service model 优化

shell/featureeng_export.sh

@@ -14,3 +14,5 @@ if [ -n "${spark_mode}" ]; then
   /opt/spark/bin/spark-submit --master yarn --deploy-mode client --queue root.strategy --driver-memory 8g --executor-memory 2g --executor-cores 1 --num-executors 8 --conf spark.pyspark.python=${python_os} --conf spark.default.parallelism=100 --conf spark.storage.memoryFraction=0.5 --conf spark.shuffle.memoryFraction=0.3 --conf spark.locality.wait=0 --jars /srv/apps/tispark-core-2.1-SNAPSHOT-jar-with-dependencies.jar,/srv/apps/spark-connector_2.11-1.9.0-rc2.jar,/srv/apps/mysql-connector-java-5.1.38.jar ${pythonFile} $day_count
 fi
 
+#/opt/spark/bin/spark-submit --master local[4] --deploy-mode client --driver-memory 8g --executor-memory 2g --executor-cores 1 --num-executors 4 --conf spark.pyspark.python=/srv/envs/serviceRec/bin/python --conf spark.default.parallelism=100 --conf spark.storage.memoryFraction=0.5 --conf spark.shuffle.memoryFraction=0.3 --conf spark.locality.wait=0 --jars /srv/apps/tispark-core-2.1-SNAPSHOT-jar-with-dependencies.jar,/srv/apps/spark-connector_2.11-1.9.0-rc2.jar,/srv/apps/mysql-connector-java-5.1.38.jar ${pythonFile} $day_count
+

spark/featureEng2.py

@@ -73,85 +73,118 @@ def getRedisConn():
     # conn = redis.Redis(host="172.18.51.10", port=6379,db=0) #test
     return conn
 
-def addItemFeatures(samples,itemDF,dataVocab,multiVocab):
-
-    itemDF = itemDF.withColumnRenamed("id", "itemid")
-    # 数据过滤：无医生
-    itemDF = itemDF.filter(col("doctor_id") != "-1")
-
-    # itemid
-    vocabList = collectColumnToVocab(itemDF, "itemid")
-    dataVocab["itemid"] = vocabList
-
-    # null处理
-    for c in ITEM_NUMBER_COLUMNS:
-        print("null count:",c,itemDF.filter(col(c).isNull()).count())
-        itemDF = itemDF.withColumn(ITEM_PREFIX+c,when(col(c).isNull(),0).otherwise(col(c)).cast("float")).drop(c)
-
-    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
-    for c, v in ITEM_MULTI_COLUMN_EXTRA_MAP.items():
-        print("null count:", c, itemDF.filter(col(c).isNull()).count())
-        itemDF = itemDF.withColumn(c, F.when(F.col(c).isNull(), "-1").otherwise(F.col(c)))
-
-        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]
-
-    samples = samples.join(itemDF, on=['itemid'], how='inner')
-    # 统计特征处理
-    print("统计特征处理...")
-    staticFeatures = samples.groupBy('itemid').agg(F.count(F.lit(1)).alias('itemRatingCount'),
-                                                   F.avg(F.col('rating')).alias('itemRatingAvg'),
-                                                   F.stddev(F.col('rating')).alias('itemRatingStddev')).fillna(0) \
-        .withColumn('itemRatingStddev', F.format_number(F.col('itemRatingStddev'), NUMBER_PRECISION).cast("float")) \
-        .withColumn('itemRatingAvg', F.format_number(F.col('itemRatingAvg'), NUMBER_PRECISION).cast("float"))
-
-    # join item rating features
-    samples = samples.join(staticFeatures, on=['itemid'], how='left')
-
-    print("连续特征处理...")
-    # todo 分桶比较耗时，可以考虑做非线性转换
-    # 连续特征处理
-    pipelineStage = []
-    # Normalization
-    # for c in ["itemRatingAvg","itemRatingStddev"]:
-    #     pipelineStage.append(MinMaxScaler(inputCol=c, outputCol=c+"Scale"))
-
-    # bucketing
-    bucketColumns = [ITEM_PREFIX+"case_count", ITEM_PREFIX+"ordered_user_ids_count", ITEM_PREFIX+"lowest_price", "itemRatingCount", "itemRatingStddev","itemRatingAvg"]
-    for c in bucketColumns:
-        pipelineStage.append(QuantileDiscretizer(numBuckets=10, inputCol=c, outputCol=c + "Bucket"))
+def parseTags(tags,i):
+    tags_arr = tags.split(",")
+    if len(tags_arr) >= i:
+        return tags_arr[i-1]
+    else:
+        return "-1"
+
+def numberToBucket(num):
+    res = 0
+    if num >= 1000:
+        res = 1000//10
+    else:
+        res = int(num)//10
+    return str(res)
+
+def priceToBucket(num):
+    res = 0
+    if num >= 100000:
+        res = 100000//1000
+    else:
+        res = int(num)//1000
+    return str(res)
+
+
+numberToBucketUdf = F.udf(numberToBucket, FloatType())
+priceToBucketUdf = F.udf(priceToBucket, FloatType())
+
+def addStaticsFeatures(samples,dataVocab):
+    print("user统计特征处理...")
+    samples = samples \
+        .withColumn('userRatingCount',F.format_number(F.count(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.count(F.collect_list(when(F.col('label') == 1, F.lit(1)).otherwise(F.lit(None))).over(sql.Window.partitionBy("userid").orderBy(F.col("timestamp")).rowsBetween(-100, -1))),NUMBER_PRECISION).cast("float")) \
+        .withColumn("userExpCount", F.format_number(F.count(F.collect_list(when(F.col('label') == 0, F.lit(1)).otherwise(F.lit(None))).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)
 
-    featurePipeline = Pipeline(stages=pipelineStage)
-    samples = featurePipeline.fit(samples).transform(samples)
+    print("item统计特征处理...")
+    samples = samples \
+        .withColumn('itemRatingCount',F.format_number(F.count(F.lit(1)).over(sql.Window.partitionBy('item_id').orderBy('timestamp').rowsBetween(-100, -1))), NUMBER_PRECISION).cast("float") \
+        .withColumn("itemRatingAvg", F.format_number(F.avg(F.col("rating")).over(sql.Window.partitionBy('item_id').orderBy('timestamp').rowsBetween(-100, -1)), NUMBER_PRECISION).cast("float")) \
+        .withColumn("itemRatingStddev", F.format_number(F.stddev(F.col("rating")).over(sql.Window.partitionBy('item_id').orderBy('timestamp').rowsBetween(-100, -1)),NUMBER_PRECISION).cast("float")) \
+        .withColumn("itemClickCount", F.format_number(F.count(F.collect_list(when(F.col('label') == 1, F.lit(1)).otherwise(F.lit(None))).over(sql.Window.partitionBy("item_id").orderBy(F.col("timestamp")).rowsBetween(-100, -1))),NUMBER_PRECISION).cast("float")) \
+        .withColumn("itemExpCount", F.format_number(F.count(F.collect_list(when(F.col('label') == 0, F.lit(1)).otherwise(F.lit(None))).over(sql.Window.partitionBy("item_id").orderBy(F.col("timestamp")).rowsBetween(-100, -1))),NUMBER_PRECISION).cast("float")) \
+        .withColumn("itemCtr", F.format_number(F.col("itemClickCount")/(F.col("itemExpCount")+1),NUMBER_PRECISION).cast("float")) \
+
+    # 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","itemRatingCount","itemRatingAvg","itemClickCount","itemExpCount"]:
+        new_col = col + bucket_suffix
+        samples = samples.withColumn(new_col, numberToBucketUdf(F.col(col))).drop(col)
+        dataVocab[new_col] = bucket_vocab
+
+    # 方差处理
+    number_suffix = "_number"
+    for col in ["userRatingStddev","itemRatingStddev"]:
+        new_col = col + number_suffix
+        samples = samples.withColumn(new_col,1/(F.col(col)+1)).drop(col)
+    for col in ["userCtr", "itemCtr"]:
+        new_col = col + number_suffix
+        samples = samples.withColumnRenamed(col, new_col)
 
-    # 转string
-    for c in bucketColumns:
-        samples = samples.withColumn(c + "Bucket",F.col(c + "Bucket").cast("string")).drop(c)
+    samples.printSchema()
+    return samples
 
-        dataVocab[c + "Bucket"] = [str(float(i)) for i in range(11)]
+def addItemFeatures(itemDF,dataVocab,multi_col_vocab):
+    # multi_col = ['sku_tags', 'sku_show_tags','second_demands', 'second_solutions', 'second_positions']
+    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']
 
+    for col in onehot_col:
+        new_c = ITEM_PREFIX + col
+        dataVocab[new_c] = list(set(itemDF[col].tolist()))
 
-    samples.printSchema()
-    # samples.show(5, truncate=False)
+    for c in multi_col:
+        multi_col_vocab[c] = list(set(itemDF[c].tolist()))
 
-    return samples
+        for i in range(1, 6):
+            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 = itemDF.withColumn(new_col, numberToBucketUdf(F.col(col))).drop(col)
+        dataVocab[new_col] = bucket_vocab
+
+    for col in ['sku_price']:
+        new_col = ITEM_PREFIX + col + bucket_suffix
+        itemDF = itemDF.withColumn(new_col, priceToBucketUdf(F.col(col))).drop(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 = itemDF.withColumnRenamed(col, new_col)
+
+    itemDF.show(10, truncate=False)
+    return itemDF
 
 def extractTags(genres_list):
+    # 根据点击列表顺序加权
     genres_dict = defaultdict(int)
-    for genres in genres_list:
+    for i,genres in enumerate(genres_list):
         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)
     return [x[0] for x in sortedGenres]
 
@@ -162,64 +195,32 @@ def arrayReverse(arr):
 
 def addUserFeatures(samples,dataVocab,multiVocab):
     dataVocab["userid"] = collectColumnToVocab(samples, "userid")
-    dataVocab["user_os"] = ["ios","android","-1"]
+    dataVocab["user_os"] = ["ios","android"]
 
     extractTagsUdf = F.udf(extractTags, ArrayType(StringType()))
-    arrayReverseUdf = F.udf(arrayReverse, ArrayType(StringType()))
-    samples = samples.withColumnRenamed("cl_id","userid")
+    # arrayReverseUdf = F.udf(arrayReverse, ArrayType(StringType()))
     print("user历史数据处理...")
     # user历史记录
     samples = samples\
-        .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))) \
-        .withColumn("userPositiveHistory", arrayReverseUdf(F.col("userPositiveHistory")))
+        .withColumn('userPositiveHistory',F.collect_list(when(F.col('label') == 1, F.col('item_id')).otherwise(F.lit(None))).over(sql.Window.partitionBy("userid").orderBy(F.col("timestamp")).rowsBetween(-100, -1))) \
+        .withColumn("userPositiveHistory", F.reverse(F.col("userPositiveHistory")))
     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"]
+        dataVocab["userRatedHistory"+str(i)] = dataVocab["item_id"]
     samples = samples.drop("userPositiveHistory")
 
-    # user历史点击分值统计
-    print("统计特征处理...")
-    samples = samples\
-        .withColumn('userRatingCount',F.count(F.lit(1)).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1))) \
-        .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")) \
-        .filter(F.col("userRatingCount") > 1)
-
     # user偏好
-    for c,v in USER_MULTI_COLUMN_EXTRA_MAP.items():
+    for c,v in multiVocab.items():
         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))))
-        for i in range(1, v+1):
+        for i in range(1, 6):
             samples = samples.withColumn(new_col + "__" + str(i),F.when(F.col(new_col)[i - 1].isNotNull(), F.col(new_col)[i - 1]).otherwise("-1"))
-
-            dataVocab[new_col + "__" + str(i)] = multiVocab[c]
+            dataVocab[new_col + "__" + str(i)] = v
 
         samples = samples.drop(new_col)
-        # .drop(c).drop(new_col)
-
-    print("连续特征处理...")
-    pipelineStage = []
-
-    # Normalization
-    # for c in ["userRatingAvg", "userRatingStddev"]:
-    #     pipelineStage.append(MinMaxScaler(inputCol=c, outputCol=c + "Scale"))
-
-    # bucketing
-    bucketColumns = ["userRatingCount","userRatingAvg","userRatingStddev"]
-    for c in bucketColumns:
-        pipelineStage.append(QuantileDiscretizer(numBuckets=10, inputCol=c, outputCol=c + "Bucket"))
-
-    featurePipeline = Pipeline(stages=pipelineStage)
-    samples = featurePipeline.fit(samples).transform(samples)
-    # 转string
-    for c in bucketColumns:
-        samples = samples.withColumn(c + "Bucket", F.col(c + "Bucket").cast("string")).drop(c)
-
-        dataVocab[c + "Bucket"] = [str(float(i)) for i in range(11)]
 
     samples.printSchema()
-    # samples.show(5,truncate=False)
-
+    samples.show(10,truncate=False)
     return samples
 
 
@@ -255,6 +256,8 @@ def splitAndSaveTrainingTestSamplesByTimeStamp(samples,splitTimestamp, file_path
     test.write.option("header", "true").option("delimiter", "|").mode('overwrite').csv(testSavePath)
 
 def collectColumnToVocab(samples,column):
+    pd.DataFrame()[""].tolist()
+    list(set(samples[column].tolist()))
     datas = samples.select(column).distinct().collect()
     vocabSet = set()
     for d in datas:
@@ -300,7 +303,7 @@ def getDataVocab(samples,model_columns):
             vocabSet.add("-1")# 空值的默认
             dataVocab[c] = list(vocabSet)
         # elif c.count("userRatedHistory") > 0:
-        #     dataVocab[c] = dataVocab["itemid"]
+        #     dataVocab[c] = dataVocab["item_id"]
         else:
             # 判断是否多值离散列
             for cc, v in multiVocab.items():
@@ -660,6 +663,7 @@ def init_es_query():
 
 def parseSource(_source):
     id = str(_source.setdefault("id",-1))
+    discount = _source.setdefault("discount",0)
     case_count = _source.setdefault("case_count",0)
     sales_count = _source.setdefault("sales_count",0)
     service_type = str(_source.setdefault("service_type",-1))
@@ -678,7 +682,7 @@ def parseSource(_source):
         sku_price_list.append(sku.setdefault("price",0.0))
 
     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_show_tags = ",".join(sku_show_tags_list) if len(sku_show_tags_list) > 0 else "-1"
     sku_price = min(sku_price_list)
 
     #merchant_id
@@ -697,6 +701,7 @@ def parseSource(_source):
     hospital_is_high_quality = str(int(hospital.setdefault("is_high_quality", False)))
 
     data = [id,
+            discount,
             case_count,
             sales_count,
             service_type,
@@ -712,7 +717,7 @@ def parseSource(_source):
             second_solutions,
             second_positions,
             sku_tags,
-            sku_show_tags,
+            # sku_show_tags,
             sku_price
     ]
 
@@ -720,7 +725,7 @@ def parseSource(_source):
 
 # es中获取特征
 def get_service_feature_df():
-    es_columns = ["id", "sales_count", "doctor", "case_count", "service_type","merchant_id","second_demands", "second_solutions", "second_positions", "sku_list"]
+    es_columns = ["id","discount", "sales_count", "doctor", "case_count", "service_type","merchant_id","second_demands", "second_solutions", "second_positions", "sku_list"]
     query = init_es_query()
     query["_source"]["includes"] = es_columns
     print(json.dumps(query), flush=True)
@@ -734,10 +739,11 @@ def get_service_feature_df():
         datas.append(data)
     print("item size:",len(datas))
 
-    itemColumns = ['id', 'case_count', 'sales_count', 'service_type','merchant_id',
+    itemColumns = ['id','discount', 'case_count', 'sales_count', 'service_type','merchant_id',
                    'doctor_type', 'doctor_id', 'doctor_famous', 'hospital_id', 'hospital_city_tag_id', 'hospital_type',
                    'hospital_is_high_quality', 'second_demands','second_solutions', 'second_positions',
-                   'sku_tags','sku_show_tags','sku_price']
+                   'tags_v3','sku_price']
+                   # 'sku_tags','sku_show_tags','sku_price']
     df = pd.DataFrame(datas,columns=itemColumns)
     return df
 
@@ -769,7 +775,7 @@ if __name__ == '__main__':
     ratingDF = clickDF.union(expDF)
     ratingDF = ratingDF.withColumnRenamed("time_stamp", "timestamp")\
         .withColumnRenamed("device_id", "userid")\
-        .withColumnRenamed("card_id", "itemid")\
+        .withColumnRenamed("card_id", "item_id")\
         .withColumnRenamed("page_stay", "rating")\
         .withColumnRenamed("os", "user_os")
 
@@ -785,44 +791,48 @@ if __name__ == '__main__':
     negCount = df.loc[df["label"]==1]["label"].count()
     print("pos size:"+str(posCount),"neg size:"+str(negCount))
 
-    itemDF = get_service_feature_df(spark)
+    #统计数据处理
+    ratingSamplesWithLabel = addStaticsFeatures(ratingSamplesWithLabel)
+
+    itemDF = get_service_feature_df()
     print(itemDF.columns)
     print(itemDF.show(10, truncate=False))
-    itemDF.to_csv("/tmp/service_{}.csv".format(endDay))
-    df.to_csv("/tmp/service_train_{}.csv".format(endDay))
+    # itemDF.to_csv("/tmp/service_{}.csv".format(endDay))
+    # df.to_csv("/tmp/service_train_{}.csv".format(endDay))
 
 
+    # 数据字典
+    dataVocab = {}
+    multiVocab = {}
 
-    # # 数据字典
-    # dataVocab = {}
-    # multiVocab = {}
-    #
-    # print("处理item特征...")
-    # timestmp1 = int(round(time.time()))
-    # samplesWithItemFeatures = addItemFeatures(ratingSamplesWithLabel, itemDF, dataVocab,multiVocab)
-    # timestmp2 = int(round(time.time()))
-    # print("处理item特征, 耗时s:{}".format(timestmp2 - timestmp1))
-    # print("multiVocab:")
-    # print(multiVocab.keys())
-    #
-    # print("处理user特征...")
-    # samplesWithUserFeatures = addUserFeatures(samplesWithItemFeatures,dataVocab,multiVocab)
-    # timestmp3 = int(round(time.time()))
-    # print("处理user特征, 耗时s:{}".format(timestmp3 - timestmp2))
-    #
-    # # user columns
-    # user_columns = [c for c in samplesWithUserFeatures.columns if c.startswith("user")]
-    # print("collect feature for user:{}".format(str(user_columns)))
-    # # item columns
-    # item_columns = [c for c in samplesWithUserFeatures.columns if c.startswith("item")]
-    # print("collect feature for item:{}".format(str(item_columns)))
-    # # model columns
-    # print("model columns to redis...")
-    # model_columns = user_columns + item_columns
-    # featureColumnsToRedis(model_columns)
+    print("处理item特征...")
+    timestmp1 = int(round(time.time()))
+    itemDF = addItemFeatures(itemDF, dataVocab,multiVocab)
+    timestmp2 = int(round(time.time()))
+    print("处理item特征, 耗时s:{}".format(timestmp2 - timestmp1))
+    print("multiVocab:")
+    print(multiVocab.keys())
+
+    samples = ratingSamplesWithLabel.join(itemDF, on=['item_id'], how='inner')
+
+    print("处理user特征...")
+    samplesWithUserFeatures = addUserFeatures(samples,dataVocab,multiVocab)
+    timestmp3 = int(round(time.time()))
+    print("处理user特征, 耗时s:{}".format(timestmp3 - timestmp2))
     #
-    # print("数据字典save...")
-    # print("dataVocab:", str(dataVocab.keys()))
+    # user columns
+    user_columns = [c for c in samplesWithUserFeatures.columns if c.startswith("user")]
+    print("collect feature for user:{}".format(str(user_columns)))
+    # item columns
+    item_columns = [c for c in samplesWithUserFeatures.columns if c.startswith("item")]
+    print("collect feature for item:{}".format(str(item_columns)))
+    # model columns
+    print("model columns to redis...")
+    model_columns = user_columns + item_columns
+    featureColumnsToRedis(model_columns)
+
+    print("数据字典save...")
+    print("dataVocab:", str(dataVocab.keys()))
     # vocab_path = "../vocab/{}_vocab.json".format(VERSION)
     # dataVocabStr = json.dumps(dataVocab, ensure_ascii=False)
     # open(configUtils.VOCAB_PATH, mode='w', encoding='utf-8').write(dataVocabStr)