IntegratedML-IRIS-Cloud-Height-prediction Program deployment

Data Initialization

1. Create a table with sql statements

 

 

create table IF NOT EXISTS MLTEST_MSG.HeightWeightPredictMSG (Id int primary key identity(101,2),number varchar(50), Height float, Weight  float);

create table IF NOT EXISTS MLTEST_MSG.HeightWeightMSG (Id int primary key identity(101,2),number varchar(50), Height float, Weight  float);

create table IF NOT EXISTS MLTEST_MSG.FamilyMSG(id int primary key identity(101,2),Csex int, Family float,Father float,Mother float, num int,Height float);

create table IF NOT EXISTS MLTEST_MSG.FamilyPredictMSG(id int primary key identity(101,2),Csex int, Family float,Father float,Mother float, num int,Height float);

      

 

2. Upload the provided csv file to the cloud data platform, and import the data into the two tables as shown in the screenshot. Note that the field ID is removed when importing.

     

 

 

Front-end deployment

1. Notepad Open the config.js file under the distML folder, modify the information and start the front-end page (you can use Tomcat)

 

 

 

   

 

①: Backend path

②: When it is 1, the page is displayed in English, and when it is 0, it is Chinese.

Back-end deployment

Open the application.yml file in the backend package and modify the database information in the file

 

 

 

 

①: database url

②: Username

③: Password

Then start the automldemo1.jar package with the java command, the default port number is 9999

 

 

 

 

After starting, enter the front-end page (if you use tomcat to start the address: ip: port/distML)

The first time you enter the page, the model will be created and trained, and you need to wait for a while. You can also create a model with the following statement after the data initialization and table creation in step 1.

 

Create model FamiyModel predicting (Height) from MLTEST_MSG.FamilyMSG

Train model FamiyModel

Create model HeightWeightMODEL predicting (Height) WITH (Weight float)  from MLTEST_MSG.HeightWeightMSG

Train model HeightWeightMODEL

 

Test Flow

 

① Select the module

② Fill in the relevant data. If there is adult sibling data, you can click add to fill in the information.

③ Click Submit and wait for the prediction result to appear in a while.