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Commit bfa8847f authored by 郭羽's avatar 郭羽
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排序模型优化

parent 75815fee
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spark/featureEng.py
View file @ bfa8847f
...@@ -315,7 +315,7 @@ def addUserFeatures(samples,dataVocab,multiVocab): ...@@ -315,7 +315,7 @@ def addUserFeatures(samples,dataVocab,multiVocab):
return samples return samples
def addSampleLabel(ratingSamples): def addSampleLabel(ratingSamples):
ratingSamples = ratingSamples.withColumn('label', when(F.col('rating') >= 5, 1).otherwise(0)) ratingSamples = ratingSamples.withColumn('label', when(F.col('rating') >= 1, 1).otherwise(0))
ratingSamples.show(5, truncate=False) ratingSamples.show(5, truncate=False)
ratingSamples.printSchema() ratingSamples.printSchema()
return ratingSamples return ratingSamples
...@@ -852,8 +852,8 @@ if __name__ == '__main__': ...@@ -852,8 +852,8 @@ if __name__ == '__main__':
df = ratingSamplesWithLabel.toPandas() df = ratingSamplesWithLabel.toPandas()
df = pd.DataFrame(df) df = pd.DataFrame(df)
posCount = df.loc[df["label"]==0]["label"].count() posCount = df.loc[df["label"]==1]["label"].count()
negCount = df.loc[df["label"]==1]["label"].count() negCount = df.loc[df["label"]==0]["label"].count()
print("pos size:"+str(posCount),"neg size:"+str(negCount)) print("pos size:"+str(posCount),"neg size:"+str(negCount))
itemDF = get_service_feature_df() itemDF = get_service_feature_df()
......
spark/featureEng_copy.py
View file @ bfa8847f
import sys import sys
import os import os
from datetime import date, timedelta from datetime import date, timedelta
from elasticsearch import Elasticsearch from elasticsearch import Elasticsearch
from elasticsearch.helpers import scan from elasticsearch.helpers import scan
...@@ -11,48 +10,24 @@ import redis ...@@ -11,48 +10,24 @@ import redis
from pyspark import SparkContext, SparkConf from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession from pyspark.sql import SparkSession
import pyspark.sql as sql import pyspark.sql as sql
from pyspark.sql.functions import when,col from pyspark.sql.functions import when
from pyspark.sql.types import * from pyspark.sql.types import *
from pyspark.sql import functions as F from pyspark.sql import functions as F
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, QuantileDiscretizer, MinMaxScaler
from collections import defaultdict from collections import defaultdict
import json import json
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__)))) sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
import utils.configUtils as configUtils import utils.configUtils as configUtils
# import utils.connUtils as connUtils # import utils.connUtils as connUtils
import pandas as pd
import math
# os.environ["PYSPARK_PYTHON"]="/usr/bin/python3" # os.environ["PYSPARK_PYTHON"]="/usr/bin/python3"
""" """
特征工程 特征工程
""" """
ITEM_MULTI_COLUMN_EXTRA_MAP = {"first_demands": 1,
"second_demands": 5,
"first_solutions": 1,
"second_solutions": 5,
"first_positions": 1,
"second_positions": 5,
"tags_v3": 10,
}
USER_MULTI_COLUMN_EXTRA_MAP = {"first_demands": 1,
"second_demands": 3,
"first_solutions": 1,
"second_solutions": 3,
"first_positions": 1,
"second_positions": 3,
"tags_v3": 5,
}
ITEM_NUMBER_COLUMNS = ["lowest_price","smart_rank2","case_count","ordered_user_ids_count"]
ITEM_CATE_COLUMNS = ["service_type","merchant_id","doctor_type","doctor_id","doctor_famous","hospital_id","hospital_city_tag_id","hospital_type","hospital_is_high_quality"]
NUMBER_PRECISION = 2 NUMBER_PRECISION = 2
VERSION = configUtils.SERVICE_VERSION VERSION = configUtils.SERVICE_VERSION
...@@ -61,174 +36,333 @@ FEATURE_ITEM_KEY = "Strategy:rec:feature:service:" + VERSION + ":item:" ...@@ -61,174 +36,333 @@ FEATURE_ITEM_KEY = "Strategy:rec:feature:service:" + VERSION + ":item:"
FEATURE_VOCAB_KEY = "Strategy:rec:vocab:service:" + VERSION FEATURE_VOCAB_KEY = "Strategy:rec:vocab:service:" + VERSION
FEATURE_COLUMN_KEY = "Strategy:rec:column:service:" + VERSION FEATURE_COLUMN_KEY = "Strategy:rec:column:service:" + VERSION
TRAIN_FILE_PATH = "service_feature_" + VERSION
ITEM_PREFIX = "item_" ITEM_PREFIX = "item_"
DATA_PATH_TRAIN = "/data/files/service_feature_{}_train.csv".format(VERSION)
def getRedisConn(): def getRedisConn():
pool = redis.ConnectionPool(host="172.16.50.145",password="XfkMCCdWDIU%ls$h",port=6379,db=0) pool = redis.ConnectionPool(host="172.16.50.145", password="XfkMCCdWDIU%ls$h", port=6379, db=0)
conn = redis.Redis(connection_pool=pool) conn = redis.Redis(connection_pool=pool)
# conn = redis.Redis(host="172.16.50.145", port=6379, password="XfkMCCdWDIU%ls$h",db=0) # conn = redis.Redis(host="172.16.50.145", port=6379, password="XfkMCCdWDIU%ls$h",db=0)
# conn = redis.Redis(host="172.18.51.10", port=6379,db=0) #test # conn = redis.Redis(host="172.18.51.10", port=6379,db=0) #test
return conn return conn
def addItemFeatures(samples,itemDF,dataVocab,multiVocab):
itemDF = itemDF.withColumnRenamed("id", "itemid") def parseTags(tags, i):
# 数据过滤：无医生 tags_arr = tags.split(",")
itemDF = itemDF.filter(col("doctor_id") != "-1") if len(tags_arr) >= i:
return tags_arr[i - 1]
else:
return "-1"
# itemid
vocabList = collectColumnToVocab(itemDF, "itemid")
dataVocab["itemid"] = vocabList
# null处理 def numberToBucket(num):
for c in ITEM_NUMBER_COLUMNS: res = 0
print("null count:",c,itemDF.filter(col(c).isNull()).count()) if not num:
itemDF = itemDF.withColumn(ITEM_PREFIX+c,when(col(c).isNull(),0).otherwise(col(c)).cast("float")).drop(c) return str(res)
if num >= 1000:
res = 1000 // 10
else:
res = int(num) // 10
return str(res)
for c in ITEM_CATE_COLUMNS:
print("null count:", c, itemDF.filter(col(c).isNull()).count())
itemDF = itemDF.withColumn(ITEM_PREFIX+c, F.when(F.col(c).isNull(), "-1").otherwise(F.col(c))).drop(c)
# 字典添加
dataVocab[ITEM_PREFIX+c] = collectColumnToVocab(itemDF,ITEM_PREFIX+c)
# 离散特征处理 def priceToBucket(num):
for c, v in ITEM_MULTI_COLUMN_EXTRA_MAP.items(): res = 0
print("null count:", c, itemDF.filter(col(c).isNull()).count()) if not num:
itemDF = itemDF.withColumn(c, F.when(F.col(c).isNull(), "-1").otherwise(F.col(c))) return str(res)
if num >= 100000:
res = 100000 // 1000
else:
res = int(num) // 1000
return str(res)
multiVocab[c] = collectMutiColumnToVocab(itemDF, c)
for i in range(1, v + 1):
new_c = ITEM_PREFIX + c + "__" + str(i)
itemDF = itemDF.withColumn(new_c, F.split(F.col(c), ",")[i - 1])
itemDF = itemDF.withColumn(new_c, F.when(F.col(new_c).isNull(), "-1").otherwise(F.col(new_c)))
dataVocab[new_c] = multiVocab[c] numberToBucketUdf = F.udf(numberToBucket, StringType())
priceToBucketUdf = F.udf(priceToBucket, StringType())
samples = samples.join(itemDF, on=['itemid'], how='inner')
# 统计特征处理 def addItemStaticFeatures(samples, itemDF, dataVocab):
print("统计特征处理...") ctrUdf = F.udf(wilson_ctr, FloatType())
staticFeatures = samples.groupBy('itemid').agg(F.count(F.lit(1)).alias('itemRatingCount'), # item不设置over窗口，原因：item可能一直存在，统计数据按照最新即可
print("item统计特征处理...")
staticFeatures = samples.groupBy('item_id').agg(F.count(F.lit(1)).alias('itemRatingCount'),
F.avg(F.col('rating')).alias('itemRatingAvg'), F.avg(F.col('rating')).alias('itemRatingAvg'),
F.stddev(F.col('rating')).alias('itemRatingStddev')).fillna(0) \ F.stddev(F.col('rating')).alias('itemRatingStddev'),
F.sum(
when(F.col('label') == 1, F.lit(1)).otherwise(F.lit(0))).alias(
"itemClickCount"),
F.sum(
when(F.col('label') == 0, F.lit(1)).otherwise(F.lit(0))).alias(
"itemExpCount")
).fillna(0) \
.withColumn('itemRatingStddev', F.format_number(F.col('itemRatingStddev'), NUMBER_PRECISION).cast("float")) \ .withColumn('itemRatingStddev', F.format_number(F.col('itemRatingStddev'), NUMBER_PRECISION).cast("float")) \
.withColumn('itemRatingAvg', F.format_number(F.col('itemRatingAvg'), NUMBER_PRECISION).cast("float")) .withColumn('itemRatingAvg', F.format_number(F.col('itemRatingAvg'), NUMBER_PRECISION).cast("float")) \
.withColumn('itemCtr',
# join item rating features F.format_number(ctrUdf(F.col("itemClickCount"), (F.col("itemExpCount"))), NUMBER_PRECISION).cast(
samples = samples.join(staticFeatures, on=['itemid'], how='left') "float"))
staticFeatures.show(20, truncate=False)
staticFeatures = itemDF.join(staticFeatures, on=["item_id"], how='left')
# 连续特征分桶
bucket_vocab = [str(i) for i in range(101)]
bucket_suffix = "_Bucket"
for col in ["itemRatingCount", "itemRatingAvg", "itemClickCount", "itemExpCount"]:
new_col = col + bucket_suffix
staticFeatures = staticFeatures.withColumn(new_col, numberToBucketUdf(F.col(col))) \
.drop(col) \
.withColumn(new_col, F.when(F.col(new_col).isNull(), "0").otherwise(F.col(new_col)))
dataVocab[new_col] = bucket_vocab
# 方差处理
number_suffix = "_number"
for col in ["itemRatingStddev"]:
new_col = col + number_suffix
staticFeatures = staticFeatures.withColumn(new_col,
F.when(F.col(col).isNull(), 0).otherwise(1 / (F.col(col) + 1))).drop(
col)
for col in ["itemCtr"]:
new_col = col + number_suffix
staticFeatures = staticFeatures.withColumn(col, F.when(F.col(col).isNull(), 0).otherwise(
F.col(col))).withColumnRenamed(col, new_col)
print("item size:", staticFeatures.count())
staticFeatures.show(5, truncate=False)
return staticFeatures
def addUserStaticsFeatures(samples, dataVocab):
print("user统计特征处理...")
samples = samples \
.withColumn('userRatingCount', F.format_number(
F.sum(F.lit(1)).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1)),
NUMBER_PRECISION).cast("float")) \
.withColumn("userRatingAvg", F.format_number(
F.avg(F.col("rating")).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1)),
NUMBER_PRECISION).cast("float")) \
.withColumn("userRatingStddev", F.format_number(
F.stddev(F.col("rating")).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1)),
NUMBER_PRECISION).cast("float")) \
.withColumn("userClickCount", F.format_number(
F.sum(when(F.col('label') == 1, F.lit(1)).otherwise(F.lit(0))).over(
sql.Window.partitionBy("userid").orderBy(F.col("timestamp")).rowsBetween(-100, -1)), NUMBER_PRECISION).cast(
"float")) \
.withColumn("userExpCount", F.format_number(F.sum(when(F.col('label') == 0, F.lit(1)).otherwise(F.lit(0))).over(
sql.Window.partitionBy("userid").orderBy(F.col("timestamp")).rowsBetween(-100, -1)), NUMBER_PRECISION).cast(
"float")) \
.withColumn("userCtr",
F.format_number(F.col("userClickCount") / (F.col("userExpCount") + 1), NUMBER_PRECISION).cast(
"float")) \
.filter(F.col("userRatingCount") > 1)
print("连续特征处理...") samples.show(20, truncate=False)
# todo 分桶比较耗时，可以考虑做非线性转换
# 连续特征处理 # 连续特征分桶
pipelineStage = [] bucket_vocab = [str(i) for i in range(101)]
# Normalization bucket_suffix = "_Bucket"
# for c in ["itemRatingAvg","itemRatingStddev"]: for col in ["userRatingCount", "userRatingAvg", "userClickCount", "userExpCount"]:
# pipelineStage.append(MinMaxScaler(inputCol=c, outputCol=c+"Scale")) new_col = col + bucket_suffix
samples = samples.withColumn(new_col, numberToBucketUdf(F.col(col))) \
.drop(col) \
.withColumn(new_col, F.when(F.col(new_col).isNull(), "0").otherwise(F.col(new_col)))
dataVocab[new_col] = bucket_vocab
# 方差处理
number_suffix = "_number"
for col in ["userRatingStddev"]:
new_col = col + number_suffix
samples = samples.withColumn(new_col, F.when(F.col(col).isNull(), 0).otherwise(1 / (F.col(col) + 1))).drop(col)
for col in ["userCtr"]:
new_col = col + number_suffix
samples = samples.withColumn(col, F.when(F.col(col).isNull(), 0).otherwise(F.col(col))).withColumnRenamed(col,
new_col)
# bucketing samples.printSchema()
bucketColumns = [ITEM_PREFIX+"case_count", ITEM_PREFIX+"ordered_user_ids_count", ITEM_PREFIX+"lowest_price", "itemRatingCount", "itemRatingStddev","itemRatingAvg"] samples.show(20, truncate=False)
for c in bucketColumns: return samples
pipelineStage.append(QuantileDiscretizer(numBuckets=10, inputCol=c, outputCol=c + "Bucket"))
featurePipeline = Pipeline(stages=pipelineStage)
samples = featurePipeline.fit(samples).transform(samples)
# 转string def addItemFeatures(itemDF, dataVocab, multi_col_vocab):
for c in bucketColumns: # multi_col = ['sku_tags', 'sku_show_tags','second_demands', 'second_solutions', 'second_positions']
samples = samples.withColumn(c + "Bucket",F.col(c + "Bucket").cast("string")).drop(c) multi_col = ['tags_v3', 'second_demands', 'second_solutions', 'second_positions']
onehot_col = ['id', 'service_type', 'merchant_id', 'doctor_type', 'doctor_id', 'doctor_famous', 'hospital_id',
'hospital_city_tag_id', 'hospital_type', 'hospital_is_high_quality']
dataVocab[c + "Bucket"] = [str(float(i)) for i in range(11)] for col in onehot_col:
new_c = ITEM_PREFIX + col
dataVocab[new_c] = list(set(itemDF[col].tolist()))
itemDF[new_c] = itemDF[col]
itemDF = itemDF.drop(columns=onehot_col)
for c in multi_col:
multi_col_vocab[c] = list(set(itemDF[c].tolist()))
samples.printSchema() for i in range(1, 6):
# samples.show(5, truncate=False) new_c = ITEM_PREFIX + c + "__" + str(i)
itemDF[new_c] = itemDF[c].map(lambda x: parseTags(x, i))
dataVocab[new_c] = multi_col_vocab[c]
# 连续特征分桶
bucket_vocab = [str(i) for i in range(101)]
bucket_suffix = "_Bucket"
for col in ['case_count', 'sales_count']:
new_col = ITEM_PREFIX + col + bucket_suffix
itemDF[new_col] = itemDF[col].map(numberToBucket)
itemDF = itemDF.drop(columns=[col])
dataVocab[new_col] = bucket_vocab
for col in ['sku_price']:
new_col = ITEM_PREFIX + col + bucket_suffix
itemDF[new_col] = itemDF[col].map(priceToBucket)
itemDF = itemDF.drop(columns=[col])
dataVocab[new_col] = bucket_vocab
# 连续数据处理
number_suffix = "_number"
for col in ["discount"]:
new_col = ITEM_PREFIX + col + number_suffix
itemDF[new_col] = itemDF[col]
itemDF = itemDF.drop(columns=[col])
return itemDF
return samples
def extractTags(genres_list): def extractTags(genres_list):
# 根据点击列表顺序加权
genres_dict = defaultdict(int) genres_dict = defaultdict(int)
for genres in genres_list: for i, genres in enumerate(genres_list):
for genre in genres.split(','): for genre in genres.split(','):
genres_dict[genre] += 1 genres_dict[genre] += i
sortedGenres = sorted(genres_dict.items(), key=lambda x: x[1], reverse=True) sortedGenres = sorted(genres_dict.items(), key=lambda x: x[1], reverse=True)
return [x[0] for x in sortedGenres] return [x[0] for x in sortedGenres]
# sql版本不支持F.reverse # sql版本不支持F.reverse
def arrayReverse(arr): def arrayReverse(arr):
arr.reverse() arr.reverse()
return arr return arr
def addUserFeatures(samples,dataVocab,multiVocab):
"""
p —— 概率，即点击的概率，也就是 CTR
n —— 样本总数，即曝光数
z —— 在正态分布里，均值 + z * 标准差会有一定的置信度。例如 z 取 1.96，就有 95% 的置信度。
Wilson区间的含义就是，就是指在一定置信度下，真实的 CTR 范围是多少
"""
def wilson_ctr(num_pv, num_click):
num_pv = float(num_pv)
num_click = float(num_click)
if num_pv * num_click == 0 or num_pv < num_click:
return 0.0
z = 1.96;
n = num_pv;
p = num_click / num_pv;
score = (p + z * z / (2 * n) - z * math.sqrt((p * (1.0 - p) + z * z / (4.0 * n)) / n)) / (1.0 + z * z / n);
return float(score);
def addUserFeatures(samples, dataVocab, multiVocab):
dataVocab["userid"] = collectColumnToVocab(samples, "userid") dataVocab["userid"] = collectColumnToVocab(samples, "userid")
dataVocab["user_os"] = ["ios","android","-1"] dataVocab["user_city_id"] = collectColumnToVocab(samples, "user_city_id")
dataVocab["user_os"] = ["ios", "android"]
extractTagsUdf = F.udf(extractTags, ArrayType(StringType())) extractTagsUdf = F.udf(extractTags, ArrayType(StringType()))
arrayReverseUdf = F.udf(arrayReverse, ArrayType(StringType())) arrayReverseUdf = F.udf(arrayReverse, ArrayType(StringType()))
samples = samples.withColumnRenamed("cl_id","userid") ctrUdf = F.udf(wilson_ctr, FloatType())
print("user历史数据处理...") print("user历史数据处理...")
# user历史记录 # user历史记录
samples = samples\ samples = samples.withColumn('userPositiveHistory', F.collect_list(
.withColumn('userPositiveHistory',F.collect_list(when(F.col('label') == 1, F.col('itemid')).otherwise(F.lit(None))).over(sql.Window.partitionBy("userid").orderBy(F.col("timestamp")).rowsBetween(-100, -1))) \ when(F.col('label') == 1, F.col('item_id')).otherwise(F.lit(None))).over(
.withColumn("userPositiveHistory", arrayReverseUdf(F.col("userPositiveHistory"))) sql.Window.partitionBy("userid").orderBy(F.col("timestamp")).rowsBetween(-100, -1)))
for i in range(1,11):
samples = samples.withColumn("userRatedHistory"+str(i), F.when(F.col("userPositiveHistory")[i-1].isNotNull(),F.col("userPositiveHistory")[i-1]).otherwise("-1"))
dataVocab["userRatedHistory"+str(i)] = dataVocab["itemid"]
samples = samples.drop("userPositiveHistory")
# user历史点击分值统计 samples = samples.withColumn("userPositiveHistory", arrayReverseUdf(F.col("userPositiveHistory")))
print("统计特征处理...")
samples = samples\ for i in range(1, 11):
.withColumn('userRatingCount',F.count(F.lit(1)).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1))) \ samples = samples.withColumn("userRatedHistory" + str(i),
.withColumn("userRatingAvg", F.format_number(F.avg(F.col("rating")).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1)),NUMBER_PRECISION).cast("float")) \ F.when(F.col("userPositiveHistory")[i - 1].isNotNull(),
.withColumn("userRatingStddev", F.format_number(F.stddev(F.col("rating")).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1)),NUMBER_PRECISION).cast("float")) \ F.col("userPositiveHistory")[i - 1]).otherwise("-1"))
.filter(F.col("userRatingCount") > 1) dataVocab["userRatedHistory" + str(i)] = dataVocab["item_id"]
samples = samples.drop("userPositiveHistory")
# user偏好 # user偏好
for c,v in USER_MULTI_COLUMN_EXTRA_MAP.items(): print("user 偏好数据")
for c, v in multiVocab.items():
new_col = "user" + "__" + c new_col = "user" + "__" + c
samples = samples.withColumn(new_col, extractTagsUdf(F.collect_list(when(F.col('label') == 1, F.col(c)).otherwise(F.lit(None))).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1)))) samples = samples.withColumn(new_col, extractTagsUdf(
for i in range(1, v+1): F.collect_list(when(F.col('label') == 1, F.col(c)).otherwise(F.lit(None))).over(
samples = samples.withColumn(new_col + "__" + str(i),F.when(F.col(new_col)[i - 1].isNotNull(), F.col(new_col)[i - 1]).otherwise("-1")) sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1))))
for i in range(1, 6):
dataVocab[new_col + "__" + str(i)] = multiVocab[c] samples = samples.withColumn(new_col + "__" + str(i),
F.when(F.col(new_col)[i - 1].isNotNull(), F.col(new_col)[i - 1]).otherwise(
samples = samples.drop(new_col) "-1"))
# .drop(c).drop(new_col) dataVocab[new_col + "__" + str(i)] = v
print("连续特征处理...") samples = samples.drop(new_col).drop(c)
pipelineStage = []
print("user统计特征处理...")
# Normalization samples = samples \
# for c in ["userRatingAvg", "userRatingStddev"]: .withColumn('userRatingCount', F.format_number(
# pipelineStage.append(MinMaxScaler(inputCol=c, outputCol=c + "Scale")) F.sum(F.lit(1)).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1)),
NUMBER_PRECISION).cast("float")) \
# bucketing .withColumn("userRatingAvg", F.format_number(
bucketColumns = ["userRatingCount","userRatingAvg","userRatingStddev"] F.avg(F.col("rating")).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1)),
for c in bucketColumns: NUMBER_PRECISION).cast("float")) \
pipelineStage.append(QuantileDiscretizer(numBuckets=10, inputCol=c, outputCol=c + "Bucket")) .withColumn("userRatingStddev", F.format_number(
F.stddev(F.col("rating")).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1)),
featurePipeline = Pipeline(stages=pipelineStage) NUMBER_PRECISION).cast("float")) \
samples = featurePipeline.fit(samples).transform(samples) .withColumn("userClickCount", F.format_number(
# 转string F.sum(when(F.col('label') == 1, F.lit(1)).otherwise(F.lit(0))).over(
for c in bucketColumns: sql.Window.partitionBy("userid").orderBy(F.col("timestamp")).rowsBetween(-100, -1)), NUMBER_PRECISION).cast(
samples = samples.withColumn(c + "Bucket", F.col(c + "Bucket").cast("string")).drop(c) "float")) \
.withColumn("userExpCount", F.format_number(F.sum(when(F.col('label') == 0, F.lit(1)).otherwise(F.lit(0))).over(
sql.Window.partitionBy("userid").orderBy(F.col("timestamp")).rowsBetween(-100, -1)), NUMBER_PRECISION).cast(
"float")) \
.withColumn("userCtr",
F.format_number(ctrUdf(F.col("userClickCount"), F.col("userExpCount")), NUMBER_PRECISION)) \
.filter(F.col("userRatingCount") > 1)
dataVocab[c + "Bucket"] = [str(float(i)) for i in range(11)] samples.show(10, truncate=False)
# 连续特征分桶
bucket_vocab = [str(i) for i in range(101)]
bucket_suffix = "_Bucket"
for col in ["userRatingCount", "userRatingAvg", "userClickCount", "userExpCount"]:
new_col = col + bucket_suffix
samples = samples.withColumn(new_col, numberToBucketUdf(F.col(col))) \
.drop(col) \
.withColumn(new_col, F.when(F.col(new_col).isNull(), "0").otherwise(F.col(new_col)))
dataVocab[new_col] = bucket_vocab
# 方差处理
number_suffix = "_number"
for col in ["userRatingStddev"]:
new_col = col + number_suffix
samples = samples.withColumn(new_col, F.when(F.col(col).isNull(), 0).otherwise(1 / (F.col(col) + 1))).drop(col)
for col in ["userCtr"]:
new_col = col + number_suffix
samples = samples.withColumn(col, F.when(F.col(col).isNull(), 0).otherwise(F.col(col))).withColumnRenamed(col,
new_col)
samples.printSchema() samples.printSchema()
# samples.show(5,truncate=False) samples.show(10, truncate=False)
return samples return samples
def addSampleLabel(ratingSamples): def addSampleLabel(ratingSamples):
ratingSamples = ratingSamples.withColumn('label', when(F.col('rating') >= 5, 1).otherwise(0)) ratingSamples = ratingSamples.withColumn('label', when(F.col('rating') >= 1, 1).otherwise(0))
# ratingSamples = ratingSamples.withColumn('label', when(F.col('rating') >= 5, 1).otherwise(0))
ratingSamples.show(5, truncate=False) ratingSamples.show(5, truncate=False)
ratingSamples.printSchema() ratingSamples.printSchema()
return ratingSamples return ratingSamples
def samplesNegAndUnion(samplesPos,samplesNeg):
def samplesNegAndUnion(samplesPos, samplesNeg):
# 正负样本 1:4 # 正负样本 1:4
pos_count = samplesPos.count() pos_count = samplesPos.count()
neg_count = samplesNeg.count() neg_count = samplesNeg.count()
...@@ -241,28 +375,30 @@ def samplesNegAndUnion(samplesPos,samplesNeg): ...@@ -241,28 +375,30 @@ def samplesNegAndUnion(samplesPos,samplesNeg):
print("dataSize:{}".format(str(dataSize))) print("dataSize:{}".format(str(dataSize)))
return samples return samples
def splitAndSaveTrainingTestSamplesByTimeStamp(samples,splitTimestamp, file_path):
def splitAndSaveTrainingTestSamplesByTimeStamp(samples, splitTimestamp, file_path):
samples = samples.withColumn("timestampLong", F.col("timestamp").cast(LongType())) samples = samples.withColumn("timestampLong", F.col("timestamp").cast(LongType()))
# quantile = smallSamples.stat.approxQuantile("timestampLong", [0.8], 0.05) # quantile = smallSamples.stat.approxQuantile("timestampLong", [0.8], 0.05)
# splitTimestamp = quantile[0] # splitTimestamp = quantile[0]
train = samples.where(F.col("timestampLong") <= splitTimestamp).drop("timestampLong") train = samples.where(F.col("timestampLong") <= splitTimestamp).drop("timestampLong")
test = samples.where(F.col("timestampLong") > splitTimestamp).drop("timestampLong") test = samples.where(F.col("timestampLong") > splitTimestamp).drop("timestampLong")
print("split train size:{},test size:{}".format(str(train.count()),str(test.count()))) print("split train size:{},test size:{}".format(str(train.count()), str(test.count())))
trainingSavePath = file_path + '_train' trainingSavePath = file_path + '_train'
testSavePath = file_path + '_test' testSavePath = file_path + '_test'
train.write.option("header", "true").option("delimiter", "|").mode('overwrite').csv(trainingSavePath) train.write.option("header", "true").option("delimiter", "|").mode('overwrite').csv(trainingSavePath)
test.write.option("header", "true").option("delimiter", "|").mode('overwrite').csv(testSavePath) test.write.option("header", "true").option("delimiter", "|").mode('overwrite').csv(testSavePath)
def collectColumnToVocab(samples,column):
def collectColumnToVocab(samples, column):
datas = samples.select(column).distinct().collect() datas = samples.select(column).distinct().collect()
vocabSet = set() vocabSet = set()
for d in datas: for d in datas:
if d[column]: if d[column]:
vocabSet.add(str(d[column])) vocabSet.add(str(d[column]))
vocabSet.add("-1") # 空值的默认
return list(vocabSet) return list(vocabSet)
def collectMutiColumnToVocab(samples,column):
def collectMutiColumnToVocab(samples, column):
datas = samples.select(column).distinct().collect() datas = samples.select(column).distinct().collect()
tagSet = set() tagSet = set()
for d in datas: for d in datas:
...@@ -273,72 +409,30 @@ def collectMutiColumnToVocab(samples,column): ...@@ -273,72 +409,30 @@ def collectMutiColumnToVocab(samples,column):
tagSet.add("-1") # 空值默认 tagSet.add("-1") # 空值默认
return list(tagSet) return list(tagSet)
def getDataVocab(samples,model_columns):
dataVocab = {}
multiVocab = {}
# 多值特征
for c in ITEM_MULTI_COLUMN_EXTRA_MAP.keys():
print(c)
multiVocab[c] = collectMutiColumnToVocab(samples,c)
samples = samples.drop(c)
# id类特征 和 类别特征
for c in ["userid"]:
print(c)
dataVocab[c] = collectColumnToVocab(samples,c)
for c in model_columns:
# 判断是否以Bucket结尾
if c.endswith("Bucket"):
datas = samples.select(c).distinct().collect()
vocabSet = set()
for d in datas:
if d[c]:
vocabSet.add(str(d[c]))
vocabSet.add("-1")# 空值的默认
dataVocab[c] = list(vocabSet)
# elif c.count("userRatedHistory") > 0:
# dataVocab[c] = dataVocab["itemid"]
else:
# 判断是否多值离散列
for cc, v in multiVocab.items():
if c.count(cc) > 0:
dataVocab[c] = v
return dataVocab
def dataVocabToRedis(dataVocab): def dataVocabToRedis(dataVocab):
conn = getRedisConn() conn = getRedisConn()
conn.set(FEATURE_VOCAB_KEY,dataVocab) conn.set(FEATURE_VOCAB_KEY, dataVocab)
conn.expire(FEATURE_VOCAB_KEY,60 * 60 * 24 * 7) conn.expire(FEATURE_VOCAB_KEY, 60 * 60 * 24 * 7)
def featureColumnsToRedis(columns): def featureColumnsToRedis(columns):
conn = getRedisConn() conn = getRedisConn()
conn.set(FEATURE_COLUMN_KEY, json.dumps(columns)) conn.set(FEATURE_COLUMN_KEY, json.dumps(columns))
conn.expire(FEATURE_COLUMN_KEY, 60 * 60 * 24 * 7) conn.expire(FEATURE_COLUMN_KEY, 60 * 60 * 24 * 7)
def featureToRedis(key,datas):
def featureToRedis(key, datas):
conn = getRedisConn() conn = getRedisConn()
for k,v in datas.items(): for k, v in datas.items():
newKey = key+k newKey = key + k
conn.set(newKey,v) conn.set(newKey, v)
conn.expire(newKey, 60 * 60 * 24 * 7) conn.expire(newKey, 60 * 60 * 24 * 7)
def collectFeaturesToDict(samples,columns,prefix): def userFeaturesToRedis(samples, columns, prefix, redisKey):
idCol = prefix+"id" idCol = prefix + "id"
timestampCol = idCol+"_timestamp" timestampCol = idCol + "_timestamp"
#根据timestamp获取每个user最新的记录
prefixSamples = samples.groupBy(idCol).agg(F.max("timestamp").alias(timestampCol))
resDatas = samples.join(prefixSamples, on=[idCol], how='left').where(F.col("timestamp") == F.col(timestampCol))
resDatas = resDatas.select(*columns).distinct().collect()
print(prefix,len(resDatas))
return {d[idCol]:json.dumps(d.asDict(),ensure_ascii=False) for d in resDatas}
def featuresToRedis(samples,columns,prefix,redisKey):
idCol = prefix+"id"
timestampCol = idCol+"_timestamp"
def toRedis(datas): def toRedis(datas):
conn = getRedisConn() conn = getRedisConn()
...@@ -349,19 +443,34 @@ def featuresToRedis(samples,columns,prefix,redisKey): ...@@ -349,19 +443,34 @@ def featuresToRedis(samples,columns,prefix,redisKey):
conn.set(newKey, v) conn.set(newKey, v)
conn.expire(newKey, 60 * 60 * 24 * 7) conn.expire(newKey, 60 * 60 * 24 * 7)
#根据timestamp获取每个user最新的记录 # 根据timestamp获取每个user最新的记录
prefixSamples = samples.groupBy(idCol).agg(F.max("timestamp").alias(timestampCol)) prefixSamples = samples.groupBy(idCol).agg(F.max("timestamp").alias(timestampCol))
resDatas = samples.join(prefixSamples, on=[idCol], how='left').where(F.col("timestamp") == F.col(timestampCol)) resDatas = prefixSamples.join(samples, on=[idCol], how='inner').where(F.col("timestamp") == F.col(timestampCol))
resDatas = resDatas.select(*columns).distinct() resDatas = resDatas.select(*columns).distinct()
resDatas.show(10, truncate=False)
print(prefix, resDatas.count()) print(prefix, resDatas.count())
resDatas.repartition(8).foreachPartition(toRedis) resDatas.repartition(8).foreachPartition(toRedis)
def itemFeaturesToRedis(itemStaticDF, redisKey):
idCol = "item_id"
def toRedis(datas):
conn = getRedisConn()
for d in datas:
k = d[idCol]
v = json.dumps(d.asDict(), ensure_ascii=False)
newKey = redisKey + k
conn.set(newKey, v)
conn.expire(newKey, 60 * 60 * 24 * 7)
itemStaticDF.repartition(8).foreachPartition(toRedis)
""" """
数据加载 数据加载
""" """
CONTENT_TYPE = "service" CONTENT_TYPE = "service"
SERVICE_HOSTS = [ SERVICE_HOSTS = [
{'host': "172.16.52.33", 'port': 9200}, {'host': "172.16.52.33", 'port': 9200},
...@@ -375,21 +484,24 @@ ES_INDEX_TEST = "gm_test-service-read" ...@@ -375,21 +484,24 @@ ES_INDEX_TEST = "gm_test-service-read"
ACTION_REG = r"""^\\d+$""" ACTION_REG = r"""^\\d+$"""
def getEsConn_test(): def getEsConn_test():
host_config = [{'host': '172.18.52.14', 'port': 9200}, {'host': '172.18.52.133', 'port': 9200}, host_config = [{'host': '172.18.52.14', 'port': 9200}, {'host': '172.18.52.133', 'port': 9200},
{'host': '172.18.52.7', 'port': 9200}] {'host': '172.18.52.7', 'port': 9200}]
return Elasticsearch(host_config, http_auth=('elastic', 'gm_test'), timeout=3600) return Elasticsearch(host_config, http_auth=('elastic', 'gm_test'), timeout=3600)
def getEsConn(): def getEsConn():
return Elasticsearch(SERVICE_HOSTS, http_auth=('elastic', 'gengmei!@#'), timeout=3600) return Elasticsearch(SERVICE_HOSTS, http_auth=('elastic', 'gengmei!@#'), timeout=3600)
def getClickSql(start, end): def getClickSql(start, end):
sql = """ sql = """
SELECT DISTINCT t1.partition_date, t1.cl_id device_id, t1.card_id,t1.time_stamp,t1.page_stay,t1.cl_type as os SELECT DISTINCT t1.partition_date, t1.cl_id device_id, t1.card_id,t1.time_stamp,t1.page_stay,t1.cl_type as os,t1.city_id as user_city_id
FROM FROM
( (
select partition_date,cl_id,business_id as card_id,time_stamp,page_stay,cl_type select partition_date,city_id,cl_id,business_id as card_id,time_stamp,page_stay,cl_type
from online.bl_hdfs_maidian_updates from online.bl_hdfs_maidian_updates
where action = 'page_view' where action = 'page_view'
AND partition_date>='{startDay}' and partition_date<='{endDay}' AND partition_date>='{startDay}' and partition_date<='{endDay}'
...@@ -399,7 +511,7 @@ def getClickSql(start, end): ...@@ -399,7 +511,7 @@ def getClickSql(start, end):
AND cl_id != '' AND cl_id != ''
AND business_id is not null AND business_id is not null
AND business_id != '' AND business_id != ''
group by partition_date,cl_id,business_id,time_stamp,page_stay,cl_type group by partition_date,city_id,cl_id,business_id,time_stamp,page_stay,cl_type
) AS t1 ) AS t1
join join
( --渠道，新老 ( --渠道，新老
...@@ -427,29 +539,31 @@ def getClickSql(start, end): ...@@ -427,29 +539,31 @@ def getClickSql(start, end):
)t3 )t3
on t3.device_id=t2.device_id on t3.device_id=t2.device_id
WHERE t3.device_id is null WHERE t3.device_id is null
""".format(startDay=start,endDay=end) """.format(startDay=start, endDay=end)
print(sql) print(sql)
return sql return sql
def getExposureSql(start, end): def getExposureSql(start, end):
sql = """ sql = """
SELECT DISTINCT t1.partition_date,t1.cl_id device_id,t1.card_id,t1.time_stamp, 0 as page_stay,cl_type as os SELECT DISTINCT t1.partition_date,t1.cl_id device_id,t1.card_id,t1.time_stamp, 0 as page_stay,cl_type as os,t1.city_id as user_city_id
from from
( --新首页卡片曝光 ( --新首页卡片曝光
SELECT partition_date,cl_id,card_id,time_stamp,cl_type SELECT partition_date,city_id,cl_type,cl_id,card_id,max(time_stamp) as time_stamp
FROM online.ml_community_precise_exposure_detail FROM online.ml_community_precise_exposure_detail
where partition_date>='{startDay}' and partition_date<='{endDay}' where partition_date>='{startDay}' and partition_date<='{endDay}'
and action in ('page_precise_exposure','home_choiceness_card_exposure') and action in ('page_precise_exposure','home_choiceness_card_exposure')
and cl_id IS NOT NULL and cl_id IS NOT NULL
and card_id IS NOT NULL and card_id IS NOT NULL
and is_exposure='1' and is_exposure='1'
and page_name='home' --and page_name='home'
and tab_name='精选' --and tab_name='精选'
--and page_name in ('home','search_result_more') --and page_name in ('home','search_result_more')
and ((page_name='home' and tab_name='精选') or (page_name='category' and tab_name = '商品'))
and card_type in ('card','video') and card_type in ('card','video')
and card_content_type in ('service') and card_content_type in ('service')
and (get_json_object(exposure_card,'$.in_page_pos') is null or get_json_object(exposure_card,'$.in_page_pos') != 'seckill') and (get_json_object(exposure_card,'$.in_page_pos') is null or get_json_object(exposure_card,'$.in_page_pos') != 'seckill')
group by partition_date,cl_id,card_id,time_stamp,cl_type group by partition_date,city_id,cl_type,cl_id,card_id,app_session_id
) t1 ) t1
join join
...@@ -478,10 +592,11 @@ def getExposureSql(start, end): ...@@ -478,10 +592,11 @@ def getExposureSql(start, end):
)t3 )t3
on t3.device_id=t2.device_id on t3.device_id=t2.device_id
WHERE t3.device_id is null WHERE t3.device_id is null
""".format(startDay=start,endDay=end) """.format(startDay=start, endDay=end)
print(sql) print(sql)
return sql return sql
def getClickSql2(start, end): def getClickSql2(start, end):
sql = """ sql = """
SELECT DISTINCT t1.partition_date, t1.cl_id device_id, t1.business_id card_id,t1.time_stamp time_stamp,t1.page_stay as page_stay SELECT DISTINCT t1.partition_date, t1.cl_id device_id, t1.business_id card_id,t1.time_stamp time_stamp,t1.page_stay as page_stay
...@@ -526,6 +641,7 @@ def getClickSql2(start, end): ...@@ -526,6 +641,7 @@ def getClickSql2(start, end):
print(sql) print(sql)
return sql return sql
def getExposureSql2(start, end): def getExposureSql2(start, end):
sql = """ sql = """
SELECT DISTINCT t1.partition_date,t1.cl_id device_id,t1.card_id,t1.time_stamp, 0 as page_stay SELECT DISTINCT t1.partition_date,t1.cl_id device_id,t1.card_id,t1.time_stamp, 0 as page_stay
...@@ -612,6 +728,7 @@ def getExposureSql2(start, end): ...@@ -612,6 +728,7 @@ def getExposureSql2(start, end):
print(sql) print(sql)
return sql return sql
def connectDoris(spark, table): def connectDoris(spark, table):
return spark.read \ return spark.read \
.format("jdbc") \ .format("jdbc") \
...@@ -622,6 +739,7 @@ def connectDoris(spark, table): ...@@ -622,6 +739,7 @@ def connectDoris(spark, table):
.option("password", "o5gbA27hXHHm") \ .option("password", "o5gbA27hXHHm") \
.load() .load()
def get_spark(appName): def get_spark(appName):
sparkConf = SparkConf() sparkConf = SparkConf()
sparkConf.set("spark.sql.crossJoin.enabled", True) sparkConf.set("spark.sql.crossJoin.enabled", True)
...@@ -641,14 +759,15 @@ def get_spark(appName): ...@@ -641,14 +759,15 @@ def get_spark(appName):
.getOrCreate()) .getOrCreate())
return spark return spark
def init_es_query(): def init_es_query():
q = { q = {
"_source": { "_source": {
"includes":[] "includes": []
}, },
"query": { "query": {
"bool": { "bool": {
"must": [], "must": [{"term": {"is_online": True}}],
"must_not": [], "must_not": [],
"should": [] "should": []
} }
...@@ -656,44 +775,55 @@ def init_es_query(): ...@@ -656,44 +775,55 @@ def init_es_query():
} }
return q return q
def parseSource(_source): def parseSource(_source):
id = str(_source.setdefault("id",-1)) id = str(_source.setdefault("id", -1))
smart_rank2 = _source.setdefault("smart_rank2",0.0) discount = _source.setdefault("discount", 0)
case_count = _source.setdefault("case_count",0) case_count = _source.setdefault("case_count", 0)
service_type = str(_source.setdefault("service_type",-1)) sales_count = _source.setdefault("sales_count", 0)
first_demands = ','.join(_source.setdefault("first_demands",[])) service_type = str(_source.setdefault("service_type", -1))
second_demands = ','.join(_source.setdefault("second_demands",[])) second_demands = ','.join(_source.setdefault("second_demands", ["-1"]))
first_solutions = ','.join(_source.setdefault("first_solutions",[])) second_solutions = ','.join(_source.setdefault("second_solutions", ["-1"]))
second_solutions = ','.join(_source.setdefault("second_solutions",[])) second_positions = ','.join(_source.setdefault("second_positions", ["-1"]))
first_positions = ','.join(_source.setdefault("first_positions",[])) tags_v3 = ','.join(_source.setdefault("tags_v3", ["-1"]))
second_positions = ','.join(_source.setdefault("second_positions",[]))
tags_v3 = ','.join(_source.setdefault("tags_v3",[])) # sku
ordered_user_ids_count = len(_source.setdefault("ordered_user_ids",[])) sku_list = _source.setdefault("sku_list", [])
sku_tags_list = []
lowest_price_arr = _source.setdefault("lowest_price",[]) sku_show_tags_list = []
lowest_price = lowest_price_arr[0].setdefault("price",0.0) if len(lowest_price_arr) > 0 else 0.0 sku_price_list = []
for sku in sku_list:
#merchant_id sku_tags_list += sku.setdefault("sku_tags", [])
merchant_id = _source.setdefault("merchant_id","-1") # sku_tags_list += sku.setdefault("sku_tags_id",[])
sku_show_tags_list.append(sku.setdefault("show_project_type_name", ""))
price = sku.setdefault("price", 0.0)
if price > 0:
sku_price_list.append(price)
# sku_tags = ",".join([str(i) for i in sku_tags_list]) if len(sku_tags_list) > 0 else "-1"
# sku_show_tags = ",".join(sku_show_tags_list) if len(sku_show_tags_list) > 0 else "-1"
sku_price = min(sku_price_list) if len(sku_price_list) > 0 else 0.0
# merchant_id
merchant_id = str(_source.setdefault("merchant_id", "-1"))
# doctor_type id famous_doctor # doctor_type id famous_doctor
doctor = _source.setdefault("doctor",{}) doctor = _source.setdefault("doctor", {})
doctor_type = doctor.setdefault("doctor_type","-1") doctor_type = str(doctor.setdefault("doctor_type", "-1"))
doctor_id = doctor.setdefault("id","-1") doctor_id = str(doctor.setdefault("id", "-1"))
doctor_famous = str(int(doctor.setdefault("famous_doctor",False))) doctor_famous = str(int(doctor.setdefault("famous_doctor", False)))
# hospital id city_tag_id hospital_type is_high_quality # hospital id city_tag_id hospital_type is_high_quality
hospital = doctor.setdefault("hospital", {}) hospital = doctor.setdefault("hospital", {})
hospital_id = hospital.setdefault("id", "-1") hospital_id = str(hospital.setdefault("id", "-1"))
hospital_city_tag_id = str(hospital.setdefault("city_tag_id", -1)) hospital_city_tag_id = str(hospital.setdefault("city_tag_id", -1))
hospital_type = hospital.setdefault("hospital_type", "-1") hospital_type = str(hospital.setdefault("hospital_type", "-1"))
hospital_is_high_quality = str(int(hospital.setdefault("is_high_quality", False))) hospital_is_high_quality = str(int(hospital.setdefault("is_high_quality", False)))
data = [id, data = [id,
lowest_price, discount,
smart_rank2,
case_count, case_count,
sales_count,
service_type, service_type,
ordered_user_ids_count,
merchant_id, merchant_id,
doctor_type, doctor_type,
doctor_id, doctor_id,
...@@ -702,20 +832,21 @@ def parseSource(_source): ...@@ -702,20 +832,21 @@ def parseSource(_source):
hospital_city_tag_id, hospital_city_tag_id,
hospital_type, hospital_type,
hospital_is_high_quality, hospital_is_high_quality,
first_demands,
second_demands, second_demands,
first_solutions,
second_solutions, second_solutions,
first_positions,
second_positions, second_positions,
tags_v3 tags_v3,
# sku_show_tags,
sku_price
] ]
return data return data
# es中获取特征 # es中获取特征
def get_service_feature_df(spark): def get_service_feature_df():
es_columns = ["id", "lowest_price", "smart_rank2", "doctor", "case_count", "service_type", "first_demands", "second_demands", "first_solutions", "second_solutions", "first_positions", "second_positions", "tags_v3","ordered_user_ids"] es_columns = ["id", "discount", "sales_count", "doctor", "case_count", "service_type", "merchant_id",
"second_demands", "second_solutions", "second_positions", "sku_list", "tags_v3"]
query = init_es_query() query = init_es_query()
query["_source"]["includes"] = es_columns query["_source"]["includes"] = es_columns
print(json.dumps(query), flush=True) print(json.dumps(query), flush=True)
...@@ -727,27 +858,16 @@ def get_service_feature_df(spark): ...@@ -727,27 +858,16 @@ def get_service_feature_df(spark):
_source = res['_source'] _source = res['_source']
data = parseSource(_source) data = parseSource(_source)
datas.append(data) datas.append(data)
print("item size:",len(datas)) print("item size:", len(datas))
dataRDD = spark.sparkContext.parallelize(datas) itemColumns = ['id', 'discount', 'case_count', 'sales_count', 'service_type', 'merchant_id',
itemColumns = ['id', 'lowest_price', 'smart_rank2', 'case_count', 'service_type', 'ordered_user_ids_count','merchant_id',
'doctor_type', 'doctor_id', 'doctor_famous', 'hospital_id', 'hospital_city_tag_id', 'hospital_type', 'doctor_type', 'doctor_id', 'doctor_famous', 'hospital_id', 'hospital_city_tag_id', 'hospital_type',
'hospital_is_high_quality', 'first_demands', 'second_demands', 'first_solutions', 'hospital_is_high_quality', 'second_demands', 'second_solutions', 'second_positions',
'second_solutions', 'first_positions', 'second_positions', 'tags_v3'] 'tags_v3', 'sku_price']
df = dataRDD.toDF(schema=itemColumns) # 'sku_tags','sku_show_tags','sku_price']
df = pd.DataFrame(datas, columns=itemColumns)
return df return df
# mysql中获取用户画像
def get_user_portrait(spark):
return spark.read \
.format("jdbc") \
.option("driver", "com.mysql.jdbc.Driver") \
.option("url", "jdbc:mysql://172.16.50.175:3306/doris_olap") \
.option("dbtable", "user_tag3_portrait") \
.option("user", "doris") \
.option("password", "o5gbA27hXHHm") \
.load()
def addDays(n, format="%Y%m%d"): def addDays(n, format="%Y%m%d"):
return (date.today() + timedelta(days=n)).strftime(format) return (date.today() + timedelta(days=n)).strftime(format)
...@@ -756,43 +876,55 @@ def addDays(n, format="%Y%m%d"): ...@@ -756,43 +876,55 @@ def addDays(n, format="%Y%m%d"):
if __name__ == '__main__': if __name__ == '__main__':
start = time.time() start = time.time()
#入参 # 入参
trainDays = int(sys.argv[1]) trainDays = int(sys.argv[1])
print('trainDays:{}'.format(trainDays),flush=True) print('trainDays:{}'.format(trainDays), flush=True)
endDay = addDays(0) endDay = addDays(0)
startDay = addDays(-int(trainDays)) startDay = addDays(-int(trainDays))
print("train_data start:{} end:{}".format(startDay,endDay)) print("train_data start:{} end:{}".format(startDay, endDay))
spark = get_spark("service_feature_csv_export") spark = get_spark("service_feature_csv_export")
spark.sparkContext.setLogLevel("ERROR") spark.sparkContext.setLogLevel("ERROR")
itemDF = get_service_feature_df()
print(itemDF.columns)
print(itemDF.head(10))
# 行为数据 # 行为数据
clickSql = getClickSql(startDay,endDay) clickSql = getClickSql(startDay, endDay)
expSql = getExposureSql(startDay,endDay) expSql = getExposureSql(startDay, endDay)
clickDF = spark.sql(clickSql) clickDF = spark.sql(clickSql)
expDF = spark.sql(expSql) expDF = spark.sql(expSql)
# ratingDF = samplesNegAndUnion(clickDF,expDF) # ratingDF = samplesNegAndUnion(clickDF,expDF)
ratingDF = clickDF.union(expDF) ratingDF = clickDF.union(expDF)
ratingDF = ratingDF.withColumnRenamed("time_stamp", "timestamp")\ ratingDF = ratingDF.withColumnRenamed("time_stamp", "timestamp") \
.withColumnRenamed("device_id", "userid")\ .withColumnRenamed("device_id", "userid") \
.withColumnRenamed("card_id", "itemid")\ .withColumnRenamed("card_id", "item_id") \
.withColumnRenamed("page_stay", "rating")\ .withColumnRenamed("page_stay", "rating") \
.withColumnRenamed("os", "user_os") .withColumnRenamed("os", "user_os") \
.withColumn("user_city_id", F.when(F.col("user_city_id").isNull(), "-1").otherwise(F.col("user_city_id"))) \
.withColumn("timestamp", F.col("timestamp").cast("long"))
print(ratingDF.columns) print(ratingDF.columns)
print(ratingDF.show(10, truncate=False)) print(ratingDF.show(10, truncate=False))
itemDF = get_service_feature_df(spark)
print(itemDF.columns)
print(itemDF.show(10, truncate=False))
print("添加label...") print("添加label...")
ratingSamplesWithLabel = addSampleLabel(ratingDF) ratingSamplesWithLabel = addSampleLabel(ratingDF)
posCount = ratingSamplesWithLabel.filter(F.col("label")==1).count() df = ratingSamplesWithLabel.toPandas()
negCount = ratingSamplesWithLabel.filter(F.col("label")==0).count() df = pd.DataFrame(df)
print("pos size:"+str(posCount),"neg size:"+str(negCount))
posCount = df.loc[df["label"] == 1]["label"].count()
negCount = df.loc[df["label"] == 0]["label"].count()
print("pos size:" + str(posCount), "neg size:" + str(negCount))
itemDF = get_service_feature_df()
print(itemDF.columns)
print(itemDF.head(10))
# itemDF.to_csv("/tmp/service_{}.csv".format(endDay))
# df.to_csv("/tmp/service_train_{}.csv".format(endDay))
# 数据字典 # 数据字典
dataVocab = {} dataVocab = {}
...@@ -800,22 +932,39 @@ if __name__ == '__main__': ...@@ -800,22 +932,39 @@ if __name__ == '__main__':
print("处理item特征...") print("处理item特征...")
timestmp1 = int(round(time.time())) timestmp1 = int(round(time.time()))
samplesWithItemFeatures = addItemFeatures(ratingSamplesWithLabel, itemDF, dataVocab,multiVocab) itemDF = addItemFeatures(itemDF, dataVocab, multiVocab)
timestmp2 = int(round(time.time())) timestmp2 = int(round(time.time()))
print("处理item特征, 耗时s:{}".format(timestmp2 - timestmp1)) print("处理item特征, 耗时s:{}".format(timestmp2 - timestmp1))
print("multiVocab:") print("multiVocab:")
print(multiVocab.keys()) for k, v in multiVocab.items():
print(k, len(v))
print("dataVocab:")
for k, v in dataVocab.items():
print(k, len(v))
itemDF_spark = spark.createDataFrame(itemDF)
itemDF_spark.printSchema()
itemDF_spark.show(10, truncate=False)
# item统计特征处理
itemStaticDF = addItemStaticFeatures(ratingSamplesWithLabel, itemDF_spark, dataVocab)
# 统计数据处理
# ratingSamplesWithLabel = addStaticsFeatures(ratingSamplesWithLabel,dataVocab)
samples = ratingSamplesWithLabel.join(itemStaticDF, on=['item_id'], how='inner')
print("处理user特征...") print("处理user特征...")
samplesWithUserFeatures = addUserFeatures(samplesWithItemFeatures,dataVocab,multiVocab) samplesWithUserFeatures = addUserFeatures(samples, dataVocab, multiVocab)
timestmp3 = int(round(time.time())) timestmp3 = int(round(time.time()))
print("处理user特征, 耗时s:{}".format(timestmp3 - timestmp2)) print("处理user特征, 耗时s:{}".format(timestmp3 - timestmp2))
#
# user columns # user columns
user_columns = [c for c in samplesWithUserFeatures.columns if c.startswith("user")] user_columns = [c for c in samplesWithUserFeatures.columns if c.startswith("user")]
print("collect feature for user:{}".format(str(user_columns))) print("collect feature for user:{}".format(str(user_columns)))
# item columns # item columns
item_columns = [c for c in samplesWithUserFeatures.columns if c.startswith("item")] item_columns = [c for c in itemStaticDF.columns if c.startswith("item")]
print("collect feature for item:{}".format(str(item_columns))) print("collect feature for item:{}".format(str(item_columns)))
# model columns # model columns
print("model columns to redis...") print("model columns to redis...")
...@@ -828,33 +977,27 @@ if __name__ == '__main__': ...@@ -828,33 +977,27 @@ if __name__ == '__main__':
dataVocabStr = json.dumps(dataVocab, ensure_ascii=False) dataVocabStr = json.dumps(dataVocab, ensure_ascii=False)
open(configUtils.VOCAB_PATH, mode='w', encoding='utf-8').write(dataVocabStr) open(configUtils.VOCAB_PATH, mode='w', encoding='utf-8').write(dataVocabStr)
# item特征数据存入redis
itemFeaturesToRedis(itemStaticDF, FEATURE_ITEM_KEY)
timestmp6 = int(round(time.time()))
print("item feature to redis 耗时s:{}".format(timestmp6 - timestmp3))
"""特征数据存入redis======================================""" """特征数据存入redis======================================"""
# user特征数据存入redis # user特征数据存入redis
featuresToRedis(samplesWithUserFeatures, user_columns, "user", FEATURE_USER_KEY) userFeaturesToRedis(samplesWithUserFeatures, user_columns, "user", FEATURE_USER_KEY)
timestmp5 = int(round(time.time())) timestmp5 = int(round(time.time()))
print("user feature to redis 耗时s:{}".format(timestmp5 - timestmp3)) print("user feature to redis 耗时s:{}".format(timestmp5 - timestmp6))
# userDatas = collectFeaturesToDict(samplesWithUserFeatures, user_columns, "user")
# featureToRedis(FEATURE_USER_KEY, userDatas)
# itemDatas = collectFeaturesToDict(samplesWithUserFeatures, item_columns, "item")
# featureToRedis(FEATURE_ITEM_KEY, itemDatas)
# item特征数据存入redis
# todo 添加最近一个月有行为的item，待优化:扩大item范围
featuresToRedis(samplesWithUserFeatures, item_columns, "item", FEATURE_ITEM_KEY)
timestmp6 = int(round(time.time()))
print("item feature to redis 耗时s:{}".format(timestmp6 - timestmp5))
"""训练数据保存 ======================================""" """训练数据保存 ======================================"""
timestmp3 = int(round(time.time())) timestmp3 = int(round(time.time()))
train_columns = model_columns + ["label", "timestamp", "rating"] train_columns = model_columns + ["label", "timestamp", "rating"]
trainSamples = samplesWithUserFeatures.select(*train_columns) trainSamples = samplesWithUserFeatures.select(*train_columns)
print("write to hdfs start...") train_df = trainSamples.toPandas()
splitTimestamp = int(time.mktime(time.strptime(addDays(0), "%Y%m%d"))) train_df = pd.DataFrame(train_df)
splitAndSaveTrainingTestSamplesByTimeStamp(trainSamples, splitTimestamp, TRAIN_FILE_PATH) train_df.to_csv(DATA_PATH_TRAIN, sep="|")
print("write to hdfs success...")
timestmp4 = int(round(time.time())) timestmp4 = int(round(time.time()))
print("数据写入hdfs 耗时s:{}".format(timestmp4 - timestmp3)) print("训练数据写入success 耗时s:{}".format(timestmp4 - timestmp3))
print("总耗时m:{}".format((timestmp4 - start)/60)) print("总耗时m:{}".format((timestmp4 - start) / 60))
spark.stop() spark.stop()
\ No newline at end of file
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