Empirical Probability Density Chart - Platform For AI - Alibaba Cloud Documentation Center

This topic describes the Empirical Probability Density Chart component provided by Machine Learning Designer.

The component uses kernel distribution to estimate the probability density of sample data. Similar to the function of a histogram, kernel distribution indicates the distribution of sample data. However, kernel distribution overlays the contributions of all parts to generate a smooth and continuous distribution curve, whereas a histogram only generates discrete descriptions. If the kernel density estimation function is used, the probability density of non-sample data points is not zero. Instead, the probability density is an overlay of the weighted probability densities of all sampling points in a specific kernel distribution. The Empirical Probability Density Chart component uses Gaussian distribution as the kernel density estimation function.

Configure the component

You can configure the parameters of the Empirical Probability Density Chart component by using one of the following methods:

Method 1: Configure the component in the Machine Learning Platform for AI (PAI) console

Configure the component parameters on the pipeline page of Machine Learning Designer. The following section describes the parameters.

Tab	Parameter	Description
Field Setting	Input Columns	The input columns. You can select only columns of the BIGINT or DOUBLE data type.
Field Setting	Label Column	The label column. If you configure this parameter, the input columns are aggregated based on the values of the label column. For example, if a label column has two values (0 and 1), two results are returned.
Parameter Setting	Number of Calculation Intervals	The number of calculation intervals. A greater value indicates higher accuracy. The value of this parameter is calculated based on the range of values in each column.
Execution Tuning	Cores	The number of cores that you want to use. The value must be a positive integer.
Execution Tuning	Memory Size	The memory size of each core. Valid values: 1 to 65536. Unit: MB.

Method 2: Configure the parameters by using PAI commands

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

PAI -name empirical_pdf
-project algo_public
-DinputTableName="test_data"
-DoutputTableName="test_epdf_out"
-DfeatureColNames="col0,col1,col2"
-DinputTablePartitions="ds='20160101'"
-Dlifecycle=1
-DintervalNum=100

Parameter	Required	Description	Default value
inputTableName	Yes	The name of the input table.	None
outputTableName	Yes	The name of the output table.	None
featureColNames	Yes	The feature columns that are selected from the input table for training.	None
labelColName	No	The name of the label column in the input table.	None
inputTablePartitions	No	The partition that is 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 the partitions with commas (,).	None
intervalNum	No	The number of calculation intervals. A greater value indicates higher accuracy. Valid values: [1,1E14).	None
lifecycle	No	The lifecycle of the output table.	None
coreNum	No	The number of cores that you want to use. The value must be a positive integer.	Automatically allocated
memSizePerCore	No	The memory size of each core. Valid values: 1 to 65536. Unit: MB.	Automatically allocated

Sample command

Execute the following SQL statements to generate input data:

    drop table if exists epdf_test;
    create table epdf_test as
    select
      *
    from
    (
      select 1.0 as col1 from dual
        union all
      select 2.0 as col1 from dual
        union all
      select 3.0 as col1 from dual
        union all
      select 4.0 as col1 from dual
        union all
      select 5.0 as col1 from dual
    ) tmp;

Run the following PAI command:

PAI -name empirical_pdf
-project algo_public
-DinputTableName=epdf_test
-DoutputTableName=epdf_test_out
-DfeatureColNames=col1;

Input description
You can select multiple columns that you want to calculate. You can also select label columns and group these columns by label value. For example, the label columns contain the values 0 and 1. The columns are divided into two groups: label=0 and label=1. Then, the probability density of each group is provided.
Note
You can specify up to 100 label columns.

Output description

A diagram and a result table are generated. The following table describes the columns that are contained in the result table. If no label columns are specified, NULL is displayed for the label column in the output table.

Column	Data type	Description
colName	string	The input column.
label	string	The label column.
x	double	Indicates the value of the x-axis. The value is calculated based on the interpolation results, not the actual value.
pdf	double	The probability density.

Output table

    +------------+------------+------------+------------+
    | colname    | label      | x          | pdf        |
    +------------+------------+------------+------------+
    | col1       | NULL       | 1.0        | 0.12775155176809325 |
    | col1       | NULL       | 1.0404050505050506 | 0.1304256933829622 |
    | col1       | NULL       | 1.0808101010101012 | 0.13306325897429525 |
    | col1       | NULL       | 1.1212151515151518 | 0.1356613897616418 |
    | col1       | NULL       | 1.1616202020202024 | 0.1382173796574596 |
    | col1       | NULL       | 1.202025252525253 | 0.1407286844875733 |
    | col1       | NULL       | 1.2424303030303037 | 0.14319293014274642 |
    | col1       | NULL       | 1.2828353535353543 | 0.14560791960033242 |
    | col1       | NULL       | 1.3232404040404049 | 0.14797163876379316 |
    | col1       | NULL       | 1.3636454545454555 | 0.1502822610772349 |
    | col1       | NULL       | 1.404050505050506 | 0.1525381508819247 |
    | col1       | NULL       | 1.4444555555555567 | 0.1547378654919243 |
    | col1       | NULL       | 1.4848606060606073 | 0.1568801559764068 |
    | col1       | NULL       | 1.525265656565658 | 0.15896396664681753 |
    | col1       | NULL       | 1.5656707070707085 | 0.16098843325768245 |
    | col1       | NULL       | 1.6060757575757592 | 0.1629528799404685 |
    | col1       | NULL       | 1.6464808080808098 | 0.16485681490034038 |
    | col1       | NULL       | 1.6868858585858604 | 0.16669992491584543 |
    | col1       | NULL       | 1.727290909090911 | 0.16848206869138338 |
    | col1       | NULL       | 1.7676959595959616 | 0.17020326912168932 |
    | col1       | NULL       | 1.8081010101010122 | 0.17186370453638117 |
    | col1       | NULL       | 1.8485060606060628 | 0.17346369900080946 |
    | col1       | NULL       | 1.8889111111111134 | 0.17500371175692428 |
    | col1       | NULL       | 1.929316161616164 | 0.17648432589456017 |
    | col1       | NULL       | 1.9697212121212146 | 0.17790623634938396 |
    | col1       | NULL       | 2.0101262626262653 | 0.1792702373286898 |
    | col1       | NULL       | 2.050531313131316 | 0.18057720927022053 |
    | col1       | NULL       | 2.0909363636363665 | 0.18182810544221673 |
    | col1       | NULL       | 2.131341414141417 | 0.18302393829491406 |
    | col1       | NULL       | 2.1717464646464677 | 0.18416576567472337 |
    | col1       | NULL       | 2.2121515151515183 | 0.1852546770123305 |
    | col1       | NULL       | 2.252556565656569 | 0.18629177959496213 |
    | col1       | NULL       | 2.2929616161616195 | 0.18727818503109434 |
    | col1       | NULL       | 2.33336666666667 | 0.18821499601297229 |
    | col1       | NULL       | 2.3737717171717208 | 0.18910329347850022 |
    | col1       | NULL       | 2.4141767676767714 | 0.18994412426940221 |
    | col1       | NULL       | 2.454581818181822 | 0.19073848937711185 |
    | col1       | NULL       | 2.4949868686868726 | 0.19148733286168018 |
    | col1       | NULL       | 2.535391919191923 | 0.1921915315221827 |
    | col1       | NULL       | 2.575796969696974 | 0.19285188538972659 |
    | col1       | NULL       | 2.6162020202020244 | 0.19346910910630113 |
    | col1       | NULL       | 2.656607070707075 | 0.19404382424446043 |
    | col1       | NULL       | 2.6970121212121256 | 0.1945765526142701 |
    | col1       | NULL       | 2.7374171717171762 | 0.19506771059517916 |
    | col1       | NULL       | 2.777822222222227 | 0.19551760452158667 |
    | col1       | NULL       | 2.8182272727272775 | 0.19592642714194602 |
    | col1       | NULL       | 2.858632323232328 | 0.1962942551623821 |
    | col1       | NULL       | 2.8990373737373787 | 0.1966210478770638 |
    | col1       | NULL       | 2.9394424242424293 | 0.1969066468790639 |
    | col1       | NULL       | 2.97984747474748 | 0.19715077683721793 |
    | col1       | NULL       | 3.0202525252525305 | 0.19735304731663747 |
    | col1       | NULL       | 3.060657575757581 | 0.19751295561309964 |
    | col1       | NULL       | 3.1010626262626317 | 0.19762989056457925 |
    | col1       | NULL       | 3.1414676767676823 | 0.19770313729675995 |
    | col1       | NULL       | 3.181872727272733 | 0.19773188285349683 |
    | col1       | NULL       | 3.2222777777777836 | 0.19771522265793107 |
    | col1       | NULL       | 3.262682828282834 | 0.19765216774530828 |
    | col1       | NULL       | 3.303087878787885 | 0.19754165270453194 |
    | col1       | NULL       | 3.3434929292929354 | 0.19738254426210697 |
    | col1       | NULL       | 3.383897979797986 | 0.19717365043938664 |
    | col1       | NULL       | 3.4243030303030366 | 0.19691373021193162 |
    | col1       | NULL       | 3.4647080808080872 | 0.1966015035982942 |
    | col1       | NULL       | 3.505113131313138 | 0.19623566210464843 |
    | col1       | NULL       | 3.5455181818181885 | 0.19581487945135703 |
    | col1       | NULL       | 3.585923232323239 | 0.19533782250778076 |
    | col1       | NULL       | 3.6263282828282897 | 0.1948031623623475 |
    | col1       | NULL       | 3.6667333333333403 | 0.1942095854560816 |
    | col1       | NULL       | 3.707138383838391 | 0.19355580470939734 |
    | col1       | NULL       | 3.7475434343434415 | 0.19284057057394655 |
    | col1       | NULL       | 3.787948484848492 | 0.19206268194364004 |
    | col1       | NULL       | 3.8283535353535427 | 0.19122099686158253 |
    | col1       | NULL       | 3.8687585858585933 | 0.19031444296253852 |
    | col1       | NULL       | 3.909163636363644 | 0.1893420275936375 |
    | col1       | NULL       | 3.9495686868686946 | 0.18830284755928747 |
    | col1       | NULL       | 3.989973737373745 | 0.1871960984396676 |
    | col1       | NULL       | 4.030378787878796 | 0.18602108343567092 |
    | col1       | NULL       | 4.070783838383846 | 0.18477722169674377 |
    | col1       | NULL       | 4.111188888888897 | 0.1834640560916829 |
    | col1       | NULL       | 4.151593939393948 | 0.1820812603860928 |
    | col1       | NULL       | 4.191998989898998 | 0.18062864579383914 |
    | col1       | NULL       | 4.232404040404049 | 0.179106166873458 |
    | col1       | NULL       | 4.272809090909099 | 0.17751392674406796 |
    | col1       | NULL       | 4.31321414141415 | 0.17585218159888508 |
    | col1       | NULL       | 4.353619191919201 | 0.17412134449794325 |
    | col1       | NULL       | 4.394024242424251 | 0.1723219884250765 |
    | col1       | NULL       | 4.434429292929302 | 0.17045484859762067 |
    | col1       | NULL       | 4.4748343434343525 | 0.16852082402064342 |
    | col1       | NULL       | 4.515239393939403 | 0.1665209782808102 |
    | col1       | NULL       | 4.555644444444454 | 0.16445653957824907 |
    | col1       | NULL       | 4.596049494949504 | 0.16232889999798905 |
    | col1       | NULL       | 4.636454545454555 | 0.16013961402571825 |
    | col1       | NULL       | 4.6768595959596055 | 0.1578903963157465 |
    | col1       | NULL       | 4.717264646464656 | 0.15558311872216193 |
    | col1       | NULL       | 4.757669696969707 | 0.1532198066072439 |
    | col1       | NULL       | 4.798074747474757 | 0.1508026344442397 |
    | col1       | NULL       | 4.838479797979808 | 0.14833392073462115 |
    | col1       | NULL       | 4.878884848484859 | 0.14581612226291346 |
    | col1       | NULL       | 4.919289898989909 | 0.1432518277151203 |
    | col1       | NULL       | 4.95969494949496 | 0.1406437506896507 |
    | col1       | NULL       | 5.00010000000001 | 0.13799472213247665 |
    +------------+------------+------------+------------+