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归一化

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本文为您介绍Designer提供的归一化组件。

组件配置

您可以使用以下任意一种方式,配置归一化组件参数。

方式一:可视化方式

Designer工作流页面配置组件参数。

页签

参数

描述

字段设置

默认全选

默认全选,多余列不影响预测结果。

保留原始列

处理过的列增加“stdized_”前缀。支持DOUBLE类型与BIGINT类型。

执行调优

计算核心数

系统根据输入数据量,自动分配训练的实例数量。

每个核内存

系统根据输入数据量,自动分配内存。单位为MB。

方式二:PAI命令方式

使用PAI命令方式,配置该组件参数。您可以使用SQL脚本组件进行PAI命令调用,详情请参见SQL脚本

  • 稠密数据的命令

    PAI -name Normalize
        -project algo_public
        -DkeepOriginal="true"
        -DoutputTableName="test_4"
        -DinputTablePartitions="pt=20150501"
        -DinputTableName="bank_data_partition"
        -DselectedColNames="emp_var_rate,euribor3m"
  • 稀疏数据的命令

    PAI -name Normalize
        -project projectxlib4
        -DkeepOriginal="true"
        -DoutputTableName="kv_norm_output"
        -DinputTableName=kv_norm_test
        -DselectedColNames="f0,f1,f2"
        -DenableSparse=true
        -DoutputParaTableName=kv_norm_model
        -DkvIndices=1,2,8,6
        -DitemDelimiter=",";

参数名称

是否必选

参数描述

默认值

inputTableName

输入表的表名。

selectedColNames

输入表中,参与训练的列。列名以英文逗号(,)分隔,支持INT和DOUBLE类型。如果输入为稀疏格式,则支持STRING类型的列。

所有列

inputTablePartitions

输入表中,参与训练的分区。支持以下格式:

  • Partition_name=value

  • name1=value1/name2=value2:多级格式

说明

如果指定多个分区,则使用英文逗号(,)分隔。

所有分区

outputTableName

输出结果表。

outputParaTableName

配置输出表。

输出表1为非分区表

inputParaTableName

配置输入表。

keepOriginal

是否保留原始列:

  • true:处理过的列重命名(”normalized_”前缀),原始列保留。

  • false:全部列保留且不重命名。

false

lifecycle

输出表的生命周期,取值范围为[1,3650]

coreNum

计算的核心数目,取值为正整数。

系统自动分配

memSizePerCore

每个核心的内存(单位是兆),取值范围为(1, 65536)

系统自动分配

enableSparse

是否打开稀疏支持:

  • true

  • false

false

itemDelimiter

KV对之间分隔符。

默认”,”

kvDelimiter

Key和Value之间分隔符。

默认”:”

kvIndices

KV表中需要归一化的特征索引。

示例

  • 数据生成

    drop table if exists normalize_test_input;
    create table normalize_test_input(
        col_string string,
        col_bigint bigint,
        col_double double,
        col_boolean boolean,
        col_datetime datetime);
    insert overwrite table normalize_test_input
    select
        *
    from
    (
        select
            '01' as col_string,
            10 as col_bigint,
            10.1 as col_double,
            True as col_boolean,
            cast('2016-07-01 10:00:00' as datetime) as col_datetime
        from dual
        union all
            select
                cast(null as string) as col_string,
                11 as col_bigint,
                10.2 as col_double,
                False as col_boolean,
                cast('2016-07-02 10:00:00' as datetime) as col_datetime
            from dual
        union all
            select
                '02' as col_string,
                cast(null as bigint) as col_bigint,
                10.3 as col_double,
                True as col_boolean,
                cast('2016-07-03 10:00:00' as datetime) as col_datetime
            from dual
        union all
            select
                '03' as col_string,
                12 as col_bigint,
                cast(null as double) as col_double,
                False as col_boolean,
                cast('2016-07-04 10:00:00' as datetime) as col_datetime
            from dual
        union all
            select
                '04' as col_string,
                13 as col_bigint,
                10.4 as col_double,
                cast(null as boolean) as col_boolean,
                cast('2016-07-05 10:00:00' as datetime) as col_datetime
            from dual
        union all
            select
                '05' as col_string,
                14 as col_bigint,
                10.5 as col_double,
                True as col_boolean,
                cast(null as datetime) as col_datetime
            from dual
    ) tmp;
  • PAI命令行

    drop table if exists normalize_test_input_output;
    drop table if exists normalize_test_input_model_output;
    PAI -name Normalize
        -project algo_public
        -DoutputParaTableName="normalize_test_input_model_output"
        -Dlifecycle="28"
        -DoutputTableName="normalize_test_input_output"
        -DinputTableName="normalize_test_input"
        -DselectedColNames="col_double,col_bigint"
        -DkeepOriginal="true";
    drop table if exists normalize_test_input_output_using_model;
    drop table if exists normalize_test_input_output_using_model_model_output;
    PAI -name Normalize
        -project algo_public
        -DoutputParaTableName="normalize_test_input_output_using_model_model_output"
        -DinputParaTableName="normalize_test_input_model_output"
        -Dlifecycle="28"
        -DoutputTableName="normalize_test_input_output_using_model"
        -DinputTableName="normalize_test_input";
  • 输入说明

    normalize_test_input

    col_string

    col_bigint

    col_double

    col_boolean

    col_datetime

    01

    10

    10.1

    true

    2016-07-01 10:00:00

    NULL

    11

    10.2

    false

    2016-07-02 10:00:00

    02

    NULL

    10.3

    true

    2016-07-03 10:00:00

    03

    12

    NULL

    false

    2016-07-04 10:00:00

    04

    13

    10.4

    NULL

    2016-07-05 10:00:00

    05

    14

    10.5

    true

    NULL

  • 输出说明

    • normalize_test_input_output

      col_string

      col_bigint

      col_double

      col_boolean

      col_datetime

      normalized_col_bigint

      normalized_col_double

      01

      10

      10.1

      true

      2016-07-01 10:00:00

      0.0

      0.0

      NULL

      11

      10.2

      false

      2016-07-02 10:00:00

      0.25

      0.2499999999999989

      02

      NULL

      10.3

      true

      2016-07-03 10:00:00

      NULL

      0.5000000000000022

      03

      12

      NULL

      false

      2016-07-04 10:00:00

      0.5

      NULL

      04

      13

      10.4

      NULL

      2016-07-05 10:00:00

      0.75

      0.7500000000000011

      05

      14

      10.5

      true

      NULL

      1.0

      1.0

    • normalize_test_input_model_output

      feature

      json

      col_bigint

      {“name”: “normalize”, “type”:”bigint”, “paras”:{“min”:10, “max”: 14}}

      col_double

      {“name”: “normalize”, “type”:”double”, “paras”:{“min”:10.1, “max”: 10.5}}

    • normalize_test_input_output_using_model

      col_string

      col_bigint

      col_double

      col_boolean

      col_datetime

      01

      0.0

      0.0

      true

      2016-07-01 10:00:00

      NULL

      0.25

      0.2499999999999989

      false

      2016-07-02 10:00:00

      02

      NULL

      0.5000000000000022

      true

      2016-07-03 10:00:00

      03

      0.5

      NULL

      false

      2016-07-04 10:00:00

      04

      0.75

      0.7500000000000011

      NULL

      2016-07-05 10:00:00

      05

      1.0

      1.0

      true

      NULL

    • normalize_test_input_output_using_model_model_output

      feature

      json

      col_bigint

      {“name”: “normalize”, “type”:”bigint”, “paras”:{“min”:10, “max”: 14}}

      col_double

      {“name”: “normalize”, “type”:”double”, “paras”:{“min”:10.1, “max”: 10.5}}

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