tag2", "search_tag3","cid_id","device_id")\
        .rdd.repartition(200).map(
        lambda x: (x[0], float(x[1]), float(x[2]), app_list_func(x[3], app_list_map), app_list_func(x[4], leve2_map),
                   app_list_func(x[5], leve3_map), app_list_func(x[6], leve2_map), app_list_func(x[7], leve2_map),
                   app_list_func(x[8], leve2_map), app_list_func(x[9], leve2_map), app_list_func(x[10], leve2_map),
                   app_list_func(x[11], leve2_map), app_list_func(x[12], leve2_map),
                   [value_map.get(x[0], 1), value_map.get(x[13], 2), value_map.get(x[14], 3), value_map.get(x[15], 4),
                    value_map.get(x[16], 5), value_map.get(x[17], 6), value_map.get(x[18], 7), value_map.get(x[19], 8),
                    value_map.get(x[20], 9), value_map.get(x[21], 10),
                    value_map.get(x[22], 11), value_map.get(x[23], 12), value_map.get(x[24], 13),
                    value_map.get(x[25], 14), value_map.get(x[26], 15)],
                   app_list_func(x[27], leve2_map), app_list_func(x[28], leve3_map),x[13],x[29],x[30]
                   ))


    rdd.persist(storageLevel= StorageLevel.MEMORY_ONLY_SER)

    # TODO 上线后把下面train fliter 删除，因为最近一天的数据也要作为训练集

    train = rdd.map(
        lambda x: (x[1], x[2], x[3], x[4], x[5], x[6], x[7], x[8], x[9],
                   x[10], x[11], x[12], x[13], x[14], x[15],x[16],x[17],x[18]))
    f = time.time()
    spark.createDataFrame(train).toDF("y", "z", "app_list", "level2_list", "level3_list",
                                      "tag1_list", "tag2_list", "tag3_list", "tag4_list",
                                      "tag5_list", "tag6_list", "tag7_list", "ids",
                                      "search_tag2_list","search_tag3_list","city","cid_id","uid") \
        .repartition(1).write.format("tfrecords").save(path=path + "tr/", mode="overwrite")
    h = time.time()
    print("train tfrecord done")
    print((h - f) / 60)

    print("训练集样本总量：")
    print(rdd.count())

    get_pre_number()

    test = rdd.filter(lambda x: x[0] == validate_date).map(
        lambda x: (x[1], x[2], x[3], x[4], x[5], x[6], x[7], x[8], x[9],
                   x[10], x[11], x[12], x[13], x[14], x[15],x[16],x[17],x[18]))

    spark.createDataFrame(test).toDF("y", "z", "app_list", "level2_list", "level3_list",
                                     "tag1_list", "tag2_list", "tag3_list", "tag4_list",
                                     "tag5_list", "tag6_list", "tag7_list", "ids",
                                     "search_tag2_list","search_tag3_list","city","cid_id","uid") \
        .repartition(1).write.format("tfrecords").save(path=path + "va/", mode="overwrite")

    print("va tfrecord done")

    rdd.unpersist()

    return validate_date, value_map, app_list_map, leve2_map, leve3_map


def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
    sql = "select e.y,e.z,e.label,e.ucity_id,feat.level2_ids,e.ccity_name," \
          "u.device_type,u.manufacturer,u.channel,c.top,e.device_id,e.cid_id,cut.time," \
          "dl.app_list,e.hospital_id,feat.level3_ids," \
          "wiki.tag as tag1,question.tag as tag2,search.tag as tag3,budan.tag as tag4," \
          "ot.tag as tag5,sixin.tag as tag6,cart.tag as tag7,doris.search_tag2,doris.search_tag3," \
          "k.treatment_method,k.price_min,k.price_max,k.treatment_time,k.maintain_time,k.recover_time " \
          "from jerry_test.esmm_pre_data e " \
          "left join jerry_test.user_feature u on e.device_id = u.device_id " \
          "left join jerry_test.cid_type_top c on e.device_id = c.device_id " \
          "left join jerry_test.cid_time_cut cut on e.cid_id = cut.cid " \
          "left join jerry_test.device_app_list dl on e.device_id = dl.device_id " \
          "left join jerry_test.diary_feat feat on e.cid_id = feat.diary_id " \
          "left join jerry_test.wiki_tag wiki on e.device_id = wiki.device_id " \
          "left join jerry_test.question_tag question on e.device_id = question.device_id " \
          "left join jerry_test.search_tag search on e.device_id = search.device_id " \
          "left join jerry_test.budan_tag budan on e.device_id = budan.device_id " \
          "left join jerry_test.order_tag ot on e.device_id = ot.device_id " \
          "left join jerry_test.sixin_tag sixin on e.device_id = sixin.device_id " \
          "left join jerry_test.cart_tag cart on e.device_id = cart.device_id " \
          "left join jerry_test.knowledge k on feat.level2 = k.level2_id " \
          "left join jerry_test.search_doris doris on e.device_id = doris.device_id and e.stat_date = doris.get_date"

    features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
                "channel", "top", "time", "hospital_id",
                "treatment_method", "price_min", "price_max", "treatment_time", "maintain_time", "recover_time",
                "app_list", "level3_ids", "level2_ids", "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
                "search_tag2", "search_tag3"]

    df = spark.sql(sql)
    df = df.drop_duplicates(["ucity_id", "device_id", "cid_id"])

    df = df.na.fill(dict(zip(features, features)))
    f = time.time()
    rdd = df.select("label", "y", "z", "ucity_id", "device_id", "cid_id", "app_list", "level2_ids", "level3_ids",
                    "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
                    "ucity_id", "ccity_name", "device_type", "manufacturer", "channel", "top", "time",
                    "hospital_id", "treatment_method", "price_min", "price_max", "treatment_time",
                    "maintain_time", "recover_time", "search_tag2", "search_tag3") \
        .rdd.repartition(200).map(lambda x: (x[0], float(x[1]), float(x[2]), x[3], x[4], x[5],
                                             app_list_func(x[6], app_list_map), app_list_func(x[7], leve2_map),
                                             app_list_func(x[8], leve3_map), app_list_func(x[9], leve2_map),
                                             app_list_func(x[10], leve2_map), app_list_func(x[11], leve2_map),
                                             app_list_func(x[12], leve2_map), app_list_func(x[13], leve2_map),
                                             app_list_func(x[14], leve2_map), app_list_func(x[15], leve2_map),
                                             [value_map.get(date, 1), value_map.get(x[16], 2),
                                              value_map.get(x[17], 3), value_map.get(x[18], 4),
                                              value_map.get(x[19], 5), value_map.get(x[20], 6),
                                              value_map.get(x[21], 7), value_map.get(x[22], 8),
                                              value_map.get(x[23], 9), value_map.get(x[24], 10),
                                              value_map.get(x[25], 11), value_map.get(x[26], 12),
                                              value_map.get(x[27], 13), value_map.get(x[28], 14),
                                              value_map.get(x[29], 15)],
                                             app_list_func(x[30], leve2_map),app_list_func(x[31], leve3_map)))

    rdd.persist(storageLevel= StorageLevel.MEMORY_ONLY_SER)
    print("预测集样本大小：")
    print(rdd.count())
    if rdd.filter(lambda x: x[0] == 0).count() > 0:
        print("预测集native有数据")
        spark.createDataFrame(rdd.filter(lambda x: x[0] == 0)
                              .map(lambda x: (x[1], x[2], x[6], x[7], x[8], x[9], x[10], x[11],
                                              x[12], x[13], x[14], x[15], x[16], x[17], x[18], x[3], x[4], x[5]))) \
            .toDF("y", "z", "app_list", "level2_list", "level3_list", "tag1_list", "tag2_list", "tag3_list",
                  "tag4_list","tag5_list", "tag6_list", "tag7_list", "ids", "search_tag2_list",
                  "search_tag3_list", "city", "uid","cid_id") \
            .repartition(1).write.format("tfrecords").save(path=path + "native/", mode="overwrite")

        print("native tfrecord done")
        h = time.time()
        print((h - f) / 60)
    else:
        print("预测集native为空")

    if rdd.filter(lambda x: x[0] == 1).count() > 0:
        print("预测集nearby有数据")
        spark.createDataFrame(rdd.filter(lambda x: x[0] == 1)
                              .map(lambda x: (x[1], x[2], x[6], x[7], x[8], x[9], x[10], x[11],
                                              x[12], x[13], x[14], x[15], x[16], x[17], x[18], x[3], x[4], x[5]))) \
            .toDF("y", "z", "app_list", "level2_list", "level3_list", "tag1_list", "tag2_list", "tag3_list",
                  "tag4_list","tag5_list", "tag6_list", "tag7_list", "ids", "search_tag2_list",
                  "search_tag3_list", "city", "uid", "cid_id")\
            .repartition(1).write.format("tfrecords").save(path=path + "nearby/", mode="overwrite")
        print("nearby tfrecord done")
    else:
        print("预测集nearby为空")


if __name__ == '__main__':
    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()
    ti = pti.TiContext(spark)
    ti.tidbMapDatabase("jerry_test")
    ti.tidbMapDatabase("eagle")
    spark.sparkContext.setLogLevel("WARN")
    path = "hdfs:///strategy/esmm/"
    local_path = "/home/gmuser/esmm/"

    validate_date, value_map, app_list_map, leve2_map, leve3_map = feature_engineer()
    get_predict(validate_date, value_map, app_list_map, leve2_map, leve3_map)

    spark.stop()