Normalization - Platform For AI - Alibaba Cloud Documentation Center

This topic describes the Normalization component provided by Machine Learning Designer (formerly known as Machine Learning Studio).

Configure the component

You can use one of the following methods to configure the Normalization component.

Method 1: Configure the component on the pipeline page

Configure the component parameters on the pipeline page of Machine Learning Designer.

Tab	Parameter	Description
Fields Setting	All Selected by Default	By default, all columns in the input table are selected. Specific columns may not be used for training. These columns do not affect the prediction result.
Fields Setting	Reserve Original Columns	Specifies whether to reserve original columns. Column names are prefixed with normalized_ after normalization. Only columns of the DOUBLE or BIGINT type can be reserved.
Tuning	Cores	The number of cores. The system automatically allocates cores used for training based on the volume of input data.
Tuning	Memory Size per Core	The memory size of each core. The system automatically allocates the memory based on the volume of input data. Unit: MB.

Method 2: Use PAI commands

Configure the component parameters by using PAI commands. You can use the SQL Script component to call PAI commands. For more information, see SQL Script.

Command for dense data

PAI -name Normalize
    -project algo_public
    -DkeepOriginal="true"
    -DoutputTableName="test_4"
    -DinputTablePartitions="pt=20150501"
    -DinputTableName="bank_data_partition"
    -DselectedColNames="emp_var_rate,euribor3m"

Command for sparse data

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=",";

Parameter	Required	Description	Default value
inputTableName	Yes	The name of the input table.	No default value
selectedColNames	No	The columns that are selected from the input table for training. The column names must be separated by commas (,). Columns of the INT and DOUBLE types are supported. If the input data is in the sparse format, columns of the STRING type are supported.	All columns
inputTablePartitions	No	The partitions that are selected from the input table for training. The following formats are supported: Partition_name=value name1=value1/name2=value2: multi-level partitions Note If you specify multiple partitions, separate them with commas (,).	All partitions
outputTableName	Yes	The name of the output table.	No default value
outputParaTableName	No	The name of the output parameter table.	No default value
inputParaTableName	Yes	The name of the input parameter table.	No default value
keepOriginal	No	Specifies whether to reserve original columns. Valid values: true: renames the normalized columns with the normalized_ prefix and reserves original columns. false: reserves all columns without renaming them.	false
lifecycle	No	The lifecycle of the output table. Valid values: [1,3650].	No default value
coreNum	No	The number of cores used in computing. The value must be a positive integer.	Determined by the system
memSizePerCore	No	The memory size of each core. Unit: MB. Valid values: (1,65536).	Determined by the system
enableSparse	No	Specifies whether to support the input data in the sparse format. Valid values: true false	false
itemDelimiter	No	The delimiter used between key-value pairs.	,
kvDelimiter	No	The delimiter used between keys and values.	:
kvIndices	No	The feature indexes that require normalization in the table that contains data in the key-value format.	No default value

Example

Generate input data

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
    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
    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
    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
    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
    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
) tmp;

Run PAI commands

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";

Input

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

Output

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}}

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}}