Standardization - Platform For AI - Alibaba Cloud Documentation Center

Standardization is a data preprocessing technique that converts feature data to a unified dimension. The Standardization component unifies the dimensions of different features by setting the mean to 0 and the standard deviation to 1. Standardization can enhance the convergence and performance of the models, especially models that use the gradient descent optimization algorithm.

Algorithm description

You can standardize one or more columns in a table and save the generated data to a new table.
The following formula is used for standardization: (X - Mean)/(Standard deviation).
- Mean: the mean of samples.
- Standard deviation: the standard deviation of samples. The standard deviation is used when samples are used to calculate the total deviation. To make the value obtained after standardization closer to the mean, you must moderately increase the calculated standard deviation by using the formula .
- The formula used to calculate the standard deviation of samples is .
  represents the mean of samples X1, X2, ..., and Xn.

Configure the component

Method 1: Configure the component on the pipeline page

Add the Standardization component on the pipeline page of Machine Learning Designer and configure the component parameters described in the following table on the right side of the page.

Tab	Parameter	Description
Fields Setting	All Selected by Default	Default select all. Extra columns do not affect the prediction results.
	Reserve Original Columns	Processed columns are prefixed with "stdized_". Supports DOUBLE and BIGINT types.
	Input Sparse Matrix	Specifies whether to support the input data in the sparse format. If you enable this feature, configure the following parameters: KV Pair Delimiter KV Delimiter KV Index
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 Standardization component parameters by using PAI commands. You can use the SQL Script component to call PAI commands. For more information, see Scenario 4: Execute PAI commands within the SQL script component.

Command for dense data

PAI -name Standardize
    -project algo_public 
    -DkeepOriginal="false"    
    -DoutputTableName="test_5"
    -DinputTablePartitions="pt=20150501"  
    -DinputTableName="bank_data_partition" 
    -DselectedColNames="euribor3m,pdays"

Command for sparse data

PAI -name Standardize    
    -project projectxlib4
    -DkeepOriginal="true"
    -DoutputTableName="kv_standard_output"
    -DinputTableName=kv_standard_test
    -DselectedColNames="f0,f1,f2"
    -DenableSparse=true
    -DoutputParaTableName=kv_standard_model    
    -DkvIndices=1,2,8,6
    -DitemDelimiter=",";

Parameter	Required	Default value	Description
inputTableName	Yes	None	The name of the input table.
selectedColNames	No	All columns	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.
inputTablePartitions	No	All partitions	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 (,).
outputTableName	Yes	None	The name of the output table.
outputParaTableName	Yes	None	The name of the output parameter table.
inputParaTableName	No	None	The name of the input parameter table.
keepOriginal	No	false	Specifies whether to reserve original columns. Valid values: true: renames the standardized columns with the stdized_ prefix and reserves original columns. false: reserves all columns without renaming them.
lifecycle	No	None	The lifecycle of the output table.
coreNum	No	Automatically assigned by the system	The number of cores.
memSizePerCore	No	Automatically assigned by the system	The memory size of each core.
enableSparse	No	false	Specifies whether to support the input data in the sparse format. Valid values: true false
itemDelimiter	No	","	The delimiter used between key-value pairs.
kvDelimiter	No	":"	The delimiter used between keys and values.
kvIndices	No	None	The feature indexes that require standardization in the table that contains data in the key-value format.

Examples

Generate input data

drop table if exists standardize_test_input;
create table standardize_test_input(
    col_string string,
    col_bigint bigint,
    col_double double,
    col_boolean boolean,
    col_datetime datetime);
insert overwrite table standardize_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 standardize_test_input_output;
drop table if exists standardize_test_input_model_output;
PAI -name Standardize
    -project algo_public
    -DoutputParaTableName="standardize_test_input_model_output"
    -Dlifecycle="28"
    -DoutputTableName="standardize_test_input_output"
    -DinputTableName="standardize_test_input"
    -DselectedColNames="col_double,col_bigint"
    -DkeepOriginal="true";
drop table if exists standardize_test_input_output_using_model;
drop table if exists standardize_test_input_output_using_model_model_output;
PAI -name Standardize
    -project algo_public
    -DoutputParaTableName="standardize_test_input_output_using_model_model_output"
    -DinputParaTableName="standardize_test_input_model_output"
    -Dlifecycle="28"
    -DoutputTableName="standardize_test_input_output_using_model"
    -DinputTableName="standardize_test_input";

Input

standardize_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

standardize_test_input_output

col_string	col_bigint	col_double	col_boolean	col_datetime	stdized_col_bigint	stdized_col_double
01	10	10.1	true	2016-07-01 10:00:00	-1.2649110640673518	-1.2649110640683832
NULL	11	10.2	false	2016-07-02 10:00:00	-0.6324555320336759	-0.6324555320341972
02	NULL	10.3	true	2016-07-03 10:00:00	NULL	0.0
03	12	NULL	false	2016-07-04 10:00:00	0.0	NULL
04	13	10.4	NULL	2016-07-05 10:00:00	0.6324555320336759	0.6324555320341859
05	14	10.5	true	NULL	1.2649110640673518	1.2649110640683718

standardize_test_input_model_output

feature	json
col_bigint	{“name”: “standardize”, “type”:”bigint”, “paras”:{“mean”:12, “std”: 1.58113883008419}}
col_double	{“name”: “standardize”, “type”:”double”, “paras”:{“mean”:10.3, “std”: 0.1581138830082909}}

standardize_test_input_output_using_model

col_string	col_bigint	col_double	col_boolean	col_datetime
01	-1.2649110640673515	-1.264911064068383	true	2016-07-01 10:00:00
NULL	-0.6324555320336758	-0.6324555320341971	false	2016-07-02 10:00:00
02	NULL	0.0	true	2016-07-03 10:00:00
03	0.0	NULL	false	2016-07-04 10:00:00
04	0.6324555320336758	0.6324555320341858	NULL	2016-07-05 10:00:00
05	1.2649110640673515	1.2649110640683716	true	NULL

standardize_test_input_output_using_model_model_output

feature	json
col_bigint	{“name”: “standardize”, “type”:”bigint”, “paras”:{“mean”:12, “std”: 1.58113883008419}}
col_double	{“name”: “standardize”, “type”:”double”, “paras”:{“mean”:10.3, “std”: 0.1581138830082909}}